Spot relevant differences
Data shows where customers, situations or products differ.
Predict churn
Identify earlier which customers may be at risk of leaving.
Customer churn often only becomes apparent after a customer has cancelled. As a result, signals in their behaviour and usage are not acted on in time. A churn model uses historical patterns to estimate which customers are more likely to leave.
From an isolated possibility to a practical application
The application only becomes meaningful when it fits a specific task and the information available for it.
The prediction supports a focused assessment; it does not provide certainty about individual behaviour. That is why we define in advance what the solution should and should not do, how the outcome will be checked and who remains responsible.
What it delivers
Make more targeted decisions
The results support a specific commercial decision or action.
Track results measurably
Indicators chosen in advance show whether the application makes a difference.
Our approach
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Choose a commercial question
We determine which decision or action is the focus.
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Examine the data
We assess the available customer, product or transaction data.
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Develop a model
We choose a suitable, explainable analytical method.
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Test the impact
We compare the outcomes with the current approach.
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Integrate and monitor
We make the outcomes usable and monitor results and side effects.
Aligned with your organisation
This service is relevant to organisations with ongoing customer relationships or subscriptions. The exact setup depends on the process, the available data and the risks associated with the application.
We therefore start with a clearly defined application and only expand it once the initial setup works in practice.
Other solutions
One data platform for fragmented sourcesBring data from separate systems together in one place, where everyone sees the same figures.
Dashboard for day-to-day operationsSee how things stand on one screen, without having to build reports first.
Predicting when maintenance is neededPlan maintenance based on what the data says, not on a fixed schedule.
Frequently asked questions
This service addresses the following challenge: a churn model uses historical patterns to estimate which customers are more likely to leave. Exactly how it works depends on the process and the data available.
The service may be relevant to organisations with ongoing customer relationships or subscriptions. A clear need and usable information matter more than the size of the organisation.
That depends on the application. We start with the question you need to answer, then determine which data or sources are necessary and suitable.
No. For fixed, predictable tasks, conventional automation may be enough. We choose the simplest approach that adequately addresses the challenge and add complexity only when needed.
We define the scope of the application, check the results with users and put appropriate controls in place. Once it is in use, we monitor how it performs and adjust the solution when the data, its use or the context changes.
Discover what predicting churn could mean for your organisation
Want to explore where to start with churn prediction? Together, we’ll assess the challenge, the information available and the conditions needed.