01
See churn weeks earlier
We detect disengagement before users stop logging in - not after cancellation events show up.
Outcome
More time to intervene. Fewer surprise losses.
Churn Intelligence
Your churn dashboard is lying to you.
We predict churn using the signals your analytics tools throw away.
Most churn models rely on "clean" events - logins, upgrades, clicks.
DookieData analyzes the messy, ignored, uncomfortable behavioral data that actually predicts who's about to leave. But we don't stop there.
We measure interventions: save campaigns, onboarding fixes, support escalation rules, in-app nudges, and feature-adoption plays. .
churn_signal_scan.sh
$analyzing behavioral signals...
→partial_sessions: 847 anomalies
→hesitation_patterns: 12 accounts flagged
→silent_disengagement: 23 at-risk users
⚠3 accounts your dashboard says are "healthy"
Signal Confidence
94%
Dashboard Accuracy
31%
Hidden Risk Coverage
87%
Used by growth teams who are tired of "pretty dashboards"
- Revenix
- SynapseHQ
- ChurnLayer
- Outbound.io
- Metricflow
The Problem
Why churn models fail in the real world
Most churn tools assume perfect data. Real companies don't have that.
- 01
Events are missing
- 02
Users behave inconsistently
- 03
Teams over-filter "noise"
- 04
Dashboards hide uncertainty
So churn looks "under control" … right up until revenue drops.
The problem isn't your team.
It's what your tools refuse to look at.
It's what your tools refuse to look at.
Our Thesis
DookieData exists for the data you didn't think mattered.
We model churn using:
- Partial sessions
- Hesitation patterns
- Feature abandonment
- Silent disengagement
- "Almost" behaviors
The stuff that looks insignificant - until customers disappear.
What Changes
Three outcomes that matter
02
Stop over-optimizing the wrong users
Most teams chase high-activity users and ignore quiet ones.
We surface:
- Users who look "fine" on paper
- Accounts at high risk despite healthy usage
Outcome
Smarter retention plays, fewer wasted campaigns.
03
Trust your churn numbers again
Instead of hiding uncertainty, we model it.
We show:
- Confidence ranges
- Signal strength
- What's predictive vs. coincidental
Outcome
Better decisions. Less false confidence.
Process
How DookieData works
-
01
Connect your data
Product events, sessions, feature usage, support signals.
-
02
We analyze the ignored signals
Incomplete actions, pauses, drop-offs, inconsistencies.
-
03
We flag risk - with context
Not just who might churn, but why and how confident we are.
-
04
You act earlier
Retention, onboarding fixes, product improvements.
Social Proof
What teams are saying
"Our churn model finally matched reality. That was… uncomfortable at first."
"It pointed out risks we'd filtered out for years."
"This didn't replace our dashboards. It exposed them."
Fit Check
Built for teams who already have data - but don't fully trust it
- CMOs tired of surprise churn
- Growth marketers running retention experiments
- Data teams dealing with messy, real-world behavior
Not for:
Teams looking for "AI magic" without accountability.
Take Action
Your churn problem isn't hidden. It's ignored.
See what your dashboards filtered out.
(No credit card. No "AI magic." Just data.)
FAQ
Questions, answered
No. It's a refusal to ignore inconvenient data.
Because the most predictive signals are rarely the clean ones.
Yes.
Definitely not. That's the point.