In a telco company, there are two costs known as Acquisition Cost and Retention Cost. Acquisition Cost is the expense for a company to acquire new customers. Meanwhile, Retention Cost is the spending for the company to retain existing customers.
Due to human limitations, we are often wrong to predict which customers will churn and which customers will retain. So the allocation of funds can be wrong so that the funds issued become larger.
Moreover, according to some sources, the acquisition cost is 7x greater than the retention cost. If we are wrong in predicting a customer who will actually churn, but it turns out that we predict a customer who will retain, then we need to spend more than we should.
Through this project, I will try to build a Machine Learning model that can predict which customer is going to churn or is still with us.
The model can predict which customers that are going to leave or stay so that cost allocation can be determined as precisely as possible.
Minimized Acquisition Cost.
The dataset that I will use is from Kaggle.com. You may visit the dataset through this link.
This dataset contains 33 variables (columns) and 7043 observations (rows).