Growth & CRO Skill
A/B Test Setup
Plan, design, or implement an A/B test or experiment.
$15
Cohort Retention Analyzer
Group customers by kickoff month and turn retention into a curve, fitting decay, projecting LTV and computing NRR, GRR, Magic Number and Rule of 40 with a survivor-bias guard.
Turns the question 'are the customers who started in month X still with us?' into an answer that is a curve, not a single number. It groups customers by their kickoff month, fits retention decay (linear onboarding + exponential steady-state), projects LTV, and computes the SaaS health vitals: NRR, GRR, Magic Number and Rule of 40, with a built-in survivor-bias guard so departed customers stay in the denominator.
$15 one-time
Add to a kit → Prices include 20% VAT. · Forged on real agency work · one-time, no lock-in
- Type Skill
- Category Growth & CRO
- Delivery Email · instant
- License One-time
Inside the run · no black box
See the actual work before you buy it.
Retention math lies easily, mostly through survivor bias. Anchoring cohorts to first real payment and locking denominators in SQL, the analyzer fits decay curves, projects LTV three ways and scores its own forecasts.
- Assigns every customer to a cohort by kickoff month, derived from the date of their first completed payment, so cohorts are anchored to real money rather than signup forms.
- Builds the month-by-month retention matrix (M0 through M23) with a survivor-bias guard built into the SQL: the denominator stays locked at the cohort's starting size, so customers who left are never silently dropped from the math.
- Fits the retention curve with a hybrid model: linear decay for the first 3 onboarding months, exponential decay for steady state, then computes the cohort half-life, the number of months until half the cohort is gone.
- Projects lifetime value per customer three ways (linear, exponential, hybrid) and computes the SaaS health set on top: NRR, GRR, Magic Number and Rule of 40.
- Logs every forecast it makes against the actual that arrives a month later and scores the gap with a Brier score, so the model's calibration is continuously measured instead of assumed.
- Isolates the top 20% of cohorts by cumulative revenue (the power-law check) and asks what they share, sector, channel, onboarding intensity, turning the best cohorts into a replication target rather than a vanity stat.
One power source. 6 lines out.
cohort-retention-analyzer · core
core active · 6 lines
- ✓ answer 'which start-month
Answer 'which start-month cohort stayed longest?'
- ✓ build cohort-based ltv p…
Build cohort-based LTV projection for LTV/CAC
- ✓ report nrr, grr, magic n…
Report NRR, GRR, Magic Number, Rule of 40
- ✓ render a cohort retention
Render a cohort retention heatmap on a dashboard
- ✓ find the top 20% of coho…
Find the top 20% of cohorts driving most revenue
- ✓ calibrate retention fore…
Calibrate retention forecasts with a Brier score
Yours to keep.
Drag time forward. Watch what stays.
Forever
That's what owning means.
The rented stack
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Your forge
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Avoid inflated retention claims with a fixed-cohort-size survivor-bias guard
license: perpetual -
Forecast lifetime value with a hybrid decay model instead of naive linear math
license: perpetual -
Track whether your forecasts are actually accurate over time, not just optimistic
license: perpetual -
Surface the few cohorts that quietly produce most of your revenue
license: perpetual
subscriptions expire · deeds don't
Everything in the box.
Pick a piece up. Watch it work.
Extended PostgreSQL cohort retention view with materialized refresh and indexes
6 parts · one working system · ships instantly by email
This wasn't forged for everyone.
- Not for you if you'd rather rent a tool than own one.
- Not for you if you want someone else to run your stack.
- Not for you if you're happy guessing.
For founders, RevOps and analytics teams who need defensible retention, LTV and SaaS-health numbers grounded in cohort data rather than survivor-biased averages.
then this was forged for you.Works with
Universal by design: these run in any AI. Delivered in the open Agent Skills + MCP format (native in Claude); ChatGPT, Gemini, Cursor and Copilot adapt the same files their own way.
- Claude Native format
- ChatGPT Adapts via open standards
- Gemini Adapts via open standards
- Cursor Adapts via open standards
- Copilot Adapts via open standards
Catch what's on your mind.
the air is clear. nothing between you and the forge.
catch a spark: the forge will answer
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What do I need to feed it to get the cohort curves?
It works from your customers grouped by their kickoff month with their retention or revenue over time, which is standard subscription data. From that it fits the decay and projects forward, so the input is history, not a forecast you supply.
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Why fit a curve at all instead of just averaging retention?
Because a single average hides the shape: early churn during onboarding behaves differently from the slow steady-state decline. Modeling those two parts separately gives an LTV projection that holds up better than a flat number.
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Will the projection be trustworthy if I only have a few months of data?
A projection is only as defensible as the history behind it, so thin cohorts give wide, uncertain curves. It computes the vitals like NRR and the Rule of 40 from what exists, but it cannot invent maturity you have not lived yet.
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How is it delivered?
By email right after purchase: ready to run, downloaded instantly, no setup wait.
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One-time or subscription?
A one-time purchase; no subscription or hidden fees. VAT (20%) is included.
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Can I get a refund?
As a digital product, it can’t be refunded once downloaded. That’s why we show exactly what’s inside and who it’s for, right here.