Churn prediction and LTV modelling: who will leave and who will be worth more
We build churn prediction and LTV models on your data. Every customer gets a risk score and an expected value. Segments flow into your CRM daily, so the retention team knows who to contact first.
What hurts
You find out a customer has left when they fail to renew. Winning them back costs more than keeping them. Yet the signals were there: fewer visits, smaller baskets, a complaint to support.
Retention treats everyone the same. Discounts go to people who would have stayed anyway and to people who are already gone. Budget is spent, and nobody can measure the result.
Marketing knows acquisition cost but not what a customer brings over their lifetime. So ads optimise for cheap leads who buy once and disappear.
What we build
- Analysis of customer history: purchases, visits, tickets, payments
- A churn definition that fits your model: subscription, contract or repeat purchase
- A churn risk model with per-customer explanations
- A customer lifetime value model on the horizon you need
- Retention segments: who to call, who to email, who to leave alone
- Daily score updates in HubSpot, Pipedrive, Kommo or your CRM
- A dashboard of churn and LTV by acquisition channel and cohort
- A retention A/B test design to measure the real effect
What you get
- A working model with daily score refresh
- Risk and LTV fields on the customer record in your CRM
- A cohort, churn and LTV dashboard by channel
- Model code with feature and methodology documentation
- Retention playbooks and team training
How we measure the result
- Share of customers who churned among those flagged as high risk
- Churn in the treated group versus the control group
- LTV by acquisition channel, recalculated monthly
- Retention spend per customer saved
Systems and stack
Industries
Questions
How much does churn and LTV modelling cost?
From $8,000. The price depends on how many customer data sources there are, their quality and whether we integrate with your CRM and email tools. One CRM with payment history costs less than combining POS, app and contact centre data. We fix the estimate once we have checked which customer data actually exists.
What data do you need?
At least a year of customer history: purchases or payments, dates, acquisition channel. Visits, support tickets, email engagement and calls help a lot. Personal data can be pseudonymised before it reaches us. We sign an NDA and a GDPR DPA before access.
How accurate is the model?
Models sometimes get it wrong, so we never use the score as a verdict. We measure accuracy on customers the model did not see in training. We prove the retention effect with an A/B test against a control group. That shows the real result, not a pretty chart.
How will our team use it?
Each customer record in the CRM shows risk, LTV and the top reasons. Managers get a daily list of who to contact and what to offer. Segments can also go to a Happ voice agent for calls or to your email campaigns.
How long does it take?
Usually 6-8 weeks. The first 2-3 weeks go into collecting and validating data. Then the model, CRM integration and a launch on part of the base. The retention test with a control group needs a few more weeks after launch to show results.
How do you keep the model accurate over time?
The model retrains monthly because behaviour changes. We monitor that quality does not drift. You own the code and documentation. Under an SLA we add new features, refresh segments and help design new retention tests.
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We reply within one business day. 30 minutes to tell you whether it is worth doing and what it costs.