How to Create a Good Churn Prevention Strategy
How to Use Technology to Support Your Churn Prevention Strategy
Software or digital tools and platforms can provide powerful support for your organization’s churn prevention strategy. Many leading organizations are already leveraging artificial intelligence (AI) and machine learning to forecast future trends and behaviors and identify previously hidden indicators that help to predict churn. And as they do, they are realizing significant benefits; in fact, 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.
Interaction analytics is among the tools and techniques in use to provide context for customer data, enabling organizations to interpret that data in an actionable way to both predict and prevent customer churn. Interaction analytics enable contact centers to automatically analyze 100% of interactions, so you can discover the root causes of customer dissatisfaction by analyzing all of your customers' past and current activities. Organizations can use interaction analytics to identify customers who require immediate attention as well as patterns that allow the organization to prevent customer churn. Interaction analytics also leverages sentiment analysis to gauge the emotional state of customers and automatically identifies trending hot topics and key phrases in customer communications.
As AI interpretive and predictive models enable contact centers to autoscore soft skill behaviors and score 100% of interactions on the basis of customer behavior and whether that customer is a churn risk, they also enable organizations to interpret and measure these behaviors. They can then give agents immediate feedback on how to steer customer conversations with in-the-moment prompts and specific recommendations that increase CSAT and drive down churn. All the while, analytics can help organizations harness data on at-risk customers to refine marketing offers, tailoring to customer types and demographics.
Still other organizations are using customer journey analytics—both past and present journeys—to predict the likelihood of churn. Customer journey analytics combines data across multiple touchpoints to understand how customers navigate through brand interactions over time, which allows organizations to identify behavior that implicitly indicates that a customer is happy or dissatisfied with the service they just received.
Today’s customers are calling the shots and voting with their wallets. It’s no longer enough to simply react to churn; to succeed, organizations must predict and prevent customer churn with a holistic approach that sheds light on the end-to-end customer journey. Learn more about increasing customer loyalty with NICE Nexidia Churn Prediction. You can also read about tools and strategies to prevent churn in our Customer Experience Analytics guide.