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Database Marketing Intensive Database Marketing Intensive Document Transcript

  • 2013 DMA Database Marketing Post Intensive Program Agenda Post Intensive Session on Database Marketing Developing a 21st Century Database—The Tools, Tactics and Tests to Meet Your Business Needs Within the past few years, massive changes in data, technology and the web have significantly impacted the planning, research, marketing and sales processes. Business needs have shifted dramatically with a focus on faster analysis, broader multi-channel integration and dynamic database information systems. This nine-hour seminar is designed for the database marketer who is looking to enhance or overhaul database business operations at their company. The instructor lineup consists of leading industry professionals who regularly evaluate cutting-edge technologies and best practices in database marketing. Over the course of two days, attendees will be exposed to current and future systems, trends, recommendations and pitfalls that lie ahead in today’s and tomorrow’s database marketing landscape. Day 1 Wednesday, October 16 1:00 – 4:30 1:00 – 1:15 Program Introduction/Class Discussion—PEGG NADLER Part 1-- 1:15—2:15 Re-evaluating Your Marketing Database System: A How To BERNICE GROSSMAN, DMRS Group A “check list” of the most important items to review when re-evaluating your marketing database, your vendor, and the design and attending functionality of your current solution tool. Attendees will be provided with a proven method of what to look for and how to know what is and is not working. Before you conclude your marketing database is broken, learn how to answer the key questions that determine the state of your database. Part 2—2:15 – 3:15 A Primer on Database Systems—Deciphering Differences and Determining Directions MARCUS TEWKSBURY, VP Client Partner, Experian There is a myriad of database technologies on the market today—and this session is designed to equip attendees with the key benchmarks to assess and select marketing systems that meet their company’s existing and anticipated needs. Included in this overview will be an examination of current marketing automation application software, including traditional vendors, B2B, CRM systems and Web content systems. Part 3-- 3:30 – 4:30 Deadly Sins and the Ten Commandments: How to Achieve Best-Practices Database Content and Key Metrics Reporting JIM WHEATON, Wheaton Group A database is only as good as its content, and bad content always costs you money. There is nothing glamorous about creating and maintaining best-practices content. Data audits and other forms of quality assurance are hard work. The same is true about carefully reflecting the nuances of your business and data when creating dashboards and reports. This session will tell you why all of this, although often overlooked, is so important for database success.
  • Day 2 Thursday, October 17 8:30 – 2:45 Part 4— 8:30 – 9:45 Leveraging Your Database: Reporting, Templates & Strategic Applications AL BESSIN, Bessin Consulting Identifying the customer, their wants and needs, and what drives their behavior forms the basis for successful marketing in today’s business environment. Learn how to create a customer balance sheet; identify where mistakes are being made; and use findings to drive business transformation. Understand what media is working by looking at different ways in which results are being reported for online and offline marketing campaigns. Emphasis will be on determining the most practical and actionable methods to use including marketing performance, lifetime value and business strategy. Part 5—9:45 – 10:45 Modeling and Analytics STEVE KIM, VP, Quantitative Marketing Group, Merkle This session will highlight the critical dimensions for a successful use of database information when building and deploying models and analytics in your business. Insights, targeting and measurement are all critical components to fully understand the most effective channels in marketing programs. Big Data presents major challenges and opportunities in data collection and its manipulation in the creation of modeling and analysis. We will explore the latest in analytical hardware and software and discuss how to utilize your data to detail ROI in building the business case and calculating its impact. Part 6—11:00 – 12:00 Navigating the Data Maze JOANNE BRANSCUM, Director Management Information, Acxiom Global Services DOUG CHRISTIANSON, Senior Principal, Acxiom Global Services Database marketers are now faced with massive amounts of data, mounting privacy issues and growing regulations on the use, collection and dissemination of data. This session will look at both traditional and new sources of data used to shape database analysis programs. We will address the latest trends in Big Data for the B-C and B-B worlds. Data collection and data connection as well as best practices for determining and protecting your customer data needs will be discussed. Break & Boxed Lunch Pickup 12:00 – 12:15 (Boxed Lunch—working lunch during Part 7) Part 7—12:15 – 1:30 Integrating Digital Media Data with Your Marketing Database RANDY HLAVAC, Lecturer Professor – Northwestern University, Medill IMC (Integrated Marketing Communications) Social media, mobile, web communities and other electronic media hold the potential for providing new, high impact data to improve the ability of our marketing database systems to drive highly targeted CRM and electronic programs. But challenges exist using this data. What data is important (and legal) to add to your database? How do we monitor and assess data quality and impact? How do we entice visitors to provide data? We will examine how to integrate your social, mobile, web, and CRM marketing efforts into a single Social CRM system.
  • Part 8—1:45 – 2:45 Marketing ROI: How to Ensure Political, Technical, and Business Success for a Database Project PEGG NADLER, Pegg Nadler Associates This session will set a realistic foundation for positioning your database for success within your company. We will look at war stories and success stories and provide guidance and benchmarks for conducting a business needs/expectations survey and the justification for the continued investment and deployment in your marketing database division.
  • 9/23/2013 1 Re-evaluating Your Marketing Database System: A How To Database Marketing Post Intensive Part 1 Bernice Grossman Founder, DMRS Group bgrossman@dmrsgroup.com • DMRS has been working with client companies to maximize their data marketing efforts since 1983. We are an independent consultancy, we own no data, no software, nor any processing services or facilities. • We manage data audits/assessments and operational needs assessments: Choosing the right vendors Data / ETL, MSP / ESP, MDB / CRM, MA / SFA Implementation End-user marketing applications for off-line and on-line • Our client list spans a broad spectrum of Domestic and International businesses including Avis, Epson, Microsoft, Pfizer, United Airlines, Nestle, Simon, United States Gypsum, and United Airlines Who is DMRS ?
  • 9/23/2013 2 This Session • This session will provide a check list of the most important items to review when re-evaluating your marketing database, your vendor, and the design and attending functionality of your current solution tool. • Attendees will be provided with a proven method of what to look for and how to know what is and is not working. Before you conclude your marketing database is broken, come to this session and learn the key questions to ask that will help determine the state of your database. • Key takeaways: – What do you need now that you didn't need when your marketing database was built? – What about your data? – How should you review database integration with email and social media - what exists now that didn't exist at the time of the build? First, A Definition just so that we’re all on the same page An MDB (Marketing Database) is a single repository for all data identified as relevant to meet the goals of marketing that are defined as actionable and accessible for: • Capturing data from all channels • Consistent data hygiene and de-duplication rules • Allows for segmentation and query • Integrates Direct, E-Mail, Social Media (transactional, web site, call center, behavioral, attitudinal, events – more) • Performs complete Campaign Management • Measures media performance • Manages multi-channel marketing • Performs modeling and predicting behavior analyses • It is read only. It is NOT a contact management system.
  • 9/23/2013 3 IS YOUR MDB “BROKEN”? • What is “broken”? We’re going to look at a few examples in a moment. • Length of contract • When does your contract expire? • (If inside) Is it time to take it off-site? • Are you all integrated? • Does your MDB work? • What are the metrics you use to decide this? – Do the MDB counts match the transaction counts? – Does the geography match • Who decides that it does or does not work? • Does anyone want to use it? • Who? Why? • Who does not? • Is marketing grumbling • Is IT smirking Some “Broken” Examples • Pharmaceutical Company – Kept each drug on a separate MDB – became too expensive – realized they were paying for certain processes three times but only needed to “buy” it once • Membership Organization – The users were in silos – just like their data – Change Management was very difficult – Never contemplated the problems of moving data back and forth (especially from their SFA to the MDB) • Large Retail Shopping Installation – Never thought through how to use the response management functionality
  • 9/23/2013 4 Is Everything Still the Same at the MSP? • Corporate mission statement and customer service philosophy • Total number of staff • Key executives • Ownership information and organization chart • Quality control procedures from data receipt to MDB update • # of customer support staff • # of technical support staff • Customer mix • System software information • Percent of budget applied to R&D • Willingness to provide details pending litigation • MDB staff attrition over the last year • Company privacy policy • Primary industries that are served • Number/type of user group meetings held each year Are Their Data Center Capabilities Still the Same? • Available data center locations • Back up procedures • Real-time redundancy (servers, HVAC, etc.) • Disaster recovery and business continuity procedures • Contingency for downtime and preventive maintenance • Physical and data security measures • Connectivity options • Service levels for problem reporting and resolution – Do these meet your needs today? • Ability to provide support 24 x 7 x 365
  • 9/23/2013 5 What About ………… • Has their client list changed? How? • What have they done to enhance their look-up tables for company name, title, first name • Can their solution now support both your marketing and contact management/SFA needs? How? • Have they integrated with an ESP? – Who? – How are they integrated? – Is it really one platform or is it two that are “made” to look like one? • How are they integrating Social Media? • What is available to you in Real Time? WHY do you need real time? THE CRITICAL QUESTIONS • When was the last time your BRD was updated? • When was the last time you compared your BRD to what you are receiving? This should be done at least 1x/yr • When was the last time you looked at your ERD? • Has the staff that manages your MDB changed? • What do you need now that you didn't need when your MDB was built? How old is your MDB? • Have you reviewed the MDB integration processes with email and social media issues that didn't exist at the time of the build?
  • 9/23/2013 6 Do you still have the same “25 Questions”? WHAT 25 Questions? If you had an ideal standard and fresh marketing database, what questions would you want answered from the data? But, there are 2 conditions: • Question must be quantitative! • Question cannot use a subjective word (e.g. big or better)! For example: How many customers who purchase SKU #123 in Mississippi also purchased SKU #456 Original Business Goals and Functional Requirements Business goals • Become customer-centric by developing a complete view of the customer with all pertinent data • Increase effectiveness and efficiency of acquisition and retention marketing with better customer targeting and campaign management • Improve overall ROI by marketing to most valuable customers • Target individual customers with specific messages designed to best meet their needs • Understand customer behavior for each product within channels and across the brands Functional requirements • Provide access for query and analysis by both marketing and sales • Integrate the mail and email query and campaign management functions. • Provide accurate information on new customers, cost to acquire customers, number of inactive customers, migration of customers between value segments and the cost of migration • Use 3rd party B-to-B data to establish corporate hierarchy links of ownership and firmographic profile info • Enhance customer data through the use of 3rd party for demographics, lifestyles, behavioral, attitudinal
  • 9/23/2013 7 Has Your Team Changed? • Team Champion – Owns the Vision and Articulates it to the Team • Marketing (all channels) – Direct mail – Email – Telemarketing – Social Media – Space – Acquisition – Retention – Product • Sales • IT • Finance • Legal HAS YOUR ENVIRONMENT CHANGED? Data locations: – Oracle data warehouse – Mainframe flat files – SQL Server – SalesForce.com 2,000,000 eligible records on file. Approx. 50 Gb of data representing the last 3 years. Growth over the next 3 years is expected at a rate of 25% per year. Files included business-to-business consumer US and International data customers/prospect full postal address, just email, some “handles” Estimated # users = 20. THIS IS WHAT IS WAS: WHAT ABOUT NOW?
  • 9/23/2013 8 What about your data? • Is it the same or has it changed in scope • Have you added new products, services, bought other companies, etc., • Have you changed the channels you use for acquisition and/or retention or the amount you use of a channel? • Have you changed data vendors? Data Sources – Marketing Strategies Have You Added New Ones or Made Significant Changes? • DATA • Transactional Files • Email • Web Site Data • Operations • Complaints • Reviews • Tech Support • Social • Other • MARKETING • New Channels • Different Schedules • Re-Organized • New Management • Decided to Outsource • Added / Deleted Partners • Bought / Sold a Company • Other
  • 9/23/2013 9 Have You Recently Reviewed.. Your data enhancement sources and methodologies Have you created a “best record” and are the requirements still the same Have you reviewed data standardization and sanitization routines What about records with only: Postal Addresses. E-Mail Addresses. Social Media “handles” What about those record missing “key” data elements What About…………… • Response time – Do you need increased speed? – When was the last time you had the server sized? • Query capabilities • Multiple users – Have you added or deleted users? • Simultaneous usage – Has this stayed the same? • Multiple locations • Data feeds and updates – Have you added new ones?
  • 9/23/2013 10 Remember when you …… • Created validation rules for all of the data feeds • Developed Appropriate Audit Reports for Data feeds Database refreshes Standard reports • Developed Reject procedures – and decided what to do what to do when key checkpoints failed • Do you still follow those rules?? Created Sanity Checks…. • Standard reports that ran after database refreshes and database feeds to verify key metrics • Threshold reports If “x” metric exceeds an appropriate number does a red flag goes up? Who is advised? Are the reports still automatically distributed to the appropriate people? are those people still at your company? are the reports read?
  • 9/23/2013 11 THE 8 MUST HAVE’S – Do You Have More / or Are They Just Different? Query Calculating Reporting Direct and E Mail Campaign Management Social Media Integration Data Extract Data Import Data Mining, Analysis, Tracking & Modeling Do Any of These Still Exists? • Disparate platforms ---- not everything is connected • No common repository to store everything • Creating selections is just too complicated – almost no one knows SQL except IT • Data is still not sanitized, standardized, unduplicated nor aggregated the same way across all of the sources • Still no written set of up-to-date business rules • Sill no written BRD?
  • 9/23/2013 12 Nice to Have or Now Must Have’s • Real time access • Data from files not integrated (by name and address) with the MDB – integration is done by an ID • Social Media “handles” are matched to email addresses • Bi-synchronous feed with SFA What are your users doing? • What are the work-arounds? • Might these be the reason your MDB is “broken” • How many are there? • How can you get these to be integrated into the on-going functionality of the processes your MSP provides?
  • 9/23/2013 13 Some Final Thoughts • Politics will always rear it’s ugly head – nothing changes • This was a high emotional stressful project and it still it • There was high, often undirected, energy and its still there • Big questions like, “who really owns the data”, MUST be answered - this is like a moving target! • Although there were multiple levels of expectation for the Master Marketing Database (MDB), have you finally all agreed? Does this need to be reviewed? LIST OF PLAYERS IN THIS SPACE IS ENDLESS Customer Relationship Management (CRM) Extract, Transform, Load (ETL) Marketing Service Provider (MSP) Marketing Automation / Lead Management
  • OUR DATA IS A MESS! HOW TO CLEAN UP YOUR MARKETING DATABASE BY BERNICE GROSSMAN & RUTH P. STEVENS OCTOBER 2005 BUSINESS-TO-BUSINESS DATABASE MARKETING
  • EXECUTIVE SUMMARY Business-to-business companies are often frustrated by inaccurate customer information. But there are steps you can take to keep your data clean and up to date. The most essential action steps are manual, using processes to enter data correctly in the first place, and to conduct outbound communications to verify its ongoing accuracy. These steps can then be supplemented by the automated method, which usually means sending your data to an outside service provider for regular clean-up. The authors sent a sample of 10,000 business records to four leading vendors and share the results. OUR DATA IS A MESS! HOW TO CLEAN UP YOUR MARKETING DATABASE 1 “WHAT DO YOU THINK ABOUT YOUR CUSTOMER DATA? ” You can ask this question of anyone involved in B-to-B sales and marketing, and the answer you receive will just about always be the same. “Our data is a mess.” Of course, it’s likely that the answer will be couched in more forceful terms than “a mess.” But the implication is clear. People in business- to-business marketing are aware that they need to do a better job of collecting and maintaining accurate and up-to-date information about their customers and prospects. There are steps you can take to keep your data clean and fresh. This paper reviews the data hygiene methods available to business marketers today. It will also introduce the results of a research study among data hygiene vendors that will help you understand what you can expect a third-party service provider to do to keep your customer information clean. WHAT IS A “MESS”? THE STATE OF YOUR BUSINESS- TO-BUSINESS DATA TODAY Part of the problem faced by business marketers is definitional. While everyone says “My data is a mess,” they may mean different things by it. Marketers, for example, may be talking about situations when their direct mail arrives but doesn’t get delivered beyond the mail room. For sales people, it’s when they pick up the phone and discover the customer’s direct phone number has changed. But there’s more. The business may have moved its offices. Or the customer’s title may have changed. Or the data fields may be mixed up, for example, an old purchase order number that’s parked in the customer name field. Or the state of Nebraska may be abbreviated NB, while the post office only accepts NE. It goes on and on. Each of these problems is common in business marketing databases, and creates enormous waste — of marketing communications investment, and of business opportunity — not to mention frustration
  • at all levels. So what can you do about it? The solutions lies in data hygiene, defined as follows: Correcting inaccurate fields and standardizing formats and data elements. There are two general approaches to data hygiene: manual options and automated clean up. Let us look at what each of these can — and cannot — do for you. MANUAL PROCESSES There are two key manual methods involved in data hygiene: 1. Enter clean data in the first place 2. Institute on-going updating processes The most important is the first: If the data is entered or received incorrectly at the start, you have not only wasted a business opportunity, you have created needless extra expense to go back and correct the information. Bad data is worse than no data at all. Smart companies are using the following key methods for correct data input: • Create and maintain a set of processes known as Input Editing Standards (IES). These are the rules for data elements that must be followed at the point of entry. For example, you might standardize all references to the International Business Machines corporation as IBM. You would require that 2-digit state abbreviations conform with USPS standards. And you would require that all titles be spelled out fully. Most companies create an input standards document when they first create a computerized database of customer information. But over the years that document may get lost, out-dated or filed somewhere collecting dust. Your first step is to find that document, review it, refresh it, and put it into use. • Train data entry personnel on the IES rules, and repeat the training at least quarterly. It’s not just for new employees, but also needed as an ongoing refresher. A corollary point: Don’t expect to pay your key-entry personnel peanuts and get great results. They need substantial training and incentives to do a good job maintaining your data asset. • Use address-checking software at point of entry, to ensure deliverability. Starting out with clean data is only the beginning. Business data tends to degrade at the rate of 3-6% per month, so you must invest in ongoing maintenance. Here are the best manual methods for data cleanliness: • Train and motivate employees who have direct customer contact to request updates at each encounter. This includes call center personnel, customer service, sales people and distributors. It may be the job of marketing to keep the database clean, but data is a valuable corporate asset, and everyone has a stake in its quality. • Segment your file, and conduct outbound confirmation contacts for the highest value accounts. This can be by mail, email or telephone. • When using first-class mail, request the address correction service provided by the USPS. Put in place a process to update the addresses from the “nixies,” meaning the undeliverable mail that is returned to you. • Invite your customers to help you maintain their information correctly. Make the contact information available on a password-protected website, and ask your customers to key-enter changes as they occur. Offering them a good reason to do so, or perhaps apremium or incentive, will result in higher levels of customer compliance. HOW TO CLEAN UP YOUR MARKETING DATABASE 2
  • THE AUTOMATED METHOD Once you have manual methods underway, send your data out to a service provider for regular clean up. We recommend data cleansing at a third party at least twice a year. Large providers of business data are skilled at matching your file to their databases of standardized, updated records, and giving you back the good information. On a per-record basis, automated clean-up is inexpensive, and should be combined with an ongoing manual program of data hygiene. There is quite a bit of misapprehension about the nature of automated data hygiene. Because it involves a matching process against a larger national database, some people confuse it with other data processes. So, before we go into more detail about what it can do, let us be clear about what we don’t mean by automated data clean up for business marketers. Sending your names out for clean up is not to be confused with: • De-duplication, which means identifying records that qualify as duplicates. • Data append, which means adding extra fields like an industry code, years in business, a credit score, or company size. At the same time, it’s important to realize that automated data hygiene cannot clean up everything on your database. For example, changes to a person’s title or direct phone number are unlikely to be reported with any speed into a national database. So much of the time, the vendor will have no fresher title or phone data than you have yourself. And there’s another matter to consider: Whose data is correct? If the name you have on your file for a company or a person is different from the name on the national database, how will you decide which one to accept? Most companies give preference to the data that was most recently collected or confirmed with customers. The national databases maintained by various vendors have only one ultimate standard against which address accuracy can be measured, namely, the USPS. In fact, the only “true” addresses, street, state and ZIP code, are those recognized by the post office. So you can count on the outside vendors to clean up addresses to the point where they will support mail delivery. But you won’t have the same level of confidence in the potential clean-up of telephone numbers, fax numbers, email addresses, and job titles. For such elements, verification via outbound contact and/or inbound web-based updating is the only method to ensure accuracy and timeliness. This may be a disappointment to those who were hoping that they could simply “send our data out for clean up.” In fact, the best method for ongoing maintenance of many important data elements used by business marketers is outbound contact and verification. Because this is an expensive and time-consuming process, we recommend that you verify your most valuable accounts first, and then decide the benefit of continuing on to your lesser value accounts. For a thorough understanding of what outside vendors can and cannot do for you via automated hygiene processes, we conducted a research study in 2004 that involved clean up of a sample file by four leading suppliers who have deep experience with business data. Please review the research results presented in this report. HOW TO CLEAN UP YOUR MARKETING DATABASE 3
  • HOW TO CLEAN UP YOUR MARKETING DATABASE 4 In the spring of 2004, we invited a group of leading business-to-business data services providers to join us in a research project to compare their various approaches to data clean up. Four vendors agreed to participate: Acxiom, DataFlux, Donnelley Marketing and Harte-Hanks. We compiled a sample file of 10,000 “live” names, containing 12 fields. The names we used came from a variety of client sources. They were all names of individuals at business addresses. We asked the vendors to perform their typical hygiene processes on the data and send the results back to us within 30 days. We also asked them to answer some questions about their companies and their approaches to data hygiene. Finally, we requested that they do this work at no charge, for the benefit of members of the business marketing community. As you can imagine, this research project raised several fairly touchy issues. First, we were asking the vendors to open their doors, and reveal the results of their processes as compared to their direct competition. We gratefully acknowledge the courage and openness of the vendors who chose to participate. To reduce the competitive pressure, we are withholding the identity of the specific vendors in the report below (Table 5), which reveals the number of data elements corrected, by vendor. Second, everyone involved in the project recognized the importance of protecting the privacy of the business people and companies whose names happened to turn up on the sample file. If the vendors were to apply their actual standard data hygiene processes to the file, live data was required. To protect the privacy of those involved, we have decided not to publish the sample records of individual names and addresses after clean up. THE DATA HYGIENE COMPARATIVE ANALYSIS PROJECT TABLE 1: VENDOR DEFINITIONS OF DATA HYGIENE To make sure we were all talking about the same thing, we asked the vendors, “What is your company’s definition of data hygiene?” Acxiom “Purpose-driven data management practices and/or processes that promote data accuracy. Typically is applied to name and address data content, correction and completion.” DataFlux “A 5-phase data management cycle, including profiling (inspection), quality (correction), integration (merging and linking), augmentation (enhancement), and monitoring (auditing and control). Understand the data problems: improve the data.” Donnelley Marketing “A broad range of processes that collectively deliver the highest deliverability of an address: standardizing, correcting, updating and verifying.” Harte-Hanks “The process of solving business problems resulting from inadequate data quality: accuracy, completeness, timeliness, validity.”
  • TABLE 2: VENDOR-DESCRIBED DIFFERENTIATION We thought readers would find it helpful to understand how the vendors view themselves in comparison with their competition. So we asked the vendors,“What are the 5 most important ways your work differs from your competitors’?” HOW TO CLEAN UP YOUR MARKETING DATABASE 5 Acxiom 1. Ability to recognize and parse name, business name, and address components 2. Ability to recognize the difference between business and consumer entities 3. Ad hoc, batch, automated batch and real time support of hygiene solution delivery 4. Abilitec-enabled occupancy database tool 5. NCOA/ChangePlus DataFlux 1. Integrated data profiling and data quality technologies 2. Technologies developed in house, using the same core engine 3. Forthcoming capability to monitor data quality over time 4. Interface permits business users (non IT staff) 5. All processing done in one pass Donnelley Marketing 1. Proprietary file of 13 million businesses in the US and Canada, and over 8 million executive contacts and title elements, for verification, appending and addition of missing elements 2. 100% telephone verification of each business record at least once a year 3. Ability to track executives at their home or business addresses 4. OnePass system allows all B-to-B hygiene to be done in one continuous logical flow 5. Proprietary Mailability Score that ranks each address based on deliverability Harte-Hanks 1. Expertise in multiple vertical markets 2. Ability to provide customizable and flexible client-specific hierarchy of business rules 3. Objective selection of best-of-breed vendors of business files, to suit client needs 4. Integrated access to both USPS advanced postal products and HH proprietary data 5. Broad data management tool set
  • TABLE 3: VENDOR DEFINITIONS OF “BAD” DATA To understand any potential differences in the subject matter, we asked, “How do you characterize ‘bad’ B-to-B data?” TABLE 4: THE SAMPLE FILE Our compiled file of business names and addresses came from a variety of client sources. We aimed for 10,000 names, and resulted in 9,699 usable records. Each record contained the following fields: Last name First name Phone number Fax number Email address Business title Company name Address 1 Address 2 City State ZIP code Acxiom “Data that fails to meet specific data content requirements and/or cannot be used to fulfill a specific business purpose.” DataFlux “Any data that does not support the underlying processes or business applications built on that information.” Donnelley Marketing “There is no right answer. The answer is to look within the customer segment and identify what is ‘bad’ to them.” Harte-Hanks “Data that fails to support the mission of delivering the right message to the right individual through the right channel.” HOW TO CLEAN UP YOUR MARKETING DATABASE 6
  • HOW TO CLEAN UP YOUR MARKETING DATABASE 7 TABLE 5: TOPLINE CORRECTION COUNTS ON THE 9,699 RECORDS Each vendor reported slightly different counts, based on match rates and the data the vendors have on hand. You will notice that there are wide fluctuations in the counts on non-postal data, like fax, phone and email. This is because some vendors own current data, while others rent or lease it from third parties as needed by their clients. For this research project, we did not want the vendors to incur any out-of-pocket expense, so data owners delivered higher counts in these categories. Another reason for discrepancies is the way the vendors defined certain fields, like Address line 1 versus Address line 2. What strikes us as we look at these results is, for the most part, how similar they are. CONCLUSIONS & OBSERVATIONS This study suggests that large, reputable vendors will provide very similar services when it comes to postal address standardization and correction, such as ZIP+4 and NCOA (National Change of Address). The marketer should not ask the vendor to change a title or company name — this information must come from the customer. Marketers can request that the vendor provide phone numbers, fax numbers and email addresses, but these are not part of standard data clean up as defined by most vendors. The append rates for these elements will differ by vendor, and no vendor can provide 100% coverage. In short, it’s the marketer who must make the final call about customer data accuracy. Vendor 1 Vendor 2 Vendor 3 Vendor 4 ZIP codes corrected 344 479 446 864 ZIP+4s added 8652 9375 8706 9101 Carrier routes coded 9342 9381 9401 9121 Delivery points coded 9333 9324 9203 9101 Street addresses corrected (addr1) 1776 5387 7583 685 Street addresses corrected (addr2) 704 1588 NA 3007 City names corrected 592 472 470 648 State codes corrected 163 472 470 158 Phone numbers appended 2101 4097 NA 4183 Fax numbers appended 0 2931 NA 2834 Email addresses appended 520 861 NA 645 NCOA matches 760 760 778 689 Delivery point validated records 9151 9212 9203 9086 CASS certified records 9645 9596 9334 9589
  • This publication is part of a series entitled Business-to-Business Database Marketing, by Bernice Grossman and Ruth P. Stevens. Papers published to date include: “Our Data is a Mess! How to Clean Up Your Marketing Database” (October 2005) “Keep it Clean: Address Standardization Data Maintenance for Business Marketers” (February 2006) “Outsourcing Your Marketing Database: A ‘Request for Information’ is the First Step” (March 2006) These papers are available for download at www.dmrsgroup.com and www.ruthstevens.com BERNICE GROSSMAN is president of DMRS Group, Inc., a marketing database consultancy in New York City. She is past chair of the B-to-B Council of The DMA. Reach her at bgrossman@dmrsgroup.com RUTH P. STEVENS consults on customer acquisition & retention, and teaches marketing to graduate students at Columbia Business School. She is the author of The DMA Lead Generation Handbook, and her new book is Trade Show and Event Marketing, now available at Amazon. Reach her at ruth@ruthstevens.com
  • 9/23/2013 1 Data “Based” Marketing … an infrastructure blueprint for serving the empowered consumer Database Marketing Post Intensive Part 2 Marcus Tewksbury Global Vice President – Product Strategy Experian The Modern Consumer… HYPERCONNECTEDHYPERCONNECTED HIGHLY VOCALHIGHLY VOCAL MOBILEMOBILE EMPOWEREDEMPOWERED
  • 9/23/2013 2 … and Their Path to Purchase Not Just In Cartoons
  • 9/23/2013 3 It’s about applying what you know… where it’s needed • Data Enablement • Blueprint • Readiness Assessment • Q&A 6 Agenda
  • 9/23/2013 4 Why Are CMO’s Becoming Tech Focused? Data Obsession 2.8  Zettabytes On average, firms use less than 5% of the  data available to them. Gartner estimates that 70‐85% of data is  “unstructured”. 70‐85% The total worldwide volume  of data is growing at 59% per  year, with the number of files  growing at 88% per year. In 2012, the amount of  information created and  replicated was 2.8 zettabytes  (2.8 trillion gigabytes). 59% Data 88% Files 5%5% 60%McKinsey estimates that retailers could  improve operating margins by c. 60% by  better leveraging their customer data Source:  Microsoft, McKinsey & Co.
  • 9/23/2013 5 Following the $’s 0 5 10 15 20 25 30 35 40 45 50 2007 2010 2013 Measurement CRM DM Social Mobile Display Email Mass $50 $40 $45 Budgets swung from  Mass… and not going  back Dollars are swinging back, but  to growth areas Email volumes are  growing, but not  the % of budget.   Doing more… for  the same. Tech 10‐Year History
  • 9/23/2013 6 Tech 3‐Year History Tech Today
  • 9/23/2013 7 Big Data Digital Ubiquity = Tracking Ubiquity
  • 9/23/2013 8 Reducing to Bite Sized Chunks Linkage ONLINE OFFLINE
  • 9/23/2013 9 Addressability
  • 9/23/2013 10 Painting The Picture Tech Today
  • 9/23/2013 11 • Data Enablement • Blueprint • Readiness Assessment • Q&A 21 Agenda • Data Enablement • Blueprint • Readiness Assessment • Q&A 22 Agenda
  • 9/23/2013 12 • Data Enablement • Blueprint • Readiness Assessment • Q&A 23 Agenda
  • 9/23/2013 1 Deadly Sins and the Ten Commandments: How to Achieve Best-Practices Database Content and Key Metrics Reporting Jim Wheaton Principal, Wheaton Group 919-969-8859, jim.wheaton@wheatongroup.com www.wheatongroup.com 2 Overview of Wheaton Group • We provide the link between the data and the marketing. – Database construction, management and hosting. – Data mining and consulting. – Collaborate on multi-channel communication programs. • For example, outsourced database marketing department for Excelligence Learning Corporation, and White Cap Construction Supply. • Four Principals with over 120 years of experience across well over 100 clients, and many verticals. • A focus on B2B through our B2BMarketing.com joint venture.
  • 9/23/2013 2 3 Overview of Today’s Session • Best-Practices Marketing Database Content, the foundation for: – Analysis and measurement. – Data-driven CRM. • The First 5 Commandments of Best-Practices Content. • Insightful Key Performance Indicators (“KPIs” and “Dashboards”). • The Second 5 Commandments of Best-Practices Content. 4 The CRM Revolution: “Star Wars” Database & Business Intelligence Technologies • Access and manipulate massive amounts of data in seconds. • Powerful GUI interfaces for eye-catching dashboards & reports. • However, a caveat…
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  • 9/23/2013 5 10 Car Restorations and Best-Practices Content: Some Similarities • It’s all about hard, ugly work and attention to detail: – Data audits and other forms of quality assurance. – Capturing the essence of the business in the data. • Without all the hard work, the result will be what some car restorers call (crudely but accurately) candy-coated crap. • This is because bad content always costs you money! – An example from financial services…
  • 9/23/2013 6 11 Before We Start with the 10 Commandments, One Over-Riding Concept • The ability to rapidly create multiple past-point-in-time views is essential for just about all quality analytics. • For example: – Predictive analytics such as modeling. – Cohort analysis such as lifetime value estimations. – Monitor changes in customer inventories (KPIs). – Unanticipated back-in-time reporting capability, such as how to catch a serial killer. 12 Commandment #1: Customer Hierarchies Must Be Created & Maintained • Requires robust linkages across multiple database levels. • Supports, for example: – Insight into the true nature of multi-buyers. – Accurate performance metrics such as lifetime value. – Innovative targeting programs.
  • 9/23/2013 7 13 Commandment #2: Inquiry & Demand Transactions Must Be Maintained • Examples of demand transactions: – Retail and direct including e-commerce: orders and items. – Subscriptions and continuities: payments. • Include bill-to/ship-to (and, when applicable) sold-to linkages. – For B2B, universal applicability. – For B2B and B2C, seminal to gifting. 14 Commandment #3: Post-Demand Transactions Must Be Maintained • Track progression from Demand to Gross to Net, including: – Backorders and cancels. – Returns, refunds and rebates. – Exchanges and allowances. – Delivery issues. • Critical for large differences between Gross and Net, such as: – Trial periods at reduced or no cost. – Bad debt. – High-return businesses such as women’s apparel. • Improves predictions & customers needing remedial action.
  • 9/23/2013 8 15 Commandment #4: Promotion Transactions Must Be Maintained • Maintain all promotional contacts across all channels. – Do not forget email. – Field sales and phone “touches,” if you can get them. • Typical content: – Start date. – End date. – Coding (source codes, key codes, offer codes, etc.). – Offer terms (buy-one-get-one, percentage-off, dollars-off, etc.) • Example of a 7-figure system with no promotion history… 16 Commandment #5: Supplemental Sources Must Be Considered • For example: – Overlay demographics and psychographics. – For B2B, overlay “firmagraphics.” – Customer service (complaints, etc.). – Customer-generated gift messages. • New media inputs (e.g., social networks & complainers).
  • 9/23/2013 9 17 Key Performance Indicators: The Three Rules • Rule #1: Strive for simplicity. • Rule #2: Customer inventory report as the foundational KPI. • Rule #3: Supplement with “The Why KPIs.” 18 Key Performance Indicator: The Customer Inventory Report • Three factors determine monthly gross revenue (demand): – Number of customers. – Percent monthly buying rate. – Demand per buying customer. • Track monthly, including year-over-year. – Or, as appropriate, weekly, seasonal, etc. – For example…
  • 9/23/2013 10 19 The Customer Inventory Report: An Example 2008 2007 2006 2005 Number of Companies 50,000 45,248 41,283 38,029 Percent Monthly Buying Rate 25.0% 26.7% 26.9% 25.4% Demand Per Buying Company $300.00 $308.27 $301.83 $245.42 Total Revenue $3,755,910 $3,723,296 $3,354,804 $2,369,592 2008 2007 2006 YoY Number of Companies 10.5% 9.6% 8.6% YoY Percent Monthly Buying Rate -6.2% -0.9% 6.0% YoY Demand Per Buying Company -2.7% 2.1% 23.0% YoY Revenue 0.9% 11.0% 41.6% Segments 1-3 # of Month's Demand/ Com- Buying Buying Month panies Rate Company Revenue Oct 2008 10.5% -6.2% -2.7% 0.9% Sept 2008 11.3% 3.7% 4.1% 20.7% Aug 2008 11.8% -4.7% -5.8% 0.9% July 2008 12.4% -1.4% 2.6% 13.2% June 2008 11.9% -1.2% 1.3% 12.7% May 2008 12.6% -4.1% -3.5% 4.0% April 2008 12.4% 6.5% 1.2% 19.8% Mar 2008 11.1% -11.5% 0.1% -1.4% Feb 2008 11.4% 0.3% 2.9% 14.8% Jan 2008 11.2% 0.8% -3.0% 7.7% Dec 2007 10.2% 3.5% 8.1% 23.0% Nov 2007 10.0% -3.9% -4.3% 0.8% Oct 2007 9.6% -0.9% 2.1% 11.0% Sept 2007 9.7% -4.7% 3.9% 9.4% Aug 2007 10.5% -2.3% 11.9% 22.1% July 2007 11.6% 5.0% 8.8% 27.7% June 2007 11.7% 2.0% 11.1% 26.2% May 2007 11.4% 1.2% 9.7% 23.7% April 2007 11.5% 9.6% 9.8% 34.3% Mar 2007 11.6% -1.8% 1.9% 11.6% Feb 2007 11.4% 7.4% -1.2% 16.8% Jan 2007 10.1% 3.5% 8.3% 22.1% Dec 2006 9.0% 0.8% 6.4% 16.5% Nov 2006 8.7% 1.7% 16.1% 28.2% Oct 2006 8.6% 6.0% 23.0% 41.6% Percent Year-Over-Year Three Key Factors & Overall Revenue, Segments 1-3 20
  • 9/23/2013 11 21 Supplement with “The Why KPIs” • Include net revenue if have significant post-demand activity. • Include potential “why” factors, as appropriate, such as: – Backorder/cancel rates. – Out-of-stocks. – Returns/exchange rates. – Order-to-shipment turnaround. – Complaint levels. – Circulation variations. – Product changes. • For example, the case of the missing merchandise. • Now, for Commandments 6 through 10… 22 Commandment #6: Data Semantics Must Be Complete, Consistent & Accurate • Semantics = naming conventions & coding/classification schemes. – Beware of changes, and of different coding across divisions. • A common problem area is merchandise classification. – For example, class-department-division-season combinations. – Often reworked, but often not historically. • Add a customer point-of-view. – For example, a merchandise segmentation we did…
  • 9/23/2013 12 23 Commandment #7: The Data Must Not Be Archived or Deleted • Rolling off older data is a common phenomenon. – Disk space is cheap…so, why? • For example, models built off 36 months of data… 24 Commandment #8: The Data Must Be Maintained at the Atomic Level • Can always aggregate, but can never disaggregate. • For example, thanks to atomic-level data being maintained, the serial killer was caught.
  • 9/23/2013 13 25 Commandment #9: The Data Must Be Time-Stamped • Re-creation requires going beyond the naturally-date-driven. – Address changes, progression of change statuses, demographics, etc. • Modeling and product progression analysis. • “The Easter Monster” & other floating events that drive behavior. – The importance of relative analysis. • The serial killer was caught, but what if: – Only the most current address had been saved? – The old addresses had not been date-stamped? 26 Commandment #10: The Data Must Not Be Overwritten • After the financial services example, enough said!
  • 9/23/2013 14 27 The Ten Commandments of Best-Practices Marketing Database Content • Customer hierarchies must be created and maintained. • Inquiry and demand transactions must be maintained. • Post-demand transactions must be maintained. • Promotion transactions must be maintained. • Supplemental sources must be considered. • Data semantics must be complete, consistent and accurate. • The data must not be archived or deleted. • The data must be maintained at the atomic level. • The data must be time-stamped. • The data must not be overwritten. 28 For Additional Information • “Marketing Should Control the Marketing Database, Not IT,” Chief Marketer, April 15, 2011 • “True Marketing Databases Make Sophisticated Data Mining Possible,” Direct Newsline, August 19, 2010 • “How Marketing Databases Differ from Operational Databases,” Direct Newsline, June 29, 2010 • “The First Five Commandments of Database Content Management,” Multichannel Merchant, February 1, 2007 • “The Second Five Commandments of Database Content Management,” Multichannel Merchant, May 1, 2007
  • 9/23/2013 15 29 Deadly Sins and the Ten Commandments: How to Achieve Best-Practices Database Content and Key Metrics Reporting Jim Wheaton Principal, Wheaton Group 919-969-8859, jim.wheaton@wheatongroup.com www.wheatongroup.com
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 1 Why Marketing Should Control the Marketing Database, Not IT By Jim Wheaton Principal, Wheaton Group Original version of an article that appeared in the April 15, 2011 issue of “Chief Marketer” I have been a direct and database marketing consultant since 1984. In all that time, one consistent verity is that most internal IT departments think they can – and should – be responsible for the marketing database. In many instances, the IT department has no idea what it is talking about. Why is this? I think it has to do with the term “marketing database.” IT professionals hear the word “database,” and say, “Ah ha! That means a system, and systems are in my bailiwick.” Well, the IT guys are partly right, but they are mostly wrong. This is because, for the majority of direct marketers, the systems component of a marketing database is relatively trivial. Sure, there are some multiple-terabyte systems with near-real time update cycles, and dozens of users who need simultaneous access. But, most databases are much smaller, and with no more than one of two users accessing it at any given point in time. For these smaller applications, the real challenge lies with the content; that is, the “stuff” of which the database is constituted. This “stuff” can be very difficult to render consistent and usable because of three challenges, none of which lies within the bailiwick of an IT professional: Challenge #1: Name and Address Processing B2C account information must be aggregated to the individual and household levels. Likewise, B2B account information must be aggregated to the individual, site and organizational levels. These multiple levels of customer (and, when applicable, prospect) definition are required to: Perform accurate analysis, scoring, promotional selections, and response attribution. Properly allocate marketing-spend to each customer. In order to pull all of this off: First, address standardization, ZIP Code correction, parsing and unduplication technologies – guided by carefully-constructed business rules – must be employed to match accounts on a combination of names, company names, addresses, phone numbers, and – when applicable – bill-to/ship-to relationships. Then, the matches must be unified into a single non-circular cross-reference that: Assigns each account to one and only one individual. Assigns each individual to one and only one B2C household or B2B site. For B2B, assigns each site to one and only one organizational entity.
