Discount Data Analysis for Eniac: A Comprehensive Overview

Context

The Spanish company Eniac wants to understand how its monthly revenue and average discount rate influenced sales performance in the years 2017 to 2018.

Main Objective

To carry out an in-depth analysis of the discount strategy and its impact on Eniac's revenue. Therefore, the relationship between discounts, sales performance, seasonal shifts, special events, product categories, and various price and quantity dynamics is investigated to provide insights into how to optimize the distribution of discounts among product categories, and throughout the year.

Challenge

To distribute discounts among product categories and throughout the year.

Data Source

The data for this analysis is sourced from Eniac's discount database, covering the period from 2017 to 2018. The dataset was cleaned to address issues such as a corrupted database, incongruent data, double-dot numbers, and general data quality concerns.

Tools Used

  • Python with Pandas for data manipulation and cleaning
  • Seaborn for statistical visualization
  • Excel for additional data manipulation and cleaning

Folder Structure

  • /src: Contains Python scripts for data cleaning and analysis.
  • /docs: Project csvