Costco Data Analysis Report

The full disclosure is at my Github Repository: https://github.com/thstasy/Costco-Data-Analysis-Report/blob/main/Costco_DS_report.pdf

Overview

This repository contains a comprehensive data analysis report focused on Costco’s grocery dataset, aiming to provide actionable insights into pricing, product variety, customer satisfaction, and discount strategies. The analysis uses a structured approach to understand current trends and offers recommendations for strategic improvements.

Dataset

The dataset used for this analysis is based on the publicly available Grocery Store Dataset, which contains information about product categories, prices, customer ratings, and discounts. It is widely recommended by data science professionals as an excellent resource for market basket analysis and portfolio projects.

Structure

The project is structured into the following key components:

Data Mining & Cleaning: Preprocessing the dataset to clean and standardize values (e.g., converting prices to float, extracting rating information).

• File: data_mining.ipynb

Exploratory Data Analysis (EDA): A detailed exploration of price distributions, product categories, ratings, and discount analysis. Key insights are visualized using histograms, bar charts, and scatter plots.

• File: Costco_DS_report.pdf

Market Basket Analysis: Utilizing association rules to identify patterns in product combinations and customer preferences.

• File: GroceryDataset.csv

Why Analyze Costco?

Costco, as a global leader in retail, serves a vast and diverse customer base. Analyzing its data offers valuable insights that can:

• Optimize pricing strategies
• Enhance product variety
• Improve customer satisfaction These findings can also benefit other industries by driving growth through data-driven decision-making and fostering better retail practices.

Analysis Highlights

1. Price Distribution: Most products fall within a mid-range price, with potential opportunities in both premium and low-cost segments.
2. Customer Satisfaction: Higher customer satisfaction is noted in mid-priced products, indicating value perception at these price points.
3. Discounts: A limited discount strategy shows room for improvement, potentially increasing customer engagement through more dynamic pricing models.

Conclusion

This analysis provides data-backed recommendations to refine Costco’s product, pricing, and discount strategies. The insights not only aim to benefit Costco but also offer a model that can be adapted by other retailers and industries to drive customer satisfaction and growth.

How to Use

• Clone the repository and run data_mining.ipynb to explore the raw dataset and the analysis process.
• Access the full report in Costco_DS_report.pdf for detailed visualizations and conclusions.