CHURN PREDICTION SPRINT — $5,997 · 2-WEEK SPRINT

Jake McMahon — ProductQuant

8+ years B2B SaaS · Behavioural Psychology + Big Data (Masters)

The accounts drifting toward cancellation are already in your data. This sprint finds them.

A 2-week sprint that builds a churn prediction model on your existing product and billing data — so CS gets a weekly list of who to call, why they’re at risk, and what to say.

Start the Sprint → See what’s included ↓

CS has a live at-risk list by day 14 — or full refund

WHAT YOU HAVE AT THE END

Churn prediction model Trained on your data, not a generic template

Weekly at-risk list CS knows who to call before the cancellation email arrives

CS intervention playbook One response per signal type — no improvising

Feature importance report Which behaviours predict departure, ranked by signal strength

Monthly refresh plan The system runs continuously after the sprint ends

$5,997 · fixed price · 2-week sprint

YOU ALREADY KNOW SOMETHING IS WRONG

CS finds out when the cancellation email arrives

“We do a post-mortem every time and the pattern is always the same. They were disengaging for six weeks before they told us.”

Head of CS — B2B SaaS, $8M ARR

CS prioritises the squeaky wheel, not actual risk

“We have 200 accounts and one CS rep. She works off gut feel. We have no idea which ones are actually at risk right now.”

VP Revenue — Series A

Lagging indicators, not leading ones

“We know our churn rate. We don’t know which customers are about to become part of it.”

CEO — B2B SaaS

No consistency in how CS handles at-risk accounts

“Every save conversation is improvised. Some work. Most don’t. We have no idea what the right move is for each situation.”

CS Lead — Growth stage

WHAT THIS TYPICALLY UNCOVERS

The accounts most likely to cancel are rarely the ones CS is watching.

The loudest accounts are rarely the highest risk.

Accounts that file support tickets are signalling engagement, not departure. The accounts that go quiet — sessions dropping, feature usage narrowing — are the ones heading for cancellation. Without a model surfacing these signals, CS focuses attention in the wrong direction.

Churn signals typically appear 30–60 days before cancellation.

Usage decline, login frequency drops, feature breadth narrowing — these patterns emerge weeks before the cancellation email. Your analytics tool already captures the data. The sprint turns it into a weekly signal CS can act on in time.

Billing behaviour is one of the strongest churn predictors available.

Failed payments, downgrade inquiries, and billing support tickets often appear in the data weeks before a formal cancellation. Stripe data combined with product usage signals creates a prediction layer most teams never build.

Generic churn models miss the signals specific to your product.

A model trained on your cohort history learns which behaviours precede cancellation in your product, not in B2B SaaS generally. The feature importance report tells you exactly which signals carry weight — and which ones are noise.

WHY THIS IS DIFFERENT

Most churn analysis ends with a report. This one ends with a working prediction system your CS team uses every week.

A typical churn analysis reviews historical data, produces a list of contributing factors, and recommends instrumentation improvements. Your team reads it, agrees it’s useful, and discovers it doesn’t tell them which accounts to call next Monday.

This sprint works differently. The model is trained on your actual behavioural data — not a generic churn framework. The feature importance report tells you which signals are predictive in your product specifically. And the at-risk list is running in production at the end of Week 2 — not sitting in a document waiting for an engineering sprint to implement.

The goal is not analysis. The goal is CS starting Monday morning with a ranked list of accounts that need attention this week — with enough context on why each one is flagged to know exactly what to say when they call.

TIMELINE

Week 1: the model trains. Week 2: CS has a list of who to call and why.

WEEK 1

Audit + Model

Read-only access to your analytics tool and Stripe. Cohorts mapped. Behavioural features engineered. Churn prediction model trained on your historical data and validated against held-out churned accounts.

→

WEEK 2

List + Playbook

Model run against current active accounts. First at-risk list produced and reviewed with CS lead. Intervention playbook built with CS input — one response per signal type. Format agreed: Slack, CSV, or CRM.

→

DAY 14

Handoff + Live

60-minute handoff with CS and product leads. Every deliverable walked through. Weekly at-risk list schedule confirmed. Monthly refresh process documented. System running — not planned.

CS leaves day 14 with a ranked list of who to call and what to say

WHAT YOU GET

Your CS team contacts at-risk accounts weeks before the cancellation email would have arrived.

Week 1 · Data Audit

Data Audit & Feature Engineering

Before the model runs, the data has to be right. The audit identifies instrumentation gaps, maps the behavioural features available, and engineers the predictive variables the model will use — calibrated against your specific product and usage patterns.

