Skip to content

This repository contains an analysis of customer behavior for Tuscan Lifestyles using RFM (Recency, Frequency, Monetary) analysis. The project aims to segment customers based on their historical purchasing behavior and predict their future response rates to marketing efforts

License

Notifications You must be signed in to change notification settings

rishim9816/RFM_Analysis_Tuscan_Lifestyle

Repository files navigation

RFM Analysis for Tuscan Lifestyles

Project Overview

This repository contains an analysis of customer behavior for Tuscan Lifestyles using RFM (Recency, Frequency, Monetary) analysis. The project aims to segment customers based on their historical purchasing behavior and predict their future response rates to marketing efforts.

The analysis was performed as part of a marketing assignment where the objective was to determine the optimal marketing strategy based on RFM segmentation.

Project Files

The repository is structured as follows:

  • data/: Contains the raw data used for the analysis.
    • TuscanDataForRFMAnalysis.csv: The dataset for the RFM analysis.
  • notebooks/: Contains the R code and scripts.
    • RFM_analysis.Rmd: The R code used to perform the RFM analysis and generate the results.
  • results/: Contains the results of the analysis.
    • Assignment_Solution.pdf: The final PDF that contains the code output and results.
  • Assignment.pdf: The marketing assignment prompt outlining the tasks.
  • Case_Assessing_RFM_at_Tuscan_Lifestyles.pdf: The case study providing background information for the analysis.

Analysis Breakdown

Key Tasks:

  1. Customer Segmentation: Performed RFM segmentation based on past purchases.
  2. Predictive Modeling: Predicted response rates for different customer groups.
  3. Optimization: Calculated the expected profitability of different marketing strategies.
  4. Insights: Derived actionable insights for optimizing the marketing campaign using RFM analysis.

Summary of Findings:

  • Response Rate: 2.46% of customers responded to the test catalog.
  • Profitability of Targeted Marketing: Using an RFM-based targeting strategy significantly increases profitability and ROI compared to mass marketing.

Libraries Used

  • tidyverse
  • ggplot2
  • dplyr

How to Run

To replicate the analysis:

  1. Clone this repository.
  2. Open the R script in the notebooks folder.
  3. Run the script in an R environment or RStudio to see the output.

Results

The analysis and detailed output can be found in the results/Assignment_Solution.pdf file.

License

This project is for educational purposes.

About

This repository contains an analysis of customer behavior for Tuscan Lifestyles using RFM (Recency, Frequency, Monetary) analysis. The project aims to segment customers based on their historical purchasing behavior and predict their future response rates to marketing efforts

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published