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 2 Finally, all of this must be maintained over time so that it is easy to make adjustments and enhancements, and re-consolidate the data, per ongoing quality assurance that is conducted on the matches. Challenge #2: Transaction Processing Hopefully, your customers are doing lots of buying. Most likely, the purchases are taking place across multiple channels. You almost certainly have at least one e-commerce site, and you probably have an in-bound call center. If some of your revenue comes from B2B, then you are likely to have an outbound sales team and/or field sales force. The data from each of these sources will have its own structure and anomalies. Multiple divisions often mean even more permutations of data structures and anomalies, especially when company mergers have taken place. The bottom line is that transactional data is not particularly usable in its raw format. In order to make the data usable, the semantics must be rendered historically complete, consistent and accurate, and correspond with core business concepts. Also, the data must be time-stamped and maintained down to the atomic level, and must not be overwritten, archived or deleted. Finally, the following must be included: Demand, as opposed to “shipped” or “completed,” transactions. Promotion transactions, even for those that did not result in a purchase. The following, when applicable: inquiry and post-demand transactions, and supplemental sources such as demographics, “firmographics” and social networks. Challenge #3: The Creation of Past-Point-In-Time Views A modern database must support – on-demand – any calculation, aggregation or subset that logically can be generated from the underlying data. This requires a mechanism to allow the efficient and rapid re-creation of multiple past-point-in-time (“time-zero” or “time-0”) views. Time-0 views are necessary because all of the dimensions to be analyzed cannot be known and "frozen" in advance. These views form the basis for virtually all meaningful analytics, by allowing customers to be classified based on detailed histories only up to the appropriate past-points-in-time. Cohort analysis such as lifetime value is an important example of data mining that depends on the re- creation of multiple time-0 views. Likewise, the analysis and validation files required for predictive models are based on time-0 views. Another application of cohort analysis is the monitoring of changes in customer “inventories,” such as fluctuations in segment sizes and performance over time. Still another is the analysis of historical trends within subsets of promotional channels, products and services offered, etc. Final Thoughts How many internal IT departments have the chops to handle these three challenges of marketing database content? Not many! Those that do are typically concentrated among companies in which the scale of the application is such that it makes sense to hire a team of experienced professionals.
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 3 Things are different for smaller database applications in which it is not cost effective to hire multiple experienced professionals, much less staff to the level of job-function redundancy required to counteract the inevitable resignations and terminations. All of this, to return to my opening statement, is why most IT departments have no idea what they talking about when they think they can – and should – be responsible for the marketing database. Jim Wheaton is a Principal at Wheaton Group (www.wheatongroup.com), and can be reached at 919-969-8859 or jim.wheaton@wheatongroup.com. The firm specializes in direct marketing consulting and data mining, data quality assessment and assurance, and the delivery of cost-effective marketing databases
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 4 True Marketing Databases Make Sophisticated Data Mining Possible By Jim Wheaton Principal, Wheaton Group Original version of an article that appeared in the August 16, 2010 issue of “Direct Listline” (This topic was first covered in the June 18, 2010 Direct Listline article, “How Marketing Databases Differ from Operational Databases.”) There is a big difference between a Marketing Database and an Operational Database. A Marketing Database supports sophisticated data mining and an Operational Database does not. Sophisticated data mining, in turn, is impossible without the ability to recreate multiple past-point-in- time (“time 0”) views. This is because data mining professionals work in the present, on the past, in anticipation of the future. For example, multiple customer and house non-buyer “time 0” views make it possible to: Create the analysis and validation files required for statistics-based predictive models. Generate the data for all cohort analysis, including lifetime value. Monitor changes in customer inventories, such as fluctuations in segment sizes over time. Multiple “time 0” views also support data mining to understand how lifecycle changes affect consumer purchase behavior. Direct marketers are lucky because, as a natural consequence of running their businesses, they receive all of the detailed order, item and promotion history required to perform lifecycle analysis. Retailers are not so lucky, unless they have a mechanism for identifying customers and tracking their behavior. That is where Loyalty Programs come into play. Let’s take a vertical industry – publishing – and work through a hypothetical example. Keep in mind that, although the specifics are peculiar to publishing, the general concepts are universal across vertical industries. We’ll begin with two assumptions: A publisher of a magazine that is targeted to people in their 20's and 30's wants to understand how changes in lifecycle affect renewal rates. The publisher hypothesizes that renewal rates are adversely affected as subscribers begin to raise families. If the publisher’s hypothesis is true, then we would expect to see a drop in renewal rates as subscribers move from multiple family dwelling units ("MFDUs”) to single family dwelling units ("SFDUs"), or from urban to suburban locations. With a properly constructed Marketing Database, multiple subscriber cohorts can be analyzed over time for such relationships; that is, from when they first signed up for the magazine though all of their subsequent renewal cycles. People in their 20's and 30's are notoriously mobile. For example, from the time I entered the workforce in 1980 to when I purchased my first (SFDU) home in 1988, I lived in five different
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 5 apartments in three different cities and states. Without being able to recreate time-0 information, it would be impossible to track this sort of customer movement. The inability to track customer movement is the unfortunate outcome of any Marketing Database designed such that, every time an address change is received, the previous address is over-written. Such a database will never be able to support data mining to understand how lifecycle changes affect customer purchase behavior, no matter how many years of history have been accumulated. Does your Marketing Database over-write address information as notifications of customer relocations are received? Are you even certain that you have a Marketing Database? Many companies think they have a Marketing Database when, in fact, what they really have is an Operational Database. I have seen this countless times when talking to prospective clients. If you want to know if you have a true Marketing Database, then take the five-step data processing test outlined in the June 18, 2010 Direct Listline article, “How Marketing Databases Differ from Operational Databases”: http://directmag.com/lists/0622-lists-how/ Jim Wheaton is a Principal at Wheaton Group (www.wheatongroup.com), and can be reached at 919-969-8859 or jim.wheaton@wheatongroup.com. The firm specializes in direct marketing consulting and data mining, data quality assessment and assurance, and the delivery of cost-effective marketing databases.
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 6 How Marketing Databases Differ from Operational Databases By Jim Wheaton Principal, Wheaton Group Original version of an article that appeared in the June 29, 2010 issue of “Direct Newsline” A Marketing Database must be able to perform all of the mission-critical analytical tasks required for data-driven marketing. Many people think they have a Marketing Database when, in reality, what they have is an Operational Database. An Operational Database supports essential “nuts and bolts” tasks such as customer service, fulfillment and inventory management. But, it falls short in the support of data-driven marketing analysis. To determine if you have a Marketing Database, take the following data processing test. If you can easily and rapidly execute the five tasks within the test, with no outside-the-system processing, then you will know for sure that you have a Marketing Database: FIRST: Examine the life-to-date detail for your customers as of June 1, 2009; that is, about a year ago. This is known as a past-point-in-time (“time-0”) view, which will be impossible to recreate if any of the following is true: Some of your customers as of June 1, 2009 are no longer in the system. Some of the historical data previous to June 1, 2009, for some or all of your customers, has been deleted or overwritten. You cannot exclude from your examination all historical data subsequent to June 1, 2009. SECOND: Rank your customers from best to worst, as they would have been ranked on June 1, 2009. Do this by evaluating each customer’s year-ago view by whatever selection system you use; that is, a statistics-based predictive model (or models), or some sort of rules-based logic such as Recency/Frequency/Monetary (“RFM”) Cells. THIRD: Divide the ranked customers into deciles; that is, into equal groups of ten. FOURTH: For each decile, calculate the following subsequent performance; that is, from June 1, 2009 through May 31, 2010: Average Per-Customer Revenue and Average Per-Customer Promotional Spend. Please note that the second will be impossible to calculate if you do not maintain all-important promotion history for all your customers on a campaign-by-campaign basis, regardless of whether a given customer did or did not respond to a given campaign. If you can do all this, then you might have a Marketing Database. To know for sure, you need to be able to do one last thing: FIFTH: Simultaneously for each of three additional past-points-in-time – that is, June 1 for each of the years 2008, 2007 and 2006 – create a standard File Inventory Report. The specifics will vary by the type of business you are in, but invariably will include: 1) permutations of customer counts, purchase rates and dollar amounts, and 2) year-over-year absolute as well as percent changes.
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 7 Components of your File Inventory Report should also double as Key Performance Indicators (“KPI’s”) that are closely tracked throughout the organization. If you can do all this, then you really do have an environment worthy of being called a Marketing Database. The reasons a Marketing Database needs to be able to do these five tasks are because: Database marketing is, by definition, driven by deep-dive data mining. Deep-dive data mining, in turn, requires the ability to rapidly recreate past-point-in-time (“time 0”) views, and then manipulate and report on the data within these views. In fact, it is common for multiple such views to have to be simultaneously recreated. Without this ability, you will not be able to efficiently execute any cohort analysis such as lifetime value. Nor will you be able to easily construct any statistics-based predictive models. Whether or not the Marketing Database and the Operational Database should be the same physical resource is an entirely different issue. And, an entirely different article. Jim Wheaton is a Principal at Wheaton Group (www.wheatongroup.com), and can be reached at 919-969-8859 or jim.wheaton@wheatongroup.com. The firm specializes in direct marketing consulting and data mining, data quality assessment and assurance, and the delivery of cost-effective marketing databases.
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 8 The First Five Commandments of Database Content Management By Jim Wheaton Principal, Wheaton Group Original version of an article that appeared in the February 1, 2007 issue of “Multichannel Merchant” This is the commencement of a quarterly column that will focus on best practices in data mining. We define data mining as all of the analytical methods that are available to transform data into insight. Examples include statistics-based predictive models, homogeneous groupings (“clusters”), cohort analyses such as lifetime value, quantitative approaches to optimizing contact strategies across multiple channels, and the creation of report packages and key-metrics dashboards. What this Column Will Not Be About We will not spend a lot of time comparing predictive modeling techniques and software packages. Much has been written, for example, about the merits of regression versus neural networks. Having participated in countless model builds, I speak first-hand to the fact that technique plays only a secondary role in the success or failure of a predictive model. Discussions about modeling techniques have always reminded me of the theological debate that took place many centuries ago about how many angels can dance on the head of a pin. Today’s data miners are fixated on their own pins and angels when they wrangle about techniques! A by-product of this wrangling is the fantastic claims made by proponents of some of these techniques. Unfortunately, such claims are pabulum for the gullible. The inconvenient truth, to borrow a phrase from a prominent national politician, is that technique has very little impact on results. There is only so much variance in the data, and the stark reality is that new techniques are not going to drastically improve the power of predictive models. What this Column Will Be About The focus will be on the truly important issues; namely, just about everything else having to do with data mining. For example, this month’s topic will be the significant improvements that are possible for optimizing the raw inputs to the data mining process. The ultimate goal is to perform data mining off a platform that we at Wheaton Group refer to as Best Practices Marketing Database Content. This, in turn, supports deep insight into the behavior patterns that form the foundation for data-driven decision-making. General Characteristics of Best Practices Marketing Database Content For starters, Best Practices Marketing Database Content provides a consolidated view of all customers and inquirers across all channels. Examples of channels include direct mail, e-commerce, brick-and-mortar retail, telesales and field sales. Sometimes – and particularly in Business-to- Business and Business-to-Institution environments – prospects are included.
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 9 Best Practices Marketing Database Content is as robust as the underlying methods of data collection are capable of supporting. The complete history of transactional detail must be captured. Everything within reason must be kept, even if its value is not immediately apparent. For example: One multi-channel marketer failed to forward non-cash transactions from its brick-and-mortar operation to the marketing database. This became a problem when a test was done to determine the effectiveness of coupons sent to customers, which were good for free samples of selected merchandise. The goal was to determine whether these coupons would economically stimulate store traffic. But, because the corresponding transactions did not involve cash, there was no way to mine the database for insights into which customers had taken advantage of the offer, and what the corresponding effect was on long-term demand. The Ten Commandments of Best Practices Marketing Database Content There are Ten Commandments that, if followed, will ensure Best Practices Marketing Database Content. Five are discussed this month, and the balance will be covered in the next column: #1: The Data Must Be Maintained at the Atomic Level All customer events such as the purchase of products and services must be maintained at the lowest feasible level. This is important because, although you can always aggregate, you can never disaggregate. Robust event detail provides the necessary input for seminal data mining exercises such as product affinity analysis. “Buckets” and other accumulations created from the data should be avoided. This is particularly important for businesses that are rapidly expanding, where it can be impossible to audit and maintain summary data approaches across ever-increasing numbers of divisions. One firm learned the hard way about the need to maintain atomic-level detail when it discovered that its aggregated merchandise data did not support deep-dive product affinity analysis. This is because, by definition, it was impossible to understand purchase patterns within each aggregated merchandise category. For example, with no detail beyond “Jewelry,” there was no way to identify patterns across subcategories such as Watches, Fine/Fashion Merchandise, Bridal Diamonds, Fashion Diamonds, Pearls/Stones, Accessories and Loose Goods. #2: The Data Must Not Be Archived or Deleted Within reason, data must not be archived. Likewise, it must not be deleted except under rare circumstances. Ideally, even ancient data must be retained because you never know when you might need it. Rolling off older data is perhaps the most common shortcoming of today’s marketing databases; an ironic development because, unlike ten or twenty years ago, disk space is cheap. Data mining can be severely hampered when the data does not extend significantly back in time. One database marketing firm experienced this when it tried to build a model to predict which customers would respond to a Holiday promotion. Unfortunately, all data content older than thirty- six months was rolled off the database on a regular basis. Remarkably, it was not even archived. For
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 10 example, the database would only reflect three years of history for a customer who had been purchasing for ten years. The only way to build the Holiday model, of course, was to go back to the previous Holiday promotion. This reduced to twenty-four months the historical data available to drive the model. More problematic was the need to validate the model off another Holiday promotion; the most recent of which had – by definition – taken place two years earlier. This, in turn, reduced to twelve months the amount of available data. As you can imagine, the resulting model was far from optimal in its effectiveness! #3: The Data Must Be Time-Stamped The use of time-stamped data to describe phenomena such as orders, items and promotions facilitates an understanding of the sequence of progression for customers who have been cross-sold. This is also true if customers are found to have purchased across multiple divisions during the incorporation of acquired companies. Corresponding data mining applications include product affinity analysis and next-most-likely-purchase modeling. #4: The Semantics of the Data Must Be Consistent and Accurate Descriptive information on products and services must be easily identifiable over time despite any changes that might have taken place in naming conventions. Consider how untenable analysis would be if the data semantics were so inconsistent that – say – “item number 1956” referenced a type of necktie several years ago but umbrellas now. Also, the reconciliation of different product and services coding schemes must be appropriate to the data-driven marketing needs of the overall business, and not merely to the individual divisions. #5: The Data Must Not Be Over-Written Deep dive data mining is predicated upon the re-creation of past-point-in-time “views.” For example, a model to predict who is most likely to respond to a Summer Clearance offer will be based on the historical information available at the time of an earlier Summer Clearance promotion. The re-creation of point-in-time views is problematic when data is overwritten. A major financial institution learned this in conjunction with a comprehensive database that it built to facilitate prospecting. After months of work, the prospect database was ready to launch. The internal sponsors of the project, anxious to display immediate payback to senior management, convened a two-day summit meeting to develop a comprehensive, data-driven strategy. One hour into the meeting, the brainstorming came to an abrupt and premature end. The technical folks, in their quest for processing efficiency, had not included in the database a running history of several fields that were critical to the execution of any data mining work. Instead, the values comprising these fields were over-written during each update cycle. The incorporation of this running history necessitated a redesign of the prospect database. The unfortunate result was a two-month delay, a loss of credibility in the eyes of senior management, and a substantial decline in momentum.
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 11 Final Thoughts The next column will focus on Commandments Six through Ten of Best Practices Marketing Database Content. In the meantime, consider whether your marketing database violates any of the first five Commandments. The extent to which it does is the extent to which your firm’s revenues and profits are being artificially limited. Jim Wheaton is a Principal at Wheaton Group (www.wheatongroup.com), and can be reached at 919-969-8859 or jim.wheaton@wheatongroup.com. The firm specializes in direct marketing consulting and data mining, data quality assessment and assurance, and the delivery of cost-effective marketing databases
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 12 The Second Five Commandments of Database Content Management By Jim Wheaton Principal, Wheaton Group Original version of an article that appeared in the May 1, 2007 issue of “Multichannel Merchant” There are Ten Commandments of marketing database content management. This first five were outlined in my February 1, 2007 column. This month, we will focus on the remaining five. But first, a synopsis of the February column: Data mining is enhanced, and often dramatically, when the source data is improved. The ultimate goal is for data mining to be performed off a platform that we at Wheaton Group refer to as Best Practices Marketing Database Content. This, in turn, supports deep insight into the behavior patterns that form the foundation for data-driven decision-making. Best Practices Marketing Database Content provides a consolidated view of all customers and inquirers across all channels. The complete history of transactional detail must be captured. Everything within reason must be kept, even if its value is not immediately apparent. There are Ten Commandments that, if followed, will ensure Best Practices Marketing Database Content. The first five as discussed in the February column are: #1 – The data must be maintained at the atomic level. #2 – The data must not be archived or deleted. #3 – The data must be time-stamped. #4 – The semantics of the data must be consistent and accurate. #5 – The data must not be over-written. The following are the balance of the Ten Commandments: #6: Post-Demand Transaction Activity Must Be Kept Post-demand transaction activity can include cancels, rebates, refunds, returns, exchanges, allowances and write-offs. These are essential for important exercises such as the identification of customers who will be less likely to make future purchases without remedial action. After all, customers who are disappointed by unavailable, ill-fitting or damaged merchandise, or poorly- conceived and improperly functioning services, will be less likely to purchase in the future. One common data mining application is attrition modeling. The capture of post-demand activity is particularly important in environments such as high fashion women’s apparel where return rates can be as high as 40%. Often, customers with similar gross purchase volume can have very different return rates. This, in turn, can make the difference between a profitable customer and one who is a continuous money-loser. It makes sense for predictive models to take such discrepancies into account when rank-ordering customers on expected behavior.
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 13 Tracking post-demand transactions can be a challenge because it requires the transactions to be retained by the underlying operational systems that feed the marketing database. Unfortunately, many operational systems are not equipped for this task. Instead, post-demand transactions vanish subsequent to a change in shipping status. For example, a “backorder” status will disappear once the corresponding item has been shipped. The following hypothetical sequence of events illustrates why this is problematic: Assume that an operational system feeds a marketing database update process on the first and fifteenth of every month. Also assume that a backorder is generated on June 2, and that the corresponding shipment takes place on June 14. By definition, the customer had to wait twelve days for the merchandise to shipped, which certainly is not ideal from a CRM perspective. If the operational system does not retain backorder statuses, then the June 1 and June 15 “snapshots” that feed the marketing database will fail to reflect the twelve-day wait. With only the June 12 shipment reflected, an important aspect of the customer relationship will have been lost! #7: Ship-To/Bill-To Linkages Must Be Maintained Often, these correspond to gift-giver/receiver relationships. Ship-to/bill-to linkages allow targeted promotions to extend the customer universe beyond those who made the original purchase. In fact, savvy database marketers look upon giftees as qualified prospects. In this way, customer databases can be used to drive targeted prospecting promotions, and often with formal data mining techniques. #8: All Promotional History Must Be Kept All promotional contacts across all available channels must be retained. This is necessary to rapidly and accurately create the past-point-in-time “views” required for most data mining projects, including predictive models. For multi-divisional firms, and especially those that have acquired other companies, it is important to appropriately handle different coding practices. One marquee, multi-billion dollar retailer with a substantial catalog/e-commerce division learned the hard way the importance of including promotion history. Although it spends seven figures a year on its CRM system, the underlying marketing database does not contain promotion history. As a result, most data mining projects take a week longer than they should, because of the extraneous processing required to overcome the lack of promotion history when creating analysis files. #9: Proper Linkages Across Multiple Database Levels Must Be Maintained For Business-to-Consumer (“B-to-C”) environments, individuals must be properly linked to households. For Business-to-Business (“B-to-B”) and Business-to-Institution (“B-to-I”) environments, individuals must be linked to sites, and sites to organizations. This allows the calculation of accurate performance metrics such as promotional financials, and for understanding the true nature of multi-buyers. Such links also enable the tracking of pass-along response, and for innovative targeting programs. For example, B-to-B and B-to-I direct marketers can monitor contract compliance across multiple sites within large client organizations. In such instances, discounted pricing is predicated on purchases not being made from the competition. With Best Practices Marketing Database Content,
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 14 sites within client organizations can be identified that have not received any mission-critical merchandise. Such sites may be out of contract compliance. #10: Overlay Data Must Be Included, As Appropriate For B-to-C, overlay data can be appended to create a complete view of customers, inquirers and, when applicable, prospects. Likewise for B-to-B and B-to-I, “firmagraphics” can be added to create a complete view of customers, inquirers, sites and organizations. One form of B-to-C overlay data is demographics for existing individuals and households on the marketing database, including date of birth, age, gender, marital status and presence of children. Another is the identity of additional adults within households on the database, along with their corresponding individual-level demographics. For B-to-B and B-to-I, firmographics include SIC or NAICS Code, Number of Employees, and Revenue. Also, additional individuals can be appended to sites that are resident on the database, and additional sites to organizations. One primary data mining application is the creation of profiles to “paint a picture” of customers and inquirers. However, the possibilities go far beyond that, and are limited only by the imagination. For example, date of birth can be employed to support birthday offers. Specifically, individuals with upcoming birthdays can be targeted with offers of special savings to “treat themselves.” Also, suitable gifts can be promoted to significant-others within the households. Such programs are especially lucrative for retailers. A Case Study of What Not to Do Last year, Wheaton Group was approached about a potential data mining project by a well-known gift-oriented, multi-billion dollar retail and direct marketing company that has been in decline. It soon became apparent that the firm’s marketing database content would support neither the project nor any other form of meaningful data mining. This is because: Data is archived after 36 months and is difficult to resurrect. Some portions of the database are maintained at the surname (“last name”) level and others at the individual level. For surname-level database records, only one individual’s identity is retained. This means that if a husband orders the first time, and then the wife orders – say – five subsequent times, the database will reflect six orders from the husband. This is particularly problematic for a gift-oriented business. To complicate matters, the database does not track bill-to/ship-to linkages and the corresponding gift relationships that these imply, nor does it contain gender codes. Often, the acquisition source is inaccurate, which renders problematic many worthwhile analyses such as long-term value. Also, merchandise coding discipline does not exist, the Website does not allow source codes to be entered, and customer records generally do not reflect post-demand transactions such as merchandise returns.
