Predicting and mitigating customer churn can be an intricate process for businesses. A "good" churn rate depends on many factors, from the age of a business to its industry. Even large, widely recognised businesses experience fluctuations in churn. For example, Netflix's churn rate was 3.3% in March 2022 – more than one full percentage point higher than its March 2021 churn rate. Below, we'll look at how businesses can create and manage a churn prediction strategy that works for them.

What's in this article?

• What is churn?
  
• Types of churn
  
• How churn affects businesses
  
• Churn prediction modelling techniques
  
• Data integration and management in churn prediction
  
• Challenges in churn prediction
  
• How to create a churn prediction strategy
  

What is churn?

"Churn" reflects the percentage of customers who discontinue their use of a business's products or services over a certain period of time. This concept is important for businesses because it affects their revenue and offers insights into customer satisfaction and loyalty. For example, a high churn rate may suggest that there are issues with the product, service or overall customer experience. It's important for businesses to invest resources in reducing churn because retaining existing customers is more cost effective than acquiring new ones.

Types of churn

There are several types of churn. These include:

• Voluntary churn
  Voluntary churn occurs when customers make an active decision to stop using a service or product. There are a variety of reasons for voluntary churn. A customer might be dissatisfied with the service, they might find a better alternative or their needs might change.

• Involuntary churn
  Involuntary churn happens because of payment failures, credit cards expiring and other logistical issues – not because the customers choose to leave. Businesses can address this type of churn through better payment systems or customer service interventions.

• Active churn
  Active churn refers to customers who cancel their service or subscriptions. Usually, they communicate their decision to the business through a cancellation process.

• Passive churn
  Passive churn happens when customers' subscriptions end and are not renewed, often without any active communication or explicit cancellation. This is common in subscription-based models.

• Revenue churn
  This refers to the loss of revenue that happens when customers downgrade their plans or buy less of a product or service, rather than leaving entirely.

• Customer churn
  This is the overall term for churn in reference to the loss of customers or subscribers themselves, rather than the revenue they represent.

How churn affects businesses

Customer churn can affect all aspects of a business, from revenue to product development to marketing. The global customer success management market – a field that focuses on providing customers with exactly what they want or need (and a key factor in reducing churn) – was valued at US$1.45 billion in 2022 according to Straits Research. Furthermore, it is predicted to grow at a compound annual growth rate of almost 25% until 2031. This growth reflects just how important it is for businesses to minimise churn.

Churn can affect many areas of a business, including:

• Revenue
  Customer churn has an immediate impact on a business's revenue. Losing customers means a reduction in income, particularly for subscription-based models. Churn can have an even larger impact on the revenue of smaller or newer businesses, because each customer represents a greater proportion of the total revenue.

• Product development
  High churn rates can signal issues with a product or service. Businesses can analyse the reasons behind customer departures to identify areas that need improvement or an overhaul.

• Customer acquisition costs
  Acquiring new customers to replace those lost to churn requires considerable investment in marketing and sales efforts. These costs can escalate quickly, which makes maintaining profitability more challenging.

• Brand reputation
  Frequent customer turnover can have a negative effect on a company's reputation and deter prospective customers.

• Employee morale
  High churn rates can also affect employee morale, performance and engagement – especially for employees who are directly involved with customer service and retention.

• Market insights
  Churn also provides valuable insights into market trends and customer preferences. Businesses can use this information to adapt strategies, tweak products and meet customers' needs better.

• Overall business growth
  Churn can affect a business's overall growth trajectory over time. Sustained customer retention is key for steady growth and expansion, whereas high churn rates may hinder a company's ability to scale effectively and achieve long-term goals.

Churn prediction modelling techniques

There are multiple modelling techniques that businesses can use as a framework for churn prediction. These include:

Logistic regression

Logistic regression is a statistical technique that businesses can use to predict binary outcomes, such as whether a customer will leave or stay. This modelling technique uses a variety of predictors – such as demographics, purchase history and customer interactions – to predict whether a customer will churn.

