Data Science
Identify customer segments for online retail with the use of K-means clustering
Practice Business Analytics in R and help Snapdeal create an algorithm to identify customer segments in online retail business
About this Menternship
Opportunity:
Snapdeal's mission is to become India’s value lifestyle omni-channel leader. This means they want to be the one-stop shop for all offline and online shoppers who are looking for a budget purchase. The company is building a complete ecosystem around value commerce, where they can serve 'Bharat' consumers wherever they are on their offline to online shopping journey. Snapdeal services 96% of the country's pin codes and aims to expand its network to reach the remotest areas of India. 


Problem:
E-commerce businesses like any other business depend on Customer Relationship Management software to manage customer relationships and drive customer loyalty and retention. Understanding the behavior of their customers and dividing them into segments as per their spending habits, and frequency of platform’s use - helps an e-commerce platform sharpen its customer success policy and increase revenue coming from repeat customers. Everyone in the e-commerce business understands that in a crowded marketplace, it is cheaper to retain customers than to acquire new ones for whose attention many competitors are already trying. A simple algorithm called K-means clustering can help Data Scientists better understand customer segments and predict their purchasing behavior and needs. In this menternship, you will get to practice this tool on R software.


Deliverable:
In this menternship, you will use K-means clustering to identify customer segments in online retail



Why take up this Menternship?
On completing this Menternship, you will learn about
You will explore: 
R
K-means clustering

You will apply:
Data preparation 
Feature Engineering 
EDA - Visualization
Unsupervised Learning Algorithm (K-Means Clustering)

You will create:
A model to group customer segments in online retail using R


Expected Output
  1. Report on applications of data science in e-commerce
  2. R notebook with cleaned data (normalized) - Features, and Engineered features
  3. Complete Codes for all previous components
  4. Final Report - Recommendations for the company
  5. Video of Self
Identify customer segments for online retail with the use of K-means clustering
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