PLG team improves trial-to-paid conversion

Illustrative example. This case study describes a fictional company, built to show how a PLG team could use Funnelsight to find out why some trials convert and others don't, then act on it.

Context

The team in this example, a mid-size project management SaaS company we'll call Ridgeline, ran a 14-day self-serve trial. Signups were healthy. Paid conversions weren't. Nobody on the growth team could say with confidence what separated a trial that converted from one that didn't, beyond guesswork ("they probably didn't invite teammates").

Trial activity lived in the product database. Who actually paid lived in the CRM. Comparing the two meant a manual export every time someone wanted to check a theory, which meant nobody checked it often.

Approach

Ridgeline connected its product usage data and CRM data in Funnelsight's shared funnel view, so trial behavior and paid status sat in the same report instead of two separate systems.

The first step was straightforward: track trial-specific events (first project created, first teammate invited, first integration connected) for every account starting day one of the trial.

The second step is the one that actually produced an answer. Rather than only watching new trials go forward, the team went backward: they pulled the list of accounts that had converted to paid over the previous quarter, then traced each one back through its trial-period activity. The question wasn't "what should predict conversion" but "what did our paying accounts actually do, in fact, during their trial". This reverse-engineering step surfaced a pattern the team hadn't hypothesized on their own: accounts that invited a second teammate within the first 4 days of the trial converted at a meaningfully higher rate than accounts that invited nobody, or invited someone later.

Once that pattern was visible, Ridgeline used Funnelsight's rule-based scoring to flag trial accounts hitting that same early-invite behavior, so the team could prioritize outreach toward the trials most likely to convert instead of spreading effort evenly across all of them.

Result

In this illustrative scenario, trials that invited a second teammate within the first 4 days converted to paid at roughly twice the rate of trials that didn't, based on the reverse-engineered cohort Ridgeline built. The team started weighting early-invite behavior as a factor in their trial follow-up sequence, without redesigning the trial itself.

Tracking the events themselves needed a short, one-time setup from an engineer, standard for any product analytics tool. From there, one person on the growth team could pull the backward-looking report and act on it directly in Funnelsight — no data analyst needed for the analysis itself.