Introduction
Every user who stops using your app or service represents a real loss—not only financially but also reputationally. Did you know that retaining a customer can be up to five times cheaper than acquiring a new one? The challenge lies in understanding why users leave before it’s too late.
User churn is a key metric measuring customer attrition, serving as a crucial indicator of business health—especially in industries like SaaS, e-commerce, and telecommunications—and directly impacts revenue and customer acquisition costs.
Data analysis and the use of Machine Learning can provide precise insights into customer behavior and enable the implementation of actions that prevent their departure. Churn is a critical metric for SaaS and e-commerce companies as it helps evaluate retention strategies’ effectiveness and predict revenue loss. In this article, we discuss how data can help minimize churn and present practical recommendations and best practices based on Alterdata’s experience. By leveraging collected data, companies can implement changes that reduce churn and increase customer loyalty.
Customer churn as a real threat to business survival
Churn is the silent killer of business growth. A high number of departing users lowers customer lifetime value (LTV) and reduces marketing ROI. Additionally, revenue churn—when existing customers reduce their spending—negatively affects the company’s financial health. Moreover, negative customer experiences can damage brand reputation and erode market trust.
Common questions from companies with high churn rates:
- Which users are close to leaving?
- How to increase customer engagement to retain them?
- What is our churn rate and how to improve retention among existing customers?
Key consequences of high churn:
- Reduced revenue due to lower retention—high churn means losing existing customers, directly impacting financial stability and marketing effectiveness. Reducing churn is essential for business growth, customer retention, and profitability.
- Increased marketing costs related to acquiring new customers.
- Difficulties in scaling the business and building competitive advantage.
Focusing on current customers is crucial to sustain revenue and foster brand loyalty, as retaining them helps ensure long-term business success.
Analyzing key factors influencing churn and customer lifetime value enables more effective retention management and optimization of customer retention strategies.
Types of churn
Churn, or customer attrition, is not a homogeneous phenomenon—in practice, several types are distinguished, which are crucial for effective retention actions in mobile applications. The most common is voluntary churn, where customers decide themselves to stop using a service or product. This may result from dissatisfaction with app functionality, lack of new features, unattractive offers, or simply changing user preferences. Unmet customer expectations can also lead to dissatisfaction and increased churn.
Involuntary churn occurs when users lose access to the app for reasons beyond their control—such as technical issues, payment errors, or hardware limitations. It is also important to note churn caused by poor user experience, lack of technical support, or unintuitive interfaces, which in mobile apps can significantly affect customer loyalty.
Understanding the type of churn allows better tailoring of retention efforts and personalizing offers according to individual user needs. By segmenting users into user cohorts and analyzing their behavior over time, patterns in retention and churn can be identified, enabling more targeted interventions. By analyzing data on user behavior and preferences, early warning signs can be quickly detected and effective preventive actions implemented, increasing customer loyalty and improving retention rates in your app.
Methods of calculating churn rate
The churn rate is one of the key business health indicators for mobile apps and subscription services. It measures the percentage of customers who stop using a product or service within a specific time frame, often referred to as a specific period. To calculate churn or calculate churn rate, you divide the number of customers lost during a specific period by the total number of customers at the start of the same period. Defining the time period and ensuring you compare the same period is crucial for accurate measurement and meaningful analysis.
The most common method is monthly churn rate, calculated as the ratio of customers lost during the month to the total number of customers at the start of that period. For longer customer lifecycles, annual churn rate analysis is also valuable to better understand long-term trends and retention effectiveness. Industry benchmarks such as average churn rate and customer churn rate help companies compare their performance and set realistic goals for improvement.
In mobile apps, churn rate can also be measured based on the number of users who uninstall the app or stop using it for a defined period. Tracking how many users return or leave during a specific period, and comparing these numbers to customers acquired in the same period, provides deeper insight into user engagement and retention. Using data on user activity, engagement, and interaction history allows not only precise churn calculation but also identification of critical moments when the risk of leaving increases.
Churn directly impacts monthly recurring revenue and recurring revenue for subscription-based businesses, making it essential to monitor and manage for long-term financial health. Regular churn rate analysis and customer segmentation based on behavior enable implementation of effective retention strategies that boost customer loyalty and minimize customer attrition. By leveraging data and analytical tools, and monitoring user retention rate and customer retention rate alongside churn metrics, you can better understand your users, personalize communication, and respond faster to early warning signs, translating into tangible benefits for your app and financial stability.
Available data in the organization and its role in data analysis
Many companies underestimate the value of data they already possess. User data can provide extremely valuable insights into their behavior, preferences, and potential reasons for churn.
Key data sources for churn analysis:
- User activity data: Time spent in the app, login frequency, interactions with key features, and overall app usage.
- Transactional and subscription data: Purchase history, subscription breaks, responses to pricing changes.
- Marketing data: Traffic sources, campaign types, demographic data, and customer base segmentation by demographic traits.
How to analyze available data to identify churn risk?
- Segment users based on collected data, including demographics and behaviors (e.g., activity frequency, engagement metrics).
- Use product analytics to track usage patterns and usage metrics, which help identify at-risk users by highlighting changes in engagement or anomalies in regular behavior.
- Search for warning signs such as declining logins, inactivity in key features, or sudden subscription interruptions.
- Build user profiles to understand which groups are most at risk of leaving, and analyze user flows to map the customer journey and identify friction points.
- Gather all data in one place, e.g., in a tool like BigQuery, to create a “single source of truth.” Such data centralization facilitates key pattern identification and real-time analysis.