• How logistic regression works
  Logistic regression uses historical data about customers, including those who churned and those who didn't, and examines a range of variables that might influence their decision to leave. The model calculates the odds of churn for each variable, providing insights into which factors are most predictive of customer loss. For instance, a high positive amount for the variable "number of service complaints" would suggest that a higher number of complaints is strongly linked to higher churn odds.

• When to use this technique
  This technique is a good fit for businesses that have a large amount of customer data and operate in sectors where customer retention is key – such as telecoms, finance, subscription services and e-commerce. These industries often have clear-cut, binary outcomes (such as whether or not a customer renews a subscription), which makes logistic regression a suitable choice for their predictive modelling needs.

• Benefits of logistic regression
  Compared to other methods, logistic regression is easy to implement and learn. This model provides businesses with predictions and actionable insights. For instance, if the model points to a particular service issue driving churn, a business can focus its efforts on fixing that problem.

• Challenges of logistic regression
  Logistic regression assumes a linear relationship between the independent variables and the log odds – or the likelihood that a particular outcome will occur – which isn't always the case in real-world scenarios. It can struggle with complex relationships in data, such as interactions between variables. When the data is unbalanced (for instance, if there are far more non-churners than churners), the model might become biased towards predicting the majority class, which reduces its effectiveness.

Takeaway: Logistic regression is a good choice for churn modelling in scenarios with a simple binary outcome and a wealth of customer data. It's particularly valuable for businesses that want to predict customer churn while understanding the "why" behind it.

Decision trees

Decision trees are another popular method for modelling customer churn. This technique works well in scenarios where decisions can be broken down into a series of binary choices, making it ideal for understanding the complex pathways leading to customer churn. Decision trees map these pathways in a tree-like structure based on different customer attributes and behaviours, such as usage patterns, service levels and customer feedback.

• How decision trees work
  The decision tree method is intuitive and user friendly. It splits the dataset into smaller subsets based on different criteria, creating a tree with decision nodes and leaf nodes. Each node in the tree represents a test for an attribute (such as the frequency of service use), and each branch represents the outcome of that test, leading to the next node or a leaf that gives the final decision (churn or no churn). A decision tree might split customers based on age, for example, then further divide each group based on spending habits. Through each progressive step, the customer segment will become more and more specific, allowing the model to predict the likelihood of churn for that group.

• When to use decision trees
  Decision trees are highly effective for businesses with diverse customer bases for which a variety of factors influence churn – such as retail, telecommunications and banking. This method is helpful in situations where it's important to know the specific decision paths that lead customers to churn, because it provides businesses with options for targeted interventions.

• Benefits of decision trees
  One of the main advantages of decision trees is their simplicity. Their clear visual representation makes it easy to understand and communicate the factors leading to churn. This clarity is invaluable for strategic planning and implementing targeted retention strategies. Decision trees handle both numerical and categorical data well, making them a good choice for different types of customer data.

• Challenges of using decision trees
  Although they are useful in a variety of settings, decision trees come with challenges. They can become overly intricate if they capture too many unrelated patterns in the training data, which can lead to poor generalisation of new data. This is known as "overfitting" – where the model fits its training data exactly but it cannot interpret new data accurately, thus capturing data "noise" (unnecessary information) instead of the underlying patterns. Decision trees can struggle with tasks that require linear relationships to be captured, for which logistic regression might be a better fit.

Takeaway: Decision trees are a valuable instrument for churn modelling, especially in scenarios where it's important to dissect the specific pathways of customer decisions. The model's visual simplicity and ability to handle diverse data types make it a popular choice. While decision trees have limitations, such as susceptibility to overfitting and sensitivity to data changes, their strengths in identifying and visualising the factors leading to churn make them a useful tool for many businesses.

Neural networks

Neural networks – a type of machine learning – represent a more advanced strategy in the area of churn prediction models. Unlike traditional linear models, neural networks can capture complex, non-linear relationships within data. They are well suited to tackling the complexities of customer behaviour and churn, as the factors that influence a customer's decision to leave can be multifaceted and intertwined.

