- A top 5 credit card issuer was faced with low response rates of 0.2%-1.5% for its mail campaigns.
- Over 66 MM pieces were being dropped per year in over 20 campaigns annually.
- Acquisition efficiency needed significant improvements
- We used advanced analytics to develop models to predict response, risk, approval, spend and spend activation.
- We also created a decision engine to score population.
- The universe was segmented along response and risk to optimize for overall response and approval (ORA)
- The decision engine resulted in response and approval rate to jump up by 100%.
- The acquisition cost per card came down by 66%.
- The gross margin improved by 80%