- Visualize data with Business Intelligence tools to get a full picture and make faster decisions to prevent churn.
When defining the scope of analysis, leverage customer data from the existing customer base and existing users to improve retention strategies and focus on the most profitable segments.
For example, analysis of data from an educational platform client revealed that users who did not take initial actions within three days of registration had a higher risk of churn. This enabled the introduction of activation activities.
Analyzing data across the entire customer base and understanding customer behaviour through data analysis helps better predict churn risk and optimize retention efforts.
Using Machine Learning to increase customer retention and reduce customer churn
Machine learning opens new possibilities in data analysis and user behavior prediction. ML models can identify high-risk churn user groups, including users likely to leave, and plan preventive actions. Customer churn prediction models help predict customer churn and estimate potential future revenue loss, allowing businesses to proactively address churn risks.
Models worth considering:
- Churn risk prediction: Models like XGBoost or neural networks analyze historical data and forecast the probability of user departure, enabling early identification of at-risk users. Customer churn prediction enables companies to predict customer churn and quantify the impact on future revenue, supporting more effective retention planning.
- Customer segmentation: Clustering algorithms (e.g., K-Means) help create user segments based on behavior and needs. This allows companies to tailor marketing efforts, personalize offers, and communication to specific user groups, addressing their needs instead of using a generalized approach.
By applying ML models, companies can respond timely by identifying critical moments in the customer lifecycle and user journey before churn occurs. Predicting churn is a key benefit of using ML, as it enables the customer success team to personalize communication and offers to build customer loyalty. The effectiveness of these actions depends on adapting strategies to the company’s business model.
How to effectively implement anti-churn actions?
Using data and ML is just the beginning. The key is implementing concrete actions that help retain customers and increase their engagement. Effective customer retention strategies include loyalty programs, personalized communication, user behavior analysis, and providing how-to guides to educate and engage users.
It is essential to apply specific strategies that not only retain customers but also boost their engagement. Examples include:
- Activity monitoring: Creating dashboards in BI tools that allow tracking retention metrics and anti-churn effectiveness in real time.
- Communication automation:
- Push notifications reminding users of key app features, tailored based on user behavior analysis.
- Personalized emails with offers matched to user preferences, enriched by ML recommendations. Personalizing offers and communication better meets customer needs and increases loyalty.
- Intelligent targeting—if the ML model detects a high churn risk, an appropriate message can be sent, e.g., “We have a promotional coupon for you” or another incentive to encourage continued activity.
- How-to guides: Offering tutorials, webinars, and guides to help customers maximize product usage and enhance their experience, which can reduce churn.
User experience and customer service quality are crucial factors in why customers leave. Long wait times, unskilled consultants, or poor service quality can reduce satisfaction and cause customer departures. Analyzing these factors helps better understand why customers churn and implement effective retention strategies.
Applying effective strategies increases customer loyalty, brand loyalty, and maintains stable retention levels. Retaining existing customers is more cost-effective than acquiring new ones and is essential for long-term revenue growth and increasing customer lifetime value. By focusing on anti-churn actions, companies can retain more customers and support ongoing business growth.
Our best practices
Effective churn management requires a strategic approach based on collaboration across company departments and systematic solution testing.
Alterdata recommendations:
- Team collaboration: Analysts and data scientists should closely cooperate with business teams to jointly develop mechanisms for precise detection of customers likely to leave. This approach enables more effective retention actions and business strategy optimization.
- Data warehouse as the main source of truth: To get a complete user picture, integrate various data sources into a single coherent repository, e.g., BigQuery, creating a “single source of truth.” Moreover, BigQuery ML allows conducting the entire analysis and modeling process without external tools. This enables data collection, processing, analysis, and building ML models supporting retention efforts in one environment.
- A/B testing of actions: Testing different anti-churn strategies, such as various push messages or app feature changes, to determine the most effective solutions.
Case Study – minimizing churn in practice
Initial situation
A digital platform in the education sector noticed many users quitting the service shortly after registration. Key problems included:
- Irregular participation in platform activities.
- Rapid decline in new user engagement.
- Difficulties managing relationships between users and service providers.
- Challenges in analyzing user churn concerning company services, affecting financial stability and customer retention.
Actions taken
Data analysis: Conducted detailed data analysis considering:
- Psychological factors such as engagement decline over time or reluctance to take new actions on the site, e.g., using alternative learning activities.
- Seasonality and holiday impact on user motivation breaks.
- User engagement level over time, analyzing activity changes across days, weeks, and months.
- Usability of various additional activities, checking which features are most used and how they affect user retention.
- Churn analysis related to service quality and scope to identify which offer elements most influence customer attrition and how service improvements can reduce churn rate.
ML model building: Implemented a classification model to predict churn, assessing the likelihood of user departure from the service. The model analyzes key factors such as engagement level, activity frequency, and interactions with platform features, providing precise churn risk information for each user. This enables targeted retention actions.
Results
- 15% churn reduction within 6 months.
- Higher user engagement in the first month of platform use.
- Improved operational efficiency through better resource management.
- Maximized predictive value: The model not only enabled precise churn risk prediction but also provided early signals about users likely to leave. This allowed earlier preventive actions like personalized messages or special offers. Additionally, predictions optimized platform resource management, including precise teacher availability allocation based on user engagement levels.
Summary
Minimizing churn is a key element in building a stable and scalable digital business. By leveraging data and ML, companies can better understand their customers and make more informed and effective decisions.
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