Database Marketing Intensive


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Database Marketing Intensive

  1. 1. 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.
  2. 2. 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.
  3. 3. 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.
  4. 4. 9/23/2013 1 Re-evaluating Your Marketing Database System: A How To Database Marketing Post Intensive Part 1 Bernice Grossman Founder, DMRS Group • 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 ?
  5. 5. 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.
  6. 6. 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
  7. 7. 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
  8. 8. 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. 9. 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
  10. 10. 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 – 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?
  11. 11. 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
  12. 12. 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?
  13. 13. 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?
  14. 14. 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?
  15. 15. 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?
  16. 16. 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
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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.”
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. 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 and 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 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
  26. 26. 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
  27. 27. 9/23/2013 2 … and Their Path to Purchase Not Just In Cartoons
  28. 28. 9/23/2013 3 It’s about applying what you know… where it’s needed • Data Enablement • Blueprint • Readiness Assessment • Q&A 6 Agenda
  29. 29. 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.
  30. 30. 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
  31. 31. 9/23/2013 6 Tech 3‐Year History Tech Today
  32. 32. 9/23/2013 7 Big Data Digital Ubiquity = Tracking Ubiquity
  33. 33. 9/23/2013 8 Reducing to Bite Sized Chunks Linkage ONLINE OFFLINE
  34. 34. 9/23/2013 9 Addressability
  35. 35. 9/23/2013 10 Painting The Picture Tech Today
  36. 36. 9/23/2013 11 • Data Enablement • Blueprint • Readiness Assessment • Q&A 21 Agenda • Data Enablement • Blueprint • Readiness Assessment • Q&A 22 Agenda
  37. 37. 9/23/2013 12 • Data Enablement • Blueprint • Readiness Assessment • Q&A 23 Agenda
  38. 38. 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, 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 joint venture.
  39. 39. 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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  42. 42. 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…
  43. 43. 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.
  44. 44. 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.
  45. 45. 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).
  46. 46. 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…
  47. 47. 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
  48. 48. 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…
  49. 49. 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.
  50. 50. 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!
  51. 51. 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
  52. 52. 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,
  53. 53. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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.
  54. 54. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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.
  55. 55. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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 (, and can be reached at 919-969-8859 or The firm specializes in direct marketing consulting and data mining, data quality assessment and assurance, and the delivery of cost-effective marketing databases
  56. 56. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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
  57. 57. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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”: Jim Wheaton is a Principal at Wheaton Group (, and can be reached at 919-969-8859 or The firm specializes in direct marketing consulting and data mining, data quality assessment and assurance, and the delivery of cost-effective marketing databases.
  58. 58. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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.
  59. 59. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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 (, and can be reached at 919-969-8859 or The firm specializes in direct marketing consulting and data mining, data quality assessment and assurance, and the delivery of cost-effective marketing databases.
  60. 60. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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.
  61. 61. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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
  62. 62. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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.
  63. 63. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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 (, and can be reached at 919-969-8859 or The firm specializes in direct marketing consulting and data mining, data quality assessment and assurance, and the delivery of cost-effective marketing databases
  64. 64. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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.
  65. 65. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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,
  66. 66. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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.
  67. 67. 1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 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 (, and can be reached at 919-969-8859 or The firm specializes in direct marketing consulting and data mining, data quality assessment and assurance, and the delivery of cost-effective marketing databases
  68. 68. 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 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;
  69. 69. 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
  70. 70. 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
  71. 71. 9/23/2013 3 Identifying Your Customer Starting with a Clean Customer File • Normalizing to the unique customer is key to accurate  analysis
  72. 72. 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
  73. 73. 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
  74. 74. 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
  75. 75. 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
  76. 76. 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 
  77. 77. 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
  78. 78. 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
  79. 79. 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 $2,500,000 Catalog Natural Search ‐ Branded Natural Search ‐ Non Branded Paid Search ‐ Branded Paid Search ‐ Non Branded Web (Other) Email Grand Total Allocated Demand by Marke ng Medium Month TY Month LY
  80. 80. 9/23/2013 12 Acquisition Costs by Marketing Media • Cost per acquired new buyer is part of the equation TY 1st Order Contribution LY 1st Order Contribution TY New Buyers LY New Buyers #% TY/LY ∆% TY/LY Catalog (4.32)$ (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
  81. 81. 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
  82. 82. 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
  83. 83. 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