Marketing Analytics in a Week
 

Marketing Analytics in a Week

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Marketing Analytics in a Week Marketing Analytics in a Week Presentation Transcript

  • Marketing Analytics in a Week Stephan Sorger, The “Analytics Ambassador” www.StephanSorger.com
  • ABOUT OUR SPEAKER The “Analytics Ambassador”  Author - “Marketing Analytics: Strategic Models and Metrics” (2013)  Professional Expertise - VP Strategic Marketing, On Demand Advisors   Academic Expertise - Instructor at UC Berkeley, San Francisco Extension   Applying marketing analytics to grow revenue Teaching Marketing Analytics since 2008 Board Member - Served on UC Berkeley Ext. Marketing Metrics Board Website: http://www.stephansorger.com LinkedIn: http://www.linkedin.com/in/stephansorger
  • ABOUT THE NEW BOOK  Authoritative Guide to Marketing Analytics    Over 10 years of professional experience Over 5 years of academic research Comprehensive    Nearly 500 pages of text Nearly 400 figures, tables, and graphs Practical    Structured around marketing and products, not math Packed with examples Available on Amazon.com:  Search on “Marketing Analytics”: www.amazon.com/Marketing-Analytics-Strategic-ModelsMetrics/dp/1481900307 www.StephanSorger.com
  • ON DEMAND ADVISORS: PROCESS 2. Market Definition 3. Lead Generation 4. Lead Management 5. Sales Enablement 1. Revenue Engineering 4 www.StephanSorger.com
  • ON DEMAND ADVISORS: CLIENTS 5 www.StephanSorger.com
  • ON DEMAND ADVISORS: UPCOMING EVENTS  Revenue Engineering Workshops held every month: See OnDemandAdvisors.com for complete schedule 6 www.StephanSorger.com
  • MARKETING ANALYTICS IN A WEEK AGENDA   Why a Week?  Monday: Defining the problem and building a business case  Tuesday: Selecting the people for the project  Wednesday: Preparing the technology and data  Thursday: Executing the analysis and computing the answer  Friday: Gaining insight and presenting the results  7 Introduction Practices & Pitfalls www.StephanSorger.com
  • TRENDS DRIVING ANALYTICS ADOPTION Online Data Availability Accountability  Improve productivity  Reduce costs  “What gets measured gets done” Data-Driven Presentations  Data to back up proposals  Predict success of plans 8 Marketing Analytics Adoption  Cloud-based data storage  Online = speed  Online = convenience Reduced Resources Massive Data  Initiatives to capture customer information  What to do with all that data?    Do more with less Scrutinized budgets Marketers must show outcomes www.StephanSorger.com
  • MARKETING ANALYTICS ADVANTAGES Persuade Executives Drive Revenue  Marketing as cost center  Marketing as profit center  Correlation between spending & results Save Money 9  Old way: execute campaign & guess outcome  No longer tolerate this approach  New way: predict outcome Marketing Analytics Advantages Encourage Experimentation  Test multiple scenarios before proceeding  Run Simulations  Predict which will work best  Focus on revenue impact from marketing  Correlation between spending & results Side-Step Politics   Some CEOs do not appreciate marketing Show impact of efforts with metrics www.StephanSorger.com
  • WHAT IS MARKETING ANALYTICS? “It’s a Wall!” It must be Big Data! “It’s a Fan!” It must be Social Media! “It’s a Rope!” It must be Predictive Analytics! “It’s a Snake!” It must be Marketing Automation! 10 “It’s a Tree!” It must be Google Analytics! www.StephanSorger.com
  • THE MARKETING ANALYTICS FRAMEWORK Market Analysis Competitive Analysis Strategy and Operations Marketing Mix The 4 Ps Sales and Support Chapters 1-3 Chapter 4 Chapters 5-6 Chapters 7-10 Chapter 11 Segmentation Targeting Positioning Competitive Analysis Forecasting Big Data Predictive Anyl. Conjoint Google Analytics Social Media Marketing Auto. Strategic 11 Analytics in Action Chapter 12 Tactical www.StephanSorger.com
  • WHY A WEEK? Project Scope vs. Appetite for Quick Results:     Day(s): Not credible for all but the most trivial projects Week(s): OK for small initiatives; Easy to digest Month(s): OK for medium initiatives; Perception of “major project” Year(s): OK for large initiatives; Significant risk management required Monday Wednesday Thursday Friday Defining the problem and building the business case 12 Tuesday Selecting the people for the project Preparing the technology and data Executing the analysis and computing the solution Gaining insight and presenting the results www.StephanSorger.com
