The opening
The company was nearing completion of a long-term transformation of its foundational market-access data into a more flexible relational model. At the same time, commercial demand was growing for data and capabilities that model could now support.
The obvious next step was to bring that data into the existing product. But the new architecture made something more ambitious possible.
Seeing the bigger opportunity
I analyzed the recurring business questions clients relied on Landscape to answer and prototyped a more capable payer-intelligence experience around them. I then led blinded market research to test the concept and willingness to pay.
The research validated stronger demand for the expanded experience and gave me the evidence to make the case for a larger investment. We moved forward with Landscape iQ while the underlying enterprise data transformation was still underway, building the product experience and relational model in parallel.
Designing around how clients think
The challenge was to expose the flexibility of a fundamentally new data model without exposing its complexity. External beta sessions became central to the design process. We watched how clients navigated, listened to the language they used, and tested whether our concepts actually matched how they approached research, planning, and market-access decisions.
The product evolved continuously through those sessions. We also ran an internal beta with client-facing teams, giving us another feedback loop while building familiarity with the new experience before launch.
Interpretation, not just access
More data was useful, but clients ultimately wanted to know what had changed, why it mattered, and where they should investigate next. I wanted the product to surface those signals proactively rather than require users to know what to ask a chatbot.
I designed an approach using deterministic logic, magnitude and significance scoring, and multi-causal driver analysis to identify meaningful market changes and interpret patterns in the data. An AI layer then translated those deterministic results into clear natural-language insights.
The value was not just access to more data. It was helping clients understand what mattered and where to dig deeper.
Making the case
The broader vision required more investment than simply adding the requested data to the existing experience. I used willingness-to-pay research, competitive analysis, client feedback, and beta evidence to demonstrate that the additional scope could create meaningful customer and commercial value.
What happened next
Landscape iQ secured enterprise customers before GA and continued gaining commercial traction immediately after launch. The early results validated both the market research and the decision to build a fundamentally more capable experience instead of making an incremental product extension.
Skills demonstrated: Product strategy, customer-driven development, willingness-to-pay research, information architecture, applied AI, parallel product and data development, commercial validation, and internal influence.