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Catching Customer Churn Early

Sept. 13, 2022
Here’s how electrical distributors can hang on to at-risk customers with artificial intelligence (AI).

Losing customers is expensive. The costs required to prospect new leads, close sales and convince new customers to become repeat buyers are high. Once a buyer defects to another supplier, your team will have to put in twice the work to find a new customer to fill the gap.

It’s more cost-effective to retain customers than to secure new customers. Studies show that existing customers spend significantly more over time and are 300% more likely to buy from your brand than new customers. A mere +5% uptick in customer retention can increase profits by +25%.

Fortunately, most customer churn is preventable. In one survey, 97% of customers said a negative customer service experience would change their buying behavior, and 46% said they would continue to shop elsewhere even two years after a negative experience. Improving your customer service strategy could prevent nearly most of the customer churn among these buyers.

Because churn is preventable, it’s essential to pay close attention and find ways to mitigate it. You must understand why customers are dissatisfied and what factors turn them away from your brand. Most companies look to the past to understand why customers defect. With the help of artificial intelligence (AI), you can catch signs of churn early and take steps to prevent it.


Customer churn rate is a lagging indicator of customer satisfaction. Churn is often a slow process, with a customer who regularly buys from five categories of products slipping down to four categories, then to three and so on. Oftentimes this slippage is missed. Most distributors only realize churn has happened after customers stop buying from them altogether.

Unfortunately, once a customer leaves, they are unlikely to return. That is why identifying churn risk early and taking steps to address it is the most effective way to retain customers.

AI identifies churn risk by analyzing data such as customer behavior, transaction data, browsing behavior and demographics. As a result, it pinpoints irregularities and alerts sales teams and customer service representatives (CSRs) when they should reach out to an at-risk account. For instance, if a customer stops buying their usual products, buys at a lower frequency or stops browsing online, AI can identify that change and trigger an alert. AI models use various metrics to understand churn risk Below are several of them.

Behavior. AI uses irregularities in customer behavior to identify churn risk. For example, a customer may have a predictable reordering schedule and place an order on the same day each week. If they are several days late, AI will alert the sales team that something unusual has happened so a rep can reach out and see if there is a problem.

Similarly, sales teams must be aware of regular changes in shopping behavior. For example, some customers may have increased needs in spring and summer and will buy more during those seasons. In this case, a drop in spending during the winter months may not be a sign of churn but just part of a natural cycle. AI-driven analytics can help sales teams see variations in their customers’ behavior and determine whether they need to be concerned or not.

Brand interactions. Natural language processing AI models detect customer sentiment by analyzing tone, language cues and subtext. Some models scan social media posts, customer surveys, emails, CSR notes, online forums and service center transcripts to identify negative brand impressions and dissatisfied customers.


With the help of AI, reps will be better equipped to retain customers and prevent churn by understanding which accounts are likely to defect and why. From there, sales and customer service reps can use AI to retain customers with several digital marketing strategies.

Recommendations. AI-powered recommendations help reps make more personalized and relevant product suggestions. This feature reviews browsing behavior and past purchases and compares perceived preferences to similar accounts to identify suitable item suggestions. Great product suggestions help prevent category slippage by ensuring customers find what they need and have the option to reorder from you. AI recommendations also help customers find items they may have missed, promote complementary products, and provide accurate substitutions — boosting satisfaction, improving customer stickiness and reducing churn risk.

Sentiment analysis. Natural language processing analyzes sentiment across various touchpoints and identifies customers who may be unhappy with their product or service. AI then informs your support team of any potential issues so CSRs know who to contact and offer support to. Contacting customers as soon as they show signs of frustration or unhappiness can help your team soothe tensions and reduce the risk of customers switching to a competitor instead.

Timely offers. AI can boost retention by automatically sending personalized offers and incentives to at-risk customers. It accomplishes this by reviewing customer analytics and basing offers on purchase history, interests and behavior. Data-driven promotions ensure customers receive timely offers they are likely to engage with.

Scrambling to understand customer churn by pouring over old records and notes by hand is ineffective. With hundreds of customers buying thousands of different products, it would be impossible for a sales rep to be aware of everything going on with each account — especially when slippage happens gradually over time. AI models make it easier than ever for sales reps to keep up with their customers. With the help of AI, you can take steps to improve the customer journey and ensure your buyers continue to spend with you well into the future.