An analytics and growth-engine framework for an insurtech partnership

Context

The business entered an insurance partnership with two very different routes to market: selling cover directly to consumers, and embedding insurance into other companies’ products. Each needed its own analytics approach.

Problem

There was no shared view of who the addressable customers were, how to size the opportunity, or which data and metrics the partnership should run on. Without that, the two sides would build incompatible reporting and chase overlapping segments.

What I did

I designed a single framework covering both routes: a B2C segmentation of the direct-to-consumer base along value and propensity dimensions, and a B2B model for embedded insurance that sized partner portfolios and defined the data exchange and success metrics. I set out the analytics operating model — what each side owns, what is shared, and how performance is measured.

Result

The partnership had one framework instead of two: a shared view of the addressable customers on each route, a way to size the opportunity, and one set of metrics both sides would report against.

The framework has since become running systems. Insurance lead generation is deployed: instead of broad, unscored cross-sell outreach, a propensity-scored lead pipeline directs effort to the highest-potential customers, for higher qualification rates and more efficient outreach. The Analytics & Growth Engine (AGE) is in development — an intelligence layer of analytics, modelling, profiling and insight on partner data that orchestrates the whole insurance journey, from reach and qualification to advice and conversion, for the global platform and Bahrain brokers, with work in progress to extend it to brokers in a further stc Gulf market.

What I learned

When a partnership runs two business models, the analytics has to be explicit about which one it is serving at every step — the temptation is to average them into something that fits neither.