Regression Marketing Analytics Rajkumar Venkatesan Conservatism in Major

Regression Marketing Analytics Rajkumar Venkatesan

Conservatism in Major League BB • Batting Average = Hits/(Opportunities– Walks) • On. Base% = (Hits+Walks)/Opportunities • OVERUSED: “small ball” – Sacrifice Bunt • Give up an out to advance the runner – Stealing Bases • Risk an Out to advance the runner. • UNDERUSED – Don’t risk making outs and runs will take care of themselves. Rajkumar Venkatesan

CUSTOMER $ SPENT BY A Diagnosing Market Response: Regression Analysis NUMBER OF PROMOTIONS Marketing Analytics Rajkumar Venkatesan

Example: Shopper Card Program Units purchased = a+b 1*price paid + b 2*feature ad + b 3*display Data Marketing Analytics Rajkumar Venkatesan

Example: Regression Output From Excel Marketing Analytics Rajkumar Venkatesan

Price Elasticity Price elasticity can be derived as the ratio of change in quantity demanded (%∆Q) and percentage change in price (%∆P). PED = [Change in Sales/Change in Price] × [Price/Sales] = (∆Q/∆P) × (P/Q) Marketing Analytics Rajkumar Venkatesan

Belvedere Vodka Year Sales (units) Ln(Sales) Price (dollars ) Ln(Price) Advertising (dollars) Ln (Advertising) 2007 410 6. 016 215. 44 5. 373 20486. 1 9. 93 2006 381 5. 943 211. 45 5. 354 2923. 5 7. 98 2005 365 5. 900 207. 45 5. 335 4826. 3 8. 48 2004 369 5. 911 240. 87 5. 484 13726. 6 9. 53 2003 339 5. 826 241. 33 5. 486 10330. 2 9. 24 2002 306 5. 724 247. 55 5. 512 13473. 6 9. 51 2001 273 5. 609 240. 48 5. 483 9264. 6 9. 13 Marketing Analytics Rajkumar Venkatesan

Belvedere Price Elasticity 6 5. 9 Ln (Sales) Regression Statistics Multiple R 0. 67536 R Square 0. 45611 Adjusted R Square 0. 34733 Observations 7 6. 1 5. 8 Linear(Ln (Sales) 5. 6 5. 5 5. 4 5. 285 Intercept Ln (Price) Coefficients 12. 686 − 1. 259 Ln (Sales) 5. 7 5. 335 5. 385 5. 435 Ln (Price) 5. 485 Standard Error t Stat P-value 3. 340 3. 798 0. 013 0. 615 − 2. 048 0. 096 Marketing Analytics Rajkumar Venkatesan 5. 53

Belvedere Advertising Elasticity 6. 1 6 5. 9 Ln (Sales) Regression Statistics Multiple R 0. 06102 R Square 0. 00372 Adjusted R Square − 0. 19553 Standard Error 0. 15252 Observations 7 5. 8 Ln (Sales) 5. 7 Linear(Ln (Sales)) 5. 6 5. 5 5. 4 8 Intercept Ln (advertising) Coefficients 5. 963 − 0. 013 Standard Error 0. 850 0. 093 8. 5 t Stat 7. 018 − 0. 137 Marketing Analytics 9 9. 5 Ln (Advertising) 10 10. 5 P-value 0. 001 0. 897 Rajkumar Venkatesan

Marketing Analytics Rajkumar Venkatesan

Customer Retention: Logistic Regression • Linear regression assumes the dependent variable (DV) to be continuous (and normally distributed) Profits - + • Often we have variables where there are only 2 different values 0 • Buy (1) vs no buy (0) • Retain (1) vs lose customer (0) Marketing Analytics Rajkumar Venkatesan

Customer Retention: Logistic Regression • With categorical (1/0) dependent variables, linear regression can result in nonsensical estimated probabilities (e. g. probability of retention > 100%) • A model that allows us to do this is the so-called “logistic regression” – Predictions are bound between [0, 1] Marketing Analytics Rajkumar Venkatesan

Marketing Analytics Rajkumar Venkatesan

Logistic Regression: The connection to Bookies This is called the “odds” Chance of retention to chance of churn Marketing Analytics Rajkumar Venkatesan

