Churn Intelligence Platform
See who is leaving, before they go.
Churnico scores churn risk from behavioural signals, explains the drivers and tells your team whom to reach first.
By the time churn is recorded, the decision was made long ago.
Behaviour changes weeks before a customer cancels. Traditional reports show the outcome, not the change. And retention teams usually step in late, with the same message for everyone.
- Usage drop
- Late payment
- Support request
- Offer declined
- Digital silence
Reports describe the past
A monthly churn rate tells you how much you lost, not who is about to leave.
Teams can't reach everyone
Without a priority order, limited retention capacity is spent on the wrong customers.
Action fails without a reason
Price-driven leavers get service campaigns; service-driven leavers get discounts.
Behaviour changes before the customer leaves.
That change never sits in a single table. It is spread across billing, usage, support, contracts and digital channels. Churnico joins the pieces into one customer story.
Usage decline
Usage drops below the customer's own normal level.
Payment behaviour
Delays, channel changes, partial payments.
Support intensity
Repeated, unresolved requests in a short period.
Contract calendar
Price comparison starts as the commitment end approaches.
Declined offers
Negative responses to sales and renewal offers.
Digital silence
App and portal engagement fades out.
One customer story
Churnico Intelligence
From scattered data to a prioritised decision.
Churnico unifies data from source systems at customer level, turns it into time-aware signals, scores risk and ties every score to an action.
- 01
Data
Subscription, billing, usage, support and digital channel records are unified at customer level.
Unified customer view
- 02
Signal
Raw records become time-aware behavioural signals: decline, deviation, intensity, silence.
Signal library
- 03
Intelligence
Signals are tested against hypotheses; patterns and combinations truly linked to churn are isolated.
Validated churn patterns
- 04
Prediction
Each customer gets a churn probability, a risk tier and the drivers behind it.
Explainable risk score
- 05
Action
Risk turns into an action and timing matched to the reason; outcomes feed back into the model.
Prioritised action list
Not just a score. An explanation.
Every risk score comes with the drivers behind it. Your teams see not only who is at risk, but why, and why now.
- Scores surface customers whose behaviour departs from their own history.
- Drivers are written in business language; no data science background needed.
- Two customers at the same risk level get different actions for different reasons.
Risk drivers
Contribution to score
- Time to contract end+0.34
- Deviation from personal usage baseline+0.27
- Support requests, last 60 days+0.18
- Payment channel change+0.12
- Past campaign response+0.09
Values in this interface illustrate how the product works; they are not a customer result.
Whom to reach, when, and how.
Risk scores become the retention team's daily work list. Every row carries the main reason, the recommended action and the timing, ordered to fit team capacity.
| Customer | Segment | Risk | Main reason | Recommended action | When |
|---|---|---|---|---|---|
| 7F3A…C21 | SME | 0.82High | Contract ending | Renewal offer | This week |
| B10E…94D | Consumer | 0.77High | Price sensitivity | Tariff recommendation | This week |
| 24C8…1AF | SME | 0.71High | Service experience | Senior agent outreach | 3 days |
| E95D…07B | Enterprise | 0.66Medium | Usage decline | Account manager visit | 2 weeks |
| 3A61…F58 | Consumer | 0.58Medium | Digital silence | Personalised update | 2 weeks |
Retention budget goes where the risk really is.
- Earlier
Visible before it's recorded
Risk surfaces while there is still time to act.
- Focused
Capacity on the right customers
Teams work the list by risk and value.
- Precise
Action matched to reason
Fitting interventions instead of one campaign for all.
- Measurable
Outcomes feed back
The model is regularly tested against actual churn.
Trust
A model that doesn't claim what it can't measure.
Enterprise decision-makers deserve more than a shiny accuracy number. Churnico is designed to show honestly how well the model works in reality.
Our technology approachOut-of-time validation
The model is tested on a real period after its training window.
Leakage audits
Variables that already 'know' the outcome are systematically removed.
Explainable scores
Every prediction's drivers are visible and open to challenge.
Human in the loop
Action decisions stay under your team's control.
See your churn risk in your own data.
Let's talk through your data in a short call and assess together which signals Churnico can read in your customer base.