The purpose of churn rate is to indicate the percentage of your customer base that churned over a given time period.
We can provide a fairly accurate churn rate (percentage of people that will not repurchase) for your e-shop via E-Advisor.
We begin with the classification process.
Problem instance - A Customer
Labels - Will repurchase, will not repurchase
Features - Purchases, demographics, days since last purchase
Training Data - A large number of customers categorized as repurchased , did not repurchase.
In our platform to solve problems like the above we use the Naive Bayes algorithm.
In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.
Customer churn prediction is essential as it helps to detect customers who are likely to cancel a product, subscription etc. It is very important to build reliable models which can
predict customer churn so that e-commerce stores can escape huge loss since it is also difficult/expensive to acquire new customers.