Direct Mail is Back… on Steroids
Last year, we had the opportunity to help a leading National Funding company analyze their direct mail program. The program was on autopilot and they were generating a 0.25% to 0.33% response rate. About the typical B2B number. What they were not doing was following the response rates down the funnel to understand what attributes made up the targets that actually closed and received funding.
So, first we created a traditional funnel. One that could be compared to current digital program to measure off-line channels in the same methodology as on-line channels. Then we went back a year to review over 1 million records/targets that received our mailers. We appended the data with 9 key attributes available for each target from age, location, credit score, years in business, sales volume, etc.
We then created a machine learning model to analyze where each target that responded fell out of our funnel. Some of the findings are what you might expect. Credit score of the individual was related to the fundability of an applicant. But what was amazing to find out was that targets in certain states and cities actually never ended up funding and were simply clogging up the sales and processing teams workflows.
Score Prospects and Stop Wasting Your Mailing Costs
The alternative funding world has some unique approached to qualification, so traditional banking models that score borrows are not really appropriate. In the end, what we found was that 4 key attributes, and combinations thereof, accounted for the true variability of which prospects applied for and received business funding – versus those that either never completed their full application process, were denied credit. By scoring each new prospect through our model we ended up eliminating 30% of the printing, mailing and postage costs without reducing any funded deals. So not only did the marketing costs go down, but operational costs were reduced as well by not servicing deals that were unlikely to fund.
Deploying the Solution with a Click!
Once you create your models, they need to be deployed. We suggest the utilization of a desktop tool or webpage to automate the process and provide the predictive results your team can easily put to work. In this case we even automated the distribution of the final files to the vendors and partners that needed to support the weekly program.
Read the Case Study Here.
If you would like to improve the cost efficiency of your direct marketing program. Or explore how AI can be used to improve your marketing ROI, give me a call!