It’s been well-documented that acquiring new customers is far more costly than growing relationships with the ones you already have. It’s just one reason that building customer loyalty is a priority for most businesses, and one of the key metrics we measure with the Brand Health Score.

Happy, loyal customers can be a company’s greatest asset. Not just for their lifetime value, but for the new customers they may recruit with word-of-mouth, positive online reviews, and social shares. If you have a segment of active and engaged customers, pat yourself on the back. What you’re doing for those folks is working well. 

But what about your not-so-engaged customers? Before a customer leaves, there’s rarely an announcement or singular tell-tale sign. Rather, there’s a subtle pattern of behaviors – and non-behaviors – that insinuates an impending departure. 

Because there are many potential behaviors and variables relative to customer attrition, it can be difficult to identify customers in danger. Unless you have the right data science tools, that is. 

Artificial intelligence solutions, such as neural networks, are providing marketers and other business leaders with new windows of insight into customers with the greatest likelihood of disengaging from the brands in their lives. Rather than accept attrition as an inevitable part of business, companies are now working to become proactive and laser-focused in their retention efforts. 

Here’s an example…

A Discida client was losing market share. Throwing up their hands in despair was not an option for the company’s leadership. Neither was impulsively rolling out irrelevant marketing offers. Instead, they wanted to focus the company’s resources exactly where it would have the most impact.

With the assistance of an AI neural network, the company’s marketing team deeply analyzed customer data. The network’s ability to synthesize large amounts of multi-dimensional data revealed the behavioral patterns and factors that preceded a customer leaving. That, in turn, allowed the company to segment its customer base and deploy hyper-personalized promotions, getting the right offer to the right customer at the right time. AI and data science enabled the company to become both proactive and effective, reaching waning customers with re-engagement campaigns before they left.

Companies don’t need a crystal ball to know which customers will leave. In fact, they have the answers right now. They’re just buried deep within their data, waiting for the right tools to uncover them.