This project focuses on analyzing customer churn in a telecommunication company using the provided dataset. Customer churn, or the rate at which customers stop doing business with a company, is a critical metric for telecommunication companies as it directly impacts revenue and profitability. By understanding the factors influencing churn, companies can implement targeted strategies to improve customer retention and satisfaction.
The dataset contains information about telecommunication customers, including demographic data, usage patterns, service plans, and churn status. Key columns include customer state, account length, area code, international plan status, voice mail plan status, call details, and customer service interactions.
- State: The state where the customer is located.
- Account Length: The duration of time the customer has been with the company (in days).
- Area Code: The area code associated with the customer's phone number.
- International Plan: Whether the customer has an international calling plan (yes/no).
- Voice Mail Plan: Whether the customer has a voicemail plan (yes/no).
- Number Vmail Messages: The number of voicemail messages the customer has.
- Total Day Minutes: The total number of minutes the customer used during the daytime.
- Total Day Calls: The total number of calls the customer made during the daytime.
- Total Day Charge: The total charge for daytime usage.
- Total Eve Minutes: The total number of minutes the customer used during the evening.
- Total Eve Calls: The total number of calls the customer made during the evening.
- Total Eve Charge: The total charge for evening usage.
- Total Night Minutes: The total number of minutes the customer used during the night.
- Total Night Calls: The total number of calls the customer made during the night.
- Total Night Charge: The total charge for nighttime usage.
- Total Intl Minutes: The total number of international minutes used by the customer.
- Total Intl Calls: The total number of international calls made by the customer.
- Total Intl Charge: The total charge for international usage.
- Customer Service Calls: The number of customer service calls made by the customer.
- Churn: Whether the customer has churned (left) the company (True/False).
- Data Acquisition: The dataset was obtained and loaded into a pandas DataFrame for analysis.
- Data Inspection: The structure, size, and contents of the dataset were examined to gain an understanding of the data.
- Data Cleaning:
- Duplicate entries were checked and removed to ensure data integrity.
- Data types were examined and converted where necessary for consistency.
- Missing values were handled appropriately, either by imputation or removal.
- Exploratory Data Analysis (EDA):
- Statistical summaries, distributions, and visualizations were used to explore the dataset and identify patterns.
- Key metrics such as churn rate, customer demographics, and usage patterns were analyzed.
- Insights Generation:
- Insights were derived from the analysis to understand factors influencing customer churn and retention.
- Correlations between variables and churn status were examined to identify significant predictors of churn.
- Visualization:
- Visualizations such as bar plots, box plots, pie charts, heatmaps, pairplots were created to present findings in a clear and concise manner.
- Insights were visually represented to facilitate understanding and decision-making.
The analysis provides valuable insights into customer churn behavior in the telecommunication company. By understanding the factors influencing churn, the company can implement targeted strategies to improve customer retention and satisfaction, ultimately leading to increased revenue and profitability.
- Predictive Modeling: Develop predictive models to forecast customer churn and identify at-risk customers in advance.
- Customer Segmentation: Segment customers based on their behavior and characteristics to tailor retention strategies.
- Feedback Loop: Establish a feedback loop to continuously monitor customer satisfaction and refine retention strategies accordingly.
- Dashboard Preparation: Create interactive dashboards to visualize key metrics and track churn trends over time.
- Data: Contains the dataset used for analysis.
- Notebooks: Jupyter notebooks documenting the data wrangling and analysis process.
- Reports: Contains reports, visualizations, and findings from the analysis.
- README.md: Overview of the project and instructions for replicating the analysis.
- Telecommunication Customer Churn Dataset
- Python Libraries: Pandas, Matplotlib, Seaborn, Scikit-learn