Data Visualization to Analyze Consumer Behavior for Strategic Business Decision Making in the Retail Industry: Walmart Case Study
DOI:
https://doi.org/10.22303/csrid-.17.3.2025.354-371Keywords:
Power BI, Walmart, Business Intelligence, Consumer Behavior, Retail Industry, Data VisualizationAbstract
This research focuses on data visualization to analyze consumer behavior in an effort to make strategic business decisions in the retail industry, taking the Walmart Case Study. The main objective of this study is to explore customer consumption patterns and generate data-based insights that can be utilized in formulating marketing strategies and managing retail operations. A quantitative approach is applied through systematic stages, including problem identification, literature study, data collection, Extract, Transform, Load (ETL) process, analysis, visualization, and data interpretation. The dataset used includes 50,000 Walmart customer transactions during the period January 2024 to February 2025. The use of interactive data visualization using Microsoft Power BI successfully transformed raw data into strategic insights. Key findings from the analysis indicated that the majority of transactions came from loyal customers at $6.46 million (50.58%), emphasizing the importance of customer retention strategies. In addition, customer purchasing activity was much more dominant on weekdays, with weekday purchases totaling $9.07 million compared to weekend purchases totaling $3.70 million. The data also shows that Generation X dominates the overall purchase value compared to other age groups, with purchases totaling $5.04 million. In addition, in-depth analysis of the most popular product categories, segmentation by gender, and payment method preferences provided comprehensive insights. These visualization results significantly support fast and evidence-based business decision-making. This research contributes to retail business practice through an applicable data visualization approach, and opens up opportunities for further development such as the integration of machine learning for predictive analysis and wider exploration of BI tools to improve the accuracy and scope of business analysis in the future.
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