Skip to content

Latest commit

 

History

History
65 lines (46 loc) · 2.17 KB

README.md

File metadata and controls

65 lines (46 loc) · 2.17 KB
Raw

Customer-Segmentation-Analysis-for-Online-Retail-Store

Problem Statement:

An online retail store is trying to understand the various customer purchase patterns for their firm, you are required to give enough evidence based insights to provide the same.

Dataset Information:

The online_retail.csv contains 541909 rows and 8 columns.

  1. Invoice - Invoice number
  2. StockCode - Product ID
  3. Description - Product Description
  4. Quantity - Quantity of the product
  5. InvoiceDate - Date of the invoice
  6. Price - Price of the product per unit
  7. CustomerID - Customer ID
  8. Country - Region of Purchase

Problem Objective:

  1. Using the above data, find useful insights about the customer purchasing history that can be an added advantage for the online retailer.
  2. Segment the customers based on their purchasing behavior.

Steps involved:

  1. Data Preprocessing and EDA
  2. Feature Engineering
  3. Data Visualization
  4. Correlation analysis
  5. RFM analysis
  6. Machine Learning Model Development
  7. Model Evaluation

Conclusion:

Strategic Actions Based on Segments:

  1. Best Customers (3054 Customers, $4,150,536.51 Total Sales):

Action:

  • Focus on maintaining their loyalty.
  • Provide personalized offers, loyalty rewards, and early access to new products.
  • Regularly engage with these customers through targeted communication.
  1. Loyal Customers (1067 Customers, $512,818.85 Total Sales):

Action:

  • Re-engage with these customers.
  • Use email campaigns, special discounts, and personalized messages to remind them of your products.
  • Offer incentives to bring them back.
  1. Big Spenders (13 Customers, $1,655,398.08 Total Sales):

Action:

  • Treat them as VIPs.
  • Provide top-tier loyalty programs, exclusive access to premium products, and personalized concierge services.
  • Ensure excellent customer service and maintain close relationships.
  1. At Risk (204 Customers, $2,592,654.46 Total Sales):

Action:

  • Implement win-back strategies.
  • Send personalized offers and discounts, conduct satisfaction surveys, and offer incentives to retain them.
  • Identify potential reasons for their decline in activity and address these issues.