Personalization designed to improve lift through optimization engines is built into most banking tech stacks. Many produce some lift initially but then flatline as teams need to retain data and write new rules. This can be time-consuming and expensive as bank marketers continuously learn and relearn their customer data and the performance of their marketing collateral.
Enter AI that can sustain lift by continuously updating optimization so marketers don’t have to. After initial model training and launch, an ongoing AI-fueled bandit model can update and maintain program performance while at the same time enabling new assets, strategies and programs to be tested and optimized. The result: incremental lift without the lag (see chart).
But a few words of caution: Be sure to check under the hood to make sure what’s being called AI really can continuously teach itself, rather than depending on human intervention, to keep lift going in the right direction.
