STRIPE RFM ANALYSIS — $2,997 · 2-WEEK SPRINT

Jake McMahon — ProductQuant

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

The expansion revenue hiding in your current customer base — identified in 2 weeks.

Revenue segments mapped from your existing Stripe data. Expansion candidates, churn cohorts, and your most valuable customer profile — so your CS team knows exactly which accounts to grow, which to save, and which are already on the path to cancel.

Start the Sprint → See what’s included ↓

Revenue segments your CS team acts on this week — or full refund · 2-week delivery

WHAT YOU HAVE AT THE END

RFM segmentation model Every account placed in a segment built from your billing patterns

Expansion candidate list Accounts most likely to upgrade or expand, ranked

Churn risk cohort report Which customers are contracting before they cancel

Ideal customer profile (ICP) revenue profile What your most valuable customers actually look like in billing data

CS prioritisation framework Which accounts CS focuses on and why — not who’s loudest

$2,997 · fixed price · 2-week sprint

YOU HAVE THE DATA. NOBODY HAS READ IT.

CS and sales treat all customers as equally important

“Our CS rep manages 120 accounts. She prioritises whoever emailed last. We have no idea which of those accounts are actually worth her time right now.”

VP CS — B2B SaaS, $4M ARR

Expansion-ready vs. at-risk accounts look the same in your dashboard

“We know our net revenue retention number. We have no idea which of our current accounts are about to improve it or tank it next quarter.”

Head of Revenue — Series A

MRR dashboard without segment visibility

“We present NRR at the board meeting. One board member asked which customer cohort was driving churn. We had to say we didn’t know.”

CEO — Post-Series A

Account management spread thin with no framework for focus

“We run QBRs with our top 20 accounts. We picked those 20 by MRR size. We have no idea if they’re actually the ones at risk or the ones ready to grow.”

Head of CS — B2B SaaS

WHAT BILLING DATA REVEALS WHEN YOU READ IT

Your highest-value expansion candidates are already signalling in Stripe. Nobody is watching.

Accounts go quiet in Stripe 30–60 days before they cancel.

The billing silence is the early warning. By the time the cancellation email arrives, the decision was made weeks ago. RFM reads the silence as a signal — recency drops, frequency flatlines — so your CS team intervenes before the customer decides to leave.

Expansion candidates share a billing pattern your MRR dashboard hides.

Accounts that upgrade tend to show a cluster of billing events — seat additions, add-on purchases — in the weeks before the upgrade. The MRR dashboard shows the upgrade. RFM shows the accounts that are about to upgrade, so your team reaches out before the customer figures it out alone.

Your top 20 accounts by MRR are not necessarily your top 20 by growth potential.

Size and trajectory are different things. A large account that has been flat for 6 months is not an expansion candidate. A mid-tier account with increasing billing frequency is. RFM separates size from momentum so your team focuses on accounts that are actually moving.

Your ICP definition probably describes who signs up — not who compounds.

Most ICP profiles are built from acquisition data: industry, company size, use case. RFM builds the ICP from billing behaviour — which accounts actually expand, retain, and compound over time. The profile your sales team targets should match the customers who grow, not just the ones who convert.

WHY THIS IS DIFFERENT

Most revenue analysis looks backward. This one tells you where each account is headed.

A finance report tells you what happened to revenue last quarter. A standard cohort analysis shows you retention curves. Neither tells you which specific accounts in your active base are contracting before they cancel — or which ones are showing the billing signals that precede an upgrade.

RFM segmentation reads those signals. Recency tells you when the last meaningful billing event happened. Frequency tells you how often expansion events fire. Monetary tells you whether the revenue trajectory is growing or shrinking. Together, they map every account in your base to a segment — and each segment requires a different action from CS and sales.

Everything comes from billing data you already have. No new instrumentation. No engineering work. Two weeks from Stripe access to a prioritised account list your CS team can act on immediately.

TIMELINE

By Friday, your CS team knows which accounts to grow, which to save, and where to stop wasting time.

WEEK 1

Pull + Segment

Read-only Stripe access. Historical billing data pulled, subscription events mapped, cohort structure identified. RFM signals calculated for every account. Segments built and validated against your actual billing patterns.

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WEEK 2

Rank + Framework

Expansion candidate list ranked. Churn risk cohort report built. ICP revenue profile derived from Champion-tier accounts. CS prioritisation framework documented with actions per tier.

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DAY 14

Walkthrough + Handover

60-minute session with your CS lead and founder/CPO. Every segment explained. Expansion list walked through account by account. Monthly refresh process confirmed.

This week: CS calls the accounts most likely to expand — and saves the ones most likely to cancel

DELIVERABLES

Monday morning, your CS team knows which accounts to save, which to grow, and which are fine.

