Improve Response Rate
One of the largest member organizations with many products and a large, national customer base acknowledges it is necessary to segment every campaign to limit prospect fatigue and reduce costs. A poorly segmented campaign can result in missing thousands of sales and millions in revenue.
Reach Analytics was enlisted by this client to improve segmentation and increase the direct mail response rate. Up to that point the client had used an in-house scoring model to identify targets from a list of over twenty-five million prospects. Their model was fueled by internal customer data and a national database that provides demographic clusters that group people into pre-defined categories.
Using our proprietary software, Reach Analytics quickly determined that the cluster variable used in the client’s model was not the defining factor for driving customer response. In fact, 30% of the variables used in the client’s model had no impact at all on the outcome.
Reach Analytics initially built a new model to estimate the response rate. Within the analysis of the initial model, we identified five sub-segments that when analyzed alone provided a significant improvement over the client’s current results. Reach Analytics ultimately built four new models—and identified one segment that was so strong, it didn’t need a model.
Implementation of the Reach Analytics multi-segmentation and model scoring solution proved to be quite effective:
- Removing the pre-determined clusters and replacing the poorly-performing client model with newly modeled segments resulted in an overall 50% lift in response rate.