Customer churn rate, also known as customer attrition, refers to the percentage of customers who stop doing business with a company over a specific time period. Churn rate is an important metric that directly affects revenue and profitability. Businesses track churn rates to gauge their customer retention efforts and how well they are maintaining customer relationships. High churn rates can indicate that customers are dissatisfied with a product or service, while low churn rates can imply customer loyalty and satisfaction. Average customer churn rates vary from industry to industry. In 2022, the median customer churn rate was 13% for private software-as-a-service (SaaS) businesses.
To understand your business’s churn, you need to know how many customers you’re retaining or losing over a given time period, and why you’re losing them. Customer churn models can help you build a holistic retention strategy that addresses the issues behind your churn. Below, we’ll cover what you need to know about different kinds of churn models, how to choose and build the right one for your business, and what to do with the information you gain from it.
What’s in this article?
- How to calculate customer churn
- Why customer churn is so important
- Key predictors of customer churn
- Types of customer churn models
- How to build a customer churn model
- How to use predictive analytics to reduce customer churn
- How to measure the effectiveness of customer churn models
- Best practices to reduce customer churn
How to calculate customer churn
To calculate customer churn, divide the number of customers lost during a specific time period by the total number of customers at the start of that period. Here’s a basic formula to calculate customer churn rate.
(Number of Customers Lost During the Period / Total Number of Customers at the Start of the Period) × 100 = Customer Churn Rate
To apply this formula, follow these steps:
Determine the time period for which you want to calculate the churn rate (e.g., one month, one quarter, one year).
Identify the total number of customers you had at the beginning of this period.
Count the number of customers who left or stopped doing business with you during this period.
Divide the number of customers lost by the total number of customers at the start of the period.
Multiply the result by 100 to convert it to a percentage.
This percentage reflects your churn rate: the rate at which customers discontinue their relationship with a company within the specified time frame.
Why customer churn is so important
Churn rate can have a major impact on a business’s revenue, profitability, and reputation. Here’s how churn rate impacts business operations.
Customer acquisition costs: Generally, acquiring new customers is more expensive than retaining existing ones. When customers leave, businesses need to invest more in marketing and sales efforts to replace them, which can be costly.
Lost revenue: Losing customers creates a direct loss of income, immediately impacting the business’s financial health.
Fewer long-term customers: Long-term customers tend to buy more over time and can therefore become less expensive to serve. They may also buy higher-margin products or services. A higher churn rate means fewer of these long-term customers and can lead to lower profitability.
Brand reputation: High churn rates can be a sign of customer dissatisfaction, which can harm a company’s reputation. Satisfied customers are more likely to refer others to the business, whereas dissatisfied customers may share their negative experiences, deterring potential customers.
Long-term growth: Sustainable growth requires a stable customer base. High churn rates can undermine long-term growth strategies, making it difficult for businesses to expand or invest in new opportunities.
Key predictors of customer churn
One of the most straightforward predictors of customer churn is the level of customer satisfaction with a product or service. Businesses can measure this through surveys, net promoter scores (NPS), and feedback mechanisms. Consistently low satisfaction scores or negative feedback can be a strong indicator of potential churn.
Other factors that can influence a customer’s decision to leave a business include:
Customer support interactions: While some engaged customers may contact support to maximize value, support requests can also indicate problems or dissatisfaction. Analyzing the nature and outcome of these interactions can predict churn, especially if issues are not resolved satisfactorily.
Product or service usage: Monitoring how—and how often—customers use a product or service can offer businesses insights into customer engagement levels. Low or declining usage can signal that a customer is losing interest or failing to find value, which could lead to churn.
Billing and payment issues: Frequent billing issues or problems with payments can lead to customer frustration and churn. Tracking these incidents and their resolutions can reveal early warning signs that customers are at risk of leaving.
Changes in buying behavior: A sudden change in a customer’s usual purchase patterns, such as a decrease in order size or frequency, can signal dissatisfaction or a shift in needs, potentially leading to churn.
Customer feedback and complaints: Direct feedback or complaints are important indicators of potential churn. Customers who take the time to express dissatisfaction or concerns might consider leaving if their issues are not addressed.
Engagement with marketing communications: A decline in customer engagement with emails, newsletters, promotions, or other marketing communications can indicate waning interest or relevance, which may precede churn.
Market and competitive factors: Changes in the market and the actions of competitors can influence churn. For example, if a competitor comes out with a new product or offers a similar service at a lower value, this could lead to increased churn.
Contract and subscription renewals: For businesses with a subscription or contract-based model, an upcoming renewal period is a key time to assess churn risk. When customers evaluate whether or not to renew, there is a chance that churn can occur.
Demographic and psychographic factors: Sometimes, changes in a customer’s demographic profile or a shift in their values and preferences can predict churn. For example, a change in financial status, relocation, or lifestyle can influence their decision to continue using a particular product or service.
