The DMA Statistics & Modeling for Direct Marketers  Seminar

1. The DMAStatistics & Modeling forDirect Marketers Seminar Presented by: David Shepard Rajeev Batra Copyright DSA/DMA

2. Course Objectives • How modeling can be used to achieve your marketing objectives • Introduce the set of “statistical” modeling techniques available to direct marketers • Provide you with the information and perspective you need to manage and evaluate a modeling project Copyright DSA/DMA

3. Typical Marketing Objectives • Acquire new customers • Sell additional products or services • Retain customers • Increase usage • Convert leads to orders • Predict future customer behavior • Segment customers or prospects Copyright DSA/DMA

4. Key Modeling Concepts • Predictive Models • Find customers that will respond or behave better or worse than average • Segmentation Models • Create segments or clusters of customers or geographic areas that are similar to each other and dissimilar to other customers or geographic areas Copyright DSA/DMA

5. Modeling & Contact Strategy • Customers predicted to respond or perform better than average may receive more (or fewer) promotions, more (or less) expensive promotions…or vice versa. • Segments with different profiles may require different marketing strategies Copyright DSA/DMA

6. Program Outline – Day One • Basic Statistical Notions • Correlation & Regression Analysis • Multiple Regression • Cautionary Notes on Regression • Logistic Regression • AID/CHAID Trees • Test Analysis System Copyright DSA/DMA

7. Program Outline – Day Two • Discriminant Analysis • Factor Analysis • Review of the modeling process • Cluster Analysis • Case Studies & Applications • Summary Copyright DSA/DMA

8. Direct MarketingModeling and Statistics Correlation & Simple Regression Copyright DSA/DMA

9. The Economics of Zip Code Modeling Copyright DSA/DMA

10. Zip Code Modeling • The objective of zip code modeling is to increase response to new customer acquisition promotions • The key idea is to identify specific zip codes that should be excluded …perhaps even from the best lists • Include names found on marginal lists that would otherwise not be promoted because they come from very responsive zip codes • Modeling demographics not zip code ‘numbers” • In practice response must be balanced with back-end performance considerations Copyright DSA/DMA

11. Profits Without Modeling • Available universe …2,000,000 • Expected response rate 1.28% • Mailed…400,000 • Profits …$441,166 • CPM…$650 • Profit/order…$94.50 • Response rate 1.85% Copyright DSA/DMA

12. Profits WithModeling • Available universe …2,000,000 • Expected response rate 1.28% • Mailed…400,000 • Profits …$572,613…..+$131,000! • CPM…$650 • Profit/order…$94.50 • Response rate 2.20% Modeling is Good! Copyright DSA/DMA

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14. What If you could “Spread the Average” Copyright DSA/DMA

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18. Regression & Correlation Analysis • Correlation refers to the extent to which two variables are related to each other • The correlation coefficient measures the “strength” of the relationship • + 1 means a perfect positive relationship • -1 means a perfect negative relationship • 0 means no relationship Copyright DSA/DMA

19. r =1 r =.6 r =-.8 r =-1 r =0 r = 0….? Copyright DSA/DMA

20. Regression & Correlation Analysis • Correlation refers to the extent to which two variables are related to each other • The correlation coefficient measures the “strength” of the linear relationship • + 1 means a perfect positive relationship • -1 means a perfect negative relationship • 0 means no relationship Copyright DSA/DMA

21. A Scatter DiagramWhat is “r”? Copyright DSA/DMA

22. Regression & Correlation Analysis • Independent & dependent variables • Independent or “predictor” variables (the stuff we know about our customers) are said to “cause” changes in the dependent variable • The Dependent variable is the event you are trying to predict • Response rates • Average order size • Return rates • Payment rates Copyright DSA/DMA

23. Independent Variables • Customer performance data or variables • The big three RFM variables • Recency of purchase • Frequency of purchase • Monetary value • Others • First purchase date • Product • Times promoted • Source Copyright DSA/DMA

24. Independent Variables • Calculated variables • Frequency/times promoted • Frequency/days on file • Monetary value/Frequency • Monetary value/days on file • Returns/Frequency • Time between last transactions…much harder Copyright DSA/DMA

