How Big Data is Changing Retail Marketing Analytics


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Learn how Smart retailers are using advanced revenue attribution and customer-level response modelling to optimize their marketing spends.

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How Big Data is Changing Retail Marketing Analytics

  1. 1. MARKETING ANALYTICS AS A SERVICE Retail Marketing Analytics APRIL 2012Powered by: 1
  2. 2. Who we areCompany OverviewExperienced team with a proven history of solving difficult analyticsproblems for Fortune 500 companiesCloud-based software to manage marketing’s big data problems:customer level revenue attribution and multi-channel optimization, triggeredmarketing, and planning and reportingLocations San Francisco, Seattle, and Hyderabad John Wallace, CEO Brandon Mason, CTO 2
  3. 3. UpStream Suite 3
  4. 4. Challenges with Multi-Channel RetailMulti-channel marketers are unsure where to spend their next dollar.Messy data with many Don’t understand how spending No easy way to identify themarketing and order channels, on marketing affects conversion most profitable channels for everydisparate databases, various customerexecution platforms 4
  5. 5. What is Attribution Modeling?Assigning creditWhat marketing treatments drove my order? How should theyshare credit?TargetingWhich customers are most likely to buy?Cross-channel EffectsDoes marketing in one channel affect other channels?Incremental ResponseWhich customers are most receptive to catalog? Toremarketing? To email?Strategic AllocationWhat is the optimal way to spend my next marketing dollar for aspecific customer? For group of customers? Or my whole file? 5
  6. 6. Current State: Multi-Channel Customer AnalyticsSTRONG • Simple and flexible methods lack Attribution accuracy • Most tools lack offline and METHODOLOGY brick & mortar data Marketing mix models (CPG) • Inability to integrate disparate data sources limits multi-campaign view Complex heuristic rules • Most tools aggregate data to scale, losing customer level detail Weighted, equal or cascading Attribution Last or first click/touch Double count salesWEAK ACCURACY LOW HIGH 6
  7. 7. How do you approach the problem?Enable retailers to conduct customer-level analysis onbig data to understand what motivates individuals to buy.Assemble and standardize Apply the rigor of a medical Identify and attribute Know whomall of a marketer’s data into researcher with patented the revenue drivers to reacha Hadoop cluster methodology 7
  8. 8. Advanced Revenue AttributionWhat is it?Data-driven time-to-event statistical modeling used to establish an objective and accuraterevenue distribution, all done at the individual user levelPatent pending methodology for attributing marketing spend per user“Big Data” platform that handles all of a company’s online and offline data (sales, web analyticslogs, catalog and email send data, display and search advertising logs, etc.)BenefitsNo need to retag your site with more pixels – use existing data sourcesIncorporate non traditional elements into your attribution, the methodology is flexible.Participate in the modeling processPlan and allocate spend for each marketing channel based on actual performance 8
  9. 9. Attribution Using Time Dependent Models JANUARY FEBRUARY MARCH APRIL MAY JUNE Customer PURCHASE $100 PURCHASE 1 catalog email catalog Customer PURCHASE $100 PURCHASE 2 catalog email catalog email 2 Customer PURCHASE $100 PURCHASE 3 catalog search catalog 1 email catalog 2 email 2 affiliate search 1 RECENCY OF TREATMENTS SALES ALLOCATION customer sales catalog email search affiliate catalog email search affiliate #1 $ 100 20 40 0 0 $ 99.98 $ 0.02 $ - $ - #2 $ 100 20 15 0 0 $ 81.84 $ 18.16 $ - $ - #3 $ 100 72 60 10 30 $ 40.64 $ 0.01 $ 47.03 $ 12.32 9
  10. 10. Common Attribution BucketsMarketingCatalogEmailDisplay AdvertisingAffiliateComparison Shopping EnginesLink ShareSearch (Non Branded)Loyalty ProgramsBaseCustomer DrivenStore LocationSeasonalMass MediaNeilsen DataSpecial CasedBranded SearchEconomic Conditions 10
  11. 11. Case Study: Top Multi-Channel RetailerAttribution 180%Impact 160% Direct LoadPresented results that were contrary to 140%company’s expectation; client validated Otherresults internally 120%Within 3 months, reallocated $5MM Searchmarketing budget to another channel 100% Display Remarketingwith more changes to follow 80% CustomerInsights 60% Catalog Driven/Trade AreaMarketing is responsible for ~50% of overallsales (offline and online). The other half 40% Otheraccount for the customer’s buying habit and Searchstore trade area. 20% Display Remarketing Email CatalogEcommerce significantly more influenced by 0% Emailmarketing than retail or call-center channels Before AfterDirect Load: UpStream credits marketingactivities that drove user “navigation” towebsite. 11
  12. 12. Case Study: Top Multi-Channel RetailerOptimizationImpactAlready field tested head-to-head against industry leading model+14% lift in response rate+$270K in new revenue in a single campaignReallocated marketing circulation: identified best prospects to not mail that were likely topurchase without receiving catalogScored 22MM households with 9 models all in the cloud 12
  13. 13. Exploratory Work 13
  14. 14. Results in R 14
  15. 15. Example FindingsGoogle keywords often perform worse than you thinkIn many cases 20-40% worseDisplay Advertising performs better than you thinkCertain types of display, such as retargeting, performs better than you think and can have strong influenceespecially at retail stores, which most attribution tools fail to pick upCustom loyalty has the most impact at the retail storeOften retail sales are due to habit and loyalty, but the same trend doesn’t hold onlineRetail sales are influenced by the presence of a store near homeUnfortunately the inverse is also true, web purchases are not typically driven by having a store nearbySeasonal is much stronger at Internet than Retail or Call CenterThe impact of season purchasing is almost double that of retailTenure of customers show significant differencesNewer customers are more sensitive to marketing, seasonal factors, and store area than establishedcustomers (based on tenure). 15
  16. 16. Hadoop – Revolution IntegrationCurrent State: Revo v6 • Functions to read Hadoop output; xdf creation CUSTOM VARIABLESUPSTREAM DATAFORMAT (UDF) • Exploratory data analysis (PMML) • GAM survival models • ETL • Scoring for inference • N marketing channels • Scoring for prediction • Behavioral variables • 5 billion scores per day • Promotional data per customer • Overlay data 16
  17. 17. UpStream: Architecture DecisionsPros Cons• Commodity hardware • Complex to debug• Move the code to the data, not the data to the • Lack of standards (but improving) code • Staffing• Scale Infrastructure to meet demandPros Cons• Cost effective • Nothing major to report• Scale & Performance (increase 4x with Revo Scale R)• RevoScaleR package on 50MM records• Brilliant and growing user community, which positively impacts hiring• Ongoing Hadoop/Revo support 17
  18. 18. SummaryThe World is Changing:The way customers are purchasing services is changingManaging marketing budgets in the multi-channel world is challengingUnderstanding attribution is critical to successfully deploy your marketing budgetTo Be Successful, Your Attribution Solution Should:Cover all of your dataBoth online and offlineBe statistically relevantGuess work doesn’t countScalable and flexibleMake sure you have the right technology platform and tools 18
  19. 19. Connect with UsWe’re HiringSan Francisco & SeattleMasters/PhD in Statistics or BiostatisticsJava DevelopersHyderabadOperations engineers: Big DataConversations with marketersWe’re happy to introduce attribution and help educateabout process and methodologyContactJohn Wallace, CEO @UpStreamMPMBrandon Mason, CTO 19