Why is predicting churn so critical for businesses, and why is it a tougher task than you might expect? Zohar explains in this video — or keep reading for more.
In a nutshell:
- Predicting and preventing customer churn is crucial for businesses to reduce lost revenue and prioritize customer retention.
- Identifying at-risk customers at the right time is challenging but essential for effective retention initiatives.
- Determining the optimal treatment for different customer segments is key in reducing churn.
- AI-powered solutions like Pecan can help navigate data science hurdles and drive growth by reducing churn.
Customer churn — when existing customers stop doing business with a company — is a critical metric that all companies aim to reduce. While predicting churn may seem straightforward, doing so effectively presents some unique obstacles.
In my video above and in this post, we’ll explore why churn is so vital to understand and some of the specific difficulties in modeling it accurately.
Defining Churn and Its Importance
Before diving into the complications of predicting churn, it’s helpful to precisely define what we mean by “churn.” In its simplest terms, churn refers to customers ending their relationship or stopping their purchasing activity with a business. It can also refer to a notable decline in activity. Some businesses, like streaming video services, can suffer from what are called “serial churners.”
However you define churn at your company, high churn rates directly translate to lost revenue, making customer retention a key priority.
Churn prediction involves identifying at-risk customers likely to cancel subscriptions or close accounts using a machine learning model on past customer data.
Understanding the drivers of churn and identifying at-risk customers early allows companies to target retention initiatives more precisely. The ability to model and foresee churn has become an essential capability for modern data-driven businesses across industries.
Capturing the Right Time Horizon
One major difficulty in churn prediction is the need to identify customers at high risk while they’re still active, and you can still do something about it.
Look for at-risk customers too early, and they won’t have shown any indications. Too late, and they may have already decided to leave.
Pinpointing the appropriate time window before churn occurs requires carefully tracking longitudinal customer data to reveal behavioral signals and trends leading up to the event.
The model must also segment users based on factors like typical purchasing frequency. Getting the time component right is crucial but challenging.
Determining the Optimal Treatment
Even accurately identifying customers at high risk of imminently churning is only half the battle. Companies must also determine what incentives or outreach efforts, if any, could prevent different customer segments from leaving. Knowing which customer could or should be treated differently is going to be crucial for succeeding in reducing churn.
Understanding sensitivity to pricing, specific product issues impacting loyalty, preferred communication channels, and more can help businesses craft targeted winback or retention offers.
The most important features of machine learning algorithms that output churn predictions can also guide customized treatment strategies.
Transforming Customer Retention with AI
Predicting and preventing customer churn presents some unique data science and analytics hurdles. Capturing behavioral signals in the right time window and at the right cadence is vital but tricky. Deciding on positive interventions and offers is equally challenging.
AI-powered solutions can help businesses better navigate these persistent pain points to reduce churn and drive growth.
Want to tackle churn — before it happens? Chat with us about how Pecan can help you identify at-risk customers and get proactive about retention.
