AT&T PowerPoint® Tutorial
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AT&T PowerPoint® Tutorial

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Propensity Score Matching in Marketing Program EvaluationsApril 27, 2007NISS Affiliates Annual MeetingBackground: Common Marketing Analysis SituationDid our marketing program work, and how well? No controlled experimentStuck with Treated vs. non-Treated, or Buyers vs. non-BuyersRestrospective, observational study…But fortunately LOTS of data!!• Extensive contact, purchase, usage, switching, care calling • Demographics• Internal predictive modelsExample: Airline Miles Loyalty ProgramPage 2Background: Common Marketing Analysis SituationAre customers with X characteristic more likely to Y…all other factors being equal? • Difficult to isolate characteristic X• Important strategic question• Observational study…Again, LOTS of data!!Example: Word-of-Mouth ReferralPage 3Example 1: Airline Miles Loyalty Program or…what I wish I’d known• Optional, no cost to consumer• AT&T spend -> Airline Miles• No program-level control group• Very expensive• Unadjusted comparison possibly misleading:Self-selectionBig spenders,Occasional major highly targetedenrollment campaignsPage 4Previous ApproachIsolate program variable after “controlling for” other observable factorsModel:Leave AT&T = Program + [tenure vars] + [usage vars] + [purchase history] + [switching history] + [demographics] + [Program interactions]Unobservables an issue – survey research helpedUneasy – profile indicates profound differences between program members and ...

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Propensity Score Matching in Marketing Program Evaluations
April 27, 2007 NISS Affiliates Annual Meeting
Background: Common Marketing Analysis Situation
Did our marketing program work, and how well?
No controlled experiment Stuck with Treated vs. non-Treated, or Buyers vs. non-Buyers Restrospective, observational study
…But fortunately LOTS of data!! Extensive contact, purchase, usage, switching, care calling Demographics Internal predictive models
Example: Airline Miles Loyalty Program
Page 2
Background: Common Marketing Analysis Situation
Are customers with X characteristic more likely to Y…all other factors being equal?
Difficult to isolate characteristic X Important strategic question Observational study
…Again, LOTS of data!!
Example: Word-of-Mouth Referral
Page 3
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Example 1: Airline Miles Loyalty Program
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Very expensive
Unadjusted comparison possibly misleading:
or…what I wish I’d known
Page 4
No program-level control group
Optional, no cost to consumer AT&T spend -> Airline Miles
Self-selection
Big spenders, highly targeted
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Previous Approach
Isolate program variable af ter “controlling for” other observable factors
Model: Leave AT&T = Program + [tenure vars] + [usage vars] + [purchase history] + [switching history] + [demographics] + [Program interactions]
Unobservables an issue – survey research helped
Uneasy – profile indicates profound differences between program members and non-members.
Page 5
Despite huge non-member population – programming challenge!!
Page 6
Each member matched to non-member within +/- 5%
Re-estimated model predicting Leave AT&T Æ more conservative estimate of program impact.
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Example 2:  Word-of-Mouth Referral
or….An unexpected and very long digression
New, innovative AT&T service, early market entrant
No overt viral or buzz marketing initially – BUT we hoped that WOM would positively influence sales
Early exploratory analyses suggestive of viral activity.
Again, unadjusted comparison possibly misleading.
Page 7
Homophily
Example 2: Word-of-Mouth Referral, cont’d
How to estimate WOM effect on sales?
AT&T network provides WOM framework:
Start with early subscribers
Establish their WOM connections – people they talked to before buying
Were these WOM-connected people more likely to buy, compared to similar WOM-less people?
Page 8
Early Subscriber, WOM Connections, and Matches
Page 9
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Initial Approach
Isolate WOM effect after “controlling for” other observable factors. Population is potential buyers.
Model: Buy = WOM-connection + [tenure vars] + [usage vars] + [purchase history] + [switching history] + [demographics] + [WOM interactions]
Page 10
Page 11
With WOM Connection
Initial Approach WOM connection associated with large differences in usage and demographics.
Usage Variable
WOM-less
Wealth Variable
With WOM Connection
WOM-less
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