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Predictive methods

Predictive methods. Understanding customer preferences. Agenda. Introduction to predictive analytics Logistic regression Case study: Japanese car manufacturer exporting in the US Modelling interdependent consumer preferences Causality estimation

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Predictive methods

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  1. Predictive methods Understanding customer preferences

  2. Agenda • Introduction to predictive analytics • Logistic regression • Case study: Japanese car manufacturer exporting in the US • Modelling interdependent consumer preferences • Causality estimation • Propensity scores to estimate effectiveness of marketing interventions of a pharmaceutical company • Web Analytics

  3. Predictive methods for marketing • Predictive methods exploit patterns found on historical data to estimate the probability for a certain individual to make a decision • Three main categories: • Scoring models -rank customers by their probability of making a decision • Descriptive models -categorize customers by their preferences and life style • Decision models - describe the relationship between all the elements of a decision

  4. Predictive methods applications • Marketing planning and campaign optimization • Customer relationship management • Market basket analysis • Customer retention • Direct marketing • Fraud detection • Web click stream analysis

  5. Questions to be answered • What’s the probability of a given customer to purchase a product? • How can I categorize the customer base in homogeneous groups? • Which potential customer should a promotion be offered to? • Which website should I advertise on? • Which search keywords should I invest in?

  6. Logistic regression • Is used for prediction of the probability of occurrence of an event by fitting data to a logistic curve • Makes use of several explanatory variables that can be either numerical or categorical • Used to predict dichotomous (0 = event doesn’t occur, 1 = event does occur) or categorical values (0=event a occurs, 1=event b occurs, 2=event c occurs)

  7. Logistic regression • Logistic regression • Linear relation between the predictors and the Logistic function • P(Yi=1) is the probability of an event to occur

  8. Logistic regression • Logistic curve • Input values: any real number • Output values: • (from 0 to 1)

  9. Example • A sample of PhD students are asked to decide whether to stop a research project considered unethical by an animal rights’ group Dependent variable: student’s answer 0 = “Stop research” 1 = “Continue research Explanatory variable: gender 0 = Female 1 = Male

  10. Model summary • -2 Log likelihood stat: the smaller the number the better the model • Cox & Snell and Nagelkerke R Squares: the higher the number the better the model

  11. Model summary • The model predicts that the probability for a woman to decide to continue research is 30% while for a man is 60% • The ODDS of deciding to continue research are 3.4 times higher for men than for women

  12. ODDS from model’s equation

  13. Classification • Classify subjects with respect to what decision we think they will make ex. predict that men will continue research and women will stop • 66% of correct predictions, false positive rate 41%, false negative 30%

  14. Case study: a Japanese car manufacturer exporting in the USA • Modelling consumer preferences • What drives US consumers to purchase a Japanese car versus a non Japanese one? • What is the probability for an individual to buy a Japanese car? • Which people should be targeted ? • Are consumer preferences interdependent?

  15. The data • Purchase of mid-sized cars (1 = Japanese, 0 = non Japanese) • Difference in price (k$),Difference in options (k$) • Age of buyer (years) • Annual income of buyer ($) • Ethnic origins(1=Asian, 0=non-Asian) • Education(1=College, 0= below College) • Latitude & Longitude

  16. The model

  17. Interpreting model’s output • Young and Asian people from south west of the city are more likely to buy a Japanese car • Does price coefficient make sense ? • People with higher education are less likely to buy a Japanese car

  18. Model diagnostics Very strong in predicting Japanese cars purchases but weak in predicting non-Japanese cars purchases

  19. Model diagnostics Area under the curve: 0.754 Gini coefficient = 0.5

  20. Modelling interdependent consumer preferences • An individual preference can be influenced by preferences of others • Psychological benefits • Social identification • People who identify with a particular group often adopt the preferences of the group • Incorporate these dependences into the model

  21. Looking at models’ residuals The residuals represents what is not explained by the model R Group 1

  22. Looking at models’ residuals • The presence of interdependent networks create preferences that are mutually dependent resulting in covariance matrix with non zero off diagonal elements. • Residuals of people belonging to the same group are positively correlated • Correlation (Residual Person 1 , residual Person 2)>0

  23. Looking at models’ residuals • Creating groups by splitting individuals into neighbours • AGE (16-25 , 26-40, over 40) • Demographic (Combination of Age, education, ethnic) • Geographic influence (Postal code) • Analyze average residuals for each group

  24. Looking at models’ residuals

  25. Including customer interdependences Adding a group dummy which is equal to 1 if the individual fits in the group and 0 otherwise R Asian – 26-40 Group dummy Group dummy

  26. Spurious statistics • A high correlation between sales and TV could mean: • Either media causes sales • or sales causes media • or a third variable causes both sales and TV Sales Media Income What is the truth?

