Retail User Segmentation using RFM model

Saisree GR

0

Digital Marketer

Marketing Strategist

Data Analyst

Microsoft Excel

Python

SQL

Worked with retail chain to segment their customers based on RFM (Recency, Frequency, Monetary) model inorder to target its loyal users. It's particularly useful in understanding customer value, improving targeting strategies, and maximizing returns on marketing efforts.
Steps for RFM Segmentation:
1. Data Collection & Preparation:
Extraction of transaction data for one of outlets of grocery chain along with inputs necessary to calculate recency, frequency and monetary value for a given time period.
Convert data into a table where each row represents a customer, with columns for recency, frequency, and monetary metrics.
2. Evaluation:
Assign scores(say 1-5) to each customer based on their RFM metrics.
The highest score (5) represents the top 20% of customers for each metric, and the lowest score (1) represents the bottom 20%.
Combine RFM scores to form an overall customer score (e.g., a customer with scores of R=4, F=5, M=5 is highly valuable).
3. Segmentation:
Group customers based on their RFM scores.
Typical segments include:
Impact:
1. Market Campaign & A/B testing:
Based on RFM models, champions and loyal customers of the platform were picked to provide rewards and referral options exclusively through e-mail marketing to encourage engagement and conversions.
This was followed by A/B testing to test the success of the campaign
2. Interactive Dashboard:
A dashboard displaying the distribution of RFM scores, segment performance, and customer behavior patterns.
Filters for sales teams or marketing managers to drill down into specific segments or time periods.
Some key metrics include Customer Lifetime Value (CLV), average order value (AOV), customer retention rate per segment, etc.
Like this project
0

Posted Sep 24, 2024

Worked with retail chain to segment their customers based on RFM (Recency, Frequency, Monetary) model inorder to target its loyal users.

Likes

0

Views

0

Tags

Digital Marketer

Marketing Strategist

Data Analyst

Microsoft Excel

Python

SQL

Saisree GR

Data scientist | Data analyst | Data scraper (UBC, Canada)

Retail Sales Forecasting with Predictive Modeling
Retail Sales Forecasting with Predictive Modeling
Ed-Tech Platform Customer Engagement Dashboard | Data analyst
Ed-Tech Platform Customer Engagement Dashboard | Data analyst
Real time monitoring of call-center performance
Real time monitoring of call-center performance
Linkedin job scraping using Python | Web Scraper
Linkedin job scraping using Python | Web Scraper