How AI Can Improve Customer Retention
How AI Can Improve Customer Retention, Client attrition and churn are not new problems. Anyone who has spent time in the sales world has heard statistics on the cost of acquiring a new customer. It can be five to 25 times more expensive to acquire a new customer than to keep an existing one.
More importantly, improving customer loyalty by just 5% can increase profits by 25 to 95%, depending on your industry and the size of your business. It goes without saying that businesses cannot afford to neglect the churn rate of their customers.
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 start. The best way to harness the power of AI and machine learning is to create a smart experience.
The smart experience is to leverage AI and ML to get predictive information that can be integrated into a CRM workflow. Businesses looking for a competitive edge must find ways to make their business operations smarter.
How can the smart experience help improve customer loyalty? It starts with a change of direction. Typically, companies tackle the problem by focusing on the churn rate. They invest time to find ways to prevent churn. However, the focus must shift from unsubscribing customers to a global look at customer success.
Focusing exclusively on unsubscribing is a very reactive tactic. Often, companies are late for the party with a churn rate. They will identify customers who are likely to turn around when it is too late. Indeed, there is a major difference between a leading indicator and a lagging indicator.
For example, many companies want to view the pace of orders as a sign of unsubscription. However, it tends to be a late indicator of an earlier problem. To have an impact, companies need to look at leading indicators.
Often the best churn indicators are found further in the customer lifecycle, during acquisition and integration. Sometimes, strong customer attrition is not due to poor customer service but to poor customer acquisition efforts.
Think about what was going on in your business when a new customer started. Are you launching a new product? Have there been any changes in your manufacturing process? How long did it take the customer to start using your service after the transaction was completed?
It is crucial to assess the landscape of the acquisition period. It is often there that perceptions of the relationship begin to form. Customers will compare their initial experience to the expectations you set during the sales process. As the age-old saying goes, first impressions are hard to shake.
Have you already determined the true cost of customer churn for your business? Before taking steps to improve customer loyalty, you need to quantify the cost of unsubscribing. There are three main variables to consider.
The first is obviously the loss of recurring revenue. All that customer pays is lost money.
Second, with any existing customer, it is possible to increase sales and increase revenues. The loss of this potential income must therefore be taken into account.
Finally, take into account all of your customer acquisition costs.
By combining these factors, you can better understand the true cost of unsubscribing customers.
Once you have determined the true cost of unsubscribing customers, you can begin to assess the quality of the unsubscription. Not all customer attrition is unfortunate. You should be able to determine what is an acceptable churn level and set a benchmark using basic analysis.
For example, it may be acceptable for a customer to leave if the cost of service is high and margins are low. This assumes that you acquire new net customers at an appropriate speed and volume to compensate for the loss of business. AI is certainly exciting, but you can’t get started without having laid the groundwork for basic analysis.
Better understand customer success
Once you’ve focused solely on the churn rate for overall customer success, determine the true cost of customer churn and establish fundamental analytics, then you can start using analytics and AI to drive success customers and reduce attrition.
As I mentioned earlier, the real value is in creating a smart experience. When implementing AI in a business, gathering information is excellent, but it is not enough. You must be able to leverage the information that can be discovered from the data to identify the next steps.
Your AI project can’t just be about getting a rating on the likelihood that a client will drop out. You must prepare your team for action by integrating information about the business process. This allows the goal to move from churn to customer success.
Here’s how it actually works. To predict the probability of a customer unsubscribing, you need a logistic regression model trained on historical data. It looks for examples of clients who have churned and those who have not. He will learn from these situations and develop a probability score for each client. Then, various measures can be taken to influence this probability in the hope of changing the outcome.
Natural language processing models can be used to discern a client’s feeling. These models can be populated with large amounts of unstructured data – such as call records and web chats – to find themes. Then, customers can be classified according to how they feel: good, bad or indifferent. These classifications are then put into an unsubscribe model based on logistic regression.
You start to chain several models to isolate the customers likely to withdraw. The key is to understand how to intervene before something really happens. That’s the power of predictive analytics. It allows you to be more proactive in improving customer loyalty rather than reactive to unsubscribe from customers.
Here’s an example of how to combine insight with action: George, an internal sales representative, works at the retention office, which is a specialized team responsible for reaching out to customers who have a high likelihood of churning. He enters the office in the morning and connects to his CRM. He sees a list of calls generated by an AI model that surfaces and ranks the customers likely to turn around. He tells George why the customer is likely to opt out and provides a relevant sales game to act on.
There is even more value in what AI and ML can do after George calls and takes the recommended action. Once he enters his call notes and updates the CRM, the customer can be recalculated in real time. This system allows you to continue to make sure that you take the next best steps to build customer loyalty.
AI is the future of business operations. When considering an investment in AI, make sure you have a configuration that will allow you to integrate information into your organization’s daily workflow. With the power of AI, you can start blurring the lines between sales, service, and marketing.
Remember that the best time to have a sales conversation is right after you resolve a problem. AI can be used to improve customer loyalty in a variety of industries. Think about how implementing AI can help you change your sales operations and ultimately drive customer success.