coletangsy / Customer-Analysis-Online-Retails

This project focus on customer analysis and segmentation. Which help to generate specific marketing strategies targeting different groups. RFM Analysis, Cohort Analysis, and K-means Clusters were conducted on a UK-based online retail transaction dataset with 1,067,371 rows of records hosted on the UCI Machine Learning Repository.

Customer Segmentation Analysis

Introduction

This project focus on customer analysis and segmentation. Which help to generate specific marketing strategies targeting different groups. RFM Analysis, Cohort Analysis, and K-means Clusters were conducted on a UK-based online retail transaction dataset with 1,067,371 rows of records hosted on the UCI Machine Learning Repository.

Content

	Item	Progress	Version	Links
1	data	DONE	1	- Raw Dataset - for_dataframe.py
2	Data preparation & EDA	DONE	1	- Data_Preparation&RFM_analysis.ipynb
3	Cohort Analysis	DONE	1	- cohort_analysis.ipynb
4	RFM Analysis	DONE	2	- Data_Preparation&RFM_analysis.ipynb - Lato-Bold.ttf - eda_function.py
5	K-means Cluster	DONE	1	- K-means.ipynb

Cohort Analysis

The main target here is applying cohort analysis to find out the customers behavior changes over time and the behavior pattern, to generate marketing strategies.

Meaningful Finding

Special group

• Group of 2009 Dec, have a high retention rate, keep remaining around 20 - 40 %
• Group of 2010 Dec, have a relatively low retention rate that keeping under 12 % afterward

Special period

• All groups have a significant increase in retention rate in Oct, Nov 2010, and Nov 2011, we believe it may be related to the Black Friday events.

Special trend

• A notable increase in the retention of the group after Jan 2011 can be observed from the cohort analysis.

RFM Analysis

RFM is a method used for analyzing customer values, which is commonly used in marketing and has received particular attention in retail and professional services industries.

RFM stands for:

Recency (新客) - When is the last time the customer purchase?

• Generally, a customer who interacted or transacted with the brand more recently, the more likely that the customer will be responsive to communication with the brand.

Frequency (常客) - How often do they purchase?

• Customer with frequent interacted or transacted with the brand are more engaged, and more likely to be loyal to the brand.

Monetay Value (貴客) - How much do they spend?

• its refers to the total amounts a customer has spent with the brand during a particular period of time.

Customer Segmentation - RFM Segment

	Customer Group	RFM Segment
1	Best Customers	1-1-1
2	High-spending New Customers	1-4-1 ; 1-4-2
3	Lowest-spending Active Loyal Customers	1-1-3 ; 1-1-4
4	Churned Best Customers	4-1-1 ; 4-1-2 ; 4-2-1 ; 4-2-2

Customers Segmentation - RFM Score

	Customer Group	No. of Customer	RFM Score
1	Gold	794	3 -5
2	Sliver	3,178	6 - 8
3	Gold	1,906	9 - 12

Products Keywords in Different Group's Purchase History

Gold

Silver

Bronze

K-means Cluster

From the above graph, we can find out that:

• Group 1 (Label 0) = Middle group in Monetary Value
• Group 2 (Label 1) = Lowest Monetary Value (mostly under $5,000), also lower in Frequency
• Group 3 (Label 2) = Highest Monetary Value (mostly above $10,000), lower Recency (mostly lower than 100)

Reach out to me

• Linkedin - Nicole Tang
• Kaggle - nicoletangsy
• Matters - My study journal in Chinese