How AI Can Increase Customer Retention

Customer attrition and churn are not new problems. Anyone who has spent time in the sales world has heard statistics around the cost of acquiring a new customer. It can be five to 25 times more expensive to acquire a new customer than to retain an existing one.

More importantly, improving your customer retention by just 5 percent can increase profits by 25-95 percent, depending on your industry and company size. Needless to say, companies cannot afford to neglect their customer churn.

Today, many companies are intrigued by the idea of turning to artificial intelligence for help in the sales process. However, most do not know where or how to get started. The best way to tap into the power of AI and machine learning is by building an intelligent experience.

The intelligent experience is all about leveraging AI and ML to derive predictive insights that can be embedded into the workflow of a CRM. Companies seeking a competitive advantage must find ways to make their business operations more intelligent.

Reasons for Churn

How can the intelligent experience help improve customer retention? It starts with a shift in focus. Typically, businesses are addressing the problem by homing in on churn. They invest time in figuring out how to prevent churn. However, the focus needs to shift from customer churn alone to an overall look at customer success.

Focusing on churn exclusively is a very reactive tactic. Oftentimes, companies are late to the party with churn. They will identify customers likely to churn when it’s too late. This is because there is a major difference between a leading indicator and a lagging indicator.

For example, many businesses want to look at order cadence as a sign of churn. However, it tends to be a lagging indicator of a problem that manifested earlier. In order to make an impact, businesses must look at the leading indicators.

Often the best indicators for churn are further back in the customer lifecycle, during acquisition and onboarding. Sometimes high customer attrition is not due to poor customer service but to poor customer acquisition efforts.

Think back to what was happening in your business when a new customer started. Were you launching a new product? Were there changes in your manufacturing process? How long did it take the customer to start utilizing your service once the deal was executed?

It is crucial to assess the landscape of the acquisition time period. This often is where perceptions of the relationship start to form. Customers are going to be comparing their initial experience to the expectations you set during the sales process. As the age-old adage tells us, first impressions are hard to shake.

Examine Cost

Have you ever determined the true cost of customer churn for your business? Before you take any steps to improve customer retention, you must quantify the cost of churn. There are three major variables to consider.

First is obviously the loss of recurring revenue. Whatever that customer is paying is money lost.

Second, with any existing customer, there is an opportunity to upsell and expand revenue. So you have to take into consideration the loss of that potential revenue.

Finally, factor in all your customer acquisition costs.

By combining these factors, you can get a better understanding of the true cost of customer churn.

Once you have determined the true cost of customer churn, you can begin assessing the quality of churn. Not all customer attrition is regrettable. You should be able to determine what an acceptable level of churn is and set an established benchmark using basic analytics.

For example, it might be OK for a customer to leave if the cost-to-serve is high and the margins are low. That assumes you are acquiring net-new customers at an appropriate velocity and volume to compensate for lost business. AI is certainly exciting, but you cannot jump into it without first laying the foundation with basic analytics.

Get Smart About Customer Success

After you shift your focus from purely churn to overall customer success, determine the true cost of customer churn, and establish foundational analytics, you then can begin using analytics and AI to drive customer success and reduce attrition.

As I mentioned earlier, the real value is in creating an intelligent experience. When implementing AI into a business, gathering insights is great, but that is not enough. You must be able to leverage the insights that can be uncovered from data to identify next steps.

Your AI project cannot be simply about getting a score of how likely a customer is to churn. You need to set your team up for action by weaving insights into the business process. This allows the focus to move from churn to customer success.

Here is how it actually works. To predict the probability of a customer to churn, you need a logistic regression model that is trained on historical data. It is looking for examples of customers who have churned and ones who have not. It will learn from these situations and develop a probability score for each customer. Then various actions can be taken to influence that probability in hopes of changing the outcome.

Natural language processing models can be used to discern a customer’s sentiment. These models can be fed large amounts of unstructured data — such as call recordings and Web chats — to find themes. Then customers can be classified by how they feel: good, bad or indifferent. These classifications then are put into a logistic regression-based churn model.

You are starting to chain together multiple models to help isolate customers who are likely to churn. The key is to figure out how to intervene before something actually happens. This is the power of predictive analytics. It allows you to be more proactive in improving customer retention rather than reactive to customer churn.

Here is an example of how to pair insight with action: George, an inside sales rep, is working the retention desk, which is a specialized team tasked with reaching out to customers who have a high likelihood of churning. He enters the office in the morning and logs into his CRM. He sees a call list generated by an AI model that surfaces and ranks customers likely to churn. It tells George why the customer is likely to churn and provides a relevant sales play to take action.

There is even more value in what AI and ML can do after George makes the call and takes the recommended action. Once he inputs his call notes and updates the CRM, the customer can be re-scored in real time. This system allows you to continue ensuring you are taking the next best steps to retain the customer.

Conclusion

AI is the future of business operations. When contemplating an investment in AI, be sure you have a setup that will allow you to embed insights into the daily workflow of your organization. Through the power of AI, you can start blurring the lines between sales, service and marketing.

Remember, the best time to have a selling conversation is right after you’ve solved a problem. AI can be used to improve customer retention in a variety of industries. Consider how implementing AI can help change your sales operations and ultimately drive customer success.