Discover how B2Metric's customer churn prediction models can help you identify at-risk customers and take proactive steps to improve retention. Read our blog to learn more about the power of predictive analytics.

Murat Hacıoğlu
BMS - Customer Churn Prediction
3 Minute Read
Machine Learning
Automated Machine Learning
Churn Prediction
Risk Management
B2metric Machine Learning Studio
Customer Segmentation
Return of Investment


Table of Content

  1. How Does Prediction Work?
  2. Insurance Customer Churn Prediction


Customer churn means the loss of an existing customer to an opponent. Churn prediction is a highly important job for insurance and finance services because winning new customers is a highly costly issue, so keep your current customer base. All costs of customer churn cover both lost income and the marketing costs related to switching those old customers with new ones. Retaining existing customers is cheaper than obtaining new ones. Also, older customers, who have no voluntary deductible excess, are generally non-churning customers. Newer ones do churn more often. Hence, decreasing customer churn is the main business target of every online business. To manage customer churn, churning customers should be identified first. Then these customers must be convinced to stay.

To obtain the churn rate, associate the churning variable per case after profiling. And then calculate the percentage of churning customers per cluster.

Churn prevention demands the estimation of prospective customers on time and accurately. Therefore, prediction is the prevalent method to decrease churn. Sales specialists make predictions by using limited variables, temporary rules, and algorithms based on the available data. Instead of humans, a machine learning model can be used that can sustainably educate itself and revise itself in the customer's business cycle direction.

How Does Prediction Work?


The common way of predicting future customer churn starts with your historical data analysis with the B2Metric Data Ingestion tool.

The data selection policy starts with describing variables that can affect customer churn. The B2Metric Churn Prediction product predicts the reply for customers that already exist. It does that by setting up an AutoML approach that ties up the predictors to replies. This model pertains to the administered learning situation. Predicting future customer churn is important because it helps your business realize its future expected income. Also, it makes it easier for your business to increase in a better way and interpret long-term hopes for income.

Besides, when your business can use churn prediction to predict potential churn for a percentage of your customers, it allows you to make inferences to keep them from stopping their connections with you.



B2Metric blogpost


Insurance Customer Churn Prediction

The Churn prediction model forecasts a customer's tendency to churn by using knowledge about the customer, such as household and financial data, transactional data, and behavioral data.

The inputs for the Churn prediction model are customer demographic data, insurance diplomacies, prizes, terms, demands, grievances, and the sentiment total from past surveys.

The first step in data preparation for churn prediction is collecting all existing information about the customer. The data that is procured for predicting churn is categorized as follows:

Demographic information, such as age, education, gender, revenue, employment condition, marital condition, homeownership condition, and retirement strategy.

Diplomacy-related data, such as insurance lines, number of diplomacies in the household, household time, prize, disposable revenue, and insured cars.

Claims, such as claim settlement duration and the number of claims that are filed and denied.

Complaints, such as a number of open and closed complaints.

Survey sentiment data Sentiment scores from past surveys are captured in the latest and average note attitude score fields. The note attitude score is derived from customer negative feedback only. If the note attitude is zero, the customer is more satisfied, while as the number increases, the satisfaction level decreases.


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