Week 1 · Segmentation

RFM Segmentation Model

Every customer placed in a segment based on Recency, Frequency, and Monetary signals from your Stripe data. Segments matched to your billing patterns — not a generic framework. Each segment comes with a plain-English description and an implied CS action.

Champions: high R, high F, high M — your most engaged, highest-value customers
At-risk: previously high engagement across all three signals, now declining
Promising: recent high-frequency billing, not yet high monetary — expansion ready
Hibernating: low recent activity, low frequency — the churn pipeline to monitor

Week 1 · Growth

Expansion Candidate List

The specific accounts showing the billing patterns that precede expansion — seat additions, plan upgrades, add-on purchases. Ranked by expansion probability so your team knows where to focus outreach.

Accounts with high-frequency billing events in the last 60–90 days
Accounts currently below their plan ceiling with recent engagement signals
Expansion trigger patterns: what Stripe behaviour precedes upgrades in your data
Prioritised outreach sequence for the highest-probability expansion cohort

Week 1 · Retention

Churn Risk Cohort Report

The billing patterns that appear before cancellation in your data. Not generic churn signals — the specific Stripe behaviours that have preceded churned accounts in your own cohort history. Accounts currently matching the pre-churn profile, ranked by risk level.

Monetary contraction patterns: accounts downsizing before cancelling
Recency drop-off: accounts going quiet in billing 30–60 days before churn
Cohorts currently matching the pre-churn profile — ranked by risk
Time-to-churn estimates based on your historical cohort data

Week 2 · Profile

ICP Revenue Profile

What your most valuable customers actually look like in billing data — their acquisition channel, plan history, expansion pattern, and the billing behaviours that distinguish them from the rest of your base.

Revenue profile of Champion-tier accounts: what they have in common in Stripe
Know which acquisition channels produce customers that stay — and which produce customers that churn
Plan trajectory: the typical path from new to Champion in your billing data
Spot your next best customer before they even upgrade

Week 2 · Framework

CS Account Prioritisation Framework

A scoring model your CS team uses to prioritise which accounts to focus on each week — based on segment, risk level, and expansion probability. Not who emailed last week or who’s scheduled for the next QBR.

Priority tier per account: expansion outreach, at-risk intervention, monitoring
Recommended action per tier: what CS does with each type of account
Escalation criteria: when to loop in sales or account management
Weekly review process: how to refresh priorities as the Stripe data updates

Week 2 · Process

Monthly Refresh Process

RFM segments drift as accounts move. The monthly refresh process documents exactly how to update the segmentation and the prioritisation framework each month — so the intelligence layer stays current without rebuilding from scratch.

Documented steps to re-run the segmentation each month
Signals to watch for accounts moving between segments
Dashboard review cadence: which insights to review weekly vs. monthly
The one metric to track that tells you whether the RFM layer is working

Verified: A HIPAA-compliant healthcare forms platform with a $252K MRR baseline had RFM segmentation built across the full account base — producing 33 Stripe-connected insights that made the expansion cohorts and contraction signals visible that the monthly MRR number had obscured.

FIT CHECK

100+ Stripe accounts. No framework for which ones get attention first. This builds one in 14 days.

GOOD FIT

Post-Series A B2B SaaS with 100+ paying accounts on Stripe

Stripe billing · CS team managing renewals

The situation

You have a CS team managing renewals but no framework for prioritising which accounts get attention. Your MRR dashboard shows totals but not which cohorts are driving expansion or churn. You use Stripe as your primary payment processor with at least 12 months of billing history.

What you leave with

Every account placed in a revenue segment with a clear CS action
Expansion candidates ranked by probability — ready for outreach this week
Churn risk accounts identified before the cancellation arrives

CS stops managing by inbox and starts managing by revenue signal — expansion revenue recovered, churn risk addressed before it becomes churn.

NOT A FIT

Pre-revenue, fewer than 50 accounts, or no recurring billing

Wrong stage or wrong billing model

When this sprint doesn’t apply

If you have fewer than 50 paying accounts, the segmentation won’t produce statistically meaningful cohorts. If you’re a transactional business without recurring subscription revenue, RFM signals read differently. And if you’re looking for product usage analytics rather than billing intelligence, this sprint won’t answer that question.

Better starting points

Focused Sprint

Answer one critical product question in 5 days with existing data.

Activation Deep Dive

Map your activation funnel and rank the top 3 fixes by impact.

The Foundation

Full product analysis covering activation, retention, competitive, and GTM.

What this sprint doesn’t cover

The Stripe RFM Analysis delivers the segmentation, the ranked account lists, and the CS framework. Your team acts on the output. If you need product usage analytics layered on top of billing intelligence, that’s a different engagement.