  • RUNNING EXAMPLE Topic Description Example Straightforward marketing analytics project Performed at a Fortune 500 enterprise software firm Problem Assess customer satisfaction of major accounts Constraint Little budget availability for customer sat survey Approach Correlate customer sat with existing data Time ASAP Regional Office 13 Headquarters Regional Office Customers www.StephanSorger.com
  • MONDAY Monday Tuesday Wednesday Thursday Friday Defining the problem and building the business case Selecting the people for the project Preparing the technology and data Executing the analysis and computing the solution Gaining insight and presenting the results 14 www.StephanSorger.com
  • MONDAY Topic Description Define Problem State Problem to be Solved Completed to Estimate Project Scope Build Business Case Estimate Cost Savings or Other Benefit Completed to Obtain Budget for Project Monday Define problem 15 Build business case www.StephanSorger.com
  • BEST PRACTICES: PROBLEM DEFINITION Topic Description Problem Definition Describe clearly the problem to be solved  X: “Gauge customer satisfaction”: Too vague  OK: “Determine predictive indicators for defection.” Success Criteria Define success criteria  X: “Done once data is collected”: No outcome  OK: “Show correlation at 95% confidence” Business Case Estimate savings expected vs. cost  X: “Will improve customer sat.”: Too vague  OK: Estimate hard and soft costs 16 www.StephanSorger.com
  • POLL: PROBLEM DEFINITION Question Score How many of you have encountered the following: Project proposals without clear problem definitions? _____ Project proposals without success criteria? _____ Project proposals without dollar-based business cases? _____ VOTE 17 www.StephanSorger.com
  • RUNNING EXAMPLE: PROBLEM & BUSINESS CASE Topic Description Problem Definition Determine existing indicators of imminent defection Business Case See below Category Computation Hard Savings: Regional data collection 20 reg. mgrs. * 3hr/ea * $100/hr Soft Savings: Customer sat 1 lost customer $100,000/hr Hard Cost: Marketing analyst 40 hr * $100/hr ($4,000) Net Savings Subtotal $6,000 $2,000 www.StephanSorger.com
  • TUESDAY Monday Tuesday Wednesday Thursday Friday Defining the problem and building the business case Selecting the people for the project Preparing the technology and data Executing the analysis and computing the solution Gaining insight and presenting the results 19 www.StephanSorger.com
  • TUESDAY Topic Description Core Team Statistical modeler: M.S./Ph.D. math or econ; SAS/SPSS Data Analyst: B.S.; SAS/R/Pig/SQL; Large data sets Analytics SW Developer: OO; Scrum/Agile; SQL Extended Team Project Leader Business analyst(s) Evaluator(s)/ Tester(s) Core Team Statistical Modeler Extended Team Analyst Project Leader Evaluator(s) Developer 20 Business Analyst(s) Source: Roldan, Alberto: “Implementing Business Analytics.” Atomai blog. May 5, 2010. Link: http://atomai.blogspot.com/2010/05/implementing-business-analytics.html www.StephanSorger.com
  • SATISTICAL MODELER: SAMPLE Senior Statistical Modeler: SunTrust; Atlanta, GA Responsibilities:     Develops or analyzes quantitative models. Researches best practices and new technologies. Performs complex analysis and draws conclusions. Responsible for the analysis and/or development of quantitative models both financial and non-financial in support of the company’s risk management effort.  Consults with practitioners, the academic community, and other financial institutions in researching the development of risk management models. Qualifications:  Masters/PHD degree in a in a quantitative field such as Mathematics, Statistics, Econometrics, Actuarial Science or Engineering.  Programming skills (SAS, Matlab, Visual basic).  Demonstrated mastery of quantitative modeling requirements for non-parametric type of models.  4+ experience in building Basel compliant models and involved in the entire life-cycle of building models.  Basic understanding of financial statements.
