Predicting and preventing customer churn through machine learning
What Is Churn Prevention With AI ?
With our AI churn prediction expertise, we help companies to predict which customers will leave, for which reason, and to prioritize retention activities accordingly.
Why Prevent Churn With AI ?
It is 25 times more expensive to gain a new customer than to retain an existing one. Yet many companies only remark that a customer has left when it is too late.
What are the Business Benefits?
As a sales or marketing manager, you may have an idea of what are the drivers of churn. With AI based churn prediction, you can have a data driven assessment of: what are the drivers of churn, what is their relative importance, and what is their cost. Beyond understanding the reasons of churn at a very granular level, this enables you to take action in a data driven way.
Predict Which Customers Will Leave
What if you could predict which customers will leave? It may sound impossible to some of you, but this is a field of application of AI that works with a high level of certainty. As a marketing or customer service manager, it enables you to focus your actions on the high risk customers.
Focus On What Will Work
Within a few weeks, know exactly where you must focus your sales and marketing and customer service actions in order to have rapid ROI.In other words: aim at the right target instead of losing time testing actions.
A Case study on Churn Prediction
One of our latest projects is a churn prediction solution for a company active in the financial industry.
The company was faced with losing 25000 B2B customers per year. Some of the churners were identified, some of them were not identified because their contract was still running, even if they were not ordering. The reasons for churn were not known, and the company was not able to identify churners in advance. As a consequence, our client had no actionable way to prevent its customers from leaving.
Kantify developed an AI based solution in order to enable its client to identify what were the reasons for churn, what was the cost of churn from each variable (churn impacting event), and to predict the churn, client per client. This solution is based on internal client data and external data. One of the valuable elements from the solution is to capture and predict the impact of economic churn : when the economic situation of a client directly impacts its purchasing behaviour towards our client.
Thanks to its new churn prevention solution, our client is able to adapt its marketing, sales and customer service activities to not only predict, but also prevent its customers from leaving.