These days, economic volatility, new regulations and competitive forces all make analytic learning speed and yield critical to success. In my last post, I discussed how an “analytic learning loop” accelerates feedback about market performance, enabling banks to make decisions based on how consumers are behaving today.
By providing a systematic approach to learning from recent results, analytic learning loops improve a bank's ability to:
- Design innovative products customers need, based not on their past behavior, but on how they're responding to and using your products right now.
- Deliver more effective pre-approved lead lists. Richer customer profiles enable more granular and insightful segmentation of prospect populations. Higher-performing lead lists boost the performance of marketing campaigns, and the productivity at call centers and branch offices.
- Improve response rates and early-life account performance by pinpointing individuals who are not only most likely to accept a specific offer, but to use the product in a manner that is profitable for the bank (e.g., high usage and low balance for a card product designed for transactors).
- Boost ROI on marketing campaigns by spotting variances between what was expected and what actually happened, determining sources of variance, and adjusting profiles, segmentation, offer strategies and approval decisioning to improve results while campaigns are still going on.
You can learn more about this new approach by reading FICO's Insights paper (#51) "Analytic Learning Loops Propel Bankcard Growth."
