Using Predictive Analytics to Stop Silent Customer Churn
Silent customers pose a major risk to companies. They rarely let companies know when there is a problem or when they are dissatisfied and then they unexpectedly cancel their services or simply never return to a business.
Within the hospitality industry specifically, we have found that it’s not uncommon to see well over 50% of guests who stay for the first time do not return in the next four years. We call this the “silent guest” or the “one and done” customer and they are seen through all industries on varying scales.
Companies can stop this seemingly endless cycle through the proper use of technology, specifically, with predictive analytics. Predictive analytics stops the silent customer churn by identifying four ways to retain customers:
Identify customers who are likely to churn, well before they decide to churn.
Predictive analytics can pick up signals like decreasing engagement and increases in certain behaviors that eventually result in silent churn. Predictive analytics can act like the silent observer in the background that picks up the subtle signals about someone’s likelihood to churn. Identifying these customers early provides a window to the company to influence the decision to churn for these customers.
Identify the most effective actions that can reduce the churn.
While identifying the customers who are likely to churn is good, you then need to know what best actions to take to stop the churn. By the way, the most frequently used action–giving a discount to customers who are likely to churn—is the most expensive and least likely to work strategy. This assumes that the only reason customers are churning is because they do not find your service a good value for the cost. Predictive analytics is very effective in figuring out the specific features of your service that a specific customer is likely to value. Now you can help the customer realize and discover the great value the service offers. Not only will you win that customer based on value delivered, you won’t have to lose money by giving a discount.
Identify the best time, message, and channel to use to reach the customer.
Again, predictive analytics can help you find out the best combination of the when, what message and what channel you should use to reach each customer to reduce silent churn. This hyper personalization means you are not using a one-size fits all approach and that your message connects with each customer.
Identify the full path to retention rather than one single action.
Many times we tend to assume that one single action from a company will result in silent churn reduction. However, silent churn typically happens because of a series experiences or non-experiences that the customer has. Predictive analytics can be used to identify the full suite of actions, and their sequence, that reduce silent churn.
Companies that adopt predictive analytics and take into consideration the four ways to retain a customer will be able to identify who is likely to leave and determine what the best plan of action is stopping the churn before it even begins
(About the author: Anil Kaul is CEO at Absolutdata)