Types of customer churn models
Customer churn models are categorized based on a variety of factors such as the type of algorithm, how it treats time, and the level of prediction detail. Here’s a detailed explanation of the types of customer churn models.
Predictive churn models: These models use historical data to predict the likelihood that a customer will churn in the future. They typically employ machine learning algorithms to identify patterns and predictors of churn, outlined below.
Logistic regression: This is a statistical model that estimates the probability of a binary outcome (such as churn/no churn) based on one or more independent variables. It’s widely used for its simplicity and interpretability.
Decision trees: These models use a tree-like graph of decisions and their possible consequences. They are easy to interpret but are prone to overfitting.
Random forests: An ensemble method that uses multiple decision trees to improve predictive accuracy and control overfitting, random forests are more comprehensive than a single decision tree and often provide high accuracy.
Gradient boosting machines (GBMs): GBMs are an ensemble technique that build trees sequentially, with each new tree correcting errors made by the previously trained trees. GBMs, like XGBoost and LightGBM, are powerful for churn prediction but can be complex to tune.
Neural networks: Neural networks are deep learning models that can capture complex nonlinear relationships through layers of nodes or “neurons.” They can be very effective, especially with large datasets, but are more difficult to interpret than simpler models.
Descriptive churn models: Rather than predicting future churn, these models provide insights into past churn behavior. They help identify trends, patterns, and reasons behind churn, often using clustering techniques or principal component analysis.
Time series churn models: These models look at how churn rates evolve over time. They can be particularly useful for businesses with strong seasonal patterns or those trying to gauge the impact of specific events over time.
Cohort-based models: These models analyze the churn rates of different customer cohorts. A cohort might be defined by the date customers signed up, the product they first purchased, or any other major event. This helps identify if certain cohorts are more prone to churn than others.
Survival analysis models: Also known as time-to-event models, these models measure the time it takes for an event (churn) to occur. They’re particularly useful for predicting when a customer will churn.
Real-time churn models: These models generate instant predictions based on real-time user interactions. They require a strong data infrastructure and are used in scenarios where businesses can take immediate actions to prevent churn.
Hybrid models: These models combine elements from different types of models to use their strengths and mitigate weaknesses. For example, a hybrid model might use a combination of a predictive model for churn likelihood and a survival analysis for timing.
Choosing the right model type depends on the specific business context, the nature of the customer relationship, the available data, and the desired outcome of the modeling effort. It’s often beneficial to experiment with multiple models to determine which provides the most accurate and actionable insights for a particular use case.
How to build a customer churn model
Building a customer churn model is a multistep process that involves analyzing how you define churn, what your data shows you, and which model is the most useful for your business. Here’s a detailed guide for creating a churn model that meets your business needs.
Churn definition: Different businesses have different definitions of churn. For a subscription service, churn might be a customer canceling their subscription. For an ecommerce platform, it might be a customer who hasn’t made a purchase in a certain time period.
Data collection: Gather historical data that includes both churners and nonchurners. Your dataset should include a variety of features such as customer demographics, transaction history, product usage data, customer service interactions, and any other relevant data that can influence churn.
Data preparation: Clean the data (handle missing values, remove duplicates, etc.) and transform it into a format suitable for modeling. This might include encoding categorical variables, normalizing numerical values, or creating time windows for predictive features.
Feature development: Develop features that effectively capture the behavior and characteristics of your customers. This can include aggregating transactional data into meaningful metrics, calculating usage frequency, or deriving other insightful attributes from raw data.
Exploratory Data Analysis (EDA): Before building your model, conduct EDA to find the patterns in your data. Look for correlations between features and churn, identify outliers, and understand the distribution of key variables.
Algorithm selection: Choose a machine learning algorithm to predict churn. Logistic regression, decision trees, random forests, gradient boosting machines, and neural networks are common choices. The choice of model depends on the dataset size, feature importance, and need for interpretability.
Training and testing: Split your data into training and testing sets to evaluate the performance of your model. The training set is used to train the model, while the testing set is used to assess its predictive power on unseen data.
Model validation: Use metrics such as accuracy, precision, recall, F1-score, and Area under the ROC curve (AUC-ROC) to evaluate the performance of your model. Focus on the metrics that impact your business objectives. For instance, if the cost of false positives is high, you might want to maximize precision.
Model tuning: Adjust the model’s hyperparameters to improve its performance. Consider using techniques such as grid search, random search, or Bayesian optimization to find the optimal set of hyperparameters.
Feature analysis: Learn which features are most influential in predicting churn to guide your customer retention strategies.
Deployment: Deploy the model into a production environment where it can provide ongoing predictions. This might involve integrating the model into your business systems or setting up a batch process to periodically score customers.
