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. Same trial volume in, nearly twice the customers out.

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.

Cypher AI · SaaS · Onboarding emails, in-app nudges, and trial lifecycle sequences
The whole story is on this page
The short version

Good trials, quietly lost.

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 they had going for them

  • A genuinely good product behind the signup
  • Steady trial volume already coming in the door
  • Acquisition spend that reliably earned signups
  • One clear goal: fix conversion before buying more trials

What was quietly costing them

  • An onboarding that said almost nothing
  • Nobody followed up when a trial went quiet
  • A feature-tour email sequence nobody finished
  • The spend that earned each signup, wasted a week later
Where the trials went

Same trials in. Nearly twice the customers out.

Out of every 100 trials coming in, 9 were becoming customers. The rest leaked out of an onboarding that never pointed anyone at the product's first real win. Same volume today, and 16 of every 100 come out the other side as customers.

Out of every 100 trials, how many became customers
Before
9 of 100
After
16 of 100
The onboarding behind it
Onboarding done, before
41%
Onboarding done, after
68%
Activation, before
baseline
Activation, after
up 27%

The rebuilt onboarding, drawn as the journey it is. Every email and nudge aims at the same first win.

The leak, found

One action predicted a paid plan. Trials that reached it converted at several times the base rate, and nothing in the old onboarding pointed anyone at it. Every email and nudge now does.

The check-up

Then and now.

Every engagement starts with the same audit we'd run on your business. Here's how Cypher AI's funnel graded when we plugged in, and how it grades today.

What we checkedWhere it stood when we plugged inThen → now
The onboarding
Said almost nothing; a feature-tour email sequence nobody finished
FA

The feature tour got killed and one win-focused setup path replaced it, with every email rebuilt around the first real win. Onboarding completion went from 41% to 68%.

Trial follow-up
Trials went quiet and nobody reached out
FB+

A sequence now catches trials that stall partway, and in-trial nudges point at the one action that predicted a paid plan.

Trial-to-paid
9 of every 100 trials became customers
DB+

16% now, on the same trial volume. Nearly double, without a single dollar of new acquisition spend.

Time to value
Nothing pushed users toward the win that mattered
DA

Time to value was cut roughly in half once every nudge aimed at that first win, and activation rose 27%.

The demo path
A confusing booking path for anyone who wanted a demo
CA

Cleaned up as part of the plan, so a trial that wanted a human had a straight line to one.

Tap any line for the detail →

Grades are how we score a marketing check-up. Every engagement is different; results shown are not a guarantee. How we present results

What we ran

Same three rituals, aimed at one win.

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.

Days 1 to 30

The 30-Day Plug-In

  • Audit, access, and the baseline
  • Found the action that predicted a paid plan
  • New onboarding live by day 30
Every week since

The Operator Cadence

  • A working session every week, a Friday update every week
  • A sequence set up for trials that stalled partway
  • The confusing demo booking path cleaned up
Day 90 and on

The 90-Day Rebuild

  • Every email and in-app nudge aimed at the first real win
  • Onboarding completion from 41% to 68%
  • Trial-to-paid from 9% to 16% on the same volume
What we killed

A feature-tour email sequence nobody finished, replaced with one win-focused setup path.

The numbers

What the fix bought.

Same trial volume in, no new acquisition work. Everything below moved because the same trials started reaching the win.

9% to 16%trial-to-paid, on the same trial volume
41% to 68%onboarding completion after the rebuild
+27%activation, with time to value cut roughly in half
The rest of the movement
Time to value, before
baseline
Time to value, after
cut roughly in half
Expansion MRR, before
baseline
Expansion MRR, after
up 19%
The compounding kind of fix
Same volumenot one new dollar of acquisition spend
One winevery email and nudge rebuilt around the first real win
9% → 16%trial-to-paid, nearly doubled
+19%
expansion MRR after the rebuild. The fix kept paying past the first conversion.
Nearly 2x
every future marketing dollar now buys nearly twice the customers it used to.

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

The turn

Patch the bucket before you pay to fill it.

Conversion fixes compound. Every future marketing dollar now buys nearly twice the customers it used to.

16%

Trial-to-paid, up from 9%, on the same trial volume. Not one new dollar of acquisition spend.

68%

Onboarding completion, up from 41%, once the feature tour died and one win-focused setup path replaced it.

+27%

Activation, with time to value cut roughly in half. Every nudge now points at the action that predicted a paid plan.

+19%

Expansion MRR after the rebuild. The compounding kind of fix.

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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