Response Modeling is the process of analysing previous purchasing behaviour of customers over a logical period of time.

Customer response date, coupled with other pertinent and relative data, such as demographic, geographic, psychotropic and consumer spending information is by far the most reliable source to reveal trends and tendencies of customer purchasing behaviour and patterns. Response Modeling digs deep into the databases to correlate meaningful patterns unobtainable any other way.

Modeling on response data results in improved customer response to direct marketing programs. This is obtained by targeting only prospects that are predicted as most likely to respond to a particular advertisement or promotion. Instead of mailing to or calling on (in the case of telemarketing) every prospect on a list in a database, only the ones with a high probability of responding positively are selected. Randomness and subjectivity are removed from the list selection process.
Marketing Introduction and Lifetime Value

Make the 80/20 and 50/50 rules work for you

Canada Post Requirements for Address Accuracy

Know your customers and create more profitable Direct Marketing programs with Data Mining

What is Data Mining?

Response Modeling?

Segmentation and Profiling

Customer Valuation

Cross Selling

Working the Modules Together




The benefits of Response Modeling are widely recognized as the following:

Higher returns from smaller segments of database.
Removal of "randomness" of direct mail.
Higher response for equal dollars.
Improved Return-On-Investment (ROI) for marketing dollar.
Improved inventory control and supply-side management.
Improved customer relationships and retention.
Savings on communication costs (mailing, printing costs, telephone costs etc.)