Businesses all over the world are growing every day. With the help of technology, they have access to a wider market and hence, a large customer base. Customer segmentation refers to categorizing customers into different groups with similar characteristics.
This project aims to identify major customer segments on a transnational data set for a UK-based online retail.
In this project, your task is to identify major customer segments on 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. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers. rfm_model
The differences in customers' behaviour, demographics, geographies, etc. help in classifying them in groups. Learning about different groups in the customer can help with following:
Target Marketing
Client understanding
Optimal product placement
Revenue growth
The RFM model is quite useful model in retail customer segmentation where only the data of customer transaction is available. RFM stands for the three dimensions:
Recency – How recently did the customer purchase?
Frequency – How often do they purchase?
Monetary Value – How much do they spend?
A combination of these three attributes can be defined to assign a quantitative value to customers. e.g. A customer who recently bought high value products and transacts regularly is a high value customer.
Data inspection
EDA
Data preparation
Create RFM model
Implementing various clustering Models and validating
Descriptive Analysis: The data exploration of Online customer segmentation dataset shows :
Missing and duplicate values were found.
Most of the purchases are from the United Kingdom.
Most of the customers have purchased items on Thursday, Wednesday, Tuesday.
Most of the customers have purchased items in November, October, December, and the least number of purchases in April, January, February.
Most of the customers purchase in the afternoon time.
Using a recency, frequency and monetary(RFM) analysis, the customers have been segmented into various clusters.
By applying different clustering algorithm to our dataset, we get the optimal number of cluster is equal to 3.
The business can focus on these different clusters and provide customer with services of each sector in a different way, which would not only benefit the customers but also the business at large.