  • DATA ANALYST: SAMPLE Data Scientist: Cisco; San Bruno, CA Responsibilities:  The Data Scientist will apply disciplined analysis to explore and develop new techniques for identifying and mitigating internet security threats (spam, malware, etc.).  Deliverables include research proposals, research documents describing a technique and quantitative measures of expected efficacy improvement, prototypes, functional specifications and ad-hoc measurement tools  As a leading team within Cisco STG, the Analysis Team is responsible for developing new techniques to identify and mitigate network security threats, as well as for assessing the efficacy of those techniques in defending against security threats. Qualifications:       5+ years of big-data experience including applied techniques in data mining, machine learning, or graph mining. Experience with Hadoop, Hive, MapReduce, or column stores, as well as working with large, unfiltered data sets. Able to persuade stakeholders and champion effective techniques through product development. Understanding of network security, including email and/or web threats highly desirable. Proficiency with Unix and databases, as well as working knowledge of PERL or Python. Advanced degree in a relevant field is desirable.
  • ANALYTICS SOFTWARE DEVELOPER: SAMPLE Software Engineer, Analytics Big Data Quality: Salesforce.com; SF, CA Responsibilities:  Understand and perform analysis on the unique requirements in on-demand multi-tenancy model for Analytics tools assuring that changes to existing functionality are truly required and correctly deployed.  Participate in the scrum team under our agile development process utilizing principles such as test-driven-development  Perform both functional manual/automated testing of application features using automation tools such as Selenium and JUnit and extensive white-box testing through an application program interface (API). Qualifications:  Experienced. Experienced using automation frameworks such as Selenium and JUnit, coming up with comprehensive test plans and tests cases, as well as hands on experience with Java programming and testing.  Having BI tool testing experience is definitely a big plus.  Highly technical. Strong background in Object-Oriented programming concepts and constructs.  Solid knowledge of SQL and understanding of relational database schema design.  Testing expert. Industry experience in testing on various types of browsers (Google Chrome, Firefox, IE) and web technologies, such as HTTP, XML, Javascript, HTML5, and CSS3.  In depth knowledge of SQA methodologies, tools and approaches (black box, white box and automated testing experience) in testing multi-tier scalable applications.
  • ANALYTICS PROJECT LEADER: SAMPLE Analytics Project Manager: NYC Dept. of IT and Telecomm; Brooklyn, NY Responsibilities:  Manage Citywide Performance Reporting (CPR)/Analytics platform support releases and new application development projects  Lead the Analytics Production Support team on initiatives necessary to maintain and support the platform for City agencies  Manage vendor relationships performing ongoing Analytics support and development work, Security, PMQA, independent contractors and similar engagements, including the creation of RFPs, review/selection of vendors, etc.  Ensure that applications are stable and maintainable;  Provide information to the public upon request and approval of executive management Qualifications:        3+ years’ experience managing large projects (end-to-end) Knowledge of SDLC and/or Agile; 2+ years’ experience in Vendor management, WBS creation, Project and resource planning Proficiency in Microsoft Project and other project management software Business analysis experience creating requirements, use cases, functional specifications preferred Experience with Oracle Business Intelligence Enterprise Edition (OBIEE); PMP certification; experience working with City of New York agencies www.StephanSorger.com
