Supermarket Sales Performance Analysis

This project presents an end-to-end analysis of a food-product sales dataset, combining SQL for data preparation and analysis, with Tableau for visualization and insight generation. The goal was to understand which products and categories drive revenue, how sales behave over time, and what patterns can support business decision-making for inventory, pricing, and demand planning

Problem Definition

The business questions guiding the analysis were:

  1. Which items generate the highest sales volume and revenue?

  2. How do sales vary across product categories?

  3. Are there identifiable high-demand periods or seasonal trends?

  4. What days and product types contribute most to overall sales performance?

Answering these questions would help stakeholders optimize procurement, product assortment, and promotional strategies

SQL & Tableau Project

Project Overview

This project focuses on analyzing over 878,000 supermarket transactions across multiple food categories. Using SQL for data cleaning and analysis, followed by Tableau for visualization, the objective was to identify the top-performing products and categories, understand seasonal and weekly sales patterns, and uncover insights to support revenue optimization and inventory planning

The workflow included:

  • Raw data assessment and cleaning

  • Removal of duplicates

  • Feature engineering (timestamps, revenue metrics)

  • Exploratory and descriptive SQL analysis

  • Exporting curated datasets to Excel

  • Dashboard construction in Tableau

  • Executive insights for business decision-making

Methodology Summary

1. Data Cleaning & Validation (MySQL)

  • Removal of duplicates

  • Casting date/time fields

  • Standardization of item/category references

  • Feature engineering

2. Exploratory SQL Analysis

  • Daily/weekly/monthly revenue

  • Units sold by category and product

  • Discount effects

  • Selling price vs wholesale price

3. Data Export

Creation of clean analytical tables for Tableau

4. Visualization & Storytelling

  • Interactive dashboard with filters for time, product, and category

  • Insights presented through KPIs, bar charts, trend lines, and heatmaps

SQL queries

Data Cleaning SQL queries

Feature Engineering Queries

Exploratory Analysis Queries