  • 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com 15 Promotion history is essentially unusable because the database tracks massive amounts of “spurious” activity; for example, “event occurrences” such as records that have been sent to the service bureau for National Change of Address (“NCOA”) processing. Also, there are significant problems with tying promotion history to specific names and addresses, and email promotions are not tracked at all. Finally, on the Retail side, distance-to-store calculations are based on imprecise ZIP-to-ZIP Centroids. And, they reflect only the nearest store, not where the actual purchase activity has taken place. Clearly, unless the company rectifies the appalling state of its marketing database content, it will have little chance of reversing its decline! Final Thoughts Consider whether you are working with Best Practices Marketing Database Content. The extent to which you are not is the extent to which you are artificially limiting the size of your firm’s revenues and profits. Also consider what methods you might employ to improve database content by enhancing the functionality of your operational systems. There are all sorts of ways to do this. But, that is the topic of a future article. Jim Wheaton is a Principal at Wheaton Group (www.wheatongroup.com), and can be reached at 919-969-8859 or jim.wheaton@wheatongroup.com. The firm specializes in direct marketing consulting and data mining, data quality assessment and assurance, and the delivery of cost-effective marketing databases
  • Introduction to Wheaton Group Wheaton Group LLC, launched in 1989 as “Strategic Insight” and renamed in January 2000, is a direct and database marketing services firm led by four Principals with over 120 years of experience across well over 100 clients and spanning: Business-to-business, business-to-consumer and B2B/B2C hybrids. Many vertical industries including catalog, consumer package goods, financial services, non-profit, publishing and telecommunications. All major selling and distribution channels including retail, direct (mail, phone and e- commerce) and field sales. Wheaton Group’s work is grounded in a continuous focus on data quality assessment and assurance. The firm’s core competencies include: The creation of marketing databases that offer the best-practices content required to support the most advanced forms of analytics, and hosted and maintained either by us or the client. Robust data management services including the execution of selects for multi-channel promotional campaigns. The leveraging of marketing database content through advanced analytics, reporting and quantitatively-grounded consulting. Wheaton Group also provides its services through the B2BMarketing.com joint venture. Biographies of Wheaton Group’s Four Principals Jim Wheaton has been a direct and database marketer since 1981. He began in line management. Then, he was a consultant at Kestnbaum & Company, Vice President of Research & Consulting at Wiland Services, Senior Vice President of Strategic Consulting at KnowledgeBase Marketing, and Co-Founder of Wheaton Group. Jim has authored well over 200 articles and speeches, is former Chairman of The DMA Analytics Council, and holds an MBA from The University of Chicago and a BA from Brown University. Cynthia Wheaton has been a direct and database marketer since 1978. She began in line management, spearheading new venture development at Sara Lee Direct and then at World Book Encyclopedia. One such venture was “Just My Size,” the national retail brand. Cynthia later served as VP of Marketing for GRI Corp. She became a consultant in 1986 at Kestnbaum & Company. In 1989, she launched Strategic Insight, the precursor to Wheaton Group. Cynthia has an MBA from the University of North Carolina at Chapel Hill as well as a BA in English. Boris Gendelev has specialized in marketing data warehousing, software development and analytics since joining the direct and database marketing consulting profession in 1983. He began at Foote Cone & Belding Direct Marketing Systems. Then, Boris was a Vice President at Precision Marketing, a position that he maintained throughout the Direct Marketing Technology (“Direct Tech”) and Experian acquisitions. Boris joined Wheaton Group in 2002 as a Principal. He has an MBA from The University of Chicago as well as a BS in Computer Science. Leo Sterk has specialized in strategic analytics since joining the direct and database marketing consulting profession in 1984. He began in the industry as a consultant at Kestnbaum & Company. Then, Leo was a Vice President at Precision Marketing, a position that he maintained throughout the Direct Tech and Experian acquisitions. Leo joined Wheaton Group in 2004 as a Principal. He has an MBA from The University of Chicago, and bachelors and masters degrees from the University of Illinois-Urbana in the field of urban planning. For more information, contact Jim Wheaton (919-969-8859; jim.wheaton@wheatongroup.com).
  • 9/23/2013 1 Leveraging Your Database:  Reporting, Templates and Strategic Applications Database Intensive: Part 4 Al Bessin, Bessin Consulting The Big Points • Background • Who is My Customer?  • Customer Balance Sheet • Media Reporting • Performance Measures  • Putting It All Together • Q&A
  • 9/23/2013 2 Premise: Marketing Database is Just A Tool • Need a Platform to support marketing – Objectives – Planning – Execution – Analysis Goal: To Maximize Customer Value • Relevant & timely communication increases value • Understand customer purchasing velocity • Proactively adjust your marketing and message
  • 9/23/2013 3 Identifying Your Customer Starting with a Clean Customer File • Normalizing to the unique customer is key to accurate  analysis
  • 9/23/2013 4 Customer Balance Sheet • Measuring changes in customer mix is an essential exercise • Take snapshots on a quarterly basis to account for seasonality Customer Balance Sheet Reporting • Summarize changes in customer file composition for better view of trends • Combine the Balance  Sheet with the  “Income Statement”  or view of what  happened in the  period
  • 9/23/2013 5 Customer File Views • Use views that support your  business trends and objectives 2009 2010 2011 Q1 210,926 233,993 259,835 New 23,278 24,067 25,263 Reactivated 10,751 12,372 13,545 Retained 176,897 197,554 221,027 Q2 213,272 231,955 259,211 New 18,808 16,815 16,260 Reactivated 7,657 8,653 9,225 Retained 186,807 206,487 233,726 Q3 214,355 235,929 256,320 New 14,374 17,568 15,486 Reactivated 9,002 9,996 9,854 Retained 190,979 208,365 230,980 Q4 229,730 255,929 - New 54,041 62,980 - Reactivated 21,169 24,637 - Retained 154,520 168,312 - Last Order 2006 2007 2008 2009 2010 2011 Total 2006 86,692 - - - - - 86,692 2007 16,615 72,524 - - - - 89,139 2008 15,344 14,111 65,794 - - - 95,249 2009 17,094 13,616 14,059 73,842 - - 118,611 2010 23,647 16,380 14,822 19,017 94,520 - 168,386 2011 41,249 22,889 18,767 20,790 29,011 91,927 224,633 Total 200,641 139,520 113,442 113,649 123,531 91,927 782,710 First Order Year Media Reporting
  • 9/23/2013 6 The Challenge • Number and types of marketing media have exploded • Consumer behavior continues to evolve  • Campaign analysis is increasingly complex  • Intense competition for marketing $s • Results are overstated Actual Total Demand Paid Search Catalogs Email Organic Search Social Marketing CSEs 60% Mass Media Don’t use 160% of actual  results to justify campaigns! Attribution Methodologies • Vendor reporting alone does not work • Matchback to contact files – Assumes all demand within windows is driven by the one contact – Only factors in push campaigns • Web order referring source (last touch) – Credits only the incoming medium – may simply be convenient • Last touch online plus matchback – Simple to implement, normalizes total demand – Can use weighting and fractional allocation • Multi‐touch attribution – Very complex, but still a model – Perils of cumulative errors for smaller populations
  • 9/23/2013 7 Resolve Demand Across Media • Most important – develop a holistic view of demand across all media • Start simply, if necessary, and then evolve Strive for a holistic view of marketing Order Management Feed Allocation Model Output Reporting Channel Demand Allocated Demand by Medium Demand Catalog $371,587 Catalog $527,742 Website $322,694 Email $108,128 Amazon $18,245 Paid Search ‐ Brand $7,843 Email $89,435 Paid Search ‐ Competitive $45,889 Total $801,961 Natural Search ‐ Brand $8,457 Natural Search ‐ Competitive $86,384 Comparison Shopping Engines $0 Google Analytics Feed Marketplaces $17,518 Allocated Demand by Medium Demand Total $801,961 Paid Search $85,971 Natural Search $134,674 Comparison Shopping Engine $0 Other $191,484 Total $412,129 Resolve Test to Determine Validity • For email, direct mail and telemarketing, holdout campaigns  are ideal tests – Simple – measure total purchases by each population – Sometimes management resists  Tips: • Test over sufficient time • Hold test groups constant • Ensure sample size is sufficient to get statistical significance
  • 9/23/2013 8 Performance Measures Background Overview of Financial Metrics • Financial Metrics – Sales – Gross Margin (differentiate between product margins and gross  margin) – S&H Contribution (Expense) – Variable Transaction Expenses – Marketing Expenses – Semi Variable Expenses – Fixed Expenses • Contribution Analysis – Typically to make incremental decisions • Solve for the cost to drive n+1 revenue – Relevant expenses are variable 
  • 9/23/2013 9 Defining Order Contribution • Order contribution  analysis is critical to  evaluating marketing  effectiveness • May require data  external to a marketing  database to complete Marketing Contribution and Breakeven • Solve for the demand needed to cover variable marketing expense – Catalog/Direct Mail/Email examples are simplest
  • 9/23/2013 10 Customer Value Analysis • Defining “Lifetime” – Financial ROI windows are typically one to two years – Buyer behavior typically falls off rapidly after a few years • Application of Value Analysis – Establish metric for acquisition cost – Basis to compare different media and quality of acquisitions “Lifetime Value” is the variable contribution in the first and  second years after initial purchase • Lifetime Value = Sum of Order Gross Margin less Variable  Transaction Expense less Marketing Expense for orders in a  one‐ or tw0‐year window after the initial order Example: Traditional vs. Digital Media LTV
  • 9/23/2013 11 Media Evaluation • Evaluate marketing programs based on – Cost of new buyer account acquisition – Comparative value of acquired buyers – Contribution across retained buyers – Marketing ROI • Tailor for maximum effectiveness for each target group – Contact type – Contact cadence Media Performance • Report on Media rather than by Order Method – It is much more relevant for marketing (and more predictive) Demand by Promotional Media Promotional Media Month TY Month LY ∆% TY/LY Catalog $1,282,145 $1,155,720 10.9% Natural Search ‐ Branded $124,778 $41,710 199.2% Natural Search ‐ Non Branded $58,486 $14,586 301.0% Paid Search ‐ Branded $52,313 $0 Paid Search ‐ Non Branded $26,032 $200 12915.9% Web (Other) $277,457 $307,173 ‐9.7% Email $179,419 $114,522 56.7% Grand Total $2,000,630 $1,633,911 22.4% $0 $500,000 $1,000,000 $1,500,000 $2,000,000 $2,500,000 Catalog Natural Search ‐ Branded Natural Search ‐ Non Branded Paid Search ‐ Branded Paid Search ‐ Non Branded Web (Other) Email Grand Total Allocated Demand by Marke ng Medium Month TY Month LY
  • 9/23/2013 12 Acquisition Costs by Marketing Media • Cost per acquired new buyer is part of the equation TY 1st Order Contribution LY 1st Order Contribution TY New Buyers LY New Buyers #% TY/LY ∆% TY/LY Catalog (4.32)$ (4.75)$ 2,345 2,568 (223) ‐9% Email 14.20$ 16.19$ 212 84 128 152% SEM ‐ Branded 8.56$ 10.02$ 217 21 196 933% SEM ‐ Competit (1.31)$ (0.05)$ 648 500 148 30% SEO ‐ Branded 8.56$ 10.86$ 213 123 90 73% SEO ‐ Competit 10.31$ 10.21$ 875 722 153 21% Comparison Sh 6.32$ 7.52$ 236 ‐ 236 ‐ Marketplace 5.17$ 6.23$ 145 ‐ 145 ‐ Total 1.43$ (0.48)$ 4,891 4,018 873 22% Customer Value by Acquiring Media  • Understand the real value of media by looking at the  downstream value of buyers – that is the rest of the equation 1st Time Buyer 12 Month Activity Post Initial Purchase 1st Order Promotional Media 12M 1st Time Custs 1st Order Demand 1st Order AOV Subsequent 12 Mo Orders Subsequent 12 Mo Demand Orders/New Customer Demand/Ne w Customer Catalog 22,111 $1,587,013 72$ $5,820 $468,445 0.26 $21.19 Natural Search ‐ Branded 5,906 $443,547 75$ $1,195 $118,929 0.20 $20.14 Natural Search ‐ Non Branded 4,961 $287,175 58$ $786 $65,575 0.16 $13.22 Paid Search ‐ Branded 966 $78,075 81$ $95 $10,680 0.10 $11.06 Paid Search ‐ Non Branded 935 $57,866 62$ $54 $2,890 0.06 $3.09 Advertising 18,637 $1,319,660 71$ $4,063 $411,634 0.22 $22.09 Email 7,054 $782,065 111$ $1,554 $231,113 0.22 $32.76 Grand Total 60,570 $4,555,400 75$ $13,567 $1,309,265 0.22 $21.62 22,111 5,906 4,961 966 935 18,637 7,054 $21.19 $20.14 $13.22 $11.06 $3.09 $22.09 $32.76 $0.00 $5.00 $10.00 $15.00 $20.00 $25.00 $30.00 $35.00 ‐ 5,000 10,000 15,000 20,000 25,000 Catalog Natural Search ‐ Branded Natural Search ‐ Non Branded Paid Search ‐ Branded Paid Search ‐ Non Branded Adver sing Email $DeamdnaerAcquision #ofNewBuyers 12‐Mo Demand per Buyer by Media a er Acquisi on 12M 1st Time Custs Demand/New Customer
  • 9/23/2013 13 Downstream Media Response • Identify variances in how customer acquired by different  media respond to other media downstream Campaign Performance • Compare campaigns across different media using the same metrics Customer
  • 9/23/2013 14 Product Performance by Media • Compare merchandise sales by different media Demand by Media Catalog SEO‐Brand SEO‐Other SEM‐Brand SEM‐Other Email Other Online Total Appliances $283,477 $23,247 $17,886 $3,028 $31,093 $28,883 $132,816 $520,430 Food/Cooking $609,122 $67,180 $37,970 $37,705 $481,807 $147,761 $107,151 $1,488,696 Garden $167,907 $16,453 $5,999 $6,823 $9,928 $32,600 $22,997 $262,707 Books $75,566 $7,158 $2,877 $2,951 $1,159 $15,994 $12,774 $118,479 Other $76,382 $9,357 $1,807 $2,653 $463 $9,446 $24,909 $125,017 Personal Care/Clothing $88,309 $5,332 $2,866 $2,756 $349 $13,677 $9,047 $122,335 Books/DIY $12,610 $1,388 $267 $421 $1,005 $6,167 $7,109 $28,967 Gifts $42,168 $2,222 $1,912 $1,399 $2,075 $6,726 $4,588 $61,091 Housewares $240,118 $27,985 $20,271 $12,776 $8,244 $56,617 $39,067 $405,077 Tools $173,669 $21,974 $15,946 $9,247 $14,575 $50,377 $33,193 $318,981 Lighting $167,641 $24,718 $17,882 $14,796 $3,585 $55,212 $37,360 $321,194 Toys $22,422 $2,301 $1,787 $1,059 $564 $6,596 $2,717 $37,445 Total $1,959,391 $209,315 $127,468 $95,614 $554,847 $430,057 $433,727 $3,810,419 Percent of Total for Media Catalog SEO‐Brand SEO‐Other SEM‐Brand SEM‐Other Email Other Online Total Appliances 14.5% 11.1% 14.0% 3.2% 5.6% 6.7% 30.6% 13.7% Food/Cooking 31.1% 32.1% 29.8% 39.4% 86.8% 34.4% 24.7% 39.1% Garden 8.6% 7.9% 4.7% 7.1% 1.8% 7.6% 5.3% 6.9% Books 3.9% 3.4% 2.3% 3.1% 0.2% 3.7% 2.9% 3.1% Other 3.9% 4.5% 1.4% 2.8% 0.1% 2.2% 5.7% 3.3% Personal Care/Clothing 4.5% 2.5% 2.2% 2.9% 0.1% 3.2% 2.1% 3.2% Books/DIY 0.6% 0.7% 0.2% 0.4% 0.2% 1.4% 1.6% 0.8% Gifts 2.2% 1.1% 1.5% 1.5% 0.4% 1.6% 1.1% 1.6% Housewares 12.3% 13.4% 15.9% 13.4% 1.5% 13.2% 9.0% 10.6% Tools 8.9% 10.5% 12.5% 9.7% 2.6% 11.7% 7.7% 8.4% Lighting 8.6% 11.8% 14.0% 15.5% 0.6% 12.8% 8.6% 8.4% Toys 1.1% 1.1% 1.4% 1.1% 0.1% 1.5% 0.6% 1.0% Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Catalog 54% SEO‐Brand 4% SEO‐Other 3% SEM‐Brand 1% SEM‐Other 6% Email 6% Other Online 26% Appliances Product Purchase Propensity • Marketing Databases  with order detail contain   a wealth of predictive  value – Note that product  categorization useful for  marketing is often not the  same as categorization  used by merchants
  • 9/23/2013 15 Order Value Analysis • Replace “Average Order” with Order Value profiles for added  insight • Averages don’t tell the story • Compare different media Putting It Together
  • 9/23/2013 16 Top Down Reporting • Create a high level marketing dashboard relevant to your business • Detail can be provided in a series of supporting reports, as needed Campaign Support • Classic Predictive Response – Recency, Frequency, Monetary – Simple Bracketing – Model Scoring • Order Method – Store, Website, Call Center – Less predictive for direct channels than in the past • Media Responsiveness – Predictive, but hard to measure – Test to validate measurement criteria • Product Preferences – Categorization should be customer‐driven – Often not the same categories as merchants use
  • 9/23/2013 17 Reporting and Tools • Business Intelligence – Classic Approach – Business Objects – Cognos – SAS • Analysis Tools – Interactive Approach Tableau Tips • Remember, tailor your reporting and target metrics  to your business • Behavioral science requires both qualitative and  quantitative analysis • Don’t be afraid to apply an 80:20 solution – remember opportunity cost • Start at the top and then drill down – When stuck go up a level • When presenting to management, start minimalist  • Transactional costs not reported through your  marketing database can be estimated from a P&L  and added as a cost per order
  • 9/23/2013 18 The Future • Understanding the full consumer picture – moving from unknown to known behaviors Acquire Engage Convert Maximize Clicked on a handbag display ad Searched for shoes Browsed shoes, pants High-value shoe content / offer Email cross- sell pants, handbag Target site content pants, handbag Purchased shoes Purchased handbag Loyalty club invite – high- value offer • Display click: pants • Search on shoes • Site visit: shoes, pants • Displaying high-value browsing behavior • Interest in shoes, pants, handbags • Purchased shoes • Email & location • High value Questions and Answers Competitive advantage in the future will live in how effectively an organization can understand, track, engage, measure & influence consumer behavior. Al Bessin Principal, Bessin Consulting abessin@bessin-co.com 512.745.9070
  • 9/23/2013 1 Modeling & Analytics Database Marketing Intensive Part 5 Steven C. Kim VP, Quantitative Marketing Group Merkle How can you turn your kid into a  world‐class footballer?