  • BUSINESS ANALYST: SAMPLE Business Analyst: Magenic; San Francisco, CA Responsibilities:      Developing use case based requirements specifications to capture project business requirements Managing functional and non-functional requirements artifacts through all development and QA iterations Facilitation of requirements analysis sessions with project stakeholders Collaboration with project stakeholders to establish requirements baseline. Stakeholders include client business team, Magenic development team, third party development teams, QA team Qualifications:     Hands on experience as a business analyst in a software production environment Must have experience working with end users and/ or product owners Ideally, some level of experience developing software TFS ideally, and an understanding of how to use it to drive requirements <TFS: Microsoft Visual Studio Team Foundation Server>  Expression Blend experience a plus <Microsoft Expression Blend: Software UI Tool>  A sense of humor and perspective  Experience with Agile, or Agile-based, development methodologies www.StephanSorger.com
  • EVALUATOR/TESTER: SAMPLE Analytics Software Tester: JMP (SAS); Cary, NC Responsibilities:     As a JMP Analytics Software Tester, you will validate statistical features of JMP. Interact directly with developers to test the numerical accuracy of statistical algorithms during the development life cycle. Ensure quality and functionality of software code that is used to make critical decisions. Understand the needs of JMP's customer base and give usability feedback in order to make data-based analytical problem solving accessible to a wide audience.  Research technical literature, maintain test scripts and participate in the documentation review process Qualifications:  Master's degree in statistics or a related quantitative field including extensive coursework in mathematics.  2 or more years of experience using JMP in a professional capacity.  Ability to think analytically and to effectively communicate problems and suggest fixes. 26 www.StephanSorger.com
  • TUESDAY Topic Description Statistical modeler Data analyst Analytics SW developer Project Leader Business Analyst Evaluator +Executive Sponsor Core Team Statistical Modeler Developer 27 No dedicated modeler due to simple model Data analysis done by product manager No dedicated developer due to simple model Leadership done by product manager Worked with financial business analyst to get data Testing done by product manager VP Products Extended Team Analyst Project Leader Business Analyst(s) Evaluator(s) www.StephanSorger.com
  • WEDNESDAY Monday Tuesday Wednesday Thursday Friday Defining the problem and building the business case Selecting the people for the project Preparing the technology and data Executing the analysis and computing the solution Gaining insight and presenting the results 28 www.StephanSorger.com
  • ANALYTICS TECHNOLOGY CATEGORIES Category Affiliate Marketing Attribution Analytics Big Data Analytics Customer Acquisition Analytics Data Visualization Direct/email Marketing Analytics Extract, Transform, Load (ETL) Marketing Automation Marketing Intelligence/BI Marketing Tools and Templates Predictive Analytics Social Media Analysis Statistical Software Web Analytics 29 Sample Companies Linkshare Adometry, Apsalar, VisualIQ Hadoop, Oracle RTD, Teradata Angoss, Nettpositive, Vertex Group Leftronic, QlikView, Tableau Software Icontact, Litmus Astera, Informatica, Snaplogic Eloqua (Oracle), Marketo, Pardot, Act-On IBM, PivotLink, Sybase (SAP) Demand Metric Angoss, Fair Isaac, KXEN Radian6, SproutSocial, Visible Technologies R, SAS, SPSS CoreMetrics, Google, Omniture, WebTrends
  • DATA ANALYSIS: PREPARATION Step Selection Pre-Processing Transformation Data Select portion of data to target Data cleansing; Removing duplicate records Sorting; Pivoting; Aggregation; Merging Data Mining Interpretation Selection Description Find patterns in data Form judgments based on the patterns Pre-Processing Target Data Transformation PreProcessed Data Data Mining Transformed Data Interpretation Patterns Actionable Information www.StephanSorger.com
  • POLL: DATA PREPARATION Question Score How many of you have encountered the following: Problems with selecting the right data to analyze? Problems with pre-processing the data? (de-duping, etc.) Problems with transforming the data? (merging, etc.) _____ _____ _____ VOTE 31 www.StephanSorger.com
  • RUNNING EXAMPLE: DATA ANALYSIS PREP Step Description Selection Pre-Processing Transformation Limit data to customers served by regional centers Remove duplicate records Merged two databases Selection Data 32 Pre-Processing Target Data Transformation PreProcessed Data Data Mining Transformed Data Interpretation Patterns Actionable Information www.StephanSorger.com
