CHURN PREDICTION 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 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 → View deliverables page → See what’s included ↓

CS has a live at-risk list with 30-60 days of lead time.

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

Fixed price · Sprint delivery

We build a weekly list of customers about to leave.

You get a simple report sent to your customer success team every Monday. It shows who to call, why they're at risk, and the best way to save them.

CUSTOMER SUCCESS

A customer hasn't logged in for 3 weeks.

Our model flags them. Your CS rep sees they're at high risk and gets a suggested script to re-engage them. This turns a silent exit into a saved account.

BILLING ALERTS

A finance manager asks, "Which big accounts are up for renewal soon?"

The report shows clients with expiring contracts who are also showing low product usage. Your team can proactively offer support or a check-in before they decide not to renew.

PRODUCT FEEDBACK

A product team asks, "What features do at-risk customers never use?"

The model connects churn risk with specific features. You learn which parts of your product are critical for retention, so you can improve onboarding and training.

SALES PIPELINE

A sales director says, "We need to upsell our safest customers."

The report also identifies your most loyal, low-risk accounts. Your team can confidently reach out to these happy customers with new offers, boosting revenue.

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 weeks 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

The model trains. CS gets a list of who to call and why.

PHASE 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.

→

PHASE 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.

→

HANDOFF

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 with a ranked list of who to call and what to say

WHAT YOU GET

Eighteen deliverables. Your CS team gets a ranked list of who is likely to leave and what to do next.

Week 1 · Data Readiness

Full Data Audit & Feature Engineering

Before a model gets built, we find out whether your data can support one. Product usage, billing, support, and account metadata are checked for signal quality. Then the behaviours that might predict churn are turned into usable model features instead of staying trapped in dashboards.

Analytics, billing, support, account, and cancellation data reviewed together
Instrumentation gaps identified before they distort model output
Predictive features created from the behaviours your product can actually observe

Week 1 · Model

Trained Churn Prediction Model

The model is trained on your own product history, not a generic SaaS scoring template. It learns which behaviour patterns usually appear before cancellation in your product. The result is a practical risk ranking your CS team can act on, not an academic model nobody trusts.

Model trained and validated against your historical churn data where available
Risk scores tuned for operational usefulness, not just statistical neatness
False positives reviewed so CS does not waste the week chasing noise

Week 2 · CS Output

Weekly At-Risk Account List & Risk Tiers

Your CS team gets a ranked list of accounts to contact this week, with the reason each account is flagged. Risk tiers make the next action obvious: call now, watch closely, automate a nudge, or ignore. The list is delivered in the format your team will actually use.

Accounts ranked by risk score with context on which signals fired
Tiered action guidance so CS knows who needs human outreach first
CSV, Sheet, Slack, or dashboard-style delivery depending on your workflow

Week 2 · Explanation

Feature Importance Report & Signal Reference Guide

CS should not have to trust a black box. The report explains which behaviours drive the prediction and what each one means in a customer conversation. Your team can see whether the model is flagging real risk, not random correlation.

Top predictive behaviours translated into plain English
Early and late warning signals separated so timing is clear
Segment differences called out where the same behaviour means different things

Week 2 · Handoff + Refresh

CS Playbook, Model Methodology & Monthly Refresh Plan

Each risk signal gets a matching intervention, so CS knows what to say instead of improvising from a score. The methodology and refresh plan show how the model was built, how to regenerate the list, and when to retrain it as your product changes. Your team owns the system after the sprint.

Intervention playbook for common signals: inactivity, feature drop-off, billing friction, support spikes, and failed activation
Escalation criteria for when account management needs to step in
Team training session so CS understands the list, tiers, and recommended actions
Monthly refresh support so the model does not go stale after the first export

Everything above for $5,997. No hourly billing. No scope creep. Everything stays with your team.

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 weeks 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

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 the end of the sprint — or full refund. If the data can’t support a reliable model, we tell you early 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 weeks 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 a few 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.