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Quant5 planning ness-050613_final Quant5 planning ness-050613_final Presentation Transcript

  • © 2013 Quant5, Inc.How to applyPredictive AnalyticstoMarketing ChallengesFor the Planning-ness ConferenceMay 10th, 2013Doug Levin | doug@quant5.com
  • © 2013 Quant5, Inc.AgendaPlanning-ness Approach ~Time Allocation(Minutes)Teaching 45Putting teaching into practice 45 – 60Evaluation and discussion 20-302
  • © 2013 Quant5, Inc.Teaching3
  • © 2013 Quant5, Inc.Challenges Facing Marketing• Frequently make critical decisions without:– The information they need– The insights in their business & environment they need– Access to data in other parts of their organization– A supporting cast & crew (aka data scientists)• Hours are spent each week searching for data– “Connecting silos”
  • © 2013 Quant5, Inc.Challenges Facing Marketing Departments• Roadblocks to Success:– Being asked to come up with brilliant new insights• Shortage of data scientists to do statistics, math, etc.• No tools– An avalanche of data from mobile, social media and othersources… and growing by the minute!– Data located in legacy systems run by IT• Have to connecting silos through organizational means not directreporting authority5
  • © 2013 Quant5, Inc.How ambitious are you?• Do you want to have a “data centric” business? Business decisions no longer based on gut instinct• Do you want to have a “data centric” marketing dept.?Fact-driven relying onmeasurement & feedbackReal-time dataAt the point of impactEveryone’sInvolved &ConnectedUbiquitousoptimizationAutomatedRelying on predictive analytics & validationCharacteristics6
  • © 2013 Quant5, Inc.Data in your organization that can help you…discover trends and opportunities• The top 5 sources of data tagged for predictiveanalytics:• 54% Sales• 67% Marketing• 69% Customer• 55% Product• 51% FinancialIn addition, 40% of companies surveyed indicated that “Social (Facebook,Twitter & LinkedIn) had potential value in predictive analyticsSource: SAP Analytics 02/08/13All related to revenue7
  • © 2013 Quant5, Inc.Persistent, Deep QuestionsWho are our• core customers?• frequent buyers?• best customers?• poorest payers?• Best sales guys?The Who?How do we:• Attract the best customers to buymore?• Reduce the cost of customeracquisition?• increase first purchase size?• increase subsequent purchase size?• increase cross-product purchases?• Reduce fraudThe How?8
  • © 2013 Quant5, Inc.Predictive Analytics CAN help? A LOT!• Predict market trends• Predict customer needs• Predict price volatility• Create customized offers foreach segment and channel• Predict changes in demandand supply across the entiresupply chain9
  • © 2013 Quant5, Inc.Use Predictive Analytics…When your spreadsheet runs out of gasDataVariablesLogicSpeed10
  • © 2013 Quant5, Inc.Predictive Analytics SolutionsHorizontal VerticalEmbeddedDatabaseAnalytics•Hadoop•Unstructured andStructuredDatabasesConsulting Services11
  • © 2013 Quant5, Inc.© 2013 Quant5, Inc.Functional / Business Unit OutcomesGoal: More new & incremental salesSalesGoal: ROI + efficiencies + incremental rev’sMarketingGoal: Better products, prices & competitivenessProduct12• Customer Analytics• Prospect analytics• Sales cycle analytics• Price analytics• Competitive Prospects& Intelligence• Industry trends
  • © 2013 Quant5, Inc.© 2013 Quant5, Inc.Functional / Business Unit OutcomesGoal: More new & incremental salesSalesGoal: ROI + efficiencies + incremental rev’sMarketingGoal: Better products, prices & competitivenessProduct13• Market trends & Drivers• Competitors, threats &vulnerabilities• Opportunities & BudgetOptimization• Improving positioning &messaging• New products, marketsand partners• Marketing activityoptimizations• Business risks• Threat detection
  • © 2013 Quant5, Inc.© 2013 Quant5, Inc.Functional / Business Unit OutcomesGoal: More new & incremental salesSalesGoal: ROI + efficiencies + incremental rev’sMarketingGoal: Better products, prices & competitivenessProduct14• Product ManagementAnalytics• Actionable ProductIntelligence• Competitive Analysis• Partner Analysis• Supply Chain Analysis• Launch Plans & Positioning• Price Analytics
  • © 2013 Quant5, Inc.Steps to Successful Predictive AnalyticsDesign Implement Measure• Goal setting• ResourceAssessment• Questions tobe answered• Tests• Deployment(s)• Feedback• Assessment of KPIs• Improvements• Validation15
