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Direct Marketing When There Are Voluntary Buyers Yi-Ting Lai, Ke Wang Simon Fraser University {llai2, Daymond Ling, Hua Shi, Jason Zhang.

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Presentation on theme: "Direct Marketing When There Are Voluntary Buyers Yi-Ting Lai, Ke Wang Simon Fraser University {llai2, Daymond Ling, Hua Shi, Jason Zhang."— Presentation transcript:

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

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: 80% are voluntary buyers! Assumption: All profits are generated by the campaign! 4.3% 5.4%

3 Three Classes of Customers Based on their purchasing behaviors Each customer belongs to exactly one class Decided these customers voluntarily buy the product, regardless of a direct promotion. Undecided these customers will buy the product if and only if the product is directly promoted to them. Non these customers will not buy the product, regardless of a direct promotion. The only customers who can be positively influenced.

4 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 non M1 decided non M2 undecided decided The traditional paradigm favors M1. undecided non M1 decided non M2 undecided decided M2 has targeted more buyers! The difference: # of undecided customers targeted.

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 marketing aims 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 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 RR of Control

7 Model Construction : training set, : validation set. Response Treatment (T1) Control (C1) YesNo decided (3) non (2) decided + undecided (1) non + undecided (4) The learning matrix Contact positive negative Characteristics exclusive to positive class: those of undecided customers.

8 MT – MC (UBR) Proposed Solution – Model Evaluation Rank –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. Response YesNo Treatment(1) 1,182(2) 20,816 Control(3) 108(4) 2,400 Traditional paradigm Lo’s Our influential approach Our influential approach – oversample (3)

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


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