Customer churn prediction using machine learning
Making computers able to identify patterns in data is a major feature of machine learning a part of the artificial intelligence discipline that includes algorithms and methodologies to learn from data without explicitly scripting how to. Customer churn prediction means using machine learning to identify conduct characteristics that show that the use of a company’s services/products decreases over time.
Any marketer, business analyst, Sales Manager, or CEO can experience a rising churn or attrition. Of course, not only income but also reputations are affected when clients do not prolong contracts or quit. Since customer acquisitions normally cost far more than retention, companies are having challenges, particularly when the cause of departure is unclear.
In this article, we will look at how machine learning can help with customer churn prediction in other words how it can be used to identify churn and help mitigate attrition.
Benefits of Customer Churn Prediction Using Machine Learning
Identify clients at risk
First, machine learning can be used to identify clients at risk, before is too late. For example, machine learning algorithms can be trained to accurately estimate churn rates in order to find out the patterns of behavior of customers/partners who have already terminated their contracts or any other relationship with a particular organization and to compare those with the current, existing customers. Correlations are then established between active and inactive customer activities. The system, therefore, recognizes the most probable clients to leave.
Identify pain points
It is a fact, for various reasons, various companies are losing their customers. The “pain points” are usually unknown to the product or service company. In most situations, there are many factors such as poor quality and missing functionality of unattractive design, and awful customer service. Even if your product is almost perfect there will still be areas where improvements can be made to shoot for a 0% attrition. This is where machine learning can be used.
How does Customer Churn Prediction Machine Learning function?
The major aim here is essentially to predict and determine the cause of customer churn using machine learning. If companies do so in time, the churn rate may be reduced (the number of customers who opt to cancel their renewals, stop buying and migrate to competitors) and the rate of retention may be increased (number of customers who continue using services or buying goods).
First, you need to specify whatever data you want to collect. Thus, the collection, preparation, and processing of data in a way that machine learning models can understand is the first step. Subsequently, the machine learning algorithms will learn from the processed data.
For example, in a retail store, the software may record, analyze and view the data while offering specific forecasts depending on the shopping pattern, the number of check-ups and the amount of shopping carried out by a user.
Churn risk detection helps take preliminary action to keep customers
Customer churn prediction and modeling can go a step further to identify a selection of successful retention activities. The customer churn prediction system helps customer success managers to specify which clients they should approach. In other words, staff can be sure that at the proper time they are talking to the right customers (the ones at risk of attrition).
The insight derived from data analysis can be used by sales, customer success, and marketing teams to align their actions. If a customer is at risk of churning, we should not try up-sale instead we should involve the customer success team and understand the current status of the customer and if anything is needed to improve the satisfaction level with the current offering.
Conclusion
Customer churn prediction is an important activity to mitigate attrition and keep a high satisfaction level for all customers.
In this article we explored how customer churn prediction models can be used by all customer-facing teams to identify churn, prevent churn and increase the customer lifetime value for the company.