What is Customer Churn?

Customer churn rate is simply the percentage of customers that stopped using your company's product or service during a certain time period.

common methods used to calculate customer churn rate 

As legendary management guru Peter Drucker once said, the true purpose of a business is to create and keep customers. Customer churn, in particular, can be very damaging to an organization. The cost of churn, includes not only lost revenue but also the marketing costs associated with replacing churned customers with new ones.

Leveraging Existing Customers

On average, 65% of an organization's business comes from existing customers, and new customer acquisition costs are 5 to 25 times higher than retaining customers, according to Harvard Business Review. Increasing customer retention rates by 5% increases profits by 25% to 95%, according to research published by Bain & Company.

That makes calculating customer churn rates – and understanding why it's happening – critically important. Customer churn analysis leverages data in order to prevent churn and can have a dramatic effect on the contact center. Among the common methods used to calculate churn rate:

  • Simple: Total number of customers churned divided by the number of customers you had on the first day of the period you’re looking at. This method is quick and easy, but it can be unreliable if your company is growing rapidly.
  • Adjusted: Total number of customers churned divided by the midpoint of the customer count for the month. This approach normalizes changes in your total number of customers but fails to expand or contract to include different time periods.
  • Predictive: A weighted average churn rate that attempts to predict the churn rate on any given day.

The challenge is that customer attrition has historically been a lagging indicator: Typically, organizations have only assessed what went wrong after losing customers.

“By the time you see an increase in your churn rate, it is six or eight months after the point in time when you actually failed the customer,” Jonah Lopin, HubSpot’s vice president of services, told Harvard Business Review. “…You’re always six months too late to influence your future.”

Improving Customer Retention

Churn prediction modeling techniques attempt to understand the precise customer behaviors and attributes that signal the risk and timing of customer churn. This can be accomplished by fitting statistical models to historical data and trying to find a pattern in existing customers that may result in churn.

Forward-thinking organizations are also leveraging artificial intelligence (AI) and machine learning to forecast future trends and behaviors and identify previously hidden indicators that help to predict churn. And they’re reaping the rewards – a recent study by Aberdeen Research found that enterprises that use AI in their contact centers realize 3.3 times higher customer retention, 3.5 times more satisfied customers and an 8-fold decrease in customer effort. All great signals for improved customer lifetime value.

AI contact center technology can deliver behavioral insights that reside in the so-called “big data,” where pre-built models have been developed based on millions of hours of customer interactions. And with increasing access to data today, organizations can now leverage predictive churn rate analysis capabilities to identify at-risk customers and proactively prevent them from leaving. Being able to predict that a particular customer is at a high risk of churning – while there is still time to proactively address the situation – helps organizations retain customers and avoid loss of revenue. And doing so in real time multiplies that opportunity exponentially, enabling contact centers to prompt agents on how to keep a customer from leaving. 

Reducing Customer Churn by Using Interaction Analytics

Customer satisfaction is driven not by the severity of a customer’s issue but rather by agents – specifically, how agents behave toward that customer. While many organizations pay a lot of people to listen to a lot of calls to listen to and score agent interactions with customers, human listening is not cost-effective or consistent – two people evaluating the same interaction can disagree whether the agent demonstrated ownership of the customer’s issue, for example. In addition, agents don’t trust being measured on just a handful of interactions each month.

Interaction analytics enables contact centers to automatically analyze 100% of interactions, both in real time and post-interaction. A full interaction analytics program empowers organizations to discover the root causes of customer dissatisfaction by analyzing all of its customers' past and current activities, highlighting customers who require immediate attention and revealing patterns in order to preemptively prevent customer churn. It uses sentiment analysis to gauge the emotional state of customers through tell-tale variations in pitch or tone and automatically identifies trending hot topics and key phrases in customer communications. By enabling organizations to predict churn, it empowers them to take steps to prevent it.

Using AI Interpretive and Predictive Model for Churn Prediction

Research has identified nine specific behaviors agents can use to mitigate difficult situations and turn an unhappy customer into a satisfied one. These behaviors – soft skills that include empathy, rapport and ownership, among others – are key to improving customer service.

AI enables contact centers to autoscore not only these soft skill behaviors but also to score 100% of interactions on the basis of customer behavior and whether that customer is a churn risk. NICE Enlighten AI uses AI to interpret and measure these behaviors then gives agents immediate feedback on how to steer customer conversations with easy-to-understand prompts and specific recommendations. That real-time guidance helps agents steer customer conversations toward an excellent experience, increasing CSAT and decreasing churn rate.

It’s no longer enough for organizations to simply react to churn. Prevention and prediction of customer churn requires a comprehensive AI framework for customer engagement, and NICE Enlighten AI delivers actionable insights in addition to the ability to predict – and reduce – customer churn in real time. It’s easy to implement and operate and increases your organization’s effectiveness, now and over the long term. Learn more about how NICE Enlighten AI with Real-time Guidance guides agents while an interaction is in progress, giving them the coaching they need in the moment to deliver a satisfactory customer experience.

Learn more about customer experience analytics >>