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Customer Relationship Management: A Database Approach

MARK 7397 Spring 2007. Customer Relationship Management: A Database Approach. Class 5. James D. Hess C.T. Bauer Professor of Marketing Science 375H Melcher Hall jhess@uh.edu 713 743-4175. Brand Choice Models What Might Have Happened. time. Regular ABB Service, S. ABB Purchases. X.

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Customer Relationship Management: A Database Approach

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  1. MARK 7397 Spring 2007 Customer Relationship Management:A Database Approach Class 5 James D. Hess C.T. Bauer Professor of Marketing Science 375H Melcher Hall jhess@uh.edu 713 743-4175

  2. Brand Choice Models What Might Have Happened time Regular ABB Service, S ABB Purchases X X X X X X X X X X X X X X X GE Purchases 1.0 Year 0 0.5

  3. Brand Choice Models What You See Heavyup Service, S+D Regular ABB Service, S ABB Purchases X X X X X X X X X X X X X X X GE Purchases 1.0 Year 0 0.5

  4. Brand Choice Models What You Infer Heavyup Service, S+D Regular ABB Service, S ABB Purchases X X X X X X X X X X brand switches X X X X X X X X X GE Purchases 1.0 Year 0 0.5

  5. Regression Model of Brand Choice Brand Choice ? ABB 1 ? GE 0 Service ?

  6. ea+bA 1+ea+bA Logit Model Of Brand Choice Brand Choice ABB 1 GE 0 Service

  7. Explanation: Random Utility Model Daniel McFadden 2000 Nobel Laureate ABB GE The part ai + biS is the “deterministic” part of utility. The terms e i are aspects of the situation which we are unable to observe, and hence give a feeling of randomness to the choice. The customers are not really random, but simply know their situation better than us.

  8. Random Utility Model(continued) a b If eA-eG has a logistical distribution, then this probability is where a=aG-aA and b=bG-bA.

  9. Calibrating the Logit Model Suppose there were n brand choices and m times ABB was chosen and n-m times GEwas chosen, each with a different service S. The likelihood of this is the probability: ABB GE The values of a and b are chosen to “maximize likelihood” of observing the sample we did.

  10. Interpretation of Logit Coefficients Logit is not linear like regression, so its coefficients have a slightly different interpretation. Odds that ABB is chosen over GE: Odds = Pr(ABB)/Pr(GE) = ea+bS. How much do odds of ABB go up if S increases by 1? New Odds = ea+b(S+1) = ea+bSeb = Odds x exp(b) Exp(b) tells the factor by which the odds of ABB rise when S is one unit higher. Examples: b =2, exp(b)= 2.712 = 7.3, so when b=2, increasing S by +1 increases the odds of ABB being chosen by a factor of roughly seven. If they had been 3:1, they are now 22:1. b = - 1, exp(b)= 2.712-1 = 0.37, so when b= -1, increasing S by +1 decreases the odds of ABB being chosen by a factor of roughly one-third. If they had been 3:1, they are now 1:1.

  11. Let’s look at a very simple Excel version of a logit model. Please download the file “ABB Logit illustration.xls” from WebCT. It should look like the following.

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