Telecom Customer Churn

For this project, I analyzed a dataset provided by a telecom company (source: Maven Analytics) that included information about their customers such as demographics, location, services, usage patterns, and churn status. The dataset had a total of 7,043 customer records of California, out of which 1,869 customers had churned, 454 customers had joined recently in the last quarter, and the rest (4,720) had stayed with the company.

To start my analysis, I first cleaned and preprocessed the data to remove any missing values, duplicates, or irrelevant data. I then performed exploratory data analysis to gain insights into the characteristics of the customer dataset, including their demographic profiles and usage patterns. Next, I conducted a comparative analysis of the customers that churned, joined, and stayed with the company. I looked at key demographic factors such as age, gender, marriage, income level, and location.

I also identified the key drivers of customer churn by performing a correlation analysis between various customer attributes and churn status. This helped me to understand which factors were most strongly associated with customer churn feedback, such as Attitude, Competitor, Dissatisfaction, Price and other types of drivers.

Furthermore, I analyzed the reasons for customer churn by creating a pivot table with a pie chart that showed the top reasons why customers churned. These reasons included dissatisfaction with the quality of service, pricing, competitor's bonanzas offers and customer support.

I also conducted a geographical analysis to identify the top cities in California that churned, which helped the telecom company to focus their retention efforts on customers in those areas.

Finally, I analyzed the effectiveness of retention offers by identifying which offers were most preferable to customers who were at risk of churning. This analysis helped the telecom company to develop targeted retention strategies and improve their customer retention rates.

RECOMMENDED ANALYSIS :

1. How many customers joined the company? How many customers joined?

   Ans : "454 customers" joined the company during the last quarter (Q2-2022) in which male count was 243 and female count was 211. The analysis was carried out on the basis of tenure of months, customer status and gender.

2. What is the customer profile for a customer that churned, joined, and stayed? Are they different?

   Ans : Customer profile is dependent on its gender, age, married, unmarried and customer status data for a customer that churned, joined and stayed. They are different from each other on the basis of the charts been showed. In churn data, 1869 customers have churned whereas in joined data, 454 customers have joined and lastly, in stayed data, 4720 of customers have been styed for a long time and also being loyal to the company. Other part of analysis is been showed in churned, joined and stayed section.

3. What seem to be the key drivers of customer churn?

   Ans : Attitude, Competitor, Dissatisfaction, Price and other types of drivers are been responsible for Customer churn but the key drivers from the data we get is "Competitor". There can be many reasons like services, offers, bonuses, etc.

4. Is the company losing high value customers? If so, how can they retain them?

   Ans : As per the revenue generated from the data, we come to know that around 1869 (34%) of the customer have churned from the total of 7,043 customers. So, somehow it is in big percentages and we can say the company is loosing high value customer(not that much). The company can retain it by giving better referral bonuses, range of support, good offers with greater cashbacks, etc.

5. Out of the 3 customer status, stayed, churned and joined, which has the highest percentage?

   Ans : Out of the 3 customer status, stayed, churned and joined, "Stayed (67%)" customer status has the highest%.

6. What payment method was preferred by churned users?

   Ans : "Bank Withdrawal" payment method was preferred by churned users.

7. What churn offers were more preferable by the customers?

   Ans : Total churn customer count was 1869 in which 1051 customers didn’t respond or preferred to the offers. So, from the data we received, we can say that 818 customers preferred for offers in which "Offer E" were more preferable by the customer

CONCLUSION:

The telecom companies can use data analytics algorithms to identify customers who are at a higher risk of churn and implement targeted retention strategies. Reducing customer churn is critical for telecom companies to maintain their customer base and revenue. By implementing effective retention strategies, telecom companies can improve customer satisfaction, loyalty, and ultimately, their bottom line by doing the perfect analysis for their loyal customer (few enhancement) and preparing to join or buy their services.

Overall, this Excel project provided valuable insights into the customer churn behavior of the telecom company and helped them to identify key areas for improvement in their retention efforts.