  • 9/23/2013 2 Analytics defined… …the extensive use data, statistical and quantitative  analysis, explanatory and predictive models, and  fact‐based management to drive decisions and  actions.  The analytics may be input for human  decisions or may drive fully automated decisions.   Analytics are a subset of what has come to be  called business intelligence: a set of technologies  and processes that use data to understand and  analyze business performance (Davenport, 2007). There are six core CRM capabilities that must be mastered  in order to create sustainable competitive advantage Insights Interactions Measurement Optimization Segmentation enterprise | behavioral | needs | lifestage | affluence Predictive Modeling regression | latent class | bayesian | neural networks Attribution matchback | cross-channel | probabilistic | digital Marketing Mix media data | mix models | media plans Customer Value lifetime value | engagement | social influence Personalization offer & content arbitration | recommender systems Test & Learn experimental design | testing roadmaps | controls Forecasting time-series | simulation | scenario planning Customer Lifecycle definitions | historical analysis | extrapolations Contact Strategy timing models | investment decisions | cadence Reporting & BI dashboards | visualization | diagnostics & benchmarks Optimization goal maximization | optimized scenarios Core Analytic PillarsCore Analytic Pillars Information Customer Profiling descriptive comparisons | opportunity analysis Data Mining exploratory analysis | text mining | visualization Research market research | conjoint | hierarchical value maps Agility Competition market sizing | share of wallet | share of voice Organizational goal alignment | decision making requirements Improvement learning roadmaps | business case development
  • 9/23/2013 3 So what is a model? OR Or this? OR
  • 9/23/2013 4 Modeling defined… …a formalization of relationships between variables  in the form of mathematical equations.  A model  describes how one or more random variables are  related to one or more other variables. In other words, we take a bunch of data, do some  complex math, in order to predict some cool stuff Inputs Modeling Outputs Data Math Prediction Price Regression Model Sales Web Activity Logistic Regression Churn
  • 9/23/2013 5 Regression Model Total Sales = w + bSE TV GRPs Sales w b 1 unit 1. If there is no TV investment, you still have “W” sales 2. The slope of the relationship is the coefficient that predicts “b”. Or said another way, it’s the increase in sales due to an additional unit of sponsorship spend 3. Coefficient also predicts the decrease in sales due to decreased sponsorship spend So how can you predict box office receipts? Gross Cost ROI $1.2 Billion $200 Million $1 Billion $243 Million $225 Million $18 Million
  • 9/23/2013 6 Neural Network Model Source: R. Sharda, D. Delen / Expert Systems with Applications 30 (2006) 243–254 What have you predicted already and what  do you want to predict going forward? Inputs Modeling Outputs Data Math Prediction ? ? Table  Discussion
  • 9/23/2013 7 The Case for Customer Centricity Q1 The Case for Customer Centricity Q1 Customer  Segmentation as  the basis for  Customer Strategy Incremental  Measurement to  drive Decision  Making Alignment on  Customer Value Source: Big Data’s Biggest Role Aligning The CMO & CIO Greater Partnership Drives Enterprise‐Wide Customer Centricity March 2013
  • 9/23/2013 8 Why It’s Hard What is Currency? Currency, n.  (pl. –cies) 1. Medium of exchange that is in current  use in a particular country 2. General acceptance or circulation;  prevalence
  • 9/23/2013 9 What is Customer Currency? The Role of Customer Currency A common definition of the customer is in order  to define the desired market and a differentiated  value proposition to those customers Segmentation A common methodology that defines the value of  a customer in order to be used for measurement and investment decisions  Customer Value A system to determine the true return and impact  of an event or action in order to drive fact‐based  decision making Incremental  Measurement 
  • 9/23/2013 10 Why Customer Value? “If you can't sort out your customers  ‐‐ if you can't look at them and know  who is good and who is bad ‐‐ then  you can't be customer centric.   That's step one.” Peter Fader Wharton Marketing Professor Customer Value Phenomenon 20% 40% 60% 80% 100% 120% 20% 40% 60% 80% 100% ~20% of  customers  drive 80% of  value ~85% of customers contribute  positively to aggregate value ~15% of  customers  are value  detractors High Value Medium to Low Value Value Detractors cumulative % of profits
  • 9/23/2013 11 Customer Value Example Value Tier % of Customers % of  Revenue % of  Margin Rev / Customer  (Index) Mar /  Customer (Index) Diamond 5% 29% 32% 572 644 Platinum 15% 36% 39% 237 262 Gold 60% 36% 35% 56 59 Silver 15% 1% 1% 7 5 Bronze 5% 1% ‐8% 25 ‐155 Total 100% 100% 100% 100 100 Prioritization of customers to invest in versus those to minimize spend Customer Value Model Customer  Value Monetary Value Engagement Value= + Logical components of the customer value equation + Monetary Value Engagement Value+ Life‐to‐Date Expected Future Deterministic Include direct revenue and  costs of servicing client Predicted Modeled based on factors such  as prior behavior Monetary Value:    Variable revenues and costs associated with a customer Engagement Value:  Non‐monetary customer interactions with the brand 
  • 9/23/2013 12 Customer Value Applications Previous View of NPS NPS through Customer Value Understanding the value of  each customer and  differentiating marketing,  sales, and service experience  accordingly, can ideally grow  individual current and future  potential value of each  customer to the individually  optimal level Customer Value Applications Affiliat e Index Compared to Overall 4 Star 3 Star 2 Star 1 Star 0 Star #1 143 156 157 120 39 #2 167 146 132 106 47 #3 129 120 103 91 86 #4 108 120 119 96 86 #5 117 98 98 97 96 #6 98 92 92 96 108 #7 81 98 98 93 112 #8 75 79 93 97 119 #9 70 71 76 97 129 #10 62 67 74 90 138 • Affiliates drove a significant  number of online transactions  and orders • Lack of insight and consistent  measurement of affiliate  performance • Applied customer value lens  to the actual affiliate  performance • Top affiliates bring in five  times as many 4‐Stars and  one‐fifth as many 0‐Stars as  the bottom affiliates
  • 9/23/2013 13 Segmentation Role CUSTOMER CENTRICITY Customer Segmentation  Customer Strategy Strategy Execution segment brief customer initiatives How to Segment? Behavioral (what are my customers  doing) Attitudinal (understand how my  customers feel) Motivational (understand WHY my  customers make  decisions to engage)
  • 9/23/2013 14 Means End Theory Means‐End Theory proposes consumers make purchasing decisions not simply because  of the product features, but more specifically how those features provide benefits that  get them to deeply seated personal goals Most research does not address the Emotional & Personal Values layer and thus cannot  create personal relevance bridges between product attributes and personal values The things directly associated with the product or brand Example:  Dropped call guarantees from mobile provider The positive aspects of the attributes, why they are important Example:  Call quality and service The emotional benefits of the functional benefits Example:  Good cell service makes me feel safe The personal values why those emotions are important to us  Example:  Safety links to my value of family security  Personal Values Emotions Psychosocial Consequences Benefits Functional Consequences Brand / Product Attributes MAVEN Approach Personal Values Emotions Psychosocial Consequences Benefits Functional Consequences Brand / Product Attributes Mapping Attitudes Values & Emotional Needs Applying means‐end theory to segmentation enables us to first  identify the personal relevance bridges of motivational drivers of  consumer choice Segmentation then focuses on splitting based on motivational  drivers, not simply our attitudes or behaviors with a brand – WHY  we do what we do Segment A Segment B
  • 9/23/2013 15 Consumer Decision Making Emotions Psychosocial Consequences Benefits Functional Consequences Brand / Product Attributes Personal Values Self‐Esteem Personal Accomplishment Competitive Advantage Earn More  Money More Productive Real‐Time Record of Package Handling Get PromotedSaves Time and Effort Makes Me Look GoodConvenient Drop Box Package Tracking Software Reliable On‐Time Delivery Not Responsible  for Someone  Else’s Error Less Worry  about Unknowns Peace of Mind Personal Control The optimal segmentation brings primary  research and database together Meet… Darby Susan Angela Christopher Demographics Younger Upper‐mid income Mature Upper‐mid income Young/middle age Mid income Young/middle age Lower‐mid income Motivated by • Tech savvy, early adopter • Status & recognition • Driven, risk taker • Personal pride • Decision‐maker in family • Researcher, knowledgeable • Family oriented • Child‐focused lifestyle • Price sensitive • Means to an end • Recent graduate • Price conscious Shopping  Behavior • Shops at Nordstrom • Shops online at Amazon • Shops at Target • Shops at Walmart Young Trendsetter Very Rich Actionable Primary Research • View of US • Attitudes, Behaviors, Psychographics • Brand Relationships, and Sentiment Mapping • Tie to each individuals in US • Demographics • Customer Behaviors • Syndicated Data Time Savvy Mom ThrifterEnlightened Consumer
  • 9/23/2013 16 Segment Brief PRODUCT PRICE CHANNEL MEDIA Mobile phone  accessories Ahead of the curve Exclusive with value Increased product  exclusivity while  retaining relatively  low price points Social media, email,  message boards Sharing product  information, getting  feedback from peers $29M 5% Direct mail 52% Email 14% TV/radio 29% Social media 32% share  |  Customer Loyalty: 90  |  Up Sell: 160  |  Retention: 85 Increase social media exposure (sharing = additional unpaid advertisement) Lower in‐store prices slightly for increased value proposition 20% increase in in‐store traffic= 150% increase in revenue Impacts other segments through word of mouth, aspiration PERFORMANCE ACTION PLAN FINANCIAL IMPACT Young  Trendsetter Monetizing Segmentation Determine where investments should be made ROIC EBITDA Capital Charge Marketing Expenses Operating Expenses Revenue Other Expenses Basket Size Invested Capital # of Trips Cost of Capital Number of Trips Cost per trip Promo Dollars COGS Fixed Working Business Case Framework Opportunity: Increase  Segment Traffic Incremental $48 million LTV ‐ ‐ ‐ x x x Assumptions • Average LTV: $1,000  • Discount rate: 11% • In store conversion: 58% • Segment mix from 32%  50% 32% 50%
  • 9/23/2013 17 Got ROI?
  • 9/23/2013 18 Incremental Measurement • Incremental measurement is the evaluation of changes in  KPIs given a decision or event • An Incremental Metric is the difference between the KPI  given you made a decision minus the KPI had you not made  that decision • Why Incremental? • Asking what benefit would be realized from a decision applies  universally, whether talking about a choice of media, what new  product to launch, or whether an ecommerce site should be  launched • When combined with a standard metric of customer value any  decision within an organization can be evaluated based on the  question, “If we did this, what would be the impact on total  customer value?” The Fundamentals Predictive model based creation  of baseline measures Scientific based experimental  testing using controls There are really only two ways to measure things and  both involve the creation of a baseline metric to  measure against to determine what would’ve happened Observed Experiments  Predictive Estimation
  • 9/23/2013 19 Observed Experimentation Desired  Learning A|B Test Multivariate  Test Single  Factor Multiple  Factors Analysis MethodTesting Type Benefits & Uses Creation of single  control group for two  way comparisons Design of experiments  enabling measurement of  multiple factors or testing  elements at once T‐Test Multivariate  Analysis (anova / ancova) • Proper test and learn strategies require a statistical based approach to  testing design • Effectively there are two types of scientific tests:  Predictive Estimation The use of predictive modeling for measurement purposes typically involved  econometric‐based modeling methods, generally forecasting and mix modeling 0 20 40 60 80 100 120 1 2 3 4 5 6 7 8 9 10 11 12 Sales Volume Time Periods Actual Baseline Measurement  Area between the lines is  incremental predicted actual
  • 9/23/2013 20 Holy Grail of Marketing • From a marketing perspective, the greatest application  for incremental measurement is attribution • True attribution of marketing tactics is not simple as  return on marketing is a function of two dimensions: • The ability to properly allocate media dollars by vehicle  type • The ability to properly target and personalize that media  dollar into relevant experiences  • The ability to incrementally measure both of the above  requires both observed and predictive methodologies True attribution is not easy First/Last Click Approach Advanced Analytic Approach The customer purchase path has become more complex Data proliferation has made data mining increasingly difficult Organizations lack support to drive deeper marketing insights Source: Forrester Research “Cross‐channel attribution presents a clear path to marketing ROI” September 20, 2012 
  • 9/23/2013 21 Attribution defined… TV viewDirect mail Newspaper view Display view Social visitWebsite visit Paid search click Mass and Offline Digital Day 8‐30 Day 1‐7 Day 0‐1 New  Customer Actual  experience $ 0% 100% Credit over applied to bottom  of funnel touches; other  touches often ‘invisible’ Creates flawed financial  view of performance Direct or Rules  Based  3% 14% 3% 5% 5% 5% 15% 5% 5% 40% Modeled Model‐adjusted  interaction Most accurate and  actionable  …assess media performance by measuring the  incremental impact of each marketing activity How to do it National media (TV & radio) $140 Local media (TV & radio) $200 Direct mail $180 Digital $83 Cost per inquiry by tactic Top‐down media mix model (Traditional media mix model: DMA by week level, 12+ months of data)  Calibration layer Bottom‐up customer modeling (Consumer level, machine learning algorithm, 6+ weeks of data  Display/ Video $60 Email $80 Paid Search $91 Social $113 Campaign $ Cost per inquiry by tactic Direct mail $180 Placement $ Creative $ Digital Attribution Anonymous (Cookie Level) Direct Attribution PII (Email, Physical Address) + @ Network $ Program $ Creative $ Segment $ Engine $ Branded $ Keyword Segment $ Campaign $ Program $ Campaign $ Segment $ Creative $ Program $ Campaign $ Offer $ Creative $
  • 9/23/2013 22 Calculate your own ROI Assess how you are currently measuring ROI within and across all marketing channels Start the conversation with your organization
  • 9/23/2013 1 NAVIGATING THE DATA MAZE Database Marketing Post Intensive: Part 6 Presented by: Joanne Branscum & Doug Christiansen Acxiom Global Consulting Leveraging the ability to collect, connect, analyze & act on  massive amounts of messy data to make money (or do good) About Acxiom • Acxiom is an enterprise data, analytics and software as a  service company that uniquely fuses trust, experience and  scale to fuel data‐driven results • For over 40 years, Acxiom has been an innovator in  harnessing the most important sources and uses of data to  strengthen connections between people, businesses and  their partners • Utilizing a channel and media neutral approach, we  leverage cutting‐edge, data‐oriented products and services  to maximize customer value • Every week, Acxiom powers more than a trillion  transactions that enable better living for people and better  results for our 7,000+ global clients
  • 9/23/2013 2 Roadmap For The Discussion The Definition  Of Big Data Challenges and  Opportunities Group Participation Discussion Data is becoming the new raw material of business: an economic input almost on a par with capital and labor. “Every day I wake up and ask, ‘how can I flow data better, manage data better, analyze data better?” Rollin Ford – CIO of Wal‐Mart.
  • 9/23/2013 3 What Is Big Data?  How 6 Blind Men Describe an Elephant The REAL Definition of “Big Data” Big Data is about the ability to collect, connect, analyze, and act on massive amounts of messy data to make money (or do good)
  • 9/23/2013 4 DRIVERS FEATURES What’s Driving Big Data BIG  DATA NON‐ TRADITIONAL  DATA TYPES DATA  VOLUMES DATA  SOURCES TOOLS AND  TECHNOLOGIES STORAGE AND  COMPUTE  ECONOMICS BUSINESS  INSIGHTS AND  OPPORTUNITIES It’s not about technology The business opportunities drive the data It’s about making the data usable You have to have a plan It is not a once and done project Our View On Big Data You’ve been doing ‘big data’‐‐ it’s just gotten more  interesting Change takes time
  • 9/23/2013 5 The Components of Big Data CONNECT THE INFORMATION ANALYZE TO CREATE INSIGHTS ACT TO DRIVE VALUE COLLECT THE DATA ORGANIZE TO INITIATE ACTION Roadmap For The Discussion The Definition  Of Big Data Challenges and  Opportunities Group Participation Discussion
  • 9/23/2013 6 Acxiom’s Big Data Capabilities Model Dimensions (Distinctive feature of a capability) Capabilities (Ability to perform strategic actions) CONNECT THE INFORMATION ANALYZE TO CREATE INSIGHTS ACT TO DRIVE VALUE COLLECT THE DATA ORGANIZE TO INITIATE ACTION People and  Places Products and  Services Interactions and  Outcomes Anonymous Entities  to Known Individuals  Business Level Algorithms People Resource Capabilities Analytic  Processes Tools and  Technology Consumer  Decision  Process Preferences and  Propensities Channel  Agnostic Delivery Optimized Marketing  Actions 1.1 1.2 1.3 Privacy and Data  Usage Rights 1.4 2.1 Data Stewardship  and Governance Information Supply Chain 2.2 Information Assets and Flows 2.3 3.1 3.2 3.3 3.4 4.1 4.2 4.3 4.4 Organizational  Alignment Strategic  Clarity Organizational  Change Quotient Innovation to  Action 5.1 5.2 5.3 5.42.4 Group Participation Discussion
  • 9/23/2013 7 Roadmap For The Discussion The Definition  Of Big Data Challenges and  Opportunities Group Participation Discussion Harnessing large and diverse data sets  Navigating privacy concerns  Making the data actionable  Bridging digital and traditional sides of the business   Known and anonymous data linkage Key Big Data Challenges Noise – “needle in the haystack” Dealing with all the partners and changing ecosystems
  • 9/23/2013 8 Guardrails aren’t intended to  stop us from driving, just  prevent us from going over the edge… …the same applies to privacy compliance and Big Data. Newer Sources of Data Are Changing  The Landscape Imagine The Possibilities Experiment beyond the current scale Shorten the learning cycles  Improve the consumer experience Create a “frictionless” experience  Optimize marketing spend & decision  making Innovate new business models,  products & services
  • 9/23/2013 9 Have a Plan! Change the Game!
  • Big Data, Big Deal Better Connections. Better Results. By: Jed Mole, David McKee and Ian Fremaux
  • Five steps to identifying the most valuable 5% of your data — and making Big Data a Big Deal for your business An Acxiom White Paper 1 Introduction The premise of this white paper is: accept Big Data is real; but don’t just believe the hype, focus on the desired short- and long- term results and work back from those objectives. It also sets out a five-step plan that will help marketers and brands generate real competitive advantage from Big Data. What is Big Data? To paraphrase an old advertising adage1 modern marketers could be forgiven for regularly thinking, “I know up to 95% of data is of little value to me; I just don’t know how to find the most valuable 5%.” Data has been getting exponentially bigger in terms of rapidly increasing volumes for many years, but Big Data has emerged as a buzzword relatively recently. So what’s changed? When the term Big Data is used today, it generally refers to data that has one or more of three commonly accepted attributes. Some analysts and organisations add others but the accepted three are: Volume, Velocity and Variability. Volume is the most obvious of the “three Vs” but also the most deceptive. Of course, there is an exponential growth in volume caused by the consumer increasingly emailing, surfing, tweeting, blogging and the like but how much of it is real and interesting? How many of the estimated 4.5 petabytes of videos added online in 2011 (see diagram) were either (a) relevant to the marketer or (b) copies? The fact the individual has copied it may be of interest to the marketer but it’s not new content. Big volume is not new in other fields. The largest single coherent data store in the world is the World Data Centre for Climate at 220 petabytes. It is not volume alone which is changing the world, but volume combined with the other two Vs. Velocity is a key driver of change in approach brought about by Big Data; the essence of this is that the more interesting behavioural data (think product search) has a limited shelf life and must be acted upon quickly. Variability of data is unprecedented and a real game changer. Some is transient, some structured and an enormous amount now unstructured yet incredibly powerful. There is a great challenge in taking raw data from the huge variety of possible sources, integrating these into systems and producing actionable insights which feed operational systems. 1 Depending on which source you believe, this line was originally used by US retail guru John Wanamaker or British household goods magnate Lord Leverhulme: “I know half of my advertising budget is wasted — I just don’t know which half.” Jun-07 Aug-07 Oct-07 Dec-07 Feb-08 Apr-08 Jun-08 Aug-08 Oct-08 Dec-08 Feb-09 Apr-09 Jun-09 Aug-09 Oct-09 Jun-10 Aug-10 Oct-10 Dec-09 Feb-10 Apr-10 40 Hours of video 35 30 25 20 15 10 5 0 Source: YouTube Global HOURS OF VIDEO UPLOADED PER MINUTE
  • 2 Big Data, Big Deal 5 exabytes of data were created between the dawn of civilisation and 2003 — that much information now becomes available every two days. As Big Data trends ever higher as a topic, it is of concern that many of the recommended solutions involve enormous and urgent investments in IT; the idea being that if you can capture and store all of the data then you stand a chance of generating value from it. The opinion of this white paper is that this is not the answer for all organisations, rather a more efficient solution lies in every marketer asking “so what?” and first understanding how relevant Big Data is to them, how much it can support their business goals and then how to turn potential into reality. So why should marketers care? The fact is the explosion of data has been caused by the aforementioned changes in consumer behaviour and consumption; the way we shop, work and relax. The growth in numbers and kinds of channels, devices such as Smartphones and all the real-time data they provide through Apps and social networking, products and services has led to Big Data. Consumers have created it, the same people who buy goods and services from the world’s brands. Marketers absolutely must care — this is their space. Most professionals using data with privacy as a design principle to understand and engage consumers accept a great deal of data is of limited worth. The problem is how to separate the signal from the noise. Somewhere within the avalanche of data are buried significant patterns and behaviours which signpost buying, churn, brand support or aversion. A successful strategy needs to have a mechanism for uncovering these patterns and signals and adapt the operational systems to respond to them. To achieve the end goal of making their brands more successful by better understanding, targeting, engaging and serving consumers using Big Data, marketers should consider the analogy of human vision which features both ‘focused’ and ‘peripheral’ abilities. We walk down the forest track looking at the path ahead, not seeing every leaf around us, but when we sense movement our eyes immediately turn to the source and respond to the threat or opportunity. Marketers need to create the ability to achieve exactly this, to manage signals but continuously be aware of new signals within the noise and react appropriately. In the face of much hyperbole around Big Data, marketers and brands first need to avoid panic responses — new trends will always bring big statistics. Instead, think differently about the multidimensional insight Big Data affords. Many businesses have already proven they are excellent at using what consumer insights they have. But could even Apple say it’s driving the maximum possible value from customer data across all its devices and consumer touchpoints? The challenge is going from Big Data potential to the reality of improved business results. If harnessed properly, all kinds of data can be used to improve a brand’s ability to win, service, keep and grow customers. For example, customer service or product usage data may not traditionally be termed marketing, but in tomorrow’s world, the marketer must want to bring this data to bear having identified its previously unimagined potential value. Marketing is perfectly placed to make Big Data a business imperative that can serve the entire organisation, not just its own function or the consumer. The modern-day volume, velocity and variability and greater need for validity of data can be managed to make clear the benefits of Big Data far beyond the walls of the marketing department. The starting point isn’t always clear. However, by implementing the following five-step plan, you will generate quick wins and put the business on a strategic path to better results by making Big Data deliver significant, measurable benefits.
