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The weakness of Response Modeling in Direct Marketing

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Response modeling has the axiom "garbage in, garbage out" similar to any other statistical models. The quality of the data used for model building affects the goodness of the model. Response rates are not promising when the data is wrong, responder's definition is wrong, the model used is not updated, or when it is weak and apathetic in its creativity. Risk models are more dynamic if compared with credit risk models. In direct modeling there is a disadvantage - the disadvantage relates to the negative effect on some customers. And this happens when the customers level of spend is diminished. In stable condition person who have not been aberrant on the loans is likely to pay the bills on time. There are many things which can be observed and only a few can be taken into account to build the model. With a small change in area's demography response rates are affected highly. Over use of catch-phrase leads to reduction of potential customers. That's why marketing models are re-build rather than credit risk. In response models a wide variety of people is required. This includes marketing manager, the financial group, the statistician, the artist, the mail shop. The product should be worthwhile for the consumers. Coordination of production and people in response mailing matters a lot in response mailing.

Response Models

What is Response Modeling?

In response modeling the similarities between the responders of a direct marketing campaign are indentified and a statistical model is created and builds which is used as an application to the new data for determination of the customer's response.

The functions of Response modeling:

  • Marketing costs is decreased.

  • The new customers are acquired more cost effectively.

  • Those who are most likely to respond can be identified.

  • The customer's profitability can be maximized.

  • The sales and response rates is increased by 20% to 80%.

  • The customers accounting for 70% of your profit can be identified

  • The customers who are being undersold can be learned

  • The most profitable prospects can be targeted

  • A better understanding of customers and prospects can be gained

  • The best market segments can be discovered

  • How demographic and other factors have an impact on profit and responses can be learned

Statistical Model

Missed Opportunity

A missed opportunity is created when marketing campaigns avoid purchasers.


A wrong decision occurs when the wrong customers are predicted. So a danger occurs because of wrong decisions. To avoid such danger the customers should be predicted correctly.


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