Customer Segmentation Using Exploratory Data Analysis and Clustering Methods: A Grocery Store Case Study
Session Title
STEM, Health, and the Economy
Faculty Mentor
Anna Romanova, Ph.D.
College
College of Business Administration
Department
Computer Science & Quantitative Methods
Abstract
Customer segmentation is the process of uncovering behavioral patterns and characteristics that various customer groups may share. This process makes it easier to create marketing, service and sales efforts tailored to the needs of a specific group and enables marketers to better understand their audience, increase revenue, and earn greater market share. The goal of this study is to identify customer segments for a retail grocery store. We use the data set from Kaggle that contains information about customers’ purchasing behavior and monetary value, shopping modality, recency, as well as their campaign response patterns. We use exploratory data analysis and a modified version of the RMF segmentation technique to identify customer tiers within each shopping modality and use customer demographic characteristics to create customer profiles for each tier. In the next step we employ K-means clustering to create customer segments based on customer monetary value and recency. We then evaluate the responsiveness of each cluster to marketing campaigns and discounts.
Course Assignment
BADM 571 – Romanova
Previously Presented/Performed?
Winthrop University Showcase of Undergraduate Research and Creative Endeavors, Rock Hill, SC, April 2023.
Type of Presentation
Oral presentation
Start Date
15-4-2023 12:00 PM
Customer Segmentation Using Exploratory Data Analysis and Clustering Methods: A Grocery Store Case Study
Customer segmentation is the process of uncovering behavioral patterns and characteristics that various customer groups may share. This process makes it easier to create marketing, service and sales efforts tailored to the needs of a specific group and enables marketers to better understand their audience, increase revenue, and earn greater market share. The goal of this study is to identify customer segments for a retail grocery store. We use the data set from Kaggle that contains information about customers’ purchasing behavior and monetary value, shopping modality, recency, as well as their campaign response patterns. We use exploratory data analysis and a modified version of the RMF segmentation technique to identify customer tiers within each shopping modality and use customer demographic characteristics to create customer profiles for each tier. In the next step we employ K-means clustering to create customer segments based on customer monetary value and recency. We then evaluate the responsiveness of each cluster to marketing campaigns and discounts.