  • THURSDAY Monday Tuesday Wednesday Thursday Friday Defining the problem and building the business case Selecting the people for the project Preparing the technology and data Executing the analysis and computing the solution Gaining insight and presenting the results 33 www.StephanSorger.com
  • DATA ANALYSIS: EXECUTION Step Selection Pre-Processing Transformation Data Select portion of data to target Data cleansing; Removing duplicate records Sorting; Pivoting; Aggregation; Merging Data Mining Interpretation Selection Description Find patterns in data Form judgments based on the patterns Pre-Processing Target Data Transformation PreProcessed Data Data Mining Transformed Data Interpretation Patterns Actionable Information www.StephanSorger.com
  • POLL: DATA MINING Question Score How do you analyze data for patterns: “Eyeball it”: Look over columns of numbers and identify patterns “Sort it”: Sort the data and examine trends “Analyze it”: Conduct regression or other types of analysis _____ _____ _____ VOTE 35 www.StephanSorger.com
  • RUNNING EXAMPLE: DATA ANALYSIS - EXECUTION Step Description Data Mining Pre-Processing Transformation Limit data to customers served by regional centers Remove duplicate records Merged two databases Selection Data 36 Pre-Processing Target Data Transformation PreProcessed Data Data Mining Transformed Data Interpretation Patterns Actionable Information www.StephanSorger.com
  • FRIDAY Monday Tuesday Wednesday Thursday Friday Defining the problem and building the business case Selecting the people for the project Preparing the technology and data Executing the analysis and computing the solution Gaining insight and presenting the results 37 www.StephanSorger.com
  • COMMUNICATIONS WITH ANALYTICS: BEFORE Engineering Department Status  Engineering resources are very low; definitely need more engineers  Some engineers working many hours per week  Engineers risk getting burned out from working so many hours  New projects coming up will require more resources than we have  Engineering resource types  Engineering resource type A: have 10 engineers; need at least 12  Engineering resource type B: have 3 engineers; need at least 4  Engineering resource type C: have 5 engineers; need at least 6  Engineering resource type D: have 15 engineers; need at least 20  Possible slips to schedule can occur unless we hire more engineers  Recommend hiring at least 2 additional engineers in next month  Many engineers complaining to their management about workload www.StephanSorger.com
  • COMMUNICATIONS WITH ANALYTICS: AFTER Department Revenue and Resources Professional Services Organization Department Status Will Stop Producing Incremental Revenue Here Current Resource Level Projected Revenue Revenue to Date Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
  • RUNNING EXAMPLE: DATA PRESENTATION Step Description Conclusion Secondary Outcome Money Savings Increased Accuracy Primary Outcome 40 Problem solved; Correlated variable identified Big deal in enterprise software world Ghost-wrote article; “Authored” by EVP Company positioned as expert in analytics www.StephanSorger.com
  • KEY TAKE-AWAYS  Monday: State clear definitions, success criteria, and business cases  Tuesday: Identify the right people for the job  Wednesday: Adopt skill sets in preparing and merging data  Thursday: Be on the lookout for patterns in data; Be open to new ones  Friday: Develop presentations that scream Action and Insight 41 www.StephanSorger.com
  • QUESTIONS? Q&A 42 www.StephanSorger.com
  • SPONSOR Act-On is a leading provider of integrated marketing automation software. Using Act-On, more than 1700 companies tie inbound, outbound and nurturing programs together -across email, web, mobile, and social -- and achieve a superior Return on Marketing Investment. www.act-on.com 43 www.StephanSorger.com
  • HOST Demand Metric is a marketing advisory firm serving a membership community of over 30,000 marketing professionals and consultants in 75 countries with consulting methodologies, advisory services, and a library of 500+ premium marketing tools and templates. These tools allow Demand Metric members to plan more efficiently and effectively, and answer the difficult questions about their work with authority and conviction. Demand Metric tools enable members to complete marketing projects more quickly and with greater confidence, boosting the respect of the marketing team and making it easier to justify resources the team needs to succeed. www.demandmetric.com 44 www.StephanSorger.com