Customer churn means that the loss of an extending customer to an opponent. Churn prediction is a highly important job for insurances 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 customers. Retain existing customers is cheaper than obtaining new customers. 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, the prediction is the prevalent method to decrease churn. Sales specialists do prediction by using limited variables and temporary rules and algorithms 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 Works?
The common way of predicting future customer churn starts with your historical data analysis with B2Metric Data Ingestion tool.
The data selection policy starts with describing variables that can affect customer churn. B2Metric Churn Prediction product predicts the reply for customers which already exist. It does that by setup an AutoML approach that ties up the predictors to replies. This model pertains to the administer learning situation. Predicting future customer churn is important because it helps your business' winnings a better realization of future expected income. Also, it makes 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 flow potential churn for the percentage of your customer, it let able to you who does inference to keep them attention from stopping their connections with you.
Insurance Customer Churn Prediction
The Churn prediction model forecasting a customer's tendency to churn by using knowledge about the customer such as household and data like 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 of data preparation for churn prediction is collecting all existing information about the customer. The data that is procured for predicting the churn is categorized in the following :
Demographic data, 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, 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, satisfaction level decreases.