Read-only data access — analytics platform and Stripe
Cohort mapping: active, churned, and at-risk account populations
The specific behaviours that predict cancellation — so CS knows what to look for
Instrumentation gap report: what signals to add to sharpen the model over time

Week 1 · Model

Churn Prediction Model

A predictive model trained on your historical behavioural data. Not a generic scoring template — a model that has learned which patterns in your specific product precede cancellation. Validated against held-out churned accounts before deployment.

Model trained on 6+ months of product usage behaviour
Validated against held-out churned cohorts before the list is produced
Your CS team knows which 20 accounts to call this week — ranked by churn risk
CS chases real risk, not noise — the model is tuned to minimize false alarms

Week 2 · Report

Feature Importance Report

Which behaviours are actually driving the predictions — ranked by signal strength. Not a list of everything that correlates with churn: the 3–7 behaviours your model is actually weighting most heavily, and what each one means for a CS conversation.

Top predictive features ranked by importance score
What each signal means in plain English for a CS rep
Which signals fire early (30–60 days) vs. late (7–14 days) in the churn window
Segments where the signal pattern is different — enterprise vs. SMB, for example

Week 2 · CS Handoff

Weekly At-Risk Account List

The first live list of accounts your CS team should contact this week. Integrated into Slack or delivered as a CSV — whichever your team uses. Ranked by risk score and estimated cancellation window, with context on why each account is flagged.

Accounts ranked by risk score and estimated cancellation timeframe
Context per account: which signals fired and when
Recommended next action for each risk tier
Format your CS team reads without needing a data analyst to interpret

Week 2 · Playbook + Roadmap

CS Intervention Playbook & Monthly Refresh Plan

For each churn signal, a specific intervention. One response per signal type so CS runs the right play without improvising. Plus a monthly model refresh plan so the system stays accurate as your product and user base evolve.

Intervention per signal: inactivity, feature drop-off, billing friction, support spike
Message framing matched to context — power user going quiet vs. new user never activating
Escalation criteria for when to loop in account management
Monthly model refresh process: how to retrain as your data grows

From one engagement: A B2B SaaS platform discovered that accounts going quiet in weeks 3–4 were 6x more likely to cancel than accounts filing support tickets. CS had been prioritising the loudest accounts — the ones actively complaining — while the accounts silently drifting toward cancellation went uncontacted. The model surfaced the real risk signals and CS redirected outreach the following Monday.

FIT CHECK

Your CS team has the data. They just can’t see who is leaving until it’s too late.

GOOD FIT

B2B SaaS with 50+ accounts, 6+ months of usage data, and a CS team managing renewals

Event data available · CS team in place

The situation

Your CS team finds out about churn when the cancellation email arrives. You have product usage data in an analytics tool — PostHog, Amplitude, Mixpanel, Heap, or similar — and Stripe billing data. The signals that predict departure are in the data already. Nobody is surfacing them in a form CS can act on.

What you leave with

A weekly at-risk account list ranked by risk score with context on why each account is flagged
An intervention playbook so CS runs the right play for each signal type
A feature importance report showing which behaviours predict departure in your product

CS contacts at-risk accounts 30–60 days before they would have cancelled — turning reactive saves into proactive retention.

NOT A FIT

Pre-product, no analytics, or churn isn’t the constraint

Wrong stage or wrong problem

When this sprint doesn’t apply

If you have fewer than 50 active accounts, there isn’t enough data to train a reliable model. If you have no usage event tracking at all, there are no behavioural signals to model. And if users are activating but not converting — an activation problem, not a retention problem — this sprint is pointed at the wrong stage of the funnel.

Better starting points

Chameleon Flows

Trigger in-app nudges when accounts show churn signals. 3 flows live in 10 days.

Activation Deep Dive

Find where new users drop off when activation is the bottleneck.

The Foundation

Covers activation, retention, competitive, and go-to-market together.

What this sprint doesn’t cover

The Churn Prediction Sprint delivers the model, the at-risk list, and the intervention playbook. Your team runs the conversations and retention plays. If you need the full picture — including ongoing model management — that’s a different engagement.