  • Big Data, Big Deal 3 Step 1 Put the Consumer First Agree the consumer-related business problems you’re trying to address with Big Data Every marketer knows that for a brand to be successful, it has to have a compelling offer for consumers delivered via the right channel at the right time: the classic Four Ps and the expanded Seven Ps of services marketing. Big Data does not change this, but creates new opportunities. Data can be used to enhance the product, improve the price and make the promotion far more relevant to the place. One of the challenges is deciding where to begin. Some big organisations have started by loading their largest datasets into emerging technology platforms that solve the volume and variability challenges for their analysts, which allows them to mine these super-size datasets for new insights. Although this is a great way of learning the technology it only provides part of the information you need to define a Big Data roadmap. The first step of a journey into the fast-moving world of Big Data should be to ensure that you are aligning your efforts to your business objectives. So, step one is to identify some of the key issues and opportunities that affect your target market today and their relationship with your brand. A brainstorming workshop involving a range of business stakeholders can be used to establish a list of up to 10 priority items to be addressed. This workshop should ensure the business’s strategic goals and key initiatives are considered, taking care to identify which involve the consumer and brand and have the potential to be affected by Big Data. Also, a mixed group of stakeholders will provide ideas for this list from people with different perspectives of your consumers and how you can better align your products and services to their needs. An estimated incremental revenue or cost saving should be associated with each item on the list. This ROI estimate only needs to be approximate and shouldn’t require an in-depth period of analysis. At this stage it is not necessary to predict whether the challenges identified can be resolved through a Big Data initiative — that will come later. The tangible deliverable from step one is a list documented in the form of a project brief, with a problem statement describing each item on the list and an impact statement to demonstrate the potential value to the business.
  • 4 Big Data, Big Deal Step 2 Define the Starting Line Audit the data you have and identify the data you need In addition to defining the problems and opportunities that Big Data could impact, it is also necessary to have a good view of what Big Data your organisation has. The way to approach this task is by conducting a consumer-centric data audit. Who should be involved in the process? Outside marketing, IT should definitely play a part as the department is likely to be most at home with data, and may be able to help uncover silos of data unknown to the rest of the team. Data analysts should also be involved as they are already familiar with combining and using disparate data sources and will ask the right questions of the data owners when compiling the data audit. The privacy team should also be engaged to ensure the boundaries of data usage are properly respected. The audit should create a register of all the touchpoints consumers have with your brand and, for each, should ask questions including: • What data is generated • Is it captured anywhere • Do you have access to it • Do you have permission to use it • Does any documentation exist about the data’s structure and content • Is it structured, semi-structured or unstructured • Can I join it to any other customer data sources • Can I get a snapshot of this data for use in a proof of concept? As recently as five years ago, Acxiom saw an average of 15 to 25 data sources for complex marketing solutions. Now, even mid-tier companies are arriving with 50 to 100 data silos which require integration. Since the advent of social media it is even possible to start capturing information about what your consumers think of your indirect marketing campaigns. The next step is to collate more detailed information about the sources you can access. Hopefully for some of these you’ll have lots of information, but others may be reliant upon key expert users, and some will be relatively unknown. At this stage you should aim to obtain a profile of the main data entities and attributes from each system — the ‘data dictionary’ — which includes the spread of values and their frequencies for the attributes. Software tools can be used to automate some of this work. This process will highlight where data generated at a consumer touchpoint is unavailable. Filling in these data gaps is something that could be included in the strategic roadmap later in the process at step five. Each data source should be scored on a number of measures that can be used to assess whether it is a valuable source for inclusion in a proof of concept. For example, your web analytics data might score high on granularity of information available but low on completeness if you can only access the last few days’ worth. The final thing to include is data sources that you would need to have a better understanding about your customers and prospects. The specific deliverable from step two is production of a comprehensive list of the raw materials you have available for any Big Data initiatives and identification of the gaps where the data is unavailable.
  • Big Data, Big Deal 5 Step 3 Create ‘Plan A’ — A Proportionate Response Plan to test and invest, proportional to the strength of your analysis and findings Having understood the objectives and the current landscape in steps one and two you must now build a strategy for managing and making Big Data actionable — a ‘Plan A’. The challenges here will be familiar if you’ve ‘sweated blood’ building your corporate data warehouse, but are magnified in the context of Big Data by the volumes and variety of data involved. Furthermore, the velocity factor means that unless the data gets processed and actioned rapidly then the data quickly becomes stale. Throwing all of the data into a new platform like a large Hadoop cluster is not really going to solve the problem in isolation. Yes, you are going to need some hardware and processing power, but a strategy for doing something useful with raw data is critical. Enlisting the skills of an entrepreneurial senior analyst at this stage could prove vital. Solutions will vary from brand to brand but any Big Data plan must address how to: • rapidly on-board new data sources • analyse raw data to determine when and how it can be used • make data operational quickly and efficiently • identify, and if necessary discard, junk data which will clog the system • automate decisioning to accommodate the variety and velocity of Big Data • ensure all activity is executed in a privacy-compliant manner. Consider the enormous volumes of website tracking information generated by a large consumer site. Some facts will be useful, if only for a short time. For example, a recommendation engine may take a real-time feed of product searches to determine which adverts to display to users; an abandoned basket is a trigger for retargeting. The problem is much of this data is just noise. However, data mining can identify patterns or activities which are predictive of some desired behaviour and once the pattern is identified the data can be actioned by using it to feed the campaign or decision system. It is the agility in the face of an everchanging data landscape, and not just volumes, which distinguishes Big Data solutions from previous incarnations. By bringing together the learnings from steps 1 and 2 with an appreciation of the possibilities offered by the new technologies, the specific deliverable from this step should be a practical, initial vision and plan of how Big Data can work for you and your organisation. Traditionally, this is the beginning of the tender or RFP stage where firm decisions are made about hardware, software and service providers. However, given the scale of investment, and to ensure ‘the Emperor really is wearing some clothes’, the next step is a Big Data reality check.
  • 6 Big Data, Big Deal STEP 4 Test Big Data Deploy a proof of concept to test and learn Clearly there may be a significant investment required to establish a Big Data environment, not just in terms of hardware but also in terms of skills. Demonstrating ROI is therefore mandatory in securing sponsorship from the business. One of the paradoxes of Big Data is that many of the use cases do not actually require Big Data. The volumes are so large that the decisioning necessarily becomes simplified. With the exception of some parts of the automated decisioning aspect, there is nothing fundamentally new about Big Data. It’s just a new mindset that’s required. What this means is that it should be perfectly possible to conceive of and execute Big Data use cases without building a full-blown Big Data environment — especially for a proof of concept. It’s fair to say that if you can’t demonstrate a handful of use cases from a marketing viewpoint, then it’s unlikely that any Big Data environment will pay its way. Consider the SETI@home project (see below). It was necessary to prove the model worked first with a small number of networked computers in a lab before upscaling to cover 3 million computers worldwide. The resources and effort required to prove a use case will vary. In some cases smaller volumes of static data may suffice, in others production-scale volume may be required. A partner with an existing large-scale analysis environment may be able to help you look for the early wins which will justify the business spend. The objectives of step four include: • establishing which data sources offer useful, predictive information. This can be done with static physical dumps of likely sources (sales, web-logs, call centre etc) • establishing which external sources are required and may need to be brought into the mix (socio- or geo-demographic data) • providing metrics for any predicted uplift, based on extrapolation from the proof of concept • manually executing the integration steps — which must be automated in live environments — so they are understood and conform to privacy regulations. Typically, the activities at this stage will include statistical analysis (mining), searching for predictive patterns within the data and then attempting to turn these into processes which can be tested in real-world scenarios. Once again, this is where third parties can help. A good external partner should be able to provide resources such as data scientists and technologists with a grasp of the new software and platforms, to fast-track learning at this stage. Moving forward from this step, the specific deliverable should be a solid body of evidence to support the business case, and a clear understanding of the resources and processes required to underpin it. You ought to be in a position to present ROI cases to leadership, and to begin validating and adapting ‘Plan A’ from step 3, producing a strategy for Big Data that will be delivered through a practical roadmap. SETI@home was the second large-scale distributed computer project set up in the 1990s (Berkeley University & US Space Labs). It had the following objectives: • to do useful scientific work by supporting an observational analysis to detect intelligent life outside Earth, and • to prove the viability and practicality of the ‘volunteer computing’ concept and conform to privacy regulations. Whilst the first objective has not yet been met the second was an almost complete success — demonstrating how home computers could be used to process huge amounts of data in a collaborative environment. SETI is one of the fathers of cloud computing.
  • Big Data, Big Deal 7 Step 5 Emulate Human Vision — Focused and Peripheral Create a roadmap to operationalise proven approaches, and identify and test new ones Finally, you are ready to build on the results of the proof of concept. As previously described, the ideal state is to achieve both focused and peripheral vision: the ability to focus on the data that matters most while being aware of other data, and be constantly on the lookout for new or previously unavailable data. Step five is about creating a strategic roadmap. This will outline how any proof of concept tests can be operationalised, part of ‘business as usual’ and how the organisation will move forward with subsequent proofs of concept already defined. It will also implement your peripheral vision. Steps three and four will hopefully have armed you with information to build the business case to move forward; an incremental return on marketing investment with well-managed risk should have the business’s budget holders happy to ask: what’s next? The effort required to make progress should not be underestimated but likewise needn’t be a cause for alarm. While Big Data can be ephemeral and difficult to tackle head-on and while the business will need to invest to make Big Data proof of concepts operational, the fact remains that the majority of what the marketer is trying to achieve, is getting the right data in the right place at the right time. The quickest, and arguably best, return will almost certainly come from a marketer’s ability to bring high-value data to bear in existing marketing systems and programmes. To this end, priority consideration should be given to what Big Data can most quickly be captured and translated into these existing environments. You need to conduct a value analysis and prioritise. For example, compare the investment in the capability to take unstructured data and turn it into large volumes of structured insight against the expenditure needed to capture a more modest volume of structured data. The yield of each option must be identified and the answer may be to do both. It is worth remembering that much of this capability can be implemented between the consumer-generated Big Data and the existing systems without the need to completely rebuild existing architectures. It should be clear by this stage that the five-step approach offered is one that allows businesses to avoid a crippling ‘knee-jerk’ investment in reaction to the opportunity and threat Big Data represents. A proportional response that takes practical, informed steps to generate significant results is vital. Once you have a clear roadmap to operationalise the initial proofs of concept and deploy the next batch - the focused vision - attention needs to be given to how the marketer achieves peripheral vision. The good news is that most of the activities undertaken in steps one to four contain much of what the marketer needs to deliver peripheral vision. You must put in place a programme of continuous activity, indeed for many organisations this should entail a separate team that specialises in scrutinising the evolving Big Data universe to identify and evaluate potentially powerful sources of Big Data. This team is like weather forecasters, always taking readings and measurements, using a range of permanent and ad hoc activities and tools to predict what’s coming next. The very nature of Big Data means the roadmap for organisational change should always be based on testing and refining. This team needs to consider the potential of the data from an analytical perspective but must also apply pragmatism around the challenge they are going to present to their colleagues tasked with trying to operationalise the data. They should also be able to assess the likely impact from a marketing systems point of view and be capable of framing their findings and recommendations in terms of return on marketing investment. The key deliverable from the final step is a strategic and tactical plan, including a roadmap that operationalises Big Data trials, generates results, enables learnings and identifies potential opportunities so that marketing, the brand and the consumer continuously benefit from the exploitation of Big Data.
  • 8 Big Data, Big Deal Conclusion If Big Data was already present in the past, albeit with its own fast-growing volumes, it was based on what now seem narrow variables such as location, age and salary. These days, the layers and levels are almost limitless; where are people located when they use HTML5 mobile applications, how many brands do they purchase from and through which channels, what do people think and how do they feel? Big Data is real and represents both an opportunity and a threat. The threat comes in the form of putting prime focus on getting and hosting as much data as possible without regard to how it can be most profitably used. Like the donkey chasing the carrot that’s always out of reach, the pursuit of being able to catch all the data available as it evolves is always likely to result in disappointment. The opportunity becomes more realistic and tangible once a business shifts from trying to solve all of its problems with all of the data available. Opportunity comes from focusing on what problems Big Data stands the best chance of solving, understanding where the organisation is today, creating a ‘Plan A’, testing and then building a roadmap that gives the marketer both the focused and peripheral vision to make Big Data deliver incremental return on marketing investment. Given consumers create Big Data and brands need to engage, serve and delight them to be successful, it follows that marketers are best-placed to plot the course towards finding that golden 5% of your Big Data, ensuring it delivers benefits to both the consumers and the brand. Big Data has been evolving and growing in the background for some time. Now it’s here, can you afford not to take it on? Just make sure you take it on in the right way and on your terms. Acxiom and Big Data Acxiom can help marketers achieve results by exploiting the potential of Big Data with deep understanding of how to identify the right data and put it to profitable use. Combining marketing and consumer expertise, technology and data, Acxiom provides solutions that are fully compliant with privacy regulations and support the five-step plan, grounded in a ‘proportional response’ approach, and can evidence the benefits with case studies that showcase more than 40 years of data expertise. Contact details For more information call 020 7526 5265. www.acxiom.co.uk ukenquiries@acxiom.com
  • AC-0309-13 3/13 © 2013 Acxiom Corporation. All rights reserved Acxiom and InfoBase are registered trademarks of Acxiom Corporation. All other trademarks and service marks mentioned herein are property of their respective owners. 17 Hatfields London SE1 8DJ acxiom.co.uk Acxiom Acxiom is an enterprise data, analytics and software as a service company that uniquely fuses trust, experience and scale to fuel data-driven results. For more than 40 years, Acxiom has been an innovator in harnessing the most important sources and uses of data to strengthen connections between people, businesses and their partners. Utilizing a channel and media neutral approach, we leverage cutting-edge, data-oriented products and services to maximize customer value. Every week, Acxiom powers more than a trillion transactions that enable better living for people and better results for our 7,000+ global clients.
  • Data is the new black Better Connections. Better Results.
  • Data is the new black An Acxiom White Paper At this moment, a number of factors are creating opportunities that will transform marketing: increasingly addressable media, enhanced data aggregation and recognition, and the increased appetite of “C” leaders to drive improved shareholder value and accountability into marketing investments. Direct marketing leaders need to seize the opportunity to define the value of their assets within the new, digitally connected world and communicate this capability within their organization. Collaboration with digital marketers will enable the blending of rich insight and will be indispensable to achieving the multichannel vision shared by more than two-thirds of marketing organizations. Data-savvy marketers are now the cool kids on the block! addressable media inherently enables the identification and connection of individual marketing stimulus to response, thereby making it possible to link audience interaction across multiple sources. Examples include: email, home address, or phone number or mobile number. Leaders crave multichannel solutions today but they can’t execute 68 percent of U.S.-based e-business managers say that their company desires a vision for a consistent, non-fragmented, cross-channel experience, but only 29 percent feel they have the ability to follow through.1 Within the same organizations, data-driven marketers have been investing in the aggregation, delivery and optimization of programs across an alternate set of channels for generations: call center, point of sale, direct mail and others. Globally, most firms are focused on understanding and enhancing multichannel communication to ensure digital and offline communication support and improve one another in order to provide optimized customer engagement, but challenges remain. Similar to the dawn of household-level direct marketing which became addressable in the 1980s, digital media holds a similar promise of ushering new, bigger marketing opportunities into multiple channels and should be seen as a similar enabler of direct marketing principles. These new digital channels are becoming increasingly addressable, the sweet spot of direct marketers. 1
  • 2 Data is the new black Direct marketing systems optimize marketing investment, regardless of channel As mentioned, direct marketers already operate in a multichannel world. While the channels may not be directly comparable to digital, the concepts of identification, recognition and engagement are consistent and, in many cases, already in place. What is compelling is the ability of these systems to deliver results in a responsible, predictable and cost-effective manner. With nearly three times the channel spend ($45B) compared to digital channels ($17B),2 and with per-impression costs that are dramatically high, direct marketers have demonstrated a strong competence to proactively identify high-value audiences, measure stimulus to response and optimize programs to a high performance standard. For example, the DMA reported in 2010 that catalog efforts delivered an order at an average cost of $75.32, compared to paid search costs of $99.47 per order.3 This isn’t a point to refute digital marketing. Rather, it is an opportunity to demonstrate the effectiveness, predictability and efficiency of direct marketing systems to operate in a multichannel world. If this scenario sounds familiar and is important in your organization, how then can you repurpose and reposition direct marketing assets so that others in the organization will understand and begin to use them with addressable, digital media? How to position your direct marketing assets Data-driven marketers should evangelize their assets as necessary to ensure their target audience will have a seamless, comprehensive brand experience and to help shape perceptions, expectations and usage of their direct marketing assets in the new, digitally-connected world. Digital marketing IS direct marketing. What was old is new again! In order for your organization to succeed in the new digitally connected world, you must; Communicate the value of the data insight present in your marketing database — Your team has the data on prospects and customers and the ability to leverage that insight to deliver, measure and optimize highly relevant communications. Digital marketing efforts will need these assets and capabilities to successfully execute a multichannel customer engagement vision. Advocate the value of adjoining this data with other sources of valuable insight — Combining data found in digital channels leads to better focused, more intelligent marketing efforts across all channels. Examine the data and response within email, web logs and analytics, cookies, customer support, surveys, coupons and social interactions (if accessible). Completing an accurate picture of customer and prospect interaction with your marketing efforts across these channels will enable richer insight, better segmentation and set the table for enabling coordination of messages. So, how do you take your marketing database to the next phase of utility in the digitally connected marketing world? You’ll want to build a system which acts and reacts much like a central nervous system in that it is a series of synapses that will send and receive signals about customer behavior and is then able to intelligently recalibrate based on what they do or don’t do. The goal is to remember every interaction and learn. This evolved marketing system has broad ambitions — to not only send signals that influence customers, but also to sense behavior and intelligently respond. This has significant technology implications. When customer behavior is influenced or sensed, marketing leaders deploy automated decision technology to optimize the outcome.
  • Data is the new black 3 Viewing and transforming your systems for the digital world But how does one get started? First off, resist the temptation to embark on a multi-year data warehouse project. This could consume precious cycle time to be in-market with the right solution. In an environment where 37 percent of all advertising is wasted4 , this is your opportunity to show incremental gains in the near term. Utilize the data where it resides; call it from its native source. Assembling all of the pieces Acxiom suggests that you build this “marketing central nervous system” based upon the following three principles and their respective implementation steps. 1. Build a Data Mart — A data mart approach helps to focus on delivering results with an ROI that can be realized in the near term. This will resound internally and focus the conversation on the value your systems already deliver and ensure you can be in-market with a solution in a matter of months. Consider the following high-level approach to this data mart that will serve as a multichannel marketing central nervous system: Gain access to the data by building a logical layer to access it from its source. This goes to the heart of direct marketing as a practice — connection of stimulus to response and the recalibration of efforts designed to optimize response. Integrate non-transactional data. The data mart will benefit greatly by having access to information that includes customer discussion threads, blogs, chat, social and other insights that may be helpful to capturing dynamic indexes of customers and customer segments. Integrate your email database with those who have opted in for email communications and connect it with the CRM data. Augment your database with the right fields to track individuals as well as campaign elements (campaign ID, incentive used/promotional code, etc.). Build templates with pre-filled forms which the user can correct if necessary. This smaller, data mart approach is more adept and leaves ample flexibility to incorporate additional data sets while remaining nimble enough to continue executing campaigns with the data and insight you have. Use ETL (extract, transform and load) tools when you only care about certain fields, international domains and as you specify the certain data you care about as you bring it in. This makes the path to insight a lot quicker and a lot less expensive. Plug in your systems that handle analytics, reports, campaigns and marketing execution data. Take all results and lay them out in a visual dashboard to display real-time results. Cleanse and edit the data mart on a regular basis. This will be critical to keeping the system vibrant and meaningful.
  • 4 Data is the new black 2. Leverage Data Beyond the Data — The stakes are higher; your system will be viewed by all parties managing customer and prospect-touching programs. Integrating data across channels is a priority for trying to amass the insight necessary to deliver upon cross-channel coordination. Also, as a single point of reference, this system will become the source for segmentation and optimization. While even the world’s largest brands can only capture a sliver of each person’s life, maybe 1 or 2 percent, information is available that can help fill in the gaps of understanding and increase accuracy in identification and segmentation. Sources will include digital channels as well as third-party insight you may already leverage on the direct side of the house. With a bigger view of each member of your target audience and their response across channels, this enriched asset will be indispensable. Ensure that your digital marketing colleagues can see and understand how to use this insight. This is critical to delivering a consistent and engaging customer experience. Perform contact sequencing where you plan out the pace of communications you’ll do in each channel. You need to have some view of your target audience around customer segments such as where they spend time, profitability and predictive analytics so that you can assess propensities, and so forth. It’s presumed that you’ll have a base idea of customer value which you should use to select within segments to develop your contact sequencing as it will give you the ability to drive profitability. Work with your digital counterparts to help understand how audience lists are built (compiled vs. subscriber lists). Consider quality versus quantity. Invest in this process with great scrutiny. You will want to ensure consistency and collaboration. Employ analytics and insight via third-party, non-SQL data to expand the knowledge of your customers and target audience while building customer intelligence and insight. Using your regular customer database isn’t enough. While soliciting third parties, work with knowledgeable, reputable data vendors who can offer multiple, flexible options. 3. Assemble the Execution Engine — Here’s where you’ll connect your marketing systems and bring everything to life. Identify the channel pieces you need to have working in tandem; focus initially on low-risk campaigns and have a vision for expansion. With the need for near real-time processing and incorporation of new data as it becomes available, avoid a “big bang” or a calendar-driven approach to capitalize on new assets in favor of a much more dynamic model required to play and add value in the digital space. Focus on being in-market with integration of the highest value deployment channels first. Multichannel efforts integrate information from other channels in order to better target and engage your audience through their preferred channels. You’ll want to start monitoring, logging and correlating interactions in order to identify patterns which will help you to establish behavior and preferences. This in turn will help you create better offers and timing of those offers, and increasingly refine and hone your marketing efforts to optimize outcomes. Discuss and brainstorm scenarios and examples of where they will be used. For example, you’ll want to understand how direct mail and email campaigns work together simultaneously and how these systems are fully fused where you can fuel the stimulus to the audience, measure their response and take the next step with the right channel.
