Download Database Marketing: Analyzing and Managing Customers by Robert C. Blattberg PDF

By Robert C. Blattberg

I train advertising spreadsheet types to MBA's at Indiana U's Kelley university. i admire this booklet. lots of invaluable info on patron worth, pricing, optimizing junk mail earnings and lots of different subject matters. which will compensate for a few of the new advancements in "marketing engineering" you want to commence right here!

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Extra resources for Database Marketing: Analyzing and Managing Customers

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5 Revenues from field-tested cross-selling campaign (From Knott et al. 2002). 22 2 Why Database Marketing? shows one example from Knott et al. (2002). A predictive model was used to prioritize customers according to their likelihood of purchasing a home equity loan. The top prospects were then targeted with a direct mail campaign. Note that this tests the ability of the predictive model predictions to hold up when the targeting actually occurs, subsequently to the modeling. 5 shows the targeted mailing generated revenues of $93 per mailed-to customer.

These become part of the database and are used to develop future targeting strategies. 1 provides a list of database marketing activities – essentially, a list of the marketing problems addressed by database marketing. These include acquiring customers, retaining and developing customers, coordinating acquisition, retention, and development, and managing the marketing mix. Several of the sub-issues within each of these merit their own chapter in this book. For example, we will devote full chapters to cross- and up-selling, multichannel customer management, etc.

1. • Prudential Insurance tested the results of data mining for improved response rates among prospects, and found them to be doubled. • American Express used data mining to “score” customers in terms of how likely they were to purchase various items. It then used these data to generate offers that match the products of its partners with the needs of its customers. In summary, the recognition that targeting was the problem with mass marketing, that database marketing could theoretically improve targeting, that database costs were declining, and that data mining was effective in practice at developing the targeting plans, contributed mightily to the growth in database marketing as a tool for improving marketing productivity.

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