Three Ways ML Can Help With Customer Retention

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There’s an old saying in marketing that it costs five times more to get a new customer than it does to keep an old one. Of course, one could argue, as Blake Morgan did in her Forbes piece titled, “Does it still cost 5x more to create a new customer than retain an old one?,” that some customers are more valuable than others, and it’s actually good for business to let lousy customers go. But that only underscores the importance of keeping valuable customers happy.

The hard part is determining which valuable customers are happy and which are on the verge of leaving. Even when customer service or tech support is well aware that a customer is unhappy, this valuable intel may never reach that customer’s salesperson or customer success manager.

This is where machine learning (ML) can make an impact. Here are three things ML can do to help companies keep customer retention high:

1. Spot Unhappy Customers Before They Go

Odds are there are patterns among the customers that leave. Perhaps they contacted tech support repeatedly for the same problem. Or maybe they never completed the onboarding process. Machine Learning models are masters at identifying these kinds of patterns. Companies simply need to feed data about customers that have left into an ML model. That model can then be used to spot these same patterns among current customers. By using ML to predict which customers are potentially on the verge of defecting sooner rather than later, the appropriate account managers can be notified while there’s still time to rectify the situation.

2. Improve Products, Services, and Systems

These same patterns that cause customer churn also contain valuable intel about a company’s products, services, and systems. For example, there might be an unreported bug, or a larger issue like a disconnect between customers’ expectations and what the product or service actually does. Perhaps there’s a step in the onboarding process that stops a large portion of new customers in their tracks, preventing them from learning about key up-sell features.. By employing Machine Learning to identify these problem areas, it’s easier to allocate resources where they’ll have the biggest impact on customer satisfaction and retention.

3. Transform Unhappy Customers into Brand Ambassadors

Knowing which customers are on the verge of churning out provides a priceless opportunity: Account managers can connect with those customers and learn how to better serve their needs. Everybody wants to feel seen, heard, and appreciated, so the simple act of asking what’s wrong has the potential to transform those customer relationships. The key is to acknowledge a customer’s problems and take appropriate steps to solve them—or let the customer know their suggestion has been added to the roadmap for future development. Because so many customers love feeling like their input made a difference, those once-unhappy customers have the potential to become the company’s biggest evangelists and brand ambassadors.

Machine learning has the power to transform the customer retention process by enabling companies to make sense of the important patterns in their data—and automatically surface valuable intel to the right people so they can take action. It’s a potent approach that combines ML’s pattern-spotting prowess with humans’ relationship-building skills in order to grow revenue by reduce churn and improving customer satisfaction.

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