Super. Bowl 2012 Odds Green Bay Packers 3. 45 to 1 New England Patriots 4. 4 to 1 New Orleans Saints 8. 5 to 1 Baltimore Ravens 9. 5 to 1 San Deigo Chargers 10. 5 to 1 Detroit Lions 13 to 1 Houston Texans 17. 5 to 1 Pittsburg Steelers 20 to 1 Marketing Analytics Rajkumar Venkatesan

What is Odds? • If you chose a random day of the week (7 days), then the odds that you would choose a Sunday would be: – (1/7)/[1 -(1/7)] = 1/6, but not 1/7. • The odds against you choosing Sunday are 6/1 = 6 , meaning that it's 6 times more likely that you don't choose Sunday. • Generally, 'odds' are not quoted to the general public in this format because of the natural confusion with the chance of an event occurring being expressed fractionally as a probability. • A bookmaker may (for his own purposes) use 'odds' of 'one-sixth', the overwhelming everyday use by most people is odds of the form 6 to 1, 6 -1, or 6/1 (all read as 'six-to-one') where the first figure represents the number of ways of failing to achieve the outcome and the second figure is the number of ways of achieving a favorable outcome: thus these are "odds against". • An event with m to n "odds against" would have probability n/(m + n), while an event with m to n "odds on" would have probability m/(m + n). Source: http: //en. wikipedia. org/wiki/Odds Marketing Analytics Rajkumar Venkatesan

Example: Will a Physician Prescribe a Drug? Data Model Marketing Analytics Rajkumar Venkatesan

Example: XLStat Output Marketing Analytics Rajkumar Venkatesan

Logistic Regression: Coefficients • Key difference: coefficients are not interpreted as such • Need to calculate “odds ratio” – For example, if the logit regression coefficent b = 2. 303, then the odds ratio is: eb =e 2. 303 = 10 – when the IV increases one unit, the odds that the DV = 1 increases by a factor of 10, when other variables are controlled. Marketing Analytics Rajkumar Venkatesan

Example: XLStat Output What is the Odds Ratio for Sales Calls? –Caution: odds ratios that are close to one, do NOT suggest that the coefficients are insignificant – it just means there is 50/50 chance of outcome Marketing Analytics Rajkumar Venkatesan

Example: Physicians Prescriptions For each additional sales call, the odds of a physician prescribing a drug increases by 43% (holding everything else constant). 0. 36/(1 -0. 36) Prob (prescription) when sales calls is zero = exp(-0575)/[1+exp(-0. 575)] Prob (prescription) when sales calls is one = exp(-0. 575+0. 361)/[1+exp(-0. 575+0. 361)] Marketing Analytics Rajkumar Venkatesan

Reaction to econometric analysis? Rajkumar Venkatesan

Combined Effect of Age and Online Average Profit Marketing Analytics Rajkumar Venkatesan

Diagnosing Customer Profits and Retention: Common Drivers Behavioral characteristics • • • purchase volume/quantity Frequency of buying length of relationship number of product categories purchased selling costs customer satisfaction Goal: To identify key lever(s) that “drive” customer value Demographic/firmographic characteristics • Age, income, gender • Loyalty program membership • Firm size Psychographic characteristics • Attitudes, values • Interests • Activities Marketing Analytics Rajkumar Venkatesan

Model Building • Determine properties of dependent variable – Linear, + ve values, Dummy Variable • Select model that reflects dependent variable properties – Logistic regression for dummy variables Marketing Analytics Rajkumar Venkatesan

Model Building • Include the decision variable of interest among the independent variable set – Price, advertising, online • Include common control variables – Quality, Distribution, Demographics, Tenure, Competition etc. Marketing Analytics Rajkumar Venkatesan

Model Building • Does including lagged dependent variable lead to UNIT ROOT? • If UNIT ROOT, use difference as the dependent variable • Are some independent variables correlated more than 0. 8. If so, can we eliminate one of the correlated variables or combine them. Marketing Analytics Rajkumar Venkatesan

Model Building • Are some variables Missing at Random (MAR) or are they missing systematically? • If variables are missing systematically, are there proxies that can replace the missing variables Marketing Analytics Rajkumar Venkatesan

Model Building • Does the model hint @ causality or is it a correlational model? – Are dependent and independent variables measured at the same time? – Are there sufficient controls or confounding variables included – Can a reverse causation reasonably exist – Do we need to recommend an experiment? Marketing Analytics Rajkumar Venkatesan