SaaS case study

Trial-to-paid from 9% to 16%

Cypher AI. How we fixed an onboarding that was quietly losing most of the trials.

Why it mattersA couple of points of trial-to-paid is a solid quarter. Nearly doubling it without adding trial volume is the rare one.

The resultsSaaS
Trial to paid9% to 16%
ActivationUp 27%
Time to valueCut roughly in half
Onboarding done41% to 68%
Expansion MRRUp 19%
The situation

Where they started.

Cypher AI was bringing in trials and losing most of them. The product was good, but the onboarding said almost nothing, nobody followed up when a trial went quiet, and the spend that earned each signup was being wasted a week later. The goal: fix conversion before adding a single dollar of new acquisition spend.

What we ran

What we did about it.

The 30-Day Plug-In audit found the moment that mattered: trials that reached one specific action converted at several times the base rate, and the onboarding never pushed anyone toward it. The plan rebuilt every email and in-app nudge around getting users to that first real win, fast. It is the kind of work we run for SaaS companies.

01Rebuilt the onboarding emails around the first real win
02Added in-trial nudges toward the action that predicted a paid plan
03Set up a sequence for trials that stalled partway
04Cleaned up a confusing demo booking path
KilledA feature-tour email sequence nobody finished, replaced with one win-focused setup path.
Day 1Plug-In: audit, access, baseline
Day 30New onboarding live
WeeklyCadence: working session, Friday update
Day 90Rebuild: nudges aimed at the win

Trial-to-paid nearly doubled, from 9% to 16%, on the same trial volume. Activation rose 27%, time to value was cut roughly in half, and onboarding completion went from 41% to 68%. The compounding kind of fix: every future marketing dollar now buys nearly twice the customers it used to.

From the founder

“We were paying to fill a bucket with a hole in it. They found the hole and patched it.”

Founder, Cypher AI

Every engagement is different; results shown are not a guarantee. How we present results

More of the work: Scispot and RevEng.AI

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