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Reduce customer churn with AI-enhanced predictive analytics

Posted April 26, 2018
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Predictive analytics has long been an important part of corporate customer retention efforts. Research from the Aberdeen Group found that it generates an increase in customer lifetime value while also exposing the risk of customer turnover before it happens. When you add artificial intelligence to the mix, the impact on customer churn becomes even greater.

Customers consider jumping ship for many different reasons. Intervening before they make that fated decision — like the next time they reach out to your contact center — gives you an opportunity to resolve their problem and alleviate their concerns. And with a little help from AI, you can reduce friction and reveal which customers are at risk of abandoning your brand while revolutionizing your customer retention strategy in the process.

Focus on customer satisfaction

Customer retention is a byproduct of customer satisfaction — and predictive analytics is equipped to create a more satisfying experience. “When you implement a chatbot or sentiment-analysis tool, you’re able to resolve issues quickly without having any intermediary or hold time. You’re avoiding those first few minutes of the interaction that can be frustrating for customers,” says Matt DiMarsico, senior vice-president of business development with Xavient Digital – powered by TELUS International. “By increasing the efficiency of problem resolution using AI, you in turn reduce churn.”

Efficiency and problem-solving are the foundation of a good customer experience. Detelina Marinova, a marketing professor at the University of Missouri’s Trulaske College of Business, has been researching the best methods for solving customers’ needs for more than a decade.

Marinova’s findings illustrate that acknowledging the problem and immediately providing several solutions is a customer’s preferred approach, which can be easily replicated with artificial intelligence. “AI can apply these same techniques. It’s a complementary role,” Marinova says. “When machine learning algorithms can extract knowledge from customers and generate solutions, that’s a good thing.”

Marinova adds that blending these insights with a personalized human agent interaction can be especially powerful. “It’s a very promising way to tackle the problem [of customer satisfaction]” she says.

Leverage predictive modeling

AI systems can be used to improve the scale, speed and application of the tried-and-true predictive modeling approach to keep customers coming back. As an example, Xavient Digital’s Da Vinci solution was designed in part to gauge the probability of customer churn and customize strategies for preventing it by supporting chatbots, natural language processing systems and sentiment and behavioral analysis.

For instance, Da Vinci can prioritize a frustrated caller who has been flagged by an AI system and then recommend actions that will quickly address their needs.” As the technology becomes more robust, we can even complete a transaction before having to shift to an agent,” DiMarsico says. “When done well, you can layer in sentiment analysis to predict and avoid any miscommunication with the system.”

Curate customer data

When it comes to reducing churn, customer data is key. Developing an omnichannel approach that sources customer information from multiple touchpoints like the web, the contact center and retail stores, provides companies with a 360-degree view of the customer experience and identifies areas in need of improvement. “All AI is really doing is processing the data more quickly and more efficiently than a human,” explains customer-service experience expert and author Shep Hyken. “Big data is too difficult for humans to process. Now, we can ask the system questions we want answered as long as we have a really strong customer base.”

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Delivering Omnichannel Customer Experience

Everest Group, in partnership with TELUS International, has released its second paper in a two-part series, offering a practical guide to successful omnichannel execution.

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Hyken adds that predictive analytics are about comparing customers to those who came before them. It matches the data from your current customer to similar customer personas and profiles which lets you figure out what questions people are going to ask and whether they’re likely to leave.

There’s big benefits for sales teams as well. By determining which products customers purchased in the past and their utilization of certain discounts, companies can right-size their offerings and even upsell consumers. For example, Xavient’s Da Vinci is often used to predict Roll to Pay (when customers decide to continue on with a product or service after their free trial is up).

When done right, customers end up with exactly the right solution for their needs, and are thus more likely to stick around. “With uncanny accuracy you can predict what they’re going to buy, how long it’s going to take, and the questions they’ll have when they call,” Hyken says. “But you have to have that information from previous customers first, including those using a free trial.”

The benefits of predictive analytics extend beyond customer service and sales. Just as AI can determine which customers might churn, the same technology can be used to predict employee retention rates. It also serves to ensure your agents don’t burn out by repeating remedial tasks that could just as easily be handled by a chatbot. “Using AI shows employees you care enough to eliminate something anyone could do and better leverage their talents,” Hyken says.

Augmenting traditional predictive analytics methods to create an AI-enhanced experience has the potential to delight your customers, create loyal brand ambassadors and drive your company’s success. With so many applications and benefits, it’s a customer service tool not to be overlooked.

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