This is astonishing data about customer attrition. It’s no wonder that businesses of all sizes consider customer churn as one of the measures of their success. It should come as no surprise that a loyal customer is more open to exploring more of your products or upgrading to a higher service.
If you come to think of it, the measurement of customer attrition is a reactive metric rather than a proactive one because as a business you try to rectify or improve this metric only after the incident has already occurred. What this means is that, only after the customer has churned or stopped doing business with you that you ascertain what went wrong and try to fix it.
But this approach is passé. Today, businesses are empowered to access and track various touchpoints in a customer journey that attributes to customer attrition. With the emergence of disruptive technology like artificial intelligence, data can be further leveraged to solve for high impact business problems such as customer churn in a more effective way.
Customer churn costs business their revenue. Hence the ideal move is to reduce the churn rate. A quick way to derive customer churn rate for your business is to divide the number of lost customers by the total numbers of customers in a given period of time.
It is important to remember, at this point, that there are numerous factors that contribute to churn. It is never just one incident or event that drives a customer to stop doing business with you. It is usually a series of events, one leading to another, at different stages in the customer journey that results in attrition. It is necessary to consider all this data to arrive at the causes.
Customer churn, alternatively referred to as customer attrition is the percentage of customers that a business loses within a given period of time. Simply put, it is a metric that tells you how engaged you’ve kept your customers so that they stick with your business. There are different ways in which customer churn is perceived.
Absolute churn: is when a customer actively stops doing business with you and severs all ties with your company.
Presumed churn: is when a customer simply stops engaging with your business though this is not necessarily on paper.
Reactive churn: is when a customer reacts to a negative incident or event and stops doing business with you.
Prospective churn: is when a customer slow begins to disengage with your business.
At the heart of managing customer churn is the ability to identify early on the cues of possible attritors. To do that you need to take the help of analytics and data. This way you can proactively take the necessary steps to prevent those customers from leaving your business.
Here are some of the analytics that you should be looking at:
Understanding negative triggers and their effects
By examining historical data about bad customer experiences and how different customers responded to those negative events can help you build a model to predict reactive customer churn. Using this, you can begin tracking similar triggers that your existing customers are experiencing. This will give you a head start on determining how these customers are most likely to react. You can then begin implementing preventive measures to reduce the churn. While you are doing this, it is good to keep in mind that there are also external factors at play.
Understanding disengaging customer behavior
Customer reaction to leave your business could be a direct consequence of a negative experience. But that is not always the case. There are non-trigger-driven instances that cause attrition. Historical data can be used to predict such events. An ideal approach is to do a 360° assessment of your existing customers and then map them to customers who have churned in the past. This way you can foretell which customers are at a higher risk of churning in the near future.
Spotting high-risk clusters
You can use a decision tree model to classify customers based on behavioral characteristics to identify clusters of customers who are at a higher risk of churning. Based on the clusters, you can then take remedial actions.
One of the key problems with customer churn management models is the high possibility of false positives. It can have a damaging effect on the initiatives that businesses implement to curb churn. False positives can potentially increase the cost of the programs. Simply put, you will be spending money trying to retain customers who weren’t at a risk of leaving in the first place.
There are a few broad tips that you can follow to address this issue of false positively. One way to do it would be to increase the number of data points types of data and volume of data. Another approach is to explore diverse elements in the model to enhance the performance. You can do this by choosing different models that are selectively effective for specific kinds of predictions, and then bringing them all together to enhance the overall performance.
Data to consider when building AI models for customer churn management
To deal with any facet of customer behavior, you need to leverage data. The more data you have, the more accurate your analysis will be. Having a well-rounded perspective on all your customer engagement will help you take a holistic approach to customer churn management. Here are a few key datasets that you can incorporate.
You need to identify the best practices to put in place to remediate customer churn. There are a wide range of AI-powered solutions that you can use to achieve this. If you are looking for a cost-effective AI solution to manage customer churn, let us know. Brainalyzed is the artificial swarm intelligence platform you can use to address churn in your business.