RFM analysis

High-Value Customer Targeting – RFM Analysis

Exploratory Data Analysis in R / Tableau Visualization

Project Goal

The RFM analysis is an effective marketing segmentation method to gain insight into customer behavior. In the exploratory data analysis, I calculated three indexes for the RFM model, which are recency, frequency, and monetary value. Combining those three indexes, I generate important customer insights and improve our segmentation strategy using Tableau visualization.

Data

The dataset tracks 1,000 customers over 18 months, recording their purchase date, expenditure, and quantity bought.

Step One : Data wrangling

In this step , I clean the raw data by the following steps.
1 .Load in the data
2 .Detect missing data
3 . Manage Date Columns
4 .Generate New Columns (Trips)

Step Two : Generate key indexes

I calculate three key indexes for the RFM analysis
Recency : When was a customer’s last purchase?
Frequency : How often does a customer make a purchase?
Monetary Value : How much money does a customer spend on purchases?

Step Three : Generate Ranking Index

I calculate rank from 1 to 5 for each index, by dividing five equal ranges of percentile.

Final Data

I finalize the data for the RFM analysis.

Data Visualization using Tableau

I imported the R result into Tableau to obtain insights from the RFM ranking.

Dashboard Key Findings

From the plots I can discover :
1 . Distribution of Rankings
2 .Number of orders for each customer
3. Relationship between Recency & Frequency

Segmentation Interactive Tool

By Tableau, we can discover more insights.
By using this segmentation tool, we can identify the most valuable customer segments.