How to Predict and Reduce Customer Churn

Customer churn is the most important metric to get right in business growth. We’ll explore how to reduce churn, by predicting the customers at risk.

What is customer churn and why is it important?

Customer churn is probably the single most important metric to get right when it comes to business growth. It indicates if your customers are getting value from your product or service, and is the key driver of revenue and profitability. Customer churn and customer retention are opposite sides of the same coin. Put simply, churn = the proportion of customers leaving, retention = the proportion of customers staying.

The topic of churn is broad, and customer churn isn’t the only way of measuring churn in a business. Revenue churn measures recurring revenue, which takes into account customers spending less. But for the sake of scope, I’m going to stick to customer churn for this article.

Customer churn happens to the best of businesses; there’s no way to ever stop it completely. In this article, we’ll explore how you can get that churn rate down, by predicting the customers most at risk of churning, why they're churning, and when.

Measuring customer churn rate

Customer churn rate is most commonly expressed as a monthly or annual percentage, depending on the natural frequency of purchase.

Customer churn benchmarks

This is a harder question to answer than it might appear. First of all, it depends on what you’re selling, including the natural purchase frequency and the price point. Secondly, it depends on who you’re selling to.

Why reduce customer churn?

Customer churn is bound to happen, but it pays to do everything in your power to reduce it as much as possible. That’s because it’s about understanding why a customer isn’t happy.

Predicting customer churn with machine learning

Before implementing any retention strategies, you need to know who you’re targeting - those at the most risk of churning. That’s where machine learning (ML) comes in. ML can process large amounts of customer and transactional data to identify patterns and make predictions about future customer behavior.

Machine learning for churn prediction with the AI & Analytics Engine

The AI & Analytics Engine (the Engine) from PI.EXCHANGE is a no-code ML tool that makes it easy to develop ML applications. We've built a customer churn templated solution purpose-built for marketers, growth, and customer success specialists.

The Engine’s customer churn prediction process

  1. Import your customer information and transactional data.
  2. Define what churn means to your company.
  3. Build the model and analyze data using ML algorithms to determine which customers are likely to churn.

Why are customers churning?

Understanding why customers churn is crucial. You can use the “feature importance“ tool to get insights into the factors influencing churn. Perhaps your product has a high churn rate among certain demographics or after a specific time.

Common reasons for customers churning

Low perceived value

If customers feel the benefits of your product aren’t meeting their expectations, they may consider switching to a competitor.

Poor onboarding

Churn in the early stage of the customer lifecycle can often be attributed to poor customer onboarding, which shapes the new customer experience.

Poor customer service

A bad customer communication experience can lead to customers departing, especially if there are shortcomings in accuracy, friendliness, speed, and convenience.

Targeting the wrong people

If your paid advertising targets the wrong crowd, you may acquire customers who ultimately don’t meet their needs.

Reduce Customer Churn

Predicting who’s most likely to churn and understanding underlying reasons is only half the equation. Businesses need to take targeted actions to address the root causes.

Customer Retention Strategy

  1. Set up measurements to track progress and define what success looks like by setting churn rate goals.

Tracking churn by cohort

Cohort analysis separates new customers from existing ones based on when they were acquired, yielding insights into their lifecycle.

Setting churn rate goals

Divide your customer lifecycle into short, medium, and long-term timeframes to set specific churn rate goals.

Customer Retention Tactics

  • Improve (or create) the onboarding process: Enhance the new customer experience with automated emails and tutorials.
  • Engage customers with proactive, personalized communication: Use tools to make interactions relevant.
  • Collect and appreciate feedback: Establish channels for feedback to improve customer service.
  • Reward customers by offering incentives: Offer special incentives, but analyze customer lifetime value carefully.
  • Remind customers of value: Provide examples of customer success stories.
  • Bring back customers after they've left: Set up campaigns to target previous customers, addressing their pain points.

Wrapping Up

This guide has provided insights into customer churn and methods to address it.