De-Mystefying Predictive Analytics

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Keynote address at 2012 ReTechCon.com (annual conference of the Retailers Association of India), Mumbai.

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De-Mystefying Predictive Analytics

  1. 1. Galit Shmuéli SRITNE Chaired Prof. of Data AnalyticsDe-mystifying Predictive Analytics
  2. 2. Will thecustomer pay?
  3. 3. Today’s Talk1. How predictive analytics differ from Reporting and other BI tools2. The predictive analytics process3. Examples of problems that can be tackled4. Logic behind predictive analytics algorithms5. Predictive Analytics for retail in India
  4. 4. Overall BehaviourCase Studies Past Present Future “Presonalized” Behaviour
  5. 5. Today’s Talk1. How predictive analytics differ from Reporting and other BI tools2. The predictive analytics process3. Examples of problems that can be tackled4. Logic behind predictive analytics algorithms5. Predictive Analytics for retail in India
  6. 6. The Predictive Analytics ProcessProblemIdentificationMeasurement Data ModelsDetermine Draw sample, Data MiningOutcome and Split into algorithmsPredictors training/holdout & EvaluationDeploymentRe-evaluationMore data
  7. 7. Today’s Talk1. How predictive analytics differ from Reporting and other BI tools2. The predictive analytics process3. Examples of problems that can be tackled4. Logic behind predictive analytics algorithms5. Predictive Analytics for retail in India
  8. 8. Example 1: Personalized OfferProblem Who to Which WhatIdentification target? coupon? medium?Measurement Data ModelsOutcome: redemption From similar past ?Predictors: customer, campaign Expectedshop & product info (redeemers and gain per non-redeemers) offer sentDeployment (or not!)Re-evaluationMore data
  9. 9. Example 2: Employee TrainingProblem Which employees to train?IdentificationMeasurement Data ModelsOutcome: performance From past ?Predictors: employee & training efforts Expectedtraining info (successes and gain per failures) employeeDeployment (or not!)Re-evaluationMore data
  10. 10. Example 3: Customer Churn Problem Identification Which members most likely not to renew? Membership renewal Measurement Data Models Outcome: renewal Past renewal ? Predictors: customer & campaigns Expected membership info (successes and gain per Deployment (or not!) failures) customer Re-evaluation More data
  11. 11. Example 4: Product-level demand forecasting Problem Weekly Identification forecasts per clothing item Measurement Outcome: month-ahead weekly forecasts of #units purchased per item Predictors: past demand for this & related items, special events, economic outlook, social media Deployment (or not!) Data Models Re-evaluation Historic info ? More data Expected gain
  12. 12. Example 5: COD PredictionProblem Predict paymentIdentification probability Measurement Data ModelsOutcome: pay/not Past deliveries ?Predictors: customer, (payments and Expectedproduct, transaction info non-payments) gain per transactionDeployment (or not!)Re-evaluationMore data
  13. 13. Today’s Talk1. How predictive analytics differ from Reporting and other BI tools2. The predictive analytics process3. Examples of problems that can be tackled4. Logic behind predictive analytics algorithms5. Predictive Analytics for retail in India
  14. 14. Predictive Analytics:It’s all about correlation, not causationEvery time they turn on theseatbelt sign it gets bumpy!Algorithms search for correlation between theoutcome and predictorsDifferent algorithms search for different types ofstructure
  15. 15. Example: Direct MarketingMaharaja Bank wants to run acampaign for current customersto purchase a loanThey want to identify thecustomers most likely to acceptthe offerThey use data from a previouscampaign on 5000 customers,where 480 (9.6%) accepted
  16. 16. Data sample
  17. 17. Data Partitioning Training 4,000 customers Holdout 1,000 customers
  18. 18. Classification & Regression Trees No Yes No Yes No Yes
  19. 19. Regression ModelsProbability (Accept Offer) = function ofb0 + b1 Age + b2 Experience + b3 Income + b4 CCAvg +…The Regression Model Input variables Coefficient Constant term -6.16805744 Age -0.0227915 Experience 0.03030424 Income 0.06047214 ZIP Code -0.00006691 Family 0.61913204 CCAvg 0.13191609 Mortgage 0.00016262 Securities Account -0.51986736 CD Account 4.10482931 Online -1.11415482 CreditCard -1.02319455 EducGrad 3.93598175 EducProf 4.01372194
  20. 20. K-Nearest NeighboursCustomer1 = [age=25, exper=1, income=49, family=4, CCAvg=1.6, education=UG,…]Customer2 = [age=49, exper=19,income=34, family=3, CCAvg=1.5, education=UG,…]
  21. 21. Performance Evaluation: Holdout DataPredict each Overall Missed Targeted Error acceptors non-customer’s action acceptors Baseline: no offers 9.3% 9.3% 0.0% Holdout Tree 2.5% 12.9% 1.4% 1,000 customers Regression 4.3% 35.5% 1.1% K-NN 4.3% 41.9% 0.4%Different: Identify20% of customersmost likely toaccept
  22. 22. More predictive analytics methods: based on distanceCustomer1 = [age=25, exper=1, income=49, family=4, CCAvg=1.6, education=UG,…]Customer2 = [age=49, exper=19,income=34, family=3, CCAvg=1.5, education=UG,…]
  23. 23. Where do the buzzwords fit in?
  24. 24. Big Data Cloud Computing Real-time data UnstructuredSocial Media data Mobile Data
  25. 25. Today’s Talk1. How predictive analytics differ from Reporting and other BI tools2. The predictive analytics process3. Examples of problems that can be tackled4. Logic behind predictive analytics algorithms5. Predictive Analytics for retail in India
  26. 26. Step 1: Identify “classic” applications used by other companies
  27. 27. Step 2: Get Creative In India: Cash On Delivery Counter service Huge growth in ATMs Multiple languages Regional customer preferences Informative names Bargaining
  28. 28. What you’ll needTop management commitmentAnalytics team with close ties to all departments (IT, Marketing,…) understands the business and its goals creative and fearless is allowed to experiment (and fail)Data in a reachable placeSoftware
  29. 29. Last Thought: Mindful Predictive Analytics “VIP syndrome” Predictive analytics for scaling-up to public white- glove treatment Predictive analytics for reducing the burden on consumers, employees etc. (less offers & overload)
  30. 30. Asia Analytics Lab @ ISBfacebook.com/groups/asiaanalytics

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