This project focuses on analyzing telecom customer data to identify factors that influence customer churn — the likelihood of customers leaving the service provider. By examining relationships between variables such as handset type, dropped calls, and number of gadgets owned, the study provides insights that help telecom companies improve customer retention strategies and service quality.
- Identify key variables correlated with customer churn.
- Analyze how handset type, dropped calls, and gadget ownership influence churn.
- Generate descriptive statistics and mean comparisons for deeper understanding.
- Provide actionable insights to reduce churn and improve customer satisfaction.
- Software: IBM SPSS Modeler
- Dataset:
telco1xdata.txt - Techniques: Data Preparation, Cross-tabulation, Means Analysis, Descriptive Statistics, Visualization
- Import the dataset
telco1xdata.txtusing the Var. File node. - Define variable types using the Type node (set target and input fields).
- Analyze relationships between handset and churn using the Matrix node.
- Visualize handset distribution with the Distribution node.
- Compare dropped calls across churned and active users using the Means node.
- Plot data patterns using Histogram and Plot nodes.
- Generate summary statistics using the Statistics node.
The stream analyzes telecom data to identify how handset type, dropped calls, and gadget ownership influence customer churn. It provides patterns and statistical evidence to help reduce churn and improve customer satisfaction.
- Reveals which customer behaviors or attributes most influence churn.
- Supports telecom companies in developing data-driven retention strategies.
├── Identifying_Relationships.docx
├── README.md
Utkarsh Verma BCA (Data Science & AI) — Assignment IBM SPSS Modeler Project