  • Data is the new black 5 Test, measure, refine Perform an active test and run a set of strategies around multiple channels. This is a new tool for your organization. Ensure that all involved parties can see the vision and ability to coordinate the customer experience. Send out one offer to a random set of people and see what happens. Send a multi-wave campaign with an offer first through one channel, followed up by another channel and another, always measuring the response. You can test campaigns against customer-initiated conversations, measure them against your best customer profiles and map out new customer segments. Test and learn the strategy that you’ve been doing and when you figure out what kind of conversations work for you, bake that into your campaign design. The possibilities will continue to expand as newer features, channels and devices are adopted. These recommendations can help build a marketing system that pays off remarkably quickly. Several major brands have been able to deploy robust, fully functioning solutions in as little as 90-180 days. CUSTOMER DATA SOURCES MARKETING CENTRAL NERVOUS SYSTEM Enhancements Segmentation 3rd Party DataTransactional Data Closed-loop Response Analysis & Reporting Insight Mgmt. Analytic Tools & Data Marts Campaign Processes & 3rd Party Apps Client Marketing Databases Call the data from its source Integrated partner ecosystem Integrate customer data sources and touch points Send and receive signals that influence consumer behavior Correlate consumer behavior with marketing/advertising across channels over time Direct Mail Mobile Website Email Display Text Mobile Apps In-store Mobile Social Media Call Center In-store Networks Connected Devices ITV Custom Media
  • 6 Data is the new black Results Savings Bank Life Insurance of Massachusetts blended customer data with third-party insight to help craft complex “life stage” segments of their target audience. Engaging their audience in a coordinated, multichannel fashion with a personalized communications approach helped them exceed their leads-to-customers by 123 percent at a lower cost per lead against budget cuts in marketing and sales while also introducing prospects earlier in the buying process. They reduced cost per lead and increased profit in less than 120 days. Rodale is an American publisher that executed the right sequence of campaigns to their base by blending offline and online data with flexible business rules. They ran a series of 100 or more email campaigns simultaneously, seamlessly moving customers from new acquisition campaigns to cross-sell/up-sell opportunities and into maintenance or reactivation campaigns. Each customer experiences a unique conversation, tailored to their individual interests and the system suppresses products they’ve purchased or opted not to receive offers on. Rodale defines a unique rhythm of customer contacts and “rests” for each campaign, providing the ability to communicate daily with new acquisitions and less frequently but still regularly with longer-term customers. This segmentation allowed them to increase the number of products they promote from 23 per month to 151 per day while seeing an increase in gross orders of 44 percent. MOMENT OF TRUTH: Download a new white paper (www.acxiom.com/mot) that examines the overall strategy of capabilities necessary for marketing organizations to compete in the new, digitally connected world. Conclusion Direct marketing’s role in digital marketing efforts will evolve as organizations connect the concepts of addressable media to the systems they already have in place. While multichannel marketing is a desire of today’s leaders, direct marketers need to learn these trends and take the responsibility for evangelizing their capabilities within their organization. Indeed, the value of your marketing assets and processes should be evaluated to include benefits outside of the typical direct mail and customer service channels. Data marketers have great opportunities to build upon their skills, help their organizations succeed in the digital age and advance their careers by taking practical, incremental steps. They can get started now by building a logical marketing system as part of a set of capabilities that marketing will need to embrace in order to succeed in the digital age. To learn how Acxiom can work for you, call 1.888.3ACXIOM (1.888.322.9466) or visit www.acxiom.com.
  • AC-0258-13 2/13 (1) Forrester – Using Digital Channels to Create Breakthrough Multichannel Relationships, Feb 2010 (2) Winterberry report from Bruce Biegler (3) DMA 2010 Response Report (4) Briggs, Rex and Stuart, Greg. What Sticks: Why Most Advertising Fails and How to Guarantee Yours Succeeds, Kaplan Business, September 1, 2006 601 E. Third, Little Rock, AR 72201 acxiom.com 1.888.3acxiom ©2013 Acxiom Corporation. All rights reserved. Acxiom is a registered trademark of Acxiom Corporation.
  • 9/23/2013 1 #DMA2013 Marketing Synergy, Inc. Integrating Digital Media with  your Marketing Database Database Marketing Intensive: Part 7 Randy Hlavac Lecturer Professor Northwestern University, Medill IMC CEO ‐ Marketing Synergy #DMA2013 Marketing Synergy, Inc. Social Marketing with bottom line  impact Cracking the code to Social ROI Randy Hlavac Lecturer, Medill IMC Program Northwestern University CEO, Marketing Synergy Inc r-hlavac@northwestern.edu 630.328.9550 SOURCE: Medill IMC AGE: 39 [I dye my hair gray for effect] DIFFICULTY: 4 FIGHTING STYLE: Set High Expectations & Exceed Them TWITTER: @randyhlavac EMAIL: R‐hlavac@northwestern.edu HASHTAG: #NUSocialIMC SPECIAL MOVE: Shout of Earth (Left, Right, Up, Down, A, A, A, B, B, B) RATING: Awesome RANDY
  • 9/23/2013 2 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. According to IBM, managing data & social media  is what keeps CMOs “up at night” 3 IBM Global CMO Study 2012 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. And data management & social are key  pain points for companies today IBM Global CMO Study 2012
  • 9/23/2013 3 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Even CEOs are concerned about  connections, engagement & relationships IBM Global CEO study 2013 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Our Goals are Clear What is Big Data & how do I process it  for my business? What do I need to know to capture it? How is it being used today? What tools can I use?
  • 9/23/2013 4 #DMA2013 Marketing Synergy, Inc. WHAT IS BIG DATA? Identifying the data which drives your organization today #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. You need to address 4 characteristics  of Big Data…the 4 V’s 8 IBM Academic Initiative 2013
  • 9/23/2013 5 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. When you think about Big Data, think  WATER! OceansOceans RiversRivers LakesLakes #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Water helps understand the nature & velocity of data  within your organization Big  Data Data at  Rest Data in  Motion Data in  Silos Oceans Rivers Lakes
  • 9/23/2013 6 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Data at rest is your transactional and  customer data • The Calm of the Ocean – It is data periodically collected and processed – Data at rest represents: • Past purchase history • Past contact history • Predictive and behavioral measures • Demographic and lifestyle data • Life Stage and Lifestyle Clusters • We can use this type of data to service customer  requests, analyze and classify customers, and  predict future behaviors #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Data at Rest is periodically processed  and added to the other “Ocean” of  data Merge/purge  update Purchase data Customer service Marketing programs
  • 9/23/2013 7 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Data in Lakes • Siloed information – Sales data – Product data – Silos specific information – telecenter, social  networks, etc. • From a Big Data perspective, you need to: – Know where it is at – Be able to get to it when needed – Generally, not a concern #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Data in Rivers • Sources of rivers of data – Social – Website – Mobile • These data sources are data rich and always  moving • Allows for real‐time and historical analysis • Also has some interesting and very powerful  marketing opportunities
  • 9/23/2013 8 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Let’s look at an example of the power  of rivers of data • www.wefeelfine.org #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. River of Data issues • Acquiring it – You either need APIs to gather the data yourself or a  vendor – One of the best is Boardreader • www.boardreader.com • They are specialists in deep diving data across the world – You then need to analyze and classify it • Lexalytics is a good place to learn – You then need to append it to your database • This is the hard part of the process
  • 9/23/2013 9 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. To really capture & process rivers of data,  you need different integrated systems IBM Big Data Management Conference 2013 #DMA2013 Marketing Synergy, Inc. WHAT DO I NEED TO KNOW TO  CAPTURE IT? Understand your marketing and social strategies
  • 9/23/2013 10 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Big Data needs links • The key to successfully using Big Data is to get the links  to your marketing and management systems • To do that, you need to understand the way your  business markets and develops its social and mobile  programs #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. What are the links marketing built into your marketing  and contact systems? Social Varies Mobile Registration? Website Registration? Virtual  Community Registration Telecenter Phone or  Address Email Address Mobile  Phone Password?
  • 9/23/2013 11 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Why do you want individual links to  the rivers of data? Personas & Personalization Customers & Prospects Profitability metrics & business  justification Intercept & real‐time marketing #DMA2013 Marketing Synergy, Inc. BUSINESS APPLICATIONS USING BIG  DATA Why its important to link the up
  • 9/23/2013 12 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Companies deploy one of three social  strategies • Engagement Marketing – Anonymous marketing – Create Awesome & let it go viral – Key behavior is to tell others about the Awesome experience – Doesn’t attempt to target individual or add them to the database • However, it does use social monitoring systems to engage with participants in real time • For IT – No impact on your database – Need social monitoring to determine who is talking about your & capture who they are • Salesforce – Radian6 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Engagement Marketing Old Spice Manly Manhttp://youtu.be/owGykVbfgUE
  • 9/23/2013 13 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. They monitored social media to find interesting  questions to respond to…and it worked http://youtu.be/fD1WqPGn5Ag #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Social Marketing is coming on strong • Social Marketing – Create compelling & relevant content – Place it behind a wall – Have an information campaign  – While there, also put a cookie on their computer to track other activities – Require an information exchange to make it work • This is the link you are looking for • For IT – You need a database what you capture in the campaign – Link to your marketing database to classify [persona] and develop a nurture campaign • Identify their relationship with your company • Identify their product life cycle and where they are today in the process – Integrate web and social to capture cookie information on future visits • Trolling for targeted prospects • Linkage includes email & address from the form • Also get key data to classify them • And gives them limited control of the process • You also place a cookie on their computer to track their  relationship on your website & virtual community sites
  • 9/23/2013 14 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Social marketing – acquire, classify & engage #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Social IMC is the most databased of  the social strategies • Social IMC – Focus on Empowering a virtual community • Give them what they want – Goal is to create a virtual community or totally empowering program • Gives a virtual community exactly what it wants • REQUIRES a deep marketing database linkage – Get email & password – Requires you give total control to the individual • The database controls all aspects of the relationship – from the start – With Social IMC, you need to use your username and password to reap the benefits of the  program – You have total integration and total control of the relationship • For IT – You need an integrated marketing database – Real time data from the virtual community you are creating – Link to your marketing database system • Same metrics and classification as with Social Marketing – Everything moves through the information exchange
  • 9/23/2013 15 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Social IMC Carling Black Label South Africa http://youtu.be/xyDNvbUEvHg #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Social IMC Northface China http://youtu.be/6ktKwssCH‐Y
  • 9/23/2013 16 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Summary of Social #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Mobile Marketing • Mobile Apps give you the perfect chance to  get a registration • Put it behind a short, focused registration wall • People will give you the linkage data – especially their mobile phone number and  email if the mobile app is interesting
  • 9/23/2013 17 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Mobile Marketing iButterfly http://youtu.be/vEE6M0iW‐Nw #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Social IMC with Mobile Coke Chok Chok! http://youtu.be/pEDsERv‐rFA
  • 9/23/2013 18 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Gamification really integrates the app to  the marketing database http://youtu.be/qsl9NjyVpHY #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Intercept Marketing is a strong  application ‐ KLMhttp://youtu.be/pqHWAE8GDEk
  • 9/23/2013 19 #DMA2013 Marketing Synergy, Inc. WHAT ARE SOME FREE TOOLS TO  USE? #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Social Mention is the place to start • Use it to check your company, your products, and your competition • Monitor sentiment over time and note major changes [Crisis!] • Monitor keywords & top users [influencers?] and that they are saying
  • 9/23/2013 20 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Let’s Look at Men’s Fashion #DMA2013 Marketing Synergy, Inc. The left side of Social Mention gives you key  information & the right side gives you the ability to  compare searches
  • 9/23/2013 21 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Advanced Search lets you focus on  specific languages #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Alltop is great for tracking subjects • Alltop allows you to monitor topics [and create them] to identify  who is active in the social cloud
  • 9/23/2013 22 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Checking out Men’s Clothing #DMA2013 Marketing Synergy, Inc. Fashion gives us great results
  • 9/23/2013 23 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Your target audience follows key  influencers at center of a community • We follow identifies the key influencers by topic  across twitter and other social networks #DMA2013 Marketing Synergy, Inc. Wefollow let’s you search for experts by topic
  • 9/23/2013 24 #DMA2013 Marketing Synergy, Inc. Google + #DMA2013 Marketing Synergy, Inc. LinkedIn
  • 9/23/2013 25 #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Big Data Requires you to think about  data in new ways #DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc. Questions? Cracking the code toRandy Hlavac Lecturer, Medill IMC Program Northwestern University CEO, Marketing Synergy Inc Twitter: @randyhlavac Social IMC: #NUSocialIMC r-hlavac@northwestern.edu 630.328.9550
  • 9/23/2013 1 Marketing ROI: How to Ensure Political, Technical, and Business Success for a Database Project Database Marketing Post Intensive Part 8 Pegg Nadler President, Pegg Nadler Associates Inc. Pegg Nadler: Background • Database marketing consultant in media, nonprofit, publishing & retail industries • Experience: Headed DB operations at Smithsonian, Phillips Publishing, Consumers Union & HFMUS. Marketing & Sales for Metromail (now Experian), Abrams Books, Belvedere Press, The Fur Vault, Jindo Furs, Hadassah • Clients: AT&T, China Post, Corporation for Public Broadcasting, Discovery Channel, Direct Marketing Association, HFMUS, Smithsonian Institution, THIRTEEN WNET, Time Life Books, US News & World Report • Associations: Direct Marketing Club of New York Past President, DMA Ethics Policy Committee Member, DMA Annual Planning Conference Advisor, DMA Nonprofit Federation Former Advisory Council Chair
  • 9/23/2013 2 Today’s Presentation 3 • The Changing Business Landscape • Keys to Database Success • War Stories • Success Stories • Lessons Learned • Recommendations The New Business Reality 4 Integrated marketing communications Real time analytics & product offerings Data generation explosion Growth of online, mobile & social media Audience fragmentation Databases as key drivers to revenue
  • 9/23/2013 3 Challenges Still Exist Managing the customer multi- channel experience is a priority Today’s customer databases are insufficient to deliver the insight needed Measurement is critical but knowing what to measure & how to measure is a key investment theme Top Concerns Marketing’s changing needs are not met by internal IT departments Push to reduce costs internally & externally Improve ROI Integrate technologies across channels
  • 9/23/2013 4 The Big Question 7 How do we convince management to invest or reinvest in the database? What is Key to Database Success? 8 An “intelligent” business strategy The “right” team of players A “decent” database system & adequate data
  • 9/23/2013 5 #1: Key Business Issues 9 The competitive advantage comes from how analysis is handled Address the problem, not the technical solution What decisions need to be made to be successful? What questions do you want to answer to drive your sales & marketing programs? Begin with an intelligent business strategy Not data, not technology, not tools #2: A Database Champion 10 Database Leader Marketing expert Technically proficient Statistically savvy Politically astute IT independent Vendor & system knowledge
  • 9/23/2013 6 #3: The Right Team 11 Database Team Marketing experts Technically proficient DB Analysts Statistically savvy Modelers Politically astute DB Leader Senior Management Support DB Vendor & system experts #4: Top Management’s Commitment The Big C’s— CEO, CMO, COO, CFO, CTO Initial & ongoing financial backing People power— personnel for staffing Mandatory compliance & participation in DB projects
  • 9/23/2013 7 #5: A Decent Database 13 Robust systems & capabilities Budget to support ongoing operations Adequate & comprehensive data Timely updates Easy access to data by database team War Stories: Multi-Product Company 14 DB manager, no staff, multiple users with little training around the company Little DB knowledge, no standardized business rules Lack of management commitment Inadequate Funding Questionable ROI IT drives DB vendor selection & build Little or no funding for email, online or social data Opposition to use DB by various departments Modeling programs slow to test and/or rollout DB staff reductions A failed database project
  • 9/23/2013 8 Lessons Learned • Absence of dedicated trained staff undermined project successNo time for novices • No commitment from top C’s to override lack of DB cooperation throughout company.Big Guns Support • Top C’s thought they could save $$ by using IT—major mistakes since IT does not know marketingThe Black Hole of IT • Penny wise & pound foolish—the company must commit adequate $$ to fund project properlyMoney in the Bank War Stories: Membership Organization 16 DB initiatives driven by CEO CEO hires DB director Limited experience DB director Fulfillment vendor used as DB system provider “Black box” models Lack of DB knowledge across company Internal modeling team hired Data & capabilities concerns ROI unproven New DB RFP issued No budget approval DB project stalled
  • 9/23/2013 9 Lessons Learned • Don’t let your CEO or management team hire an inexperienced database director.No time to be Green • You get what you pay for. Spend what you need to hire expertise. Penny wise Pound foolish • Your fulfillment company should not serve as your database vendor. Find a DB provider with the expertise and services you require. Experience counts • Transparency in operations, analysis and modeling methodologies are necessary to encourage DB confidence, participation and success across a company. Information is power • Get the DB RFP requirements right the first time.Do Your Homework Success Story: Hearst Magazines 18 No DB, use Fulfillment System Modeling & Analytics done using disparate systems Senior Management Team makes commitment to DBM Select DB vendor Online & offline data integration Commitment to modeling & analytics VP DB Marketing hired DB build begins ROI plan detailed Program test and rollouts begin Ongoing investment to improve DB & marketing & real time online capabilities
  • 9/23/2013 10 Lessons Learned • Big C’s commitment to DB marketing for the short and long term success of the companyBig C’s Support • Company objectives and goals clearly defined Business Intelligence • Build with MDB experts, not IT expertsDB Partnership • Hiring a competent DB champion accounts for a quick start and continued success in DB programs DB Champion Demonstrating ROI: Hearst Projected DB Investment • Planned for 200% ROI in 3 years • Increased mail efficiency, higher customer response rates, reduced marketing execution resources • 30% more revenue from internet- sold subscriptions • New models to produce 5% lift on response for mail Actual ROI • DB paid for itself in one year • Consolidating information, getting clean data, buying better demographics and using online information for DM efforts • Resulted in 25-30% offline response lift • The database enabled reduction on outside lists by around 30%
  • 9/23/2013 11 Taking Inventory 21 What is happening across the company that was not included in the initial DB build? What is done in marketing, research, digital, social, editorial, customer service, email, mobile and finance? What data and campaign information can you not integrate today? What systems capture customer data across the company? What are your company’s business and customer objectives? What obstacles are in the way? Building a Case for Senior Management 22 Gather case studies & success stories that pertain to your particular business & industry Identify quick wins & gains vs. a long term detailed plan Determine a reasonable budget for funding & operations When necessary, think small using test databases & prototypes to gain approval Don’t overbuild—meet your current & near future needs since technology & business change
  • 9/23/2013 12 Critical Areas for Database Success Key Business Issues Identified A Savvy Database Champion The Right Team of Players Senior Management Commitment A Decent Database & Adequate Data Questions? 24 Thank you so very much! Please feel free to reach me at: Pegg Nadler President Pegg Nadler Associates, Inc. 212-861-0846 pegg@peggnadler.com
  • October 2009 targetmarketingmag.com
  • P egg Nadler loves the unknown. Where others see challenges, she sees opportunities. Where others fear change, she fears boredom. These are some of the qualities that have driven her 30-year direct marketing career, the bulk of which she’s spent advancing database marketing operations at commercial and nonprofit organizations and giving back to the direct marketing community. And they’re why she’s Target Marketing magazine’s Direct Marketer of the Year. Speaking over the telephone on a recent Friday evening from her New York office, the vice president of database marketing for magazine publishing empire HachetteFilipacchiMediaU.S.(HFMUS)quotesasayingfromHungarianNobel laureate Albert von Szent-Györgyi Nagyrapolt that has verbally captured her world view since she studied English and art history at the University at Albany, State University of New York: “Discovery consists of seeing what everybody has seen and thinking what nobody has thought.” “My approach to problem solving has actually always been the same,” Nadler says. “And it’s interesting how some people will find this a good approach and others will find that it could be maddening. It has always been very important for me to see the total scope of business in order to come to a decision. And this is probably one of the reasons why I love database marketing—because it really provides that wide picture.” Falling Into Love Nadler began fusing her left and right hemispheres early. The English and art history major entered direct marketing in 1979 by selling art and gift books for Harry N. Abrams. “I fell into direct marketing,” Nadler says. “When I came to New York in the late ’70s, I landed a job at Harry Abrams … and I was first their advertising By Heather Fletcher Making sense and dollars out of database marketing Direct Marketer of the Year: Pegg Vice President, Database Marketing, Hachette Filipacchi Media U.S. Nadler COVER STORY
  • manager and then moved into an area called special sales, which was selling books into areas other than bookstores. And … really it was direct marketing: catalogs, book clubs, continuity programs. That was my first exposure into direct market- ing. And I thought that it was a little bit wacky, but that it was much more fun than selling books into bookstores. And it was something that I then stayed with for the rest of my life.” From 1979 to 1990, her direct mar- keting career progressed from moving art books to selling facsimile editions of ancient manuscripts from the Vatican Library, then to hawking furs in a mostly pre-Internet, fully mid-animal rights move- ment era. “So being able to sell through the mail and through the phone became very important,” Nadler says of her 1988 to 1990 stint with Jindo Furs. Creatively working her way around the protester problem, she set up an 800 number for customers to call; secured accounts with the Home Shopping Network, Comp-U- Card, American Express and Diners Club; and mailed catalogs. Catering to the jet set, Jindo placed computer terminals at kiosks in airport waiting areas so passengers could click to buy minks before boarding. But her first taste of database market- ing, in 1990 at Metromail Corp. (now Experian), pulled her in to the direct marketing specialty. Within 18 months, she’d secured billings nearing $1 million for the marketing information, database and mail production company. “I’ve certainly always been very sys- tematic,” Nadler says. “My attraction to English was that I think that speaking very clearly and getting your message across is an imperative. And probably what has attracted me to database marketing is that I’ve always … organized … I like to get projects done. And it probably is a very neat way of wrapping up the world.” The Problem Solver Speaking of the global picture, Nadler’s strengths include all aspects of database marketing—withtheexceptionofin-depth statistical modeling, the implementation of which she supervises. So when she accepts anewchallenge,whichisusually“directing startup operations, restructuring business operations and overhauling marketing departments,” she is either in charge of or overseeing every aspect of the solution. “I’ve always been the person who can see the large business application and put the database together and then bring in the analytical people who will do the number crunching,”shesays.“SoI’mreallyamarket- er who moved into database marketing. … WhileI’vespentalltheseyearsdoingdirect anddatabasemarketing,inmyheartofhearts I’m a marketing, product-development, business-development person.” Since diving headfirst into database marketing in 1990, Nadler steadily has created and overhauled database systems and operations for some of the mightiest corporations and nonprofits in the country. Each situation is different and requires her to pull from her well-rounded direct marketing background as a vendor, con- sultant and client in the commercial and nonprofit worlds. Forinstance,duringthetimeshespentas aconsultantattheSmithsonianInstitution providing in-house database marketing expertise, Nadler managed operations first as a marketing database manager from 1992 to 1993, then as a marketing strat- egy director from 1993 to 1995. In that capacity, she analyzed the institution’s varied constituencies, including current and lapsed audiences. Identifying those high-value donor prospects, proposing a list revenue pro- gram to double sales within the first year for rented database names, developing database user training programs and estab- lishing Smithsonian’s database marketing conferences probably already sound over- whelming. But wait. There’s more. “Smithsonian had been using the data- base, but not really to the best ability,” Nadler says. “So I came in, made tweaks to the database, worked with all of the dif- ferent parts of the Smithsonian Institution to really let them realize that they had a very good resource there. My one favorite story there at the Smithsonian, and this is really not unique to Smithsonian, is that Smithsonian had a database. It might’ve been 9 million [names] when I was there. And there were names which were not housed on the database, which were in each of the development offices, includ- COVERSTORYPHOTOS:PAULGODWINPHOTOGRAPHY,NEWYORK,N.Y.
