Retail-sales-analysis
KongaGro Mart – Retail Sales Analysis
Author: Daniel Ifenna
Date: April 2025
Executive Summary
This project provides an in-depth analysis of weekly sales data across 45+ KongaGro Mart stores. Leveraging exploratory data analysis and interactive Power BI dashboards, the study highlights key factors influencing sales performance — including seasonality, markdown effectiveness, and economic variables. The insights aim to support data-driven decisions that optimize store operations, improve marketing outcomes, and enhance profitability.
Project Objectives
- Analyze weekly sales trends, especially seasonal and holiday-related spikes
- Identify top- and bottom-performing stores based on total sales
- Evaluate the influence of economic indicators (CPI, unemployment, fuel price, temperature)
- Assess the impact of markdown strategies during holiday and non-holiday periods
- Compare weekly sales performance between holiday and non-holiday periods
- Recommend strategic actions to drive revenue growth and efficiency
Key Insights
1. Sales Trends
- Strong seasonal spikes in sales are observed around major holidays (e.g. Christmas)
- A gradual decline in sales typically follows Q1, suggesting a need for targeted promotions during off-peak periods
2. Store Performance
- Highest Sales: Store 20 – $301,397,799
- Lowest Sales: Store 33 – $37,160,222
3. Economic Drivers
- CPI and unemployment show a weak negative correlation with weekly sales
- Fuel price also has a weak negative influence
- Temperature shows a modest positive relationship with product-specific sales (e.g., sunglasses in summer, coats in winter)
4. Markdown Effectiveness
- Markdowns overall have a slight positive impact on sales
- Markdown 2 and 3 perform better during non-holiday weeks
- Markdown 1 and 5 are more effective during holiday weeks
- All correlations with weekly sales are positive but less than 0.1
5. Holiday vs. Non-Holiday Sales
- Holiday Weeks: $505.3M total (7.5%)
- Non-Holiday Weeks: $6.23B total (92.5%)
- Despite fewer holiday weeks, weekly sales are significantly higher during holidays
Recommendations
- Leverage Markdown 2 and 3 during off-seasons; prioritize Markdown 1 and 5 for holiday periods
- Adjust inventory strategy based on temperature trends and seasonal product demand
- Increase inventory and marketing focus during holidays, and strategically restock based on season after peak periods
- Regularly audit store performance and set measurable targets for each location
- Investigate employee satisfaction and operational workflows at low-performing stores to uncover hidden performance drivers
Deliverables
- R scripts for data cleaning and analysis
- Power BI dashboard for interactive visualization
- Business summary report with actionable insights
Repository Contents
/dashboard preview:
/scripts: R scripts used for preprocessing and analysis R-script/dashboard: Click here to download the Power BI (.pbix) fileKongaGro_Report.md: Final analysis summary Report