A consumer health book publisher wanted to make its one-shot book direct mail programs more profitable. Specifically, its goal was to improve response without increasing returns and bad debt.


Alliant developed a model that predicted the likelihood of specific behaviors for each consumer, e.g. response, pay/no-pay, and return.  The marketer used the model to reduce the number of non-payers in each campaign and replace the under-performing names with those of more profitable customers.


With each campaign, the model identified 15% of names which would likely under perform; these names were eliminated from the mailing.


A portion of the lost mail volume was replaced with names mined from other list sources using top performing score groups from the model — the payment rate of the replaced names was 50% higher than the campaign average.


The combination of reduced campaign size and improved performance lifted overall campaign profitability for one-shot books by 32%.




Direct Mail

Insight at Work

Contact Us

Interested in working together?

Get in Touch

Acquire. Enrich. Optimize.

People-based marketing powered by billions of consumer data points