  • ing the central development office. And divisions didn’t want to share names. This is such a common occurrence. Not only in nonprofits,butincorporations:‘Don’twant you to market to my names. Don’t want you to contact my names. Want to keep these names suppressed.’ And I really had to work, very carefully, to demonstrate that the names that were within these various development offices were most probably also on the main database. “Andbybeingabletooverlaydata,bring all of these names together, we would prob- ably have a much more effective develop- ment strategy if we were able to do that,” shecontinues.“Becauseweactuallyshowed that the names that were housed in all of thesedifferentmuseumswerealreadyonthe central database. And once we understood whatthetotalcorrelationwasfromonearea to another, we were able to make a much better fundraising pitch.” Marketer for All Seasons Of all the hats she’s worn during her direct marketing career, Nadler does have a favor- ite. “I love a startup,” Nadler says. “And once the operation is going well, I’m bored. And that’s when I really like to turn it over. …That’swhatI’vedoneallalong—startup, or revamp or overhaul. … And that’s why the consultant role is really a very good role for me, because that’s how I’ve always thought as I’ve gone into companies. And I’vebeenwithsomanydifferentcompanies that it really has provided me with a very good bird’s-eye view. And it’s so important to be able to step back and look at what’s going on.” PeggNadlerAssociatesInc.ofNewYork appeared from 1997 to 1999, disappearing when Nadler accepted the full-time job of re-energizing “the marketing face” of Hadassah, a nonprofit, pro-Israel Jewish women’s organization. After a four-year stint as customer database services direc- tor for Consumers Union, publisher of Consumer Reports magazine, it was back to the milliner in 2004 to get refitted for the consultant hat. Thelistofcompaniesseekingheradvice as a consultant is so long and so filled with the “Who’s Who” of brands and nonprof- its that it simply reads alphabetically, in small type, on her résumé: AT&T, B’nai B’rith Youth Organization, Corporation for Public Broadcasting, Discovery Channel, Hachette Filipacchi Media … That’s where, in 2005, she met Hachette’sPhilippeGuelton.TheHFMUS executive vice president and COO had always wanted to build a database. “He had established a database when he was running Hachette’s operations in Japan,” Nadler explains. Guelton hired Nadler as a consultant in 2005, and she worked on the Hachette project for two years, while mixing in other consulting projects and adjunct professorships at New York University and Baruch College, City University of New York. Finally, in 2007, Guelton successfully recruited her to work full time for Hachette so she could complete building and implementing the database operations. “The last thing I wanted to do was give up my consulting,” she says. “It’s so much fun to be on the outside looking in and letting people tell you what really is troubling them. Because you’re outside the whole political arena, and people will be very honest with you about what is truly making them unhappy and what their aspirations and dreams are. So, as I say, it was a big quantum leap to go from consulting back to working in a corporate environment [at Hachette]. But, as I said, it was certainly for a really good cause. And it’s been hard. It’s been challenging. And not for one day have I been bored.” Grabbing Nadler’s attention for a few moments while she’s implementing data- baseoperationsinanenvironmentsheclas- sifies as undergoing a revolution can feel like pulling a surgeon out of an operating room. (While headlines about the publish- ing industry have been less than flattering, reflecting widespread industry trauma— from editorial layoffs to magazines folding altogether—Nadler is energized about the future. She envisions a personalized multi- channel experience that’s relevant to the consumer. More on that later.) “We’reintheprocessofputtingtogether a very strong operation,” she says during a quick call on a recent Monday, in between planning and budget meetings and search- ing for a director of analysis and modeling. Database operations, she says, are meant to determine “the new products, businesses and services Hachette should be offering. And that’s the most fun.” “Intoday’senvironment,arichandfully developeddatabaseisimperative,”Guelton relates. “We are more effective in helping our advertisers target their prime audiences and ideal prospects and in providing our subscribers with new products and better services. Since joining us in 2005, Pegg Nadler has been key in leading our efforts to expand our database capabilities …” ‘… with the lowering of processing and technology costs, we are finally able to really improve our marketing to where everything is going to be measurable and really everything’s going to morph into direct.Which is why we’re calling it integrated marketing.’ —Pegg Nadler COVER STORY
  • Influences More than just DMRS Group President Bernice Grossman’s friendship and men- toring (see sidebar below) and the wisdom of von Szent-Györgyi Nagyrapolt have provided inspiration to Nadler during her long direct marketing career. Nadler says her other direct mar- keting influences include Jack Kliger, former president and CEO of Hachette Filipacchi Media U.S. (who, as of press time, was reportedly taking over as act- ing CEO of TV Guide). Chairman of the Magazine Publishers of America from 2005 to 2007, Kliger took the unpopular stance that circulation metrics needed to change and magazine publishers needed to embrace digital technology instead of fighting it. “It is essential, I believe, that our industry moves to a more timely system of readership measurement— a system that shows the connection between distribution and readership more effectively,” according to a tran- script of Kliger’s “MPA Breakfast with a Leader” from Dec. 7, 2005. “The whole notion of the measurable audience going beyond what had been the standard magazine circulation base is actu- ally something that Jack Kliger … began talking about … years ago,” Nadler says. “And I think when he first spoke about it, a lot of people thought that he was just off-base. And he really saw this years before a lot of other parts of media and ad agencies began to glean onto this. I think he was just aware that suddenly there was a movement away from print and that the circulation counts weren’t really reflecting accurately how many people were involved with reading or being exposed to a certain product.” Tothatend,Nadlersaysnonprofitswere the first organizations to take methodical approachestounderstandingtheiraudienc- es, or members. During the ’60s, nonprofits were trouncing commercial enterprises with the exception of those like American Express and Reader’s Digest. “What were nonprofits doing early on?” Nadler asks. “They were writing down all their donor information on index cards— the earliest form of database marketing. They got it so soon. … Survival. That was theonlywaythattheyweregoingtobeable to keep the funding coming in.” Commercial entities caught on to the retention concept later, she says, when aggressive acquisition campaigns no longer worked as easily. Nonprofits, which had been cultivating their existing donor bases all along and moving them up the giving pyramid one step at a time, served as a les- son to corporate America, Nadler says. Enter the next set of visionaries Nadler cites: Don Peppers and Martha Rogers, the founding partners of Norwalk, Conn.- based customer-centric marketing strategy consultancy Peppers & Rogers Group. Nadler says the duo talks incessantly about one-to-one marketing. Or, as the group’s Web site attests, “treating different cus- tomers differently” by using data to keep and grow customer relationships. That creative rather than facts-only approach to database marketing points to thelastinfluencerNadlermentions:Arthur Middleton Hughes. Hughes is the founder of the Database Marketing Institute of Fort Lauderdale,Fla.,andaseniorstrategistwith Burlington, Mass.-based e-mail marketing firm e-Dialog. She interprets his stance as saying that there are two types of database marketers—constructors,whoassemblelists and successfully build the database, and creators, who take those names and turn them into loyal, returning customers. Finally, in Grossman’s case, the admira- tion is clearly mutual. Grossman describes Nadler as a politically savvy “overachiever” who has no use for “fluff” and will work as hard as she makes anyone else work. “Pegg is a continual learner,” Grossman says. “She is always asking questions. And so, when she’s faced with whatever today’s surpriseis,businesssurprise,shecangoback to that knowledge store of hers and pull from it. Also, she’s a really good manager. People work for her for extended periods of time. I think that there’s something to be said for being a good manager; I don’t think it’s all that easy. “Ialsothinkthatinthecompetitiveworld ofdatabasemarketing…she’sdoneextreme- ly well because she earned it,” Grossman adds. “… She has this … strategic ability, as opposedtoatacticalfunctionality.She’sable tolookatthebigpicture.[The]bigpictureis, A few of the business leaders who have been influential to Pegg Nadler: Bernice Grossman,Arthur Middleton Hughes, and Don Peppers and Martha Rogers.
  • ‘What I want to accomplish.’ And then she can go down and look at all of the different issuesshehastoaddresstoseewhetherornot she can accomplish it. … I certainly think it’s helped her move forward.” What It Is, What It Was and What It Shall Be Nadler is called on to speak to industry leaders and college students alike, and often gives them the same introduction to the craft. “Direct really demanded a response,” Nadler says of the historical difference between direct marketing and generic advertising. “Because you could actually track who was buying what and when. And, of course, database marketing then allowed us to ramp this up a notch, because we could be tracking what that individual customer was buying over time. “I just feel that we’ve made a quantum leap, and I actually talk about database marketing being the great leap backward,” she says of the current state of database marketing. “Because I’ve always said that database marketing has allowed us to get to that personal level, which, of course, is how all business transactions started years ago. [The transactions like] mom and pop shops knowing what color you liked and when you went out to buy a dress and what your favorite ice cream flavor was. But, as I said, with the lowering of processing and technology costs, we are finally able to really improve our market- ing to where everything is going to be measurable and really everything’s going to morph into direct. Which is why we’re calling it integrated marketing. I mean, even NYU, in their advanced program for direct marketing, they changed the name to integrated marketing to really reflect what was going on.” Measurement and ROI are now para- mount to marketers, no matter what chan- nel they use, instead of following nebulous metrics like Web site page views and clicks, she says. “It means that we’re not talking nonsense anymore. We’re truly talking senseanddollars.”Andadvancingtechnol- ogy will only make that more important, she predicts. Direct mail will survive and be more relevant, mobile marketing will grow exponentially, and e-mail market- ing will be more targeted—but not before consumers receive a lot more spam. Web sites will load instantly, and online video will load faster and be more fun. Moving from the future of direct mar- keting to its specific future, as married to publishing, Nadler’s excited tone doesn’t change much. “This is the most amazing time to be in what we like to say is publishing media, because it is changing dramatically,” she says. “We’re not talking about evolution anymore; this is revolution. And no one knows which species is going to make it in this catastrophic collision. Will the industry collapse? I don’t think so. I think that what we’re going to be left with will be a publishing medium that is so dynamic and so important that it’s going to go be that much better.” So after accomplishing what she set out to do at Hachette—when database operations are running smoothly—what will the next decade bring for adventure- seeking Nadler? With a full-throated laugh, she answers: “I wish I could tell you. I wish I could tell you that.” yy What does a database marketer do to have a good time? Why, hang out with other database marketers, of course. From affiliations with the Direct Marketing Association, the Direct Marketing Clubs of NewYork and Washington, D.C., and the John Caples International Awards to her former professor- ships, it might not seem like Nadler has time to do much else. For instance, Xenia “Senny” Boone, DMA’s senior vice president of corporate and social responsibility, harkens back to Nadler’s time as chairwoman of the advisory council of the DMA Nonprofit Federation (DMANF). From 2003 to 2005, Nadler led the committee while Boone was the DMANF execu- tive director. “She really helped shape what we call the [Nonprofit] Leadership Summit,” Boone says.“This was one of her brain- childs. You can appreciate putting together events could be stressful, but she always was a believer in the need for senior- level events for the fundraiser and the marketer for the non- profit community and really threw herself into it and really was committed.And when it came to working with the volunteers to get them to the event … she was somebody who would pretty much do anything to inspire and cajole and get people to attend this event and also to lead parts of the event.” Boone adds that Nadler remains active with the DMA, specifically helping shape direct marketing ethical compliance guidelines. Nadler does find time to spend with her mentor—Bernice Grossman, president and founder of data marketing consultancy DMRS Group of New York—whom she met 15 years ago at an industry event. “We usually talk about the various types of software installa- tions,” Grossman says.“We talk about different kinds of campaign management software.We talk about what are the best ways to segment and target for ultimate acquisition and retention.We talk about data and its value as it relates to enhancing the intelligence of, in her case, subscribers, to be a better marketer. “… Probably the most recent conversation would’ve been about the comparative evaluation of various software development cam- paign management tools and their effectiveness for the marketer,” Grossman continues.When asked if she could reveal that conversa- tion’s conclusions, she declines. Because they’re friends, Grossman says, she’ll provide Nadler with opinions “confidentially, for which I charge everybody else.” Extracurriculars COVER STORY Reprinted from Target Marketing® October 2009 © Copyright 2009, North American Publishing Co., Philadelphia PA 19130
  • DMA 2013 Database Post Intensive Recommended Sources for Database Marketing, CRM and Integrated Marketing The following lists include Pegg Nadler’s personal recommendations for information and reference material in your day-to-day database marketing activities. Many of the books listed are a part of my standard professional library. Some of the older titles are DB classics and provide an excellent framework for solid database marketing, best practices and guidance on DBM processes. Websites, Magazines, Newspapers & E-newsletters Ad Age www.adage.com B to B www.btobonline.com Chief Direct Marketer www.chiefmarketer.com Colloquy www.colloquy.com CRM www.destinationcrm.com Customer Think www.customerthink.com Direct www.directmag.com Direct Marketing News www.dmnews.com DMA www.the-dma.org E-Consultancy www.econsultancy.com Marketing Profs www.marketingprofs.com Marketing Sherpa www.marketingsherpa.com 1 to 1 www.1to1.com Smart Data Collective www.smartdatacollective.com Target Marketing www.targetonline.com
  • Books Arikan, Akin, Multichannel Marketing: Metrics and Methods for On and Offline Success, Sybex, 2008 Baier, Martin and Riuf, Kurtis and Chakraborty, Goutam, Contemporary Database Marketing, Racam Communications, 2002 Berry, Michael and Linoff, Gordon, Mastering Data Mining, Wiley, 2000 Brown, Stanley and Gulycz, Moosha, Performance Driven CRM, Wiley, 2002 Burnett, Ed, Database Marketing: The New Profit Frontier, Morris Lee Publishing, 1996 Cooper, Kenneth Carlton, The Relational Enterprise, American Management Association, 2002 Cross, Richard and Smith, Janet, Customer Bonding, NTC Business Books, 1995 Curry, Jany and Curry, Adam, The Customer Marketing Method, Free Press, 2000 Curry, Kay, Know Your Customers!, Kogan Page Ltd., 1992 Deloitte & Touche, Managing Database Marketing Technology for Success, Direct Marketing Association, 1992 Drozdenko, Ronald and Drake, Perry, Optimal Database Marketing, Sage Publications, 2002 Dyche, Jill, The CRM Handbook, Addison-Wesley, 2002 Paul W. Farris, Neil T. Bendle, Phillip E. Pfeifer and David J. Reibstein, Marketing Metrics: The Definitive Guide to Measuring Marketing Performance (2nd Edition), Pearson Prentice Hall, 2010 Francese, Peter, Capturing Customers, American Demographic Books, 1990 Franks, Bill, Taming the Big Data Tidal Wave, Wiley and SAS Business Series, 2012 Freeland, John, The Ultimate CRM Handbook, McGraw-Hill, 2003 Godin, Seth, Permission Marketing, Simon & Schuster, 1999 Gordon, Ian, Relationship Marketing, Wiley & Sons Canada, 1998 Greenberg, Paul, CRM at the Speed of Light, McGraw-Hill, 2002 Hartmann, Kenneth, Research and the Customer Lifecycle, Direct Marketing Association, 1995
  • Hughes, Arthur, The Customer Loyalty Solution, McGraw-Hill, 2003 Hughes, Arthur, Strategic Database Marketing (4th edition), McGraw Hill, 2012 release Jackson, Rob and Wang, Paul, Strategic Database Marketing, NTC Business Books, 1995 Jeffrey, Mark, Data-Driven Marketing, Wiley, 2010 Lee, Dick, The Customer Relationship Management Survival Guide, HYM Press, 2000 Mayer-Schonberger, Victor and Cukier, Kenneth, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Houghton Mifflin Harcourt, 2013 Nash, Edward, Database Marketing, McGraw Hill, 1993 Newburg, Jay and Marcus, Claudio, Target Smart!, Oasis Press, 1996 Newell, Frederick, loyalty.com, McGraw Hill, 2000 Newell, Frederick, The New Rules of Marketing, McGraw Hill, 1997 Nykamp, Melinda, The Customer Differential, American Management Association, 2001 Peck, Mark, Integrated Account Management, American Management Association, 1997 Peppers, Don and Rogers, Martha, Enterprise One to One, Currency Doubleday, 1997 Peppers, Don and Rogers, Martha, Extreme Trust: Honesty as a Competitive Advantage, Portfolio, 2012 Peppers, Don and Rogers, Martha, Managing Customer Relationships: A Strategic Framework, Wiley, 2011 Peppers, Don and Rogers, Martha, The One to One Fieldbook, Currency Doubleday, 1999 Peppers, Don and Rogers, Martha, The One to One Future, Currency Doubleday, 1993 Pine II, B. Joseph and Gilmore, James, The Experience Economy, Harvard Business School Press, 1999 Raphel, Murray and Raphel, Neil, Up the Loyalty Ladder, HarperCollins, 1995 Roman, Ernan, Integrated Direct Marketing, NTC Business Books, 1995 Roman, Ernan, Voice of the Customer Marketing, McGraw Hill, 2010 Schmidt, Jack and Weber, Alan, Desktop Database Marketing, NTC Business Books, 1998
  • Shaver, Dick, The Next Step in Database Marketing, Wiley, 1996 Shepard, David, The New Direct Marketing, McGraw Hill, 1999 Seybold, Patricia, The Customer Revolution, Crown Business, 2001 Smith, Ellen Reid, e-loyalty, Harper Business, 2000 Swift, Ronald, Accelerating Customer Relationships, Prentice Hall PTR, 2001 Tooker, Richard, The Business of Database Marketing, Racom, 2006 Vavra, Terry, Aftermarketing: How to Keep Customers for Life Through Relationship Marketing, Irwin, 1992 Zikopoulos, Paul and Eaton, Chris, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, McGraw Hill, 2012