Data Visualization Techniques:

khokon12

Problem: A telecommunications company wants to understand why customers are churning and identify strategies to retain them.

Data:

• Customer demographics (age, gender, location)
• Subscription details (plan type, tenure)
• Usage data (call duration, data usage, SMS)
• Customer support interactions (number of tickets, resolution time)
• Churn status (yes/no)
  

• Customer Segmentation:
  
  
  • Cluster analysis: Group customers based on similar characteristics to identify customer segments.
  • Visualization: Use scatter Phone Number plots or dendrograms to visualize the clusters.
    
• Churn Analysis:
  
  
  • Survival analysis: Analyze customer lifetime value and identify factors contributing to churn.
  • Visualization: Use survival curves or Kaplan-Meier plots to visualize churn rates.
    

• Correlation Analysis:
  
  
  • Correlation matrix: Identify relationships between variables (e.g., tenure and churn rate).
  • Visualization: Use heatmaps or scatter plots to visualize correlations.
    
• Time Series Analysis:
  
  
  • Time series plots: Analyze trends in customer usage and churn over time.
  • Decomposition: Break down time series data into components (trend, seasonality, cycle, noise).