When the customer signal is messy, or the team reads it differently, I help turn it into a product decision that moves you forward

When I'm most useful

Evidence, bias, aspiration, and sunk cost start to blur together, and the next decision gets harder to make from inside the team. And it's not for a lack of customer info. There's surveys and analytics, customer calls, sales feedback, founder instinct, and the product you've already spent time and money building.

So you end up with several plausible versions of the beachhead customer and no clean way to choose. Or the team is heads-down on velocity and needs to know whether the customer hypothesis is still holding up.

Three ways in:

• A Bullseye Customer Sprint brings the people making the decision into five live customer interviews over about four weeks. The team leaves with a documented customer direction, the evidence behind it, and a short list of what to do next.
• A Listening Cycle keeps the evidence train rolling for six weeks while the team builds, with 1-2 interviews and an immediate debrief each week to see whether the working hypothesis still holds.
• Fractional work keeps me closer to the team for a defined period, carrying what we learn into the decisions and changes that follow. More freeform than the others.

I came into this through UX design, then service design, and that 100% shapes what I look at. Sometimes what needs to change is an interface or a flow. Or sometimes it needs an ecosystem or stakeholder map to see the zoomed out view. But sometimes it's the customer definition, the workflow, or how the team decides.

The new crop of artificial intelligence (AI) tools helps boost my work across larger evidence sets and speed up synthesis, but direct customer evidence and human judgment stay at the center.