Retail-Tech / Fintech
Purchase Intelligence.
An insights product using purchase and receipt data to help merchants understand customer behaviour.
Context
Purchase and receipt data can describe customer behaviour in detail. This project explored how that information could become useful to merchants beyond individual transactions.
Purchase Intelligence started as a hypothesis about expansion revenue. Could the existing customer base unlock a second product — one that monetizes the data layer rather than the receipt itself? The work began with merchant interviews, sharp scoping, and a prototype built outside the existing roadmap.
The product had to land in a market shaped by retail-tech buying cycles, regulated data handling, and very different levels of analytics maturity across customers. Simplicity, defensibility, and a clear "so what" were non-negotiable.
Contribution
- Owned the product from problem framing through 0→1 build, working hand-in-hand with engineering, data, and commercial teams.
- Designed an AI-powered insights layer on top of the existing purchase and receipt data, prioritizing use cases with clear merchant ROI.
- Aligned product, sales, and partnerships behind a focused commercial wedge inside the existing customer base.
- Set up the discovery and delivery rhythm — outcome-focused roadmap, measurable bets, and weekly merchant feedback loops.
- Connected the product to platform decisions on pricing, packaging, and partner integrations.
Outcome
A new revenue stream inside an existing customer base — measurable expansion revenue, sharper positioning in retail-tech, and a product line that earned its place next to the core platform.
Reflection
In B2B SaaS, "AI features" are not a strategy. The leverage is in choosing the one merchant problem where AI changes the unit economics, and then defending that wedge with data quality and tight feedback loops.