• How neural networks work
  Neural networks mimic the structure and function of the human brain. They consist of layers of interconnected nodes – or "neurons" – that process input data in a hierarchical manner. Each neuron applies a calculation to its input and passes the result to the next layer. In the context of churn prediction, a neural network would take customer data as an input, process it through its layers and produce a probability of churn as an output. Neural networks learn from data through a process called training. They adjust the weights of connections between neurons based on the patterns observed in the training data, which consists of known outcomes (e.g. customers who churned and those who did not). This training allows the network to make increasingly accurate predictions about new, unseen data.

• When to use this technique
  Neural networks are particularly beneficial in scenarios where the data exhibits complex patterns that simpler models might miss. Industries with vast customer interaction data – such as telecoms, streaming services and online retail – can leverage neural networks to gain deep insights into customer behaviour. These models excel in situations where customer interactions and preferences are varied and nuanced, and do not fall into binary outcomes.

• Benefits of neural networks
  The strength of neural networks lies in their ability to model complex, non-linear relationships in data. Their ability to identify subtle patterns in large datasets can lead to more accurate and nuanced predictions of customer churn.

• Challenges of neural networks
  One of the main challenges with neural networks is their "black box" nature. Often, it's difficult to interpret exactly how or why a neural network has arrived at a particular prediction, which can be a drawback for businesses seeking transparency and actionable insights. Neural networks also require a substantial amount of data to train effectively and can be computationally intensive, requiring more resources and expertise than some other methods.

Takeaway: Neural networks are capable of handling the complexity and nuances of customer behaviour in ways that simpler models cannot. They are especially valuable in data-rich environments where learning about subtle patterns is key to predicting customer churn. While they come with challenges, such as interpretability and resource requirements, their potential for accurate and nuanced prediction makes them a valuable instrument for businesses focused on reducing customer churn.

Ensemble method

Ensemble methods are a powerful device in predictive modelling and are particularly effective in churn prediction. Ensemble methods combine multiple predictive models to improve prediction accuracy, which makes them adept at anticipating customer churn.

• How ensemble methods work
  Ensemble methods work on the principle that a group of models, working together, can achieve better results than any single model working alone. These methods involve training different models on the same data and then aggregating their predictions – usually through bagging, boosting or stacking techniques. In churn prediction, ensemble methods might involve combining different types of model, such as decision trees, neural networks and logistic regression models. Each of these models will predict customer churn independently, and then these predictions will be combined through processes such as voting or averaging to produce a final, more accurate prediction.

• When to use this technique
  Ensemble methods are particularly useful in scenarios where no single model provides satisfactory predictions or where the data is intricate and diverse. They are a great fit for industries with complex customer behaviour patterns – such as finance, telecommunications and e-commerce – where predicting churn accurately requires capturing a broad spectrum of customer interactions and behaviours.

• Benefits of ensemble methods
  The primary advantage of ensemble methods is their improved accuracy and comprehensiveness compared to individual models. By combining the strengths of multiple models, they can reduce the likelihood of overfitting (when a model produces results that are too "noisy") and are generally better at handling varied and intricate datasets. This leads to more reliable and stable predictions.

• Challenges of ensemble methods
  One challenge with ensemble methods is their increased sophistication and computational cost. They require more resources and time to train and deploy compared to a single model. Like neural networks, ensemble methods can be more difficult to interpret, making it hard to pinpoint the specific reasons behind a given prediction.

Takeaway: Ensemble methods are a sophisticated technique for churn prediction, and they can improve accuracy by combining the strengths of multiple predictive models. They are suitable for intricate data environments and for businesses that require a comprehensive strategy for addressing customer churn. While they present challenges in terms of complexity and resource requirements, their ability to deliver more accurate and stable predictions makes them a valuable strategy for businesses seeking to minimise customer churn.

