Deviniti More Than Doubled Trial Conversion by Finding the One Behavior That Predicted It

Measure the team, then the business, then the product

Before changing how anyone worked, we measured where the team actually stood. Four members of the Luna team answered a Product Model Maturity Survey in May 2025, an earlier draft of what is now the Product Model Maturity Assessment, scored against the maturity model. Scored on the same four-level ladder, the team came out at 58.5 out of 80, which puts it in the product team band rather than the delivery team band. The headline number turned out to be the least interesting part of it.

The structure of a product team was already in place, and the team agreed about it. Every question about the trio came back at the top of the scale from all four people: who does what, which ceremonies they plan and learn in together, and how value and viability are shared between them. What was missing sat one layer underneath. Focus scored lowest. All four said their planning was guided by effort estimates, story points or hours, which was the single weakest answer in the survey, and half named velocity and story points as the metrics they watched most closely.

The second signal was disagreement. On five questions the four answers spanned the full width of the scale. Asked how the team shares accountability for outcomes, two said each role focuses on its own deliverables and two said the team collectively owns success metrics and learns from failures together. Asked how often priorities move on outcome data, two said continuously and two said rarely. A team whose members describe how it works in opposite terms does not have a shared operating model yet, whatever its org chart says. That was the gap the pilot existed to close.

Then we went to the business numbers rather than to the backlog. Deviniti's own Tableau data had to settle one question first: was this a retention problem or an activation problem? Retention came back healthy. Revenue from new customers consistently exceeded churn, and every customer segment stayed beyond twelve months. Activation was where the money was leaking. Around thirty trials arrived each month, and fewer than three in ten converted, against historical peaks above fifty percent.

Six hypotheses, and one that came back at a hundred percent

We split trial instances into two cohorts in PostHog: the ones that became paid licenses, and the ones that never did. Then every hunch the team held went through the same three steps, so intuition had to survive contact with data before it could shape a roadmap.

One behavior, and a hierarchy that connects it to the money

A perfect predictor is only useful if a team can act on it. So the behavior became the target, and the target was tied explicitly to the business result it was chosen to predict.

The behavior hit its target. The business number it predicted overshot.

Three changes in how decisions get made

Goals start from the business problem, not from MRR. The business goal used to be communicated as growing monthly recurring revenue. The method every product manager learned adds a step: first decide whether the business is short on activation or on retention, then read the product's own analytics to see which one it is short on, and pick one focus for the quarter.

Each level has its own clock. Business outcomes on a one to three year horizon, team objectives for the year, product outcomes for a quarter, measured every week.

“We have analytics” means something else. Before, it meant the tracking was installed. The pilot set a different bar: the team can read the data and decide from it.

What else was going on. Deviniti was rolling out product analytics with PostHog for the first time while the pilot ran, which made the timing right, and customer success made the week-three calls. ProductTrio's part was the method: where to look, what to measure, which behavior to aim at, and how to spread it. The teams did the work.