Direct Marketing When There Are Voluntary Buyers

1. Direct Marketing When There Are Voluntary Buyers Presenter: _____________ Yi-Ting Lai, Ke Wang Simon Fraser University {llai2, wangk}@cs.sfu.ca Daymond Ling, Hua Shi, Jason Zhang Canadian Imperial Bank of Commerce {Daymond.Ling, Hua.Shi, Jason.Zhang}@cibc.com

2. Introduction: Direct Marketing • Target a selected group of customers. • Which customers should be selected for contact so that the campaign can achieve the maximum net profit? • Traditional objective: identify the customers who are most likely to respond. • A real direct marketing campaign: Assumption: All profits are generated by the campaign! 5.4% 80% are voluntary buyers! 4.3%

3. Three Classes of Customers • Based on their purchasing behaviors • Each customer belongs to exactly one class The only customers who can be positively influenced.

4. The difference: # of undecided customers targeted. Is the traditional paradigm solving the right problem? • Given a fixed number of contacts, need to maximize the set of total buyers in order to maximize net profits. undecided undecided M2 The traditional paradigm favors M1. M1 non non decided decided undecided undecided M2 has targeted more buyers! M2 non M1 non decided decided

5. Influential Marketing • S: the set of contacted customers. • DBR: the decided buyer rate of S. • UBR: the undecided buyer rate of S. • RR: the response rate of S. • Influential Marketing For a given number of contacts, influential marketingaims to maximize UBR by targeting undecided customers. • Challenges: • Customers are not explicitly labeled by the three classes. • Should require little changes to standard practices. RR = DBR + UBR

6. RR of Control Data Collection • How do we compute UBR? • Treatment: a set of customers who were contacted. • Control: a set of customer who were not contacted. • similar to Treatment. • All responders in Control must be decided buyers. UBR = RR – DBR

7. positive negative Model Construction Characteristics exclusive to positive class: those of undecided customers. Response Yes No decided + undecided non Treatment (T1) (1) (2) Contact non + undecided decided Control (C1) (3) (4) The learning matrix • <T1, C1>: training set, • <T2, C2>: validation set.

8. MT – MC (UBR) Proposed Solution – Model Evaluation • Rank <T2, C2> • T2x: top x% of the ranked list of T2 (contacted), • MT: RR of T2x, • C2x: top x% of the ranked list of C2 (not contacted), • MC: RR of C2x. • T2x and C2x are similar, • UBR = RR – DBR = MT – MC

9. Related Work – Lo’s • Predict the amount of positive influence the contact has on each customer. • Positive class: responders, • Negative class: non-responders, • Use treatment variable T to describe if a customer has responded. However, • T = 1 needs to be more strongly associated with the positive class. similar to traditional paradigm

10. Experimental Evaluation • Data: real campaign for a loan product. • 3-fold cross validation. Our influential approach Traditional paradigm Lo’s Our influential approach – oversample (3)

11. Decision Tree (SAS Enterprise Miner) Joint Comparison • Improvements of our approaches are significant in the top percentiles. Association Rule Classifier