This repository contains all techniques which can be used for customer segmentation techniques to find target customer.
In the Retail sector, the various chain of hypermarkets generating an exceptionally large amount of data. This data is generated on a daily basis across the stores. This extensive database of customers transactions needs to analyze for designing profitable strategies.
All customers have different-different kind of needs. With the increase in customer base and transaction, it is not easy to understand the requirement of each customer. Identifying potential customers can improve the marketing campaign, which ultimately increases the sales. Segmentation can play a better role in grouping those customers into various segments
The data set refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories
My goal was to segment the customers to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.
Python, sklearn, GMM, Kmeans
I implemented customer segmentation using RFM(Recency, Frequency, Monetary) analysis on the dataset which is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.
I used RFM technique to find the customer on which company should target their campaign so that the marketing campaign should drive better and bring more revenue to company.The notebook depicts how python works effectively on large data and how technology helps market to generate revenue by identifying ideal customers.
Python, sklearn, pandas