Every bank wants to send the right offer to the right customer. Most still send one message to a broad segment. The gap between the two is rarely the model. It is everything beneath the model.
Start with what the customer does
At a regional consumer bank I began with instrumentation. We set up enterprise analytics to capture granular in-app behaviour: what customers opened, where they paused, what they left. Without that, a model has nothing current to learn from.
Then let the model score people, not groups
With behaviour flowing, we embedded machine learning, predictive analytics and next-best-action into engagement. The bank moved from broad segment targeting to real-time, data-driven personalisation. We built it as a reusable framework, so it could scale across markets in the Middle East and GCC and not stay a single campaign.
Then make a decision the customer can see
The visible part was an AI-driven personalised offer engine. It delivered a 60% conversion lift across targeted engagement journeys.
What I would tell a team starting now
- Instrument first. You cannot personalise on data you never captured.
- Build the framework, then the campaign. A one-off model wins once. A framework keeps winning.
- Bring risk and privacy in early. Personalised experiences had to meet data-privacy, regulatory and risk standards in every market. Designing for that from the start is faster than retrofitting it.
- Measure at the journey. The engine is judged where the customer acts, so that is where the measurement sits.
Personalisation is a stack. The offer is the top layer, and it is only as good as the layers under it.