Telecom Customer Churn Analytics & Prediction (part I)
According to Marwanto and Komaladewi’s paper, in recent years, customer churn at data communication business in telecommunication industry could not be controlled well. There have been a lot of things done to control customer churn such as special pricing and improvement of service quality. However, it did not reduce the level of customer churn.
In this article, I use the dataset from Kaggle that contains customer level information for a telecom company in US. Various attributes related to the services used are recorded for each customer. I will try to get insight about which variables are contributing to customer churn? Who are the customers more likely to churn? And what actions can be taken to stop them from leaving.
Let’s check the correlation between ‘Churn’ variable with the other variables.
It seems that Month to month contracts, absence of online security and tech support seem to be positively correlated with churn. While, tenure, two year contracts seem to be negatively correlated with churn. Interestingly, services such as Online security, streaming TV, online backup, tech support, etc. without internet connection seem to be negatively related to churn. Let’s explore the patterns for the above correlations below before we delve into modelling and identifying the important variables.
Gender Distribution
About half of the customers in our data set are male while the other half are female
Senior Citizens
There are only 16% of the customers who are senior citizens. Thus most of our customers in the data are younger people.
Partner and dependent status
About 50% of the customers have a partner, while only 30% of the total customers have dependents.
Tenure
After looking at the below histogram we can see that a lot of customers have been with the telecom company for just a month, while quite a many are there for about 72 months. This could be potentially because different customers have different contracts. Thus based on the contract they are into it could be more/less easier for the customers to stay/leave the telecom company.
Contracts
As we can see from this graph most of the customers are in the month to month contract. While there are equal number of customers in the 1 year and 2 year contracts.
Interestingly most of the monthly contracts last for 1–2 months, while the 2 year contracts tend to last for about 70 months. This shows that the customers taking a longer contract are more loyal to the company and tend to stay with it for a longer period of time.
Let us now look at the distribution of various services used by customers
Now let’s take a quick look at the relation between monthly and total charges. We will observe that the total charges increases as the monthly bill for a customer increases.
In the part II article, we will look at predictor variable (Churn) and understand its interaction with other important variables as was found out in the correlation plot. So, see you in the next article.
BR,
Erwindra Rusli
Data Scientist Student in Purwadhika School
