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1. ABSTRACT
Customer churn, also known as Customer attrition, customer turnover, or customer defection, is when an existing customer, user, player, subscriber or any kind of return client stops doing business or ends the relationship with a company. Churn prediction is essential for businesses as it helps you detect customers who are likely to cancel a subscription, product or service. To predict whether a customer will be a churner or non-churner, there are several techniques applied for churn prediction, such as artificial neural networks, decision trees, and support vector machines. The past studies in this area indicate that Artificial Neural Networks (ANN) gives more accurate results to predict the customer churn. Therefore, this study will use the ANN to predict customer churn. The data will be collected from the telecom companies and ANN will used to predict the customer churn.
2. INTRODUCTION
The project contains of two main ideas and one particular connecting link between them. We will explain the ideas in simple way then progressively we will get to the details.
The first idea is the customer churn. The customer churn definition is any regular customer to company that depends on the regularity have stopped or cancelled the business or service with them. the term of churn has a lot of meaning but they are all agreed with when your customers or your subscribers stop, canceling their business or services with your product and also for customer loyalty it is an enemy and the sickness that will kill the business. It is calculated by number and the list of customers who left your company, also known as attrition. Why companies must take care of churn to have an idea with the customer who is with your side and to decrease the churn.
Second idea is the neural network the neural network is a computer system influenced by brain nervous.
After defining those two main ideas and accomplish them we must work on the very specific part that represent the main concept of the project which is the prediction. At the prediction we do the most of the art as we go from the neural network into the customer churn.