25. Independent Variables • Survey data • Whatever you want to know • Collected • Over the web • Coupons • Applications • Welcome package Copyright DSA/DMA

26. The Regression Line Drawn Though the Data Points r = .5 Copyright DSA/DMA

27. The Fitted Regression Line Minimizes the Sum of the Squared Errors r = .5 Y = a +bX Every estimate is wrong No other value for b gives a smaller sum of squared errors Some errors are positive, some negative Copyright DSA/DMA

28. Notes on Terminology • The following terms are equivalent • “A” = constant = intercept • “B” =slope = coefficient = “weight” • If you have more than one variable in the model, there will still be only one “a” but one coefficient (“b”) for each variable • The “computer” finds the values for “a” and the “b’s”…given the set of independent variables Copyright DSA/DMA

29. Timeline for ModelingFreezing the Vegetables • October • Select names for mailing • Make copy of mailing file including all database variables • Values in customer record are “frozen” • December • Execute mailing • January - March • Responses received • March - April • Create final response file • Match with mailing file • May • Build model Y = a +b1*x1 + b2*x2 +b3*x3….bn*Xn Copyright DSA/DMA

30. Timeline for ModelingFreezing the Vegetables • May • Build model Y = a +b1*x1 + b2*x2 +b3*x3….bn*Xn • June • Select names for August mailing based on model • August • Execute mailing • November • Evaluate model Copyright DSA/DMA

31. Creating Calibration and Validation Files/Samples Data Set Validation File Calibration File Build Model Score file Y = a + b1X1 + b2X2 + b3*X3….bn*Xn Decile Analysis Copyright DSA/DMA

32. Minimal Decile Analysis Copyright DSA/DMA

33. Using Overlay Data To Profile Results Model Built On RFM Variables No Clue As To Why or Who Copyright DSA/DMA

34. Profile the Deciles Looking For Significant Differences Copyright DSA/DMA

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37. R² • R² is the Coefficient of Determination • The coefficient represents the percentage of the total variation in the dependent variable that is “explained” by the model • If r = .7 then R² = 49% • If r = .8 then R² = 64% • If r = .9 then R² = 81%… Copyright DSA/DMA

38. The t StatisticThe Regression Coef/SE • Think of the regression coefficient (4.25) as a number coming from a sample • This means the number you see might not be the “true” number • What if the “true” number was Zero • How likely are you to pull a sample that yields a 4.25, when the true value is Zero! • Sampling theory (Basic Notions) tells that in a population with a true value of 0, 95% of your samples will result in values between 0 and +/- 1.96 standard errors. Copyright DSA/DMA

39. The T Statistic, Continued • In our example the standard error of the regression coefficient was .32 • Therefore the value of 4.25 can be expressed as 4.25/.32 or 13.34 standard errors = t • If the true value falls within a range of +/- 1.96 standard errors—95% of the time…the chances of finding a value of 13.34SE is less than 5%…much less Copyright DSA/DMA

40. The T Statistic, Continued • RULE: • if t >2, then the chances of the real value being 0 are less than 5% • If t< 2, then the chances of the real value being 0 are greater than 5% • There are exceptions to the rule • Rajeev will discuss later Copyright DSA/DMA

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44. Multiple Regression Starting With Spreadsheet Examples Copyright DSA/DMA

45. Multiple Regression • The Multiple Regression Equation • Y = a +b1*X1 +b2*X2 +b3*X3….bn*Xn • where b1 really means b1.23 • and is interpreted as • b1 measures the change in the dependent variable when X1 changes by one unit and all of the other independent variables (X2, X3, Etc. remain the same. Copyright DSA/DMA

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47. Standardized Variables To Account for Differences in Scale Frequency (6-1.98)/1.54833 = 2.6005 Monetary Value (350-176.22)/110.781 = 1.5687 Copyright DSA/DMA

48. Standardized Variables Copyright DSA/DMA

49. The F Test Measures the Significance of the Model R²/(V-1) (1-R²)/(N-V) F = Copyright DSA/DMA

50. R²/(V-1) (1-R²)/(N-V) F = Copyright DSA/DMA