Retention and churn diagnostic
A focused look at who is churning, when, and why, so the next fix targets the cause.
Book a diagnostic intro
Timeline /
2-3 weeks
Engagements typically start at /
$15,000
You receive /
5 deliverables
Who it’s for
Churn has climbed
Leadership disagrees on the cause.
Renewals are slipping
The fix needs data, not an educated guess.
Engagement is fading
No one has isolated where customers drop off.
Fit
A good match
You have a live product with customer records, and you can pull that data for analysis. Customers are leaving and the team has no agreed reason.
Not a match
You have no live product or customer base yet. Building one is a separate service.
How it works
1
First call
We cover how the business runs, your retention goals, the tech stack, and where you think customers are being lost.
2
Data handover
Table schemas with descriptions of what each table does and what populates it, or an introduction to someone who does. Then a first data pull.
3
Cohort analysis
Tuesday and Thursday check-ins on findings so far, open questions, and where the churn picture is pointing.
4
Final readout
Each churn driver ranked by size, the evidence behind it, and a prioritized set of recommendations.
5
Optional: act on it
We can keep working together to put the plan in place, scoped on its own.
Your own theory about the cause gets tested, not taken for granted. The biggest driver of churn is often not the one people expect.
What you receive
Retention and churn review
Churn driver analysis
Where to intervene
Recommended changes
90-day plan to act
If the evidence cannot confirm a cause, I will tell you rather than guess.
Examples
Users slid from weekly use to zero
Early retention was healthy, but users drifted from weekly use into inactivity; prolonged absence made recovery difficult. I built an AI-driven system to reach users before full drop-off, finding that those still active were much more likely to renew. Preventing churn improved renewals meaningfully.
People left in the first few minutes
Early drop-off called for a stronger first impression, from install to first success. I compared short and extended first-run experiences, guided and free exploration, and playtested before launch. The work shaped an onboarding approach aimed at reducing early abandonment.
Questions