Running the save conversations — your CS team handles the outreach
Rebuilding the onboarding or product experience — the sprint identifies risk, not redesigns
Ongoing model retraining — the sprint delivers the refresh process, your team runs it (or we offer a monthly add-on)

For full implementation → Growth LAB

Jake McMahon — ProductQuant

Jake McMahon

8+ years building retention, activation, and growth programs inside B2B SaaS · Behavioural Psychology + Big Data (Masters)

I run this sprint myself — the data audit, the feature engineering, the model training, the intervention map. Not a team, not a template. Your churn patterns are specific to your product and your cohorts. Generic churn frameworks miss the signals that are actually predictive in your data.

The output is built for your CS team to use without needing me in the room. If they need a data analyst to interpret the at-risk list, the sprint didn’t work. Every deliverable is written for the person who has to act on it — not the person who commissioned the analysis.

I won’t do this:

Apply a generic churn model without validating the signals against your cohort history
Deliver an at-risk list CS can’t use without a 30-minute explainer every week
Recommend instrumentation changes without a prioritised implementation sequence
Frame churn as purely a product problem when it’s often a CS process problem — or vice versa

What if our instrumentation is incomplete?

Most of it is. The sprint works with what exists and identifies what’s missing. If the data is too sparse for a full behavioural model, we shift to a signal map using Stripe billing data and support activity — and document exactly what instrumentation to add for the next iteration. You leave with a working system and a clear path to making it sharper.

Teams Jake has worked with

PRICING

30-60 days of warning before accounts cancel — and the playbook to stop them — $5,997.

$5,997

one-time · fixed price

2-week sprint

Data audit & feature engineering across your analytics stack
Churn prediction model trained on your historical cohort data
Feature importance report — which signals predict departure in your product
Weekly at-risk account list (Slack or CSV) — live at end of sprint
CS intervention playbook — one response per signal type
Monthly model refresh plan
60-minute handoff session with CS and product lead
All documentation stays with your team permanently

Works with PostHog, Amplitude, Mixpanel, Heap, Stripe, and most product analytics tools.

Book a 30-minute call →

Your CS team has 30-60 days of lead time on every at-risk account by Week 2 — or full refund. If the data can’t support a reliable model, we tell you in week 1 and scope what’s possible. The deliverable either exists or it doesn’t.

Questions.

Or book a call →

How early can you predict churn? +

The window depends on your product and usage patterns. In most B2B SaaS products with weekly or daily active usage, the leading signals appear 30–60 days before cancellation. In products with monthly usage cycles the window can be shorter — which we identify in the data audit. We set the threshold based on your data, not a generic benchmark.

What analytics tools does this work with? +

The sprint works with PostHog, Amplitude, Mixpanel, Heap, or any analytics tool that captures event-level data with a user identifier. We also use Stripe data heavily — payment behaviour is one of the most reliable churn signals available. If your instrumentation is limited, we work with what exists and document what to add to sharpen the model over time.

What does the CS team actually receive? +

Two things. The at-risk account list — accounts currently showing the leading churn signals, ranked by risk score, with context on why each one is flagged and what to do. And the intervention playbook — a guide for each signal type that tells them what message to send, when to escalate, and how to measure whether the intervention worked. Both are written for a CS person to use directly, without needing a data analyst to translate.

What if churn turns out to be a product problem, not a CS problem? +

The analysis will show this. If churned cohorts are concentrated in users who never reached the core value moment — an activation failure, not a retention failure — the sprint identifies that clearly and documents the product fix required. The deliverable shifts to a product recommendation rather than a CS playbook. You get the right answer, not the answer that fits the format.

Do we need engineering involvement? +

No engineering work is needed during the sprint. We use read-only access to your analytics tool and Stripe. The at-risk list is delivered in a format your CS team can use directly — Slack alert, CSV, or a simple Notion export. If you want to integrate it into your CRM, we document exactly what the integration requires so your team can scope it after the sprint, but it is not a prerequisite for the list to be useful.

How does the monthly refresh work after the sprint? +

The sprint delivers a documented refresh process your team can run independently. Once a month, the model is retrained on the latest cohort data — the process takes 2–4 hours if someone on your team follows the documented steps, or we offer a monthly refresh add-on if you want it handled externally. The choice is yours. The system is designed to run without a standing contract.

We already have some churn analysis internally — is this still relevant? +

It depends what “churn analysis” means. If it’s a cohort retention chart or a monthly churn rate calculation, it’s backward-looking — it tells you churn happened, not which specific accounts are at risk right now. If it’s an existing predictive model, we can audit it rather than rebuild it — the sprint output is the same (a live at-risk list), but we start from a different place. Book a call and we can assess what you have.