How to use predictive analytics to reduce customer churn
Actionable insights and intervention strategies
Use the model’s predictions to develop targeted retention strategies. For instance, consider offering customers who you have identified as high-risk for churn personalized promotions, proactive customer service, or other incentives to retain them.
Segment high-risk customers based on their characteristics or reasons for potential churn and tailor interventions accordingly.
Monitoring and feedback loop
Monitor the model’s performance on a continuous basis. Adjust and retrain the model as necessary to respond to changing patterns in customer behavior or business operations.
Establish feedback mechanisms to capture the outcomes of retention efforts and regularly monitor the effectiveness of your retention strategies. Use this data to further refine the predictive model and intervention strategies.
Collaboration and organizational integration
Share learnings and recommendations from the predictive analytics process across relevant departments to create a cohesive retention strategy.
Integrate data-driven insights into decision-making processes to address customer needs proactively and reduce churn.
How to measure the effectiveness of customer churn models
It’s important to measure how effective your customer churn models are to ensure you don’t make business decisions based on false predictions. The following metrics can evaluate how well your churn models are performing.
Accuracy: This is the percentage of total predictions that the model got right. While accuracy is a starting point, it doesn’t always give a complete picture, especially if the number of churners and nonchurners is imbalanced.
Precision: Of the customers who the model predicted would churn, how many actually did? Assessing this metric is important because the cost of false positives (predicting churn when it doesn’t happen) can be high.
Recall: Of all the customers who churned, how many did the model correctly identify? This is important if you want to capture as many true churn cases as possible, even if it means tolerating some false positives.
F1-score: This metric combines precision and recall into one number for a balanced view.
Area under the ROC curve (AUC-ROC): The ROC curve plots the true positive rate against the false positive rate at various threshold settings. The AUC measures the entire two-dimensional area underneath the entire ROC curve. A model with perfect predictions has an AUC of 1.0, while a model that makes random guesses has an AUC of 0.5.
Confusion matrix: This is a table that shows the number of true positives, false positives, true negatives, and false negatives. It helps you see the types of errors your model is making.
Lift: This metric demonstrates how much better the model is at predicting churn compared to random guessing. A lift greater than 1 indicates the model is better than random guessing.
Business impact: The true test of the model’s effectiveness is its impact on business metrics. Are you able to reduce churn rates by acting on the model’s predictions? Are customer retention strategies informed by the model’s insights leading to increased customer lifetime value (LTV) or enhanced customer satisfaction? To evaluate the business impact, you can run controlled experiments (such as A/B testing) to compare the outcomes with and without the interventions suggested by the churn model. Observing changes in churn rates, customer satisfaction scores, and profitability over time will help ascertain the model’s real value.
Best practices to reduce customer churn
Predictive behavioral segmentation: Use machine learning to predict future customer behavior based on past interactions, purchasing patterns, and engagement data. This allows you to anticipate customer needs and intervene before a churn signal registers, thereby implementing more targeted and effective retention strategies.
Advanced personalization techniques: Use advanced analytics and AI to create personalized experiences that make each customer feel valued and understood. This can include personalized product recommendations, dynamic content in communications, and tailored user experiences on digital platforms.
Customer experience improvements: Map out and analyze the entire customer journey to identify key touchpoints and potential friction points, which will help you simplify user experiences, remove obstacles, and ensure that every interaction adds value and improves satisfaction.
Proactive churn intervention: Use real-time analytics to identify customers at risk of churning and initiate proactive engagement strategies. This could involve automated triggers for personalized offers, one-on-one outreach from customer success teams, or access to exclusive content or support.
Value realization tracking: Monitor how effectively customers are achieving their desired outcomes with your product or service. Develop indicators of value realization and intervene if customers are not achieving these benchmarks. Offer guidance, support, or additional resources to help customers see the full value of their investment.
Community building: Create a sense of community among your customers through exclusive forums, user groups, or social platforms. Engaging with peers can increase product stickiness, generate additional support channels, and build customer loyalty.
Advanced loyalty programs: Move beyond loyalty programs based on transactions and embrace ones that reward engagement, advocacy, or co-creation. Encourage customers to contribute ideas, share feedback, or participate in beta tests, and reward them with exclusive benefits or recognition.
AI service enhancements: Implement AI-driven tools such as chatbots or predictive support for instant, 24/7 customer assistance. These tools can also identify when human intervention is necessary and escalate complex issues to human agents.
Data-driven product development: Use customer feedback and usage data to drive product development. By matching your product roadmap with customer needs and preferences, you can increase satisfaction and reduce the likelihood of churn due to product-market fit issues.
Strategic account management: Manage B2B or high-value customers strategically, developing deep relationships and learning about their business goals. Adjust your services or products to support their success.