Data integration and management in churn prediction

Here's what you need to know about leveraging customer data to power your churn prediction efforts:

The importance of quality data collection and integration

The foundation of any churn prediction model is the data it uses, so collecting comprehensive and accurate data is key. This involves gathering ample information and making sure that the data is relevant and up to date. Integration is equally important, as it involves combining data from various sources into a single, coherent database – which is necessary in order to create a holistic view of your customers.

Exploring data sources

A wide range of data sources can inform churn prediction. These include:

• Transactional data
  Transactional data includes purchase history, payment methods, and product or service usage patterns. Such data provides direct insights into customer behaviour and preferences.

• Customer interactions
  Records of customer service interactions, feedback and complaints are invaluable. They provide a window into customer satisfaction and potential pain points.

• Social media
  Analysing social media activity can reveal public perception and sentiment about your brand. It can also highlight trends and customer expectations.

Techniques for data cleaning and pre-processing

Once you've collected the data, it must be cleaned and pre-processed. This means removing errors, inconsistencies and irrelevant information. Techniques such as normalisation, missing values management and outlier detection can help with this important step – which verifies that the data fed into the predictive model is of the highest quality.

Safeguarding data privacy

Data privacy is a substantial and ongoing concern. Businesses must comply with data protection regulations, such as the European Union's General Data Protection Regulation, and implement comprehensive security measures. Practices such as anonymising sensitive information and gaining explicit consent for data usage help protect privacy while building trust with your customers.

Challenges in churn prediction

Churn prediction is a large-scale undertaking that can pose a number of challenges, including:

• Data quality and volume
  Data used in churn prediction must be high quality and sufficient in quantity. Poor-quality or sparse data can lead to inaccurate predictions.

• Difficulty of data integration
  When bringing together data from different sources, each source may have its own format or structure, making it complicated to merge them into a unified dataset that's ready for analysis.

• Changing customer behaviours
  Customers' preferences and behaviours can change quickly, making it difficult to stay up to speed. What worked in predicting churn last year might not work today.

• Privacy and regulatory compliance
  Using customer data for predictions while respecting customer privacy – and staying compliant with a variety of data protection laws – can be a delicate balance for businesses.

• Identifying relevant variables
  Deciding which data points are relevant for predicting churn can be a time-consuming project.

• Model accuracy and overfitting
  Building a model that predicts churn accurately without overfitting to the training data is a complex task.

• Operationalising predictive insights
  Another challenge is turning predictive insights into actionable strategies. Predicting churn is only the first step – using that information effectively to retain customers is what counts.

• Keeping pace with technological advances
  The field of data analytics is constantly evolving, which can make staying up to date with techniques and theories overwhelming.

How to create a churn prediction strategy

No single churn prediction model is a perfect fit for every business. So how do you pick a model and a strategy for churn prediction that reflects the realities and nuances of your business? Here's how to approach this process:

• Get to know your business and customers
  Develop a thorough knowledge of your business and customers. What makes your customers stay with the business, and what might drive them away?

• Choose the right data
  Identify the types of data most relevant to your business and customer behaviour. This might include transaction history, customer service interactions or social media activity. Make sure the data is accessible and usable.

• Involve your team
  Solicit input from teams within your organisation, such as sales, customer service and IT. Their insights can help you learn about different aspects of customer interactions and technical feasibility.

• Select an appropriate model
  There's no one-size-fits-all model for churn prediction. Your choice should depend on the nature of your business and the type of data you have. Examine your options closely and determine what might work best for you.

• Prepare and clean your data
  Before you can use your data, it needs to be cleaned and organised.

• Build and test the model
  Once your data is ready, build your predictive model. Test it thoroughly to verify its accuracy and effectiveness.

• Update and refine your model regularly
  Customer behaviours and market conditions change over time. Updating and refining your model regularly is necessary to keep it relevant and effective.

• Turn insights into action
  The final step is to use the insights from your churn prediction model to inform your business strategies. This might involve adjusting your marketing strategy, improving customer service or making changes to your product.