Know which customers stay, which leave, and why

Churn, segmentation, lifetime value and pricing analysis for subscription and repeat-purchase businesses.

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Recently built: churn and segmentation analytics for a national streaming platform.

Sound familiar?

Each problem links to the service that fixes it.

Churn is noticed too late

You learn a customer left weeks after it happened.

Churn & retention analytics

Everyone gets the same offer

Discounts go to customers who would have stayed anyway.

Segmentation & LTV

Price changes are a guess

Plans and prices change without knowing who upgrades or leaves.

Subscription & pricing analytics

Sign-ups don't convert

Trials start, then quietly stop at the same step.

Product & funnel analytics

Customer & revenue analytics services

Each one can be bought on its own. Most clients start with the one marked most requested.

Churn & retention analytics Most requested

Define churn once, see who is leaving and why, and get a weekly at-risk list.

Best for: Subscription and repeat-purchase businesses

Customer segmentation & lifetime value Most requested

Group customers by behaviour and value so offers and budgets go where they pay back.

Best for: Teams sending the same offer to everyone

Subscription & pricing analytics

Plan mix, upgrades, downgrades, price tests and competitor price benchmarks.

Best for: Businesses with plans or tiers

Product & funnel analytics

Find where users drop off and which features keep them coming back.

Best for: Apps, SaaS and online stores

Demand & revenue forecasting

Forecasts for sales, stock and cash planning, with a clear range of error.

Best for: Finance and operations teams
Not sure which service fits?Describe the problem on a free call, or start with a two-week, fixed-price Health Check.
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Churn starts with a definition

Move the slider. The same customers produce very different churn numbers depending on one rule, which is why we agree it with you before building anything.

7 days
Reported churn rate0%
Flagged, then came back0%

0 days45 daysShare of flagged customers who return

Sample data. The right window depends on how often your customers buy or use the product.

Proof from real work

Results in brackets are filled in once the client approves publishing them.

Case study, media and streaming

Churn and segmentation analytics for a national streaming platform

Problem
Cancellations were only noticed after they happened, and teams counted churn differently.
What we built
One churn definition per plan, behaviour segments, and a weekly at-risk list for the retention team.
Result
[x]% of churn now flagged in advance, [n] segments in use
Read the case study

How a project runs

Three ways to work with us. Stop after any of them with something useful in hand.

Plan it

Churn definition and data review

We agree the churn rule per plan and check what your data can support.

1 to 2 weeks
Build it

Segments, at-risk list, dashboards

Models and dashboards your retention and CRM teams can act on.

3 to 6 weeks
Run it

Monthly retention review

We refresh segments and review what changed with your team.

Monthly

Tools we use for customer & revenue analytics. Everything stays in your accounts.

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30 minutes, no slides, no obligation.

Questions about customer & revenue analytics

Does this only work for subscriptions?
No. Any business with repeat customers, such as online stores or B2B services, can measure churn and segments.
How much data do we need?
Usually 12 months of customer and transaction history. We check this in the first week.
Can you predict who will churn?
Yes, once churn is defined and stable. We start with clear rules and add prediction when it adds accuracy.
What does the team do with the at-risk list?
We help design the actions, such as offers, calls or emails, and measure which ones work.
Is customer data safe with you?
We work inside your systems, sign an NDA, and use anonymised IDs wherever possible.