Customer churn can make or break subscription-based businesses. I learned this while leading the turnaround of Microsoft’s consumer subscription business, Microsoft365 Personal and Family.  Building models to predict churn risk quickly became a top priority. 

In the era of the subscription economy, where businesses from media to software rely on consistent revenue streams, customer churn — the rate at which customers stop doing business with a company — is a paramount concern. Enter churn propensity models: predictive algorithms that identify customers most likely to leave, enabling companies to take proactive measures.

The Why: A Stitch in Time Saves Nine

A tiny increase in customer retention can skyrocket overall profit. According to Bain & Company, a 5% increase in customer retention can increase profits by 25% to 95%. Churn propensity models allow companies to address and mitigate potential churn before it happens, saving not just revenues but also the hefty costs associated with acquiring a new customer.

Churn Propensity Models Are Vital because they can help you:

1. Identify At-Risk Customers: Before a customer says goodbye, there are signs. Maybe it’s reduced usage, fewer log-ins, or declined engagement. Churn propensity models bring these at-risk customers into the spotlight. Armed with this insight, companies can engage these customers directly with targeted strategies.
2. Understand Key Drivers: Why did John, a two-year subscriber, suddenly cancel his subscription? Why is Maria contemplating a switch? Delving into the profiles of customers with high churn risk provides a wealth of data on pain points. Perhaps it’s an app glitch, lack of certain features, or even external competition. Recognizing these triggers is the first step to addressing and eliminating them.
3. Quantify Impact: In the realm of business, numbers reign supreme. Churn propensity models don’t just wave a vague warning flag; they quantify the potential revenue at risk. By simulating different scenarios (“What if churn reduces by 2% next quarter?”), businesses can make informed budgetary and strategic decisions.
4. Prioritize Actions: Not all churn is created equal. A long-standing premium customer contemplating departure is more urgent than a recent sign-up on a basic plan. Churn propensity models, by assessing the value and likelihood of each customer churning, allow businesses to allocate their retention resources wisely, ensuring maximum ROI

The How: Machine Learning at Its Best

Churn propensity models usually utilize machine learning techniques. Historical data is fed into the system, such as transaction history, customer service interactions, and product usage patterns. The model then identifies patterns associated with customers who have previously churned. Using this information, the model can predict the likelihood of current customers churning in the future. Here’s a detailed guide:

1. Feature Engineering: The Backbone of the Model

• Transactional Data: Every purchase, upgrade, downgrade, or refund tells a story. This could include average purchase values, frequency of transactions, or time since last purchase.
• Behavioral Data: Monitor activity logs for signs like reduced product usage, shorter sessions, or increased intervals between log-ins. Such subtle shifts often precede a customer’s decision to leave.
• Demographic Data: Age, location, occupation, and more can offer valuable context. Perhaps a certain age group finds a product feature less intuitive, or users from a particular region experience connectivity issues.
• Feedback & Interactions: Customer support tickets, reviews, and survey responses can be goldmines of insights. A spike in complaints or negative reviews can be strong churn indicators.

2. Model Selection: Choosing the Right Tool for the Job

• Logistic Regression: A good starting point due to its simplicity and interpretability. If the relationship between features and churn is linear, logistic regression can be effective.
• Random Forest: For more complex datasets with non-linear patterns, this ensemble model is valuable. It offers importance scores for each feature, highlighting the main drivers behind churn predictions.
• Neural Networks: In scenarios with large and intricate datasets, neural networks can capture intricate relationships. However, they require more data and computational power, and their “black box” nature can make interpretations challenging.

3. Model Evaluation: Ensuring Reliability and Precision

• AUC-ROC Curve: This plots the true positive rate against the false positive rate, giving an understanding of the model’s performance across various threshold values. A model with a perfect prediction ability will have an AUC of 1, while a model that predicts at random will score 0.5.
• Holdout Sample Data: To ensure the model isn’t just fitting the data it was trained on (overfitting), reserve a portion of the data (often 20-30%) for testing. This provides a more realistic view of how the model will perform on unseen data.

4. Ongoing Optimization: The Journey Never Ends

• Performance Monitoring: Like any tool, the model’s efficacy will degrade over time if left unchecked. Regularly measure its predictions against actual outcomes to assess accuracy.
• Feature Refinement: As the business grows and changes, new churn predictors might emerge. Stay flexible, and adjust the features feeding into the model as needed.
• Retraining: As fresh data comes in, retrain the model. This ensures it stays current, capturing recent customer behavior patterns and shifts.

Real-World Examples: Netflix, Spotify, and Beyond

1. Netflix

• Data Insights: With millions of users globally, Netflix captures a vast array of data – from viewing habits, search queries, and time spent per session, to the frequency of profile switches and pauses during a show.
• Strategy: Using churn propensity models, Netflix can discern if a long period of inactivity or a sudden drop in viewing hours might indicate a user’s intent to cancel their subscription.
• Actions: To counteract this potential churn:
  • Personalized Content Recommendations: Based on a user’s viewing history, Netflix suggests shows or movies they might enjoy.
  • Tailored Notifications: Users receive alerts about new seasons of their favorite series or the arrival of a highly-rated movie in their preferred genre.
  • Special Offers: For users on the brink of cancellation, Netflix might offer discounts or a free month to entice them to stay.
• Outcomes: With these tailored interventions, Netflix has successfully retained users who might have otherwise left, ensuring consistent revenue and a growing content ecosystem.

2. Spotify

• Data Insights: Spotify analyzes listening habits, playlist creation, interaction with features like “Discover Weekly”, and even the amount of time users spend exploring new genres or artists.
• Strategy: Churn models at Spotify might identify potential churn indicators, such as a decrease in daily listening time, skipping songs more frequently, or not engaging with personalized playlists.
• Actions: To re-engage such users, Spotify employs multiple strategies:
  • Curated Playlists: Users are presented with “Made for You” playlists, reigniting their interest in the platform.
  • Introducing New Features: Users are occasionally highlighted new features, like “Group Session” or “Spotify Stories”, to enhance their listening experience.
  • Special Offers: Premium users showing churn signs might be given discounts on renewals or exclusive access to premium podcasts.
• Outcomes: By understanding and addressing the reasons for potential churn, Spotify maintains a loyal user base, ensuring steady growth in subscribers and active daily users.

3. Online Gaming Platforms: Epic Games

• Strategy: With the boom of online games like Fortnite, Epic Games recognized the importance of keeping their players engaged. They implemented churn algorithms that account for gameplay frequency, in-game purchases, and interactions with other players.
• Actions: Upon detecting signs of waning engagement, they introduce personalized challenges, special in-game items, or exclusive events for the player.
• Outcomes: This has helped maintain high engagement levels, even amidst fierce competition from other online gaming platforms.

4. Health & Fitness Platforms: Peloton

• Strategy: Peloton, known for its interactive fitness equipment and classes, uses churn models to monitor workout frequency, class types taken, and user feedback.
• Actions: Users showing decreased activity might receive motivational messages, class recommendations, or promotional offers for new classes or challenges.
• Outcomes: This keeps users engaged, ensuring continued subscription renewals and active participation.

The power of churn propensity models lies in their ability to transform raw data into actionable insights. Companies like Netflix and Spotify exemplify how understanding the subtle signs of customer disengagement and proactively addressing them can make the difference between a user leaving or staying. In the hyper-competitive landscape of streaming services, these models are invaluable assets in the quest for user loyalty and long-term growth.

The Business Impact

While the direct impact is increased retention and revenues, the ripple effects are vast:

• Long-Term Customer Loyalty: Companies like Apple and Spotify prioritize user experience to create an ecosystem where customers feel connected. With predictive churn analysis, they can anticipate and address issues before they become problems, fostering long-term loyalty.
• Enhanced Personalization: Modern businesses, like Airbnb and Uber, use churn analysis not just to retain users but to personalize their experiences. By understanding what might cause a user to disengage, they tailor offerings and interactions to individual preferences, elevating the user experience.
• Optimized Marketing Spend: With businesses such as ASOS and Sephora heavily investing in digital marketing, understanding churn helps them allocate budget efficiently. They target at-risk customers with specific campaigns, getting a higher return on ad spend.

When to Apply It: Timing is Everything

Begin early, but not without reason. If you’re a startup with minimal customers, focus on product-market fit. But once you’ve got a stable customer base and start observing churn, that’s your cue.

Best Practices in Churn Propensity Modeling

Navigating the intricacies of churn propensity models requires a structured approach. While the promise of these models is undeniable, their efficacy hinges on adhering to certain best practices that enhance accuracy and utility.

1. Quality Data:

• Depth: Collect data at a granular level. For instance, instead of just knowing a user logged in, understand what they did during that session.
• Relevance: Ensure that the data points are related to the behavior you’re trying to predict. Irrelevant data can confuse the model.
• Cleanliness: Regularly audit data for duplicates, inconsistencies, or missing values to maintain accuracy.

2. Regular Updates:

• Timeliness: As market conditions and customer behaviors evolve, it’s vital to update the model to capture these shifts.
• Feedback Loop: Implement a system where the outcomes of the model’s predictions (whether they were right or wrong) feed back into the model for better accuracy over time.

3. Actionable Insights:

• Proactiveness: Don’t wait for a user to churn. Predictive insights should immediately lead to retention strategies.
• Customization: Use model outputs to tailor interventions to individual customer needs, ensuring higher effectiveness.

4. Holistic View:

• Integrate Data Streams: Combine churn predictions with customer satisfaction metrics, purchase history, and engagement levels.
• Segmentation: Recognize that not all customers are the same. Different segments might have different churn triggers.

Common Mistakes in Churn Propensity Modeling

While the potential of churn propensity models is vast, it’s not uncommon for businesses to stumble upon certain pitfalls that can hinder their effectiveness. Recognizing these can be the first step towards avoiding them.

1. Overfitting:

• Flexibility: Avoid building a model that’s too rigid. Overfit models might perform well on historical data but fail to generalize on new data.
• Validation: Regularly test your model on new, unseen data to ensure it remains robust and generalizes well.

2. Ignoring External Factors:

• Awareness: Be conscious of external events such as economic downturns, competitive launches, or social trends.
• Adaptability: Continuously adapt the model to account for these unforeseen external shifts.

3. Neglecting Feedback:

• Direct Communication: Surveys, reviews, and direct feedback are gold mines of information. They can provide qualitative insights that pure data might miss.
• Iterative Learning: Update the model based on customer feedback. If many customers cite a particular reason for churning, ensure the model can detect such signals in the future.

In Conclusion:

Churn propensity models, while powerful, are not set-it-and-forget-it tools. They require diligent attention, continuous iteration, and a combination of quantitative and qualitative insights. In the dynamic landscape of the subscription economy, businesses that wield these models wisely, avoiding pitfalls and adhering to best practices, stand to gain immeasurable advantages in customer retention and loyalty. The key lies not just in predicting churn but in effectively acting upon those predictions to create lasting customer relationships

Churn propensity models, when executed correctly, can be a game-changer for businesses in the subscription economy. As with all tools, its value lies in its judicious application and continuous refinement.