Several factors contribute to this:

1/ We pin down the exact behaviour first: who does what, where, by when. That precise behaviour is the target everything serves.

2/ We interview doers and non-doers. That’s how we handle conflicting feedback: we don’t average it away, the gap between the two groups is the insight - what doers have that non-doers lack is exactly what to fix. We can also triangulate the behaviour with user's action they do in the product - for that we experiment with product analytics integrations, such as amplitude & posthog for now.

3/ We diagnose the real barrier by applying behavioral science framework - COM-B. Every interview is coded into six buckets: capability, opportunity, motivation.

4/ The barrier tells us what to build (e.g. what type of intervention works the best, based on behavioral science principles). Different barriers need different fixes: let's say, if people don’t know how, you teach them; if it’s too inconvenient, you remove friction. Behavioural science already maps each barrier to the type of solution that works for it, so the fix isn’t a guess. The barrier itself points to a specific, concrete thing to build.

5/ Finally, every recommendation traces back to real customer voice and interviews. The line from “customer said X” to “so we’re building Y” is fully visible, end to end.

We have a blog post on it here: https://www.dipio.ai/blog/behaviour-change-design

Plus, there is a great source for all-behavioral science, including COM-B model https://thedecisionlab.com/reference-guide/organizational-be...