Every user who abandons your app or service is a real loss - not only financially, but also in terms of image. Did you know that retaining a customer can be up to five times cheaper than acquiring a new one? The challenge, however, is to understand why users leave before it's too late.
Data analysis and the use of Machine Learning can provide precise information about customer behavior and allow you to implement measures to prevent churn. In this article, we will discuss how data can help minimize churn and provide practical recommendations and best practices based on Alterdata's experience.
Churn as a real threat to a company's existence
Churn is a silent killer of business growth. If the number of users leaving is high, it lowers the customer lifetime value (LTV) and reduces the return on marketing investment. Moreover, a negative customer experience can lead to a bad brand reputation and loss of trust in the market.
The most common questions from companies with high churn rates:
- Which users are close to churn?
- How can we increase customer engagement to retain them?
Key consequences of high churn:
- Reduced revenue due to lower retention,
- Higher marketing costs associated with acquiring new customers,
- Difficulties in scaling the business and building a competitive advantage.
Therefore, minimizing churn should be a priority for any organization before achieving greater scale risks a sudden drop in revenue.
Data available within the organization
Many companies don't realize the value of the data they already have. User data can provide extremely valuable information about user behavior, preferences and potential reasons for churn.
Key data sources for churn analysis:
- User activity data: Time spent in the app, frequency of logins, interactions with key features,
- Transaction and subscription data: Purchase history, subscription breaks, reactions to price changes,
- Marketing data: Traffic sources, campaign types, demographics.
How to analyze available data to identify churn risk?
- Segment users based on behavior (e.g., frequency of activity, engagement rates).
- Look for warning signs, such as declining logins, inactivity in key features, or sudden subscription interruptions.
- Build user profiles to understand which groups are most likely to leave.
- Gather all data in one place, such as in a BigQuery tool, to create a “single source of truth.” This centralization of data makes it easier to identify key patterns and analyze in real time.
- Visualize the data with business intelligence tools for a complete picture and faster decision-making to prevent churn.
For example, data analysis in one of our client's learning platforms showed that users who did not take their first action in the app within three days of registering had a higher risk of churn. This made it possible to introduce activation activities.
Using Machine Learning to reduce churn
Machine learning opens up new possibilities in data analysis and predicting user behavior. With ML models, groups of users with a high risk of churn can be identified and preventive measures can be planned.
Models to consider:
- Predicting the risk of leaving: Models such as XGBoost or neural networks analyze historical data and predict the probability of a user leaving,
- Customer segmentation: Clustering (e.g., K-Means) helps create user segments based on user behavior and needs. This allows companies to precisely tailor marketing efforts and anti-churn strategies to specific groups of users, rather than using a generalized approach.
By using ML models, companies can respond in a timely manner, identifying critical moments in a user's path before churn occurs.
Jak skutecznie wdrożyć działania anty-churnowe?
Wykorzystanie danych i ML to tylko początek. Kluczem jest wdrożenie konkretnych działań, które pomogą utrzymać klientów i zwiększyć ich zaangażowanie.
Kluczowe jest zastosowanie konkretnych strategii, które nie tylko pomogą utrzymać klientów, ale także zwiększą ich zaangażowanie. Oto kilka przykładów:
- Monitoring działań: Tworzenie dashboardów w narzędziach BI, które umożliwiają śledzenie wskaźników retencji i efektywności działań anty-churnowych w czasie rzeczywistym.
- Automatyzacja komunikacji:
- Notyfikacje push z przypomnieniami o kluczowych funkcjach aplikacji, dostosowane na podstawie analizy zachowań użytkownika.
- Spersonalizowane maile z ofertami dopasowanymi do preferencji użytkownika, wzbogacone o rekomendacje modelu ML.
- Inteligentne targetowanie – jeśli model ML wykryje wysokie ryzyko odejścia klienta, można wysłać mu odpowiedni komunikat, np. „Mamy dla Ciebie kupon promocyjny” lub inną zachętę do dalszej aktywności.
Our best practices
Successful churn management requires a strategic approach based on collaboration between different company departments and systematic testing of solutions.
Alterdata's recommendations:
- Team collaboration: Analysts and data scientist should work closely with the business team to jointly develop mechanisms to accurately detect churn-prone customers. This approach enables more effective retention efforts and optimization of business strategy.
- Data warehouse as the main source of truth: In order to get a complete picture of the user, it is worth integrating different data sources into one coherent repository, such as BigQuery, creating a so-called "single source of truth. What's more, BigQuery ML allows you to perform the entire analysis and modeling process without the need for external tools. Thus, in a single environment, you can collect, process and analyze data and build ML models to support retention activities.
- A/B testing of activities: Testing different anti-churn strategies, such as different push messages or changes to application features, to determine the most effective solutions.
Case Study - minimizing churn in practice
Initial situation
A digital platform in the education industry noticed that many users were abandoning the service after a short period of registration. Key issues included:
- Irregular participation in activities available on the platform,
- Rapid decline in new user engagement,
- Difficulties in managing the relationship between users and service providers.
The actions taken
Data analysis: The Alterdata team conducted a detailed analysis of the data, taking into account:
- Psychological factors, such as a decline in engagement after a certain period of time or reluctance to take new actions on the site, such as using another activity to learn
- Seasonality and the impact of holidays, breaks on user motivation.
- The user's level of engagement over time, analyzing how their activity changes over days, weeks and months.
- Usability of various supplementary activities, looking at which site features are most used and how they affect user retention.
ML model construction: A classification model for churn prediction was implemented, which assesses the likelihood of a user leaving the service. The model analyzes key factors such as the level of engagement, frequency of activity and interactions with various platform features, providing precise information about the risk of churn for each user. This makes it possible to take targeted retention actions.
Results
- Reduction of churn by 15% in 6 months.
- Higher user engagement in the first month of using the platform.
- Improved operational efficiency through better resource management.
- Maximizing prediction value: The model not only enabled accurate prediction of churn risk, but also provided early signals of users prone to churn. This makes it possible to take preventive action earlier, such as personalized messages or special offers. In addition, the predictions allow optimizing the management of the platform's resources, including the precise allocation of teachers' work by adjusting their availability to the needs of users with different levels of commitment.
Summary
Minimizing churn is a key part of building a stable and scalable digital business. By leveraging data and ML, companies can not only better understand their customers, but also take more informed and effective actions.
Alterdata has a track record of delivering solutions that help companies perform better. Contact us to learn how we can help your business.
