While doing my Tableau lessons on Datacamp, one of the courses was a case study to analyze customer churn in Tableau (Case Study: Analyzing Customer Churn in Tableau). To practice my Python skill, I decided to use the data set from the course and analyze it using Python and show my analysis here.
Databel is a fictitious cell phone company. The company executes want to understand customer churn situation and recommendations to reduce churn.
Top findings -
- Current churn rate is 26.86% with 6687 customers.
- Top churn reasons from the provided dataset -
- Competitor make better offer
- Competitor had better devices
- Attitude of support person
- Don't know
- Competitor offered more data
- Majority of the customers do not belong in a group plan. Customers without group contract have higher than average churn rate (32%).
- Customers are price sensitive to the international plan charge and international call usage.
- Churn rate is higher for customers making many international calls but not on the international calling plan, as well as customers on the international calling plan but not actively making international calls.
- More than half of the customers are on month-to-month contract.
- Customers on Month-to-month contract type account for majority of the churn(87%).
- Customers age 65 and above have higher churn rate than other age group. However, seniors account for less than 20% of the total customer base.
- Customers on unlimited data plan have higher churn rate than customers not on data plan.
Recommendation -
- Review current customer base who's not part of a group or on a contract. Develop marketing and sales promotion to encourage customers to join a group or contract.
- Review current customer base who's on the international calling plan and their international call usage. Develop new pricing plan to turn on / off international calling plan automatically based on monthly usage.
- Review current customer relation management system to ensure automatic number identification system is implemented, and a call history tracking system is on place. When a customer calls the support or customer service department, all the previous interaction with the customer is listed for easy access so customers do not need to call and repeat unsolved issues.
- Conduct full review of customer support and customer service procedures for quality assurance. Provide additional trainings as needed to ensure staffs are equipped with what they need to support customers.
- Churn rate for customers on unlimited data plan is higher than customers who are not on the unlimited data plan. The dataset does not include current device offering information, or peak data download speed. This will required further research to ensure company offers the latest devices and comparable data download speed.
Assumption made based on the project document, background and provided data -
- The churn rate calculated is a yearly figure.
Lessons learned from doing this exercise -
- Be mindful of the grouping details - if it's inclusive for the upper and lower limit when doing the calculation. Test, verify and adjust the group title accordingly.
- Data is not always complete. Review the data before conducting any in-depth analysis.
- Blogger is not a good site to post Jupyter notebook file. The format of the file will take some time to fix. I will have to look into posting my files on Github in the future.
Possible additional work in the future -
- Map churn rate by state and display on a map, similar to what can be done with Tableau.
- Research further on cell phone industry churn rate and compare the statistics.
Information about the data -
Databel data from Datacamp - Databel.csv data from Datacamp
Tableau Public analysis based on Datacamp's course can be seen here - Databel Tableau Analysis
Jupyter notebook for the Python file and analysis - Databel Jupyter notebook
Tools I used -
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