Product usage analytics — this sprint reads billing data, not event data
Implementing CS process changes — the framework is delivered, your team runs it
Ongoing monitoring — the monthly refresh process is documented for your team

For the full picture → The Foundation

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 pull, the segmentation build, the cohort analysis, the CS framework. Stripe is the most underread data source in most SaaS companies. The payment behaviour tells you more about product engagement than most event-tracking setups, because it is tied directly to the moments customers decide to grow or leave.

The output is written for the people who will act on it. The CS lead needs the expansion candidate list and the at-risk account ranking. The CPO needs the ICP revenue profile. The founder needs the investor narrative. Each deliverable is formatted for the person who has to use it — not as a single analysis document that nobody reads past the executive summary.

I won’t do this:

Apply a generic RFM model without calibrating the thresholds to your billing patterns
Deliver a segmentation without a plain-English description of what to do with each segment
Frame churn cohorts without identifying the specific intervention that addresses them
Present expansion signals without a prioritised outreach sequence your team can run

What if our Stripe data is messy or we’ve had billing migrations?

Billing data is almost always messier than it looks in the dashboard. Subscription migrations, plan renames, manual adjustments — these are normal. The data pull phase is designed to identify and handle inconsistencies before the analysis begins. If the data has gaps that limit the segmentation, I document exactly what is missing and what instrumentation would fill it. You get the best analysis the data supports — not a clean model built on assumptions.

Teams Jake has worked with

PRICING

Know which accounts to grow, which to save, and what your best customers look like — $2,997.

$2,997

one-time · fixed price

2-week sprint

RFM segmentation model — built from your Stripe billing patterns
Expansion candidate list — prioritised and ready for CS outreach
Churn risk cohort report — accounts matching the pre-churn profile
ICP revenue profile — what your most valuable customers look like
CS account prioritisation framework
Monthly refresh process — documented for your team to run independently
60-minute handoff walkthrough with CS lead and founder/CPO
All deliverables stay with your team permanently

Requires read-only Stripe access only. No new instrumentation. No engineering work.

Book a 30-minute call →

Your CS team knows exactly which accounts to call and why — with the revenue at stake sized — or full refund. If the Stripe data can’t support meaningful segments, we tell you in week 1 and scope what’s possible. The deliverable either exists or it doesn’t.

Questions.

Or book a call →

Do you need write access to our Stripe account? +

No — read-only access only. No ability to change billing settings, customer data, or any Stripe configuration. The access is used solely to pull historical billing event data for the analysis. If you prefer a restricted API key scoped to read-only data, that works equally well for the data pull.

What if we’re on Chargebee or Recurly instead of Stripe? +

The sprint is designed around Stripe because the billing event data is in a queryable format that maps directly to RFM signals. If your billing is in Chargebee, Recurly, or Paddle, the same RFM analysis is possible — the data extraction approach differs. Book a call first and we can confirm whether your billing system supports the same output before starting.

What’s the difference between this and a standard finance report? +

A finance report tells you what happened to revenue. This tells you why — and what is likely to happen next. Finance reports show total MRR, total churn rate, total new revenue. RFM segmentation shows which cohorts are driving each movement type, which accounts are contracting before they cancel, and which are signalling expansion before they upgrade. The finance report is backward-looking. The RFM analysis is forward-looking — and it produces a prioritised action list, not a summary of what already happened.

We already have MRR dashboards. What does this add? +

MRR dashboards show movement — new, expansion, contraction, churn, net. RFM segmentation shows which customers are in each movement category and what billing behaviour preceded the movement. The question your MRR dashboard cannot answer is: “which accounts in our current base are about to become contraction MRR?” That is what RFM answers — from billing signals already in Stripe.

How does the monthly refresh work? +

The sprint delivers a documented monthly refresh process your team can run independently. Once a month, the segmentation is recalculated against the latest Stripe data — which takes 2–4 hours if someone on your team follows the documented steps. Accounts that have moved segments are flagged so CS knows who has changed priority. The system is designed to run without a standing contract after the sprint ends.

Can this replace product usage analytics? +

Not fully, but it produces more actionable output faster for most CS and sales motions. Billing data captures actual financial commitment signals — which are often more predictive of expansion and churn than product usage metrics for B2B SaaS. The limitation is that billing data cannot tell you which features drive engagement. If you want to combine billing intelligence with product usage signals, the full Foundation engagement is the logical next step — and the RFM segmentation carries over directly as the foundation layer.

Is this useful for investor conversations? +

Yes. Sophisticated Series A and B investors will pull apart the MRR number. Where is expansion coming from? Which cohorts churn fastest? What does net revenue retention look like by cohort? The RFM analysis produces cohort-level revenue intelligence that answers those questions directly — built on actual Stripe data, not modelled projections. The ICP revenue profile and the churn cohort report are both formatted so they can go directly into a data room.