MODELING RESPONSE TO DIRECT MAIL MARKETING ISQS 7342 – Dr. Zhangxi Lin by Junil Chang

Cross-Industry Standard Process for Data Mining According to CRISP–DM, a given data mining project has a life cycle consisting of six phases

Six phases for data mining 1. Business understanding phase 2. Dada understanding phase 3. Data preparation phase 4. Modeling phase 5. Evaluation phase 6. Deployment phase

Direct Mail Marketing Response Problem This project is to predict which customers are most likely to respond to a direct mail marketing promotion. The clothing-store data set is provided by a clothing store chain in New England. Data were collected on 33 fields for 21,740 customers

Building the Cost/Benefit Table Classification models are evaluated on accuracy rates, error rates, false negative rates, and false positive rates. four possible decision outcomes (true negative, true positive, false negative, and false positive) and assign reasonable costs to each decision outcome.

Statistics associated with the average amount spent per visit for all customers

Profit: $ *.25 = $28.40 Benefit: $ $2.00 = $ Statistics associated with the average amount spent per visit for all customers

Cost/Benefit Decision Summary for the Clothing Store Marketing Promotion Problem OutcomeClassificationActual ResponseCost True negative (TN)Nonresponse No cost True positive (TP)Response $26.40 profit False negative (FN)NonresponseResponse$28.40 lost profit False positive (FP)ResponseNonresponse$2 for the mailing cost

Clothing Store Data Set The original clothing-store data set contains customers in 51 fields Pre processed data set contains customers in 33 fields

Distribution of response variable Most people do not response to the promotion

3611 of customers or 16.61% responses for marketing campaign (1 indicates response, and 0 indicates non responses).

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