Customer churn (the loss of customers over time) is a costly challenge for businesses across industries. Studies show it costs significantly more to acquire a new customer than to retain an existing one, yet many organisations struggle to identify and address churn effectively.
By utilising data and predicting customer churn, businesses can utilise customer lifetime value to pinpoint at-risk customers and take proactive steps to retain them, thus reducing losses, and driving long-term growth.
This guide explores the concept of churn prediction, its importance, and practical strategies to integrate it into your customer retention efforts.
What is customer churn prediction?
Customer churn prediction uses data and analytics to identify which customers are likely to stop using a business’ services. By analysing past behaviours like purchasing habits or service usage, businesses can group customers into cohorts based on their traits and activity to spot warning signs early and create targeted strategies to retain those at risk, reducing losses and improving loyalty.
Churn prediction works by examining historical, live or real-time data, such as purchase history, service usage, or interactions with customer support. These insights help pinpoint early warning signs, like reduced engagement or changes in spending habits, for short term churn prediction, which indicate when a customer may be at risk of leaving. To gain a deeper understanding of customer lifetime value, long-term churn is equally important.
Advances in data technology and predictive models allow companies to analyse large datasets efficiently, uncovering trends and patterns that manual analysis might miss. With these insights, businesses can implement personalised retention strategies, such as targeted offers, better service communication, or loyalty programs, to re-engage customers before they churn. Not all churn is ‘happy churn’ as organisations everywhere will have customers that offer little to no value whatsoever. If these types of customers churn, it is very unlikely to impact the organisation negatively. Therefore, not all customers are worth saving.
By integrating churn prediction into their retention efforts, companies can move from reactive strategies to proactive solutions, protecting their revenue and building stronger, lasting customer relationships. However, timing is important, as the window of customer churn might go quickly so its not just predicting they will leave, but additionally before they have left and also not whilst they are not a churn risk.
Why is preducting customer churn important?
No business wants to lose customers, unless of course it is 'happy churn' - losing value-destroying customers. Therefore, understanding and predicting customer churn is crucial for a variety of reasons.
Customer retention is more cost-effective
Acquiring new customers is often far more expensive than retaining existing ones. In fact, studies suggest that increasing customer retention by just 5% can increase profits by 25% to 95%. By predicting which customers are at risk of leaving, companies can focus their efforts on improving retention, ultimately saving on acquisition costs.
Increased customer lifetime value (CLV)
By identifying and addressing churn risk early, companies can extend the customer lifetime, thus maximising the customer lifetime value (CLV). Predicting churn allows businesses to make data-driven decisions that improve customer satisfaction, loyalty, and revenue.
Improving customer satisfaction
Predicting churn allows businesses to understand their customers better and provide them with tailored experiences. Offering solutions to at-risk customers - such as personalised offers, better customer service, or loyalty programmes - can not only minimse the risk of churn but also improve customer satisfaction.
Better resource allocation
By focusing on customers who are most likely to churn, businesses can allocate their resources - time, effort, and budget - more effectively. It helps prioritise customer retention strategies and interventions that have the highest likelihood of success. It is important to note that without allocation of resource to the right models and modelling techniques, understanding the real value of spend on saving customers who are actually planning on staying is often prohibitive.
The churn prediction model
To predict customer churn effectively, businesses rely on churn prediction models - statistical and machine learning tools designed to analyse historical data and forecast future churn. The churn prediction model typically includes the following key components:
- Data collection: Gathering relevant customer data, such as purchase history, service usage, customer service interactions, and demographic information
- Feature engineering: Identifying which data points (features) are most predictive of churn. For example, a decline in usage or increased customer complaints could be indicators
- Model building: Using statistical or machine learning techniques to develop the model. Common techniques include decision trees, logistic regression, and neural networks
- Evaluation: Testing the model against a validation dataset to check its accuracy in predicting customer churn
- Statistical modelling: Cohort analysis and survivial analysis are as equally important as the above machine learning (ML) approaches
By leveraging a churn prediction model, businesses can quantify the likelihood of churn for each customer or in cohort groups, enabling more targeted retention efforts. The power of some of these models is that it also allows the identification of in-life events (triggers), like price increases or complaints, that might trigger churn.
How to predict customer churn
Predicting customer churn involves several steps that require a mix of data, analysis, and strategy. Here's how businesses can get started:
1. Gather relevant data
- Behavioural data: Interactions with your product or service, frequency of use, and activity patterns
- Transactional data: Purchase history, average order value, and payment patterns
- Demographic data: Age, location, and other factors that may influence customer behaviour
- Customer service data: Interactions with support teams, complaints, and resolutions
By consolidating data from various touchpoints (e.g., website analytics, CRM, support tickets, etc.), businesses can get a comprehensive view of customer behaviour and churn risk.
2. Assess churn trends
Once the data is collected, businesses need to identify churn trends. Analysing past churn events helps uncover patterns and indicators that often precede customer attrition. Look for:
- Declining engagement: A drop in usage, purchase frequency, or time spent with your product/service
- Increased complaints: A rise in customer complaints or negative feedback can signal dissatisfaction
- Contract or subscription changes: Downgrading a plan or reducing service usage is often an early sign of churn
- Churn point or similar customers: Customers or cohorts with similar traits and activity can help predict churn for existing customers
Identifying these trends allows businesses to intervene early and proactively.
3. Identify customers with high churn risk
Predictive models enable businesses to segment customers into different risk categories such as; those who are likely to churn, those who are at low risk, and those who are on the fence.
Churn models are not only effective for identifying high churn individuals, they also provide the average predicted lifetime of an individual. This, in combination with CLV can identify the ROI or payback period. The churn modelling extends beyond saving individuals at risk of churning but gives you a better understanding of how valuable your base is.
Once these customers are identified, businesses can focus on high-risk segments and develop tailored retention strategies.
Using segmentation tools, businesses can group customers based on factors like:
- Usage patterns
- Customer lifetime value
- Satisfaction levels (e.g. Net Promoter Score)
- Interactions with customer support
4. Focus on customer retention strategies
Once you’ve identified customers at risk of churn, it’s time to act. Effective retention strategies include:
- Personalised offers: Discounts, loyalty rewards, or exclusive promotions tailored to high-risk customers
- Customer support interventions: Proactively reaching out to at-risk customers to resolve issues and improve satisfaction
- Improved customer experience: Offering enhancements to your product or service that align with customer needs or preferences
- Loyalty programs: Rewarding customers for staying with your brand, offering incentives such as special perks, exclusive content, or free services
These strategies, when combined with churn prediction models, can significantly reduce customer attrition.
How we can help...
We specialise in helping businesses harness the power of data to predict and reduce customer churn. Our Customer Lifetime Value platform is designed to help you understand the lifetime value of each customer. With a modular composition, tenure and churn are key areas that can be utilised.
We apply advanced models and analytics to provide a deep understanding of customer behaviours, traits and activity to build a value-based view of a customer base. We incorporate your specific business needs and create outputs and insights for data-driven decision making. With a team of data experts and years of experience, we help businesses improve customer retention and drive long-term growth.
Leverage data and insights
Predicting customer churn is essential for businesses that want to stay competitive and drive growth. By leveraging data and insights, companies can reduce customer attrition, improve customer satisfaction, and increase profitability. Implementing churn prediction strategies and focusing on retention can lead to long-term business success.
Contact us and start making data-driven decisions that drive results. With the right tools and strategies, churn can be turned into an opportunity for growth.