What are the most common mistakes when predicting customer churn?

1 Mistake 1: Not defining churn clearly

One of the most common mistakes when predicting customer churn is not defining what churn means for your business. Churn can be measured in different ways, such as the number of customers who cancel their subscription, the number of customers who do not renew their contract, the number of customers who do not make a purchase within a certain time frame, or the number of customers who reduce their usage or engagement. Depending on your business model, industry, and customer behavior, you may need to use different definitions and metrics of churn. If you do not define churn clearly, you may end up with inaccurate or inconsistent data, which can lead to misleading or irrelevant predictions.

2 Mistake 2: Not segmenting customers

Another common mistake when predicting customer churn is not segmenting customers based on their characteristics, behavior, preferences, or needs. Customer segmentation can help you understand the different types of customers you have, their value, their churn risk, and their reasons for churning. By segmenting customers, you can tailor your churn prediction models to each segment, and design more effective and personalized retention strategies. If you do not segment customers, you may miss important patterns or differences among your customer base, and end up with a generic or inaccurate churn prediction model.

3 Mistake 3: Not choosing the right features

A feature is a variable or attribute that describes a customer or their behavior, such as age, gender, location, purchase history, frequency of usage, feedback, or satisfaction. Choosing the right features is crucial for building a reliable and robust churn prediction model, as they determine how well the model can capture the relationship between the customer data and the churn outcome. If you choose too many or too few features, or irrelevant or noisy features, you may end up with a model that is overfitting or underfitting, which means that it performs well on the training data but poorly on the new or unseen data.

4 Mistake 4: Not validating the model

Validating the model is the process of testing how well the model performs on new or unseen data, and how accurate and useful its predictions are. Validating the model can help you assess the quality and reliability of your churn prediction model, as well as identify and correct any errors or biases. If you do not validate the model, you may end up with a model that is not generalizable or applicable to your real-world data, and that may give you false or misleading results.

5 Mistake 5: Not updating the model

Updating the model is the process of retraining or refining the model based on new or changing data, customer feedback, or business goals. Updating the model can help you keep your churn prediction model relevant and accurate, as well as adapt to the changing needs and expectations of your customers. If you do not update the model, you may end up with a model that is outdated or obsolete, and that may fail to capture the current or future trends or behaviors of your customer base.

6 Mistake 6: Not acting on the predictions

The ultimate goal of predicting customer churn is not to just generate numbers or charts, but to use them to inform and improve your customer retention strategies. Acting on the predictions means using the insights and recommendations from your churn prediction model to design and implement effective and timely actions to prevent or reduce customer churn, such as offering incentives, discounts, rewards, or personalized services, improving customer service or product quality, or engaging customers with relevant and valuable content or communication. If you do not act on the predictions, you may end up wasting the time and resources invested in building and maintaining your churn prediction model, and losing the opportunity to retain and delight your customers.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?