Reena Pinto

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About Project

This project presents an end-to-end data analytics solution, built to analyze and visualize historical grocery sales data from Favorita stores in Ecuador. Using Python for data preprocessing and Power BI for interactive dashboards, the analysis uncovers sales patterns, highlights the impact of promotions and holidays, and forecasts future trends.

With over 125 million transaction records, the dashboard delivers insights into unit sales, product demand, and store performance. It also demonstrates how external factors—such as oil prices, transferred holidays, and national events like the 2016 Ecuador earthquake—influence consumer behavior.

The goal of this project is to simplify complex retail data into actionable intelligence, empowering businesses to make smarter decisions on inventory management, pricing strategies, and marketing campaigns.

Location :

Ecuador

Client :

Favorita Stores

Project Type :

Data Analytics & Visualization

Duration :

2 Weeks

Completion :

15 Apr 2022

Tools:

Python, Power BI

Deliverables:

Interactive Sales Dashboard

Summary

The Grocery Sales Analysis project successfully transformed raw transactional data from Favorita stores into actionable business insights. By leveraging Python for data preparation and Power BI for interactive dashboards, the project uncovered trends in unit sales, product demand, store performance, and the influence of promotions, holidays, and external events. The analysis of over 125 million records not only highlighted key sales drivers but also provided a foundation for accurate forecasting and strategic decision-making.

 

 

Recommendations

  1. Optimize Inventory Planning: Use sales forecasts and demand trends to align stock levels with seasonal and regional patterns, reducing overstock and stockouts.

  2. Leverage Promotions Strategically: Target promotions around holidays and high-traffic periods to maximize sales uplift and customer engagement.

  3. Enhance Regional Strategies: Tailor pricing and marketing efforts by region, as performance varies significantly across locations and store types.

  4. Monitor External Factors: Incorporate external variables (e.g., oil prices, national events) into forecasting models for more accurate predictions.

  5. Expand Dashboard Usage: Deploy the Power BI dashboard to business stakeholders for real-time monitoring, empowering faster and data-driven decisions.