  • © 2013 Quant5, Inc.Non-Obvious Knowledgeand ProbabilitiesPredictive Analytics for BusinessAnalyze current and historicaldata in order to betterunderstand customers,products and partners, andidentify potential risks andopportunities16
  • © 2013 Quant5, Inc.Putting the teachinginto Practice17
  • © 2013 Quant5, Inc.Situation Analysis• Lucy Couture:– A 3-year old eRetailer• High-end “Juicy” couture– Bags, business attire,dresses, intimate apparel,parts, shirts, shoes, skirts– Demographic:• Women (25+)• In College (19-25)• Other (gift purchasers)– Generates a couple ofmillions in gross revenuesp/year– Has 15 employees– Limited data centricityMarketerWhat arethe prices sensitivities?ProductManagerWhat are the productrelationships?MarketerWhat are thedemographics (agecohorts) of purchases?MarketerWhat sort of financialdata can be used?18
  • © 2013 Quant5, Inc.Situation Analysis• You are the Director of Marketing– With a marketing manager (“marketer”) with an MBA reportingto you• He/she is not a data scientist• The Marketing Department– Maintains the website– Uses ConstantContact as a email campaign management system– Has access to:• POS data & the customer database– Has a Limited budget– Has limited data centricity but a desire to transform thecompany into a data centric culture19
  • © 2013 Quant5, Inc.Your goals:1. Increase revenue2. Increase efficiencies of marketing activities3. Improve customer communications4. Evolve into a data centric organization• Here are the steps involved:1. Gather data from current systems2. Determine the product relationships3. Determine the customer set that is most & least receptive4. Determine the next product and price to be promoted viaemail5. Integrate back into current systems6. Measure & improve results20
  • © 2013 Quant5, Inc.Your Marketing Mix4P’s• Price• Product• Promotion• PlaceWhich element(s) of the marketing mixis most effective increasing revenue?21
  • © 2013 Quant5, Inc.Your Marketing Mix4P’s• Price• Product• Promotion• PlaceWhich type ofpromotion isgoing to bemost effective? Direct or indirect sales Advertising Marketing Promotions Events Direct marketing PREmail !Lucy has email addresses fromall kinds of potential customersand a relatively small number ofactual customers22
  • © 2013 Quant5, Inc.Which data to use?• Do not use data from:– Social Media• Facebook, Twitter, Blogs,surveys, etc.• Customer Sentiment– Mobile Data– Machine Data• RFID, sensors, etc.– Images• Video, audio, emails– “Real Time”• Use data from:– Customer transactions– Legacy systems– Web site: GoogleAnalytics23
  • © 2013 Quant5, Inc.Can A Spreadsheet Do The Analytics?24
  • © 2013 Quant5, Inc.Prospect Customer Scores –Step Two• Data needed– ( Any demographic data is ok here, we can take advantageof a lot of disparate types of information)– Customer ID– Age– Household incomeEquation: Machine learning algorithm which minescustomer demographic and descriptive data to determinewhich characteristics are indicators of success.25
  • © 2013 Quant5, Inc.Product Relationships –Step Three• Data needed– Product transactions data– Transactions in commonEquation: Determine which groups of products are highlycorrelated and purchased together.26
  • © 2013 Quant5, Inc.Targeted Offers –Step FourAvailable Data:• 10,880 emailaddresses• 1,360customers– ∑ 4,080transactions(3 per customer)– x transaction =$138.00 (2013)• Days since last purchase• Purchase prices by product,category and SKU number27
  • © 2013 Quant5, Inc.Targeted Offers–Step Four• Equation: Determines the similarity of marketbaskets by analyzing past customer behavior, anddetermining which products are most likely to bepurchased next by each customer.28
  • © 2013 Quant5, Inc.Targeted Offers –Step Four• Promotional offer:– Who should receive these targeted offers?A select group of established customers– How should the customer info be presented?Customer names and customer IDs– What other information would be helpful to know?Lifetime valueRisk of Churn• Validation– Past KPIs (# of emails per period, opens, sales)– Closed loop?29
  • © 2013 Quant5, Inc.Demo30
  • © 2013 Quant5, Inc.Discussion & Assessment31
  • © 2013 Quant5, Inc.How YOU can applyPredictive AnalyticstoMarketing ChallengesFor the Planning-ness ConferenceMay 10th, 2013Doug Levindoug@quant5.com