A financial services client sought to improve the efficiency of their direct mail campaigns by targeting high-intent prospects and reducing wasted spend.
A financial services client needed targeting models that were specifically tailored to their unique market segmentation. To solve this problem, Faraday’s data science team developed five distinct predictive models, which boosted precision and added an additional $20K in monthly revenue, driving a 10x ROI.
Rather than relying on a one-size-fits-all approach, Faraday worked with the company to refine a predictive modeling process tailored to their specific needs.
Initially, a single suppression model was used to assess all leads at once, but given the company’s four distinct customer segments, Faraday hypothesized that segment-specific models would improve precision.
Faraday then conducted rigorous validation testing, comparing conversion trends between the original model and the new segment-specific models.
These changes resulted in an additional 240K annually—and a 10x return on investment (ROI) from Faraday’s predictive lead suppression.