  27. Taking causality seriously • Using least squares regressions and data mining could lead to unreliable results: • Polishing the Ferraris rather than the Jeeps can cause Ferraris to win more races than Jeeps • Propensity scores to estimate the casual effects of marketing interventions

  28. Propensity scores • Pharmaceutical company is to promote a life-style drug and evaluate the market • The scope is to rank a list of doctors according to their likelihood to prescribe a certain drug • Marketing interventions: • Visiting a doctor describing the drug • Dining the doctor at a nice restaurant • Offering free samples of the drug

  29. Impact of marketing interventions • The marketing interventions are designed to increase the number of prescriptions written by the doctors • But how to quantify the number of prescriptions generated by the intervention? • Compare the number of prescription written after been visited with the number of prescriptions that would have been written without the intervention

  30. Prediction VS causal estimation Causality Doctor A 15 scripts Visit 10 scripts 15 scripts No visit T=1 T=2

  31. Prediction VS causal estimation Causality Doctor B 5 scripts Visit 1 script 2 scripts No visit T=1 T=2

  32. Propensity scores • We should make investment decisions comparing the expected returns when making the investment and when not making the investment • Short stop: lack of data • For a doctor who is visited by the salesperson, the number of scripts after the visit is measureable but we cannot measure the number of scripts written if the doctor wouldn’t have been visited

  33. How do we do it? • Finding clones: create matched pairs of doctors where one member of the pair has been exposed to the intervention and the other has not. The doctors must be “identical” or very similar before the time of exposure • Clones are found through the propensity score method

  34. The data • 250000 Doctors and for each one: • Number of prescription written at time 1 • Number of prescription at time 2 • Was the doctor visited by the salesperson from time 1 to time 2?(Y/N) • Doctor’s characteristics: specialty, region, date of degree and more than a hundred of such factors

  35. The data Doctor 1 Doctor 2 Doctor 3 Doctor 4

  36. Cloning by propensity score • Propensity score (Doctor A)= predicted probability of logistic regression • Dependent variable (1=“Visited by salesperson, 0=“Not Visited” • Factors: Characteristics

  37. The data Doctor 1 Doctor 2 Doctor 3 Doctor 4 What’s the causal effect ?

  38. Propensity score

  39. Campaign execution • Doctors are ranked accordingly to their causal increase in prescriptions • The first % of doctors in the list are then contacted and/or offered free samples • Priority is given to the doctors who have not been contacted yet • The % is chosen accordingly to company’s budget

  40. Propensity scores on e-commerce • Online store with membership database • Estimate the effectiveness of promotional mails and identify people to be targeted • Calculate the ROI of a free shipping initiative • Individuals receive free shipping but pay an annual fee for it • A pharmaceutical company offering on its website a coupon to encourage trial use of a drug

  41. Web analytics

  42. Purchase path Custom Attribution Algorithms Mathematical Attribution Models Rules Based Attribution Purchase Path™ Exclusions Even Attribution Last Click

  43. Purchase path In this example we drilled into the AdWords > AdWords path to see the specific ads that were clicked on en route to purchase.

  44. Timing To further increase the accuracy of attribution, an advertiser is able to choose the maximum log window.

  45. Decision influencer Uncertainty

  46. Consumer decision • Build a model to predict consumer decisions • Using data on influencers that we are able to track and measure • Representing data on influencers that we can’t yet track and measure - our uncertainty - through a statistical distribution • Calibrate the model on observed consumer decisions • Purchase - yes/no , Purchase size - dollar volume, # of units • Repeat purchases, word of mouth

  47. Decision model Consumer’s decision is a function of Our communications, Consumer Search, Competitor communications, Other sources Paid Search, Banner Ads, e-mail, On-site Promotions, Comparison shopping, Affiliate ads Site visits to us Uncertainty

  48. Decision model

  49. Data example

  50. Data example

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