Data Science
Identify customer segments for online retail with the use of K-means clustering
E-commerce industry needs data scientists for pretty much everything growth-related. Build some extraordinary proof of work in Data Science for e-commerce
Certified by
Role
Data Scientist
Industry
Technology
No. of Subscribers
70
Level
Intermediate
Time Commitment
60 Hours
Duration
45 days
Tools you’ll learn
Here’s What You Work On
About the Company
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.
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.
Explore
the following work techniques
K-means clustering
Data Science Application in E-Commerce
Bridging the gap
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 implement this algorithm using Python.
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 implement this algorithm using Python.
Apply
the following skills
Data Analysis
Data preparation
Hypothesis testing
Model formulation
Expected output
In this menternship, you will use K-means clustering to identify customer segments in online retail
Create
the following deliverables
Dataset preparation for analysis
A model to group customer segments in online retail using Python
What you’ll need before starting
Python Libraries
Segmentation Algorithms
Segmentation Algorithms
