Customer churn prediction: How to do it (and reduce customer churn)

Customers leave your company or unsubscribe to your service for a number of reasons. You don’t always see it coming, and a customer lost is a customer that needs to be replaced. With a customer churn prediction model, you may be able to spot behaviors and trends before a customer’s exit.

What is customer churn prediction?

First, let’s define the meaning of churn. More often, you’ll see the term “churn rate.” This is the percentage of customers who leave your company either by halting business or unsubscribing to your subscription service.

Churn prediction refers to a set of metrics or behavioral patterns that indicate a customer is at risk of no longer doing business with you.

Why customer churn prediction is important

By identifying customers that may leave, you can implement a retention plan to keep them on board. This matters a lot from a cost perspective. When you lose a customer, you have to fill that void with a new consumer. According to Forbes, it costs companies five times more to acquire a new customer than to retain an existing one.

The churn prediction model

A customer churn prediction model is a predictive analysis that provides an overview of a customer’s probability of churning. For a churn prediction model to be effective, you need data from former clients. Examine the metrics: Does the data reveal a pattern in the weeks or months leading up to their departure?

Various churn prediction models exist, many of which employ a machine-based learning algorithm. One of the more common methodologies is the decision tree model. Here’s an illustrated example of how it works:

The decision tree provides insight into a customer’s actions. Based on the behaviors, the model’s AI will perform a risk assessment on the customer’s churn probability. The assessment is based on the behaviors of past clients.

How does it work?

Work with your IT and coding team to develop a churn prediction model suitable for your specific business and goals. Here’s a general breakdown of the process:

1. Determine your business case

The first stage is to determine your goals as they relate to retention. What is the current retention rate? How much do you want to lower the figure by the end of the month? How much by the end of the quarter?

Furthermore, what are your initiatives for preventing at-risk customers from churning? Examples of churn preventive measures include:

• Invitations to loyalty programs
• Reminders to customers to log in or check out a newly released feature
• Recommendations for services/products compatible with the customer’s current plan

2. Collect data

Determine the metrics to input into your churn prediction model—what are useful metrics to analyze for predicting churn probability? Here are some metrics for analysis:

• Login frequency and duration of each login
• Engagement with a product or service’s features
• Open rate of subscriber emails
• Response rate and responses to satisfaction surveys
• Customer lifetime value
• Purchase history of related products or services

3. Focus on feature extraction

Determine how you want the predictive model to make assessments and probabilities based on the metrics. For example, if a customer switches off automatic billing, is he considered at high risk for churning? What’s the definition of high risk in percentage terms? 50%? 75%? Will other metrics further elevate or lower the risk? These factors should be determined based on the behavioral data of ex-customers prior to churning.

4. Determine the predictive model

Dozens of predictive models exist as well as hybrids. The above-mentioned decision tree is just one model, and it’s one many data scientists adopt in some incarnation or another for its simplicity. However, other models are available, such as random forests, binary classifications, and logic regressions. Nowadays, programmers create customized models based on offshoots of one or more of the above methodologies.

5. Deploy the system

Once the churn predictive model is set, deploy and carefully monitor the system’s performance for accuracy. Update the system as necessary and perform A/B testing on the selected metrics. You can also A/B test the prevention strategies to determine which ones yield the highest retentions.

Tools to use

Most churn prediction models can be built using a high-quality customer data platform (CDP). Many come with pre-made churn model templates in the form of default decision trees, random forests, and more. Use the template to customize your own model, plug in your own metrics, and set up machine learning for automatic predictive analysis. Whatever CDP you use, it should have the following features:

• Data integration and migration function to eliminate data silos
• Automated data-to-metrics conversion
• A pre-made and customizable churn analysis model

One tool that fits the bill is Cloud Connect. Integrate your data with Lytics’ own customer data platform. Use the vast features of this data warehouse to customize and refine your own customer churn prediction model. Get started by signing up for free today.