Direct marketing when there are voluntary buyers
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Direct Marketing When There Are Voluntary Buyers. Presenter: _____________. Yi-Ting Lai, Ke Wang Simon Fraser University {llai2, [email protected] Daymond Ling, Hua Shi, Jason Zhang Canadian Imperial Bank of Commerce {Daymond.Ling, Hua.Shi, [email protected]

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Direct Marketing When There Are Voluntary Buyers

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Direct marketing when there are voluntary buyers

Direct Marketing When There Are Voluntary Buyers

Presenter: _____________

Yi-Ting Lai, Ke Wang

Simon Fraser University

{llai2, [email protected]

Daymond Ling, Hua Shi, Jason Zhang

Canadian Imperial Bank of Commerce

{Daymond.Ling, Hua.Shi, [email protected]


Introduction direct marketing

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%


Three classes of customers

Three Classes of Customers

  • Based on their purchasing behaviors

  • Each customer belongs to exactly one class

The only customers who can be positively influenced.


Is the traditional paradigm solving the right problem

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


Influential marketing

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


Data collection

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


Model construction

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.


Proposed solution model evaluation

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


Related work lo s

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


Experimental evaluation

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)


Joint comparison

Decision Tree

(SAS Enterprise Miner)

Joint Comparison

  • Improvements of our approaches are significant in the top percentiles.

Association Rule Classifier


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