What is a churn model?
A churn model is a predictive tool that uses historical data to identify patterns suggesting whether a customer is likely to stop doing business with you. It analyses factors like buying behaviour, customer quality scores, and engagement levels.
For example, in a gym scenario, reduced attendance over time might signal churn. In a B2B context, churn might manifest through declining order volumes or diversification of suppliers. Essentially, it’s a data-driven approach to predict customer retention risks and is a key component of a wider customer retention strategy.
The benefits
Actionable insights
A churn model flags customers at risk of leaving, allowing you to put in place proactive retention strategies, such as targeted campaigns.
Data-driven clarity
It replaces subjective judgement with objective, data-led predictions, minimising biases and errors in identifying dissatisfied customers.
Streamlined access to data
A churn model consolidates customer information – such as missed orders, complaints, and future revenue predictions – into a single view, saving time and improving decision-making.
Who would benefit?
Churn models are invaluable for organisations with recurring revenue models, such as subscription services or B2B companies reliant on regular client purchases.
Customer success teams, account managers, and businesses focused on retaining long-term clients would especially benefit from implementing churn predictions.
How would we implement it?
Implementation starts with defining your organisation’s specific churn indicators. Then the data discovery phase assesses the availability, quality, and relevance of customer data. A predictive model is developed and tested to make sure it’s accurate. We’ll do this by analysing past churn cases to see if they fit what our model predicts.
Once validated, the model is integrated into tools like Power BI for visualisation, linked to platforms that allow you to take actionable insights. The model is continuously updated to adapt to evolving customer behaviours. Proof of concept can take as little as a few weeks with focused effort.
Case study
Retention in reality: A churn model success story
Our client is a global supplier of chemicals, plastics, agrosciences, and advanced materials. They were experiencing an increase in customer churn and asked us to help identify and implement actions to save at-risk clients before losing them.
The importance of identification
A crucial part was defining what churn meant for this particular complex multinational business, as it varied between units. We:
- Identified and engaged stakeholders to understand expectations, customer journeys, and churn
prevention approaches - Analysed past churn strategies to refine them
- Reviewed and aligned churn definitions with business goals
- Identified, cleaned, and analysed data sources for the churn model
- Engineered features and built a transparent, predictive model
- Presented findings in a Power BI dashboard
- Gathered stakeholder feedback, implemented changes, and deployed the model with automation
- Began monitoring its performance
Saving time, saving money
We’ve saved time for busy sales reps by delivering insights they lacked. Sales and loyalty teams now identify at-risk clients and plan interventions. Long-term, this will support financial savings through customer retention without extensive manual effort. In the proof-of-concept stage, the model has been adopted in two business areas, with plans to scale across all regions and units.
Want to learn how Salocin Group can help you identify and reduce customer churn?
Just get in touch with our friendly team today.
