Churn prediction is a critical business intelligence process that involves identifying customers who are likely to discontinue their relationship with a company or service. It's widely used across various industries, from telecommunications and subscription-based services to banking and e-commerce. Since acquiring new customers is often more expensive than retaining existing ones, churn prediction can significantly impact a company's bottom line by preserving revenue streams and maintaining customer relationships.
| Vertical | Indicators of churn | Popular counter measures |
| Subscriptions | Switch provider (e.g. mobile or streaming service), cancel SaaS tools | Incentives at renewal or upon cancellation request |
| Finance | Transfer assets / insurance elsewhere, close account / cancel credit card | Loyalty |
| Ecommerce | Buy less frequently, spend less | Promotions |
Traditional churn prediction methods rely on statistical and machine learning techniques. Logistic regression, a statistical approach, is often used for its simplicity and interpretability, providing insights into the factors influencing churn. More advanced machine learning algorithms like Random Forests and Gradient Boosting Machines have gained popularity due to their ability to handle complex, non-linear relationships in data. These models can capture intricate patterns that might be missed by simpler methods. Additionally, survival analysis techniques are employed to predict not just if a customer will churn, but when. Each approach has its strengths, and many businesses use ensemble methods, combining multiple models to improve prediction accuracy.
While traditional churn prediction methods have proven valuable, they come with several challenges and limitations. Data quality and completeness often pose significant hurdles, as these models rely heavily on historical data that may be incomplete or biased. Class imbalance is another common issue, as churned customers typically represent a small portion of the dataset, potentially skewing predictions. Feature selection and engineering is crucial for traditional machine learning, yet notoriously difficult, requiring domain expertise and can be time-consuming. Moreover, many traditional models struggle with interpretability, making it difficult for businesses to understand and act upon the predictions. Real-time prediction capabilities and scalability can also be challenging, especially for larger datasets or when rapid decision-making is required. Lastly, these approaches may fail to capture the full context of customer behavior, potentially missing important signals of impending churn.
The following table provides criteria that may guide your selection of approach:
| Challenge is related to... | Specific problem | What you can do |
| Data | Data imbalance | Apply SMOTE. Try detecting churn risk as an outlier. |
| Quality | Watch out for any bias in your data | |
| Model | Selecting algorithm | Use AutoML or neural approach |
| Interpretability | Prefer interpretable approach, or apply model interpretability (e.g. LIME, Shapley values) | |
| Operations | Scalability, Real-time performance | Use small models, select scalable platform |
| Integration into your application | Consider platform that provides API | |
| Accuracy may deteriorate over time (data drift) | Monitor |
Table 2: Challenges in churn prediction and their mitigation
Large Language Models (LLMs) and Generative AI have revolutionized churn prediction by broadening the types of data the model can consider, as well as the depth of analysis and recognition of relationships in the data. LLMs are very good at analyzing unstructured data, and that can contain valuable cues for how customers feel towards your product that aren’t obvious in transactional or behavioral data, including customer reviews, support tickets, and social media interactions. Thus, LLM can base their prediction on a more holistic view of customer sentiment and behavior. Further, LLMs excel at identifying subtle patterns and contextual clues that traditional models might miss, potentially improving prediction accuracy. Going beyond prediction, LLM can generate human-like summaries of customer interactions, helping businesses understand the "why" behind potential churn, not just the "who" and "when". Clearly, an AI-driven approach allows for more dynamic and adaptive churn prediction that can evolve with changing customer behaviors and market conditions.
Featrix simplifies the process of building predictive models by offering a unique blend of efficiency, sophistication, and ease of use.
Unlike traditional methods that often require extensive, iterative experiments, Featrix enables developers to build high-performance prediction models in just a few steps. And particularly useful for churn prediction is the ability to create hybrid models that combine a more traditional predictor with advanced techniques leveraging unstructured data through embeddings from Large Language Models (LLMs). This is way, you can get the best of both worlds: an interpretable predictor and the power of LLM analyzing text such as customer reviews and social media feedback!
Lastly, Featrix's simple API integration makes it remarkably accessible, allowing developers to incorporate powerful churn prediction capabilities into your existing systems minimal additional code.
We’d love to hear about your specific situation and challenges. Drop a note to hello@featrix.ai, or comment on this blog.