2. Customer Segmentation & RFM Analysis

Description

• Customer segmentation involves companies acknowledging the diversity of their customer base and tailoring marketing strategies to smaller, specific groups.

Project Objectives:

• Determine the number of existing customer segments based on product purchase behavior (fish, fruit, meat, sweet wines).

• Profile each customer segment.

• Prioritize the customer segments.

Result

Key highlights:

1. Data Collection and Analysis:

• Utilized the Customer Personality Analysis dataset from Kaggle, which includes demographics, household composition, engagement metrics, and purchase patterns.

• Conducted a literature review, including a paper on RFM Model and K-Means Clustering Analysis of Transit Traveler Profiles.

• Implemented demographic, psychographic, geographic, and behavioral segmentation.

2. Cluster and RFM Analysis:

•  Assessed clustering tendency using Hopkins and Visual Method.

• Defined the number of clusters and validated them through methods like the Elbow method, Silhouette, and Gap statistic.

• Analyzed customer data for RFM metrics (Recency, Frequency, Monetary) to determine customer lifetime value and segment prioritization.

3. Cluster Characteristics:

•  Described clusters based on characteristics like education level, marital status, average income, and average purchase number of various items.

4. RFM Analysis:

•  Prioritized customer segments based on RFM metrics, identifying groups like "Cannot lose them," "At risk," "Loyal Customer," "Champion," and others.

• Developed potential strategies for each cluster, like welcome programs, win-back offers, and personalized communication, depending on their engagement levels and purchasing behavior.

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GROUP 3 - DANA 4840 Fall 2023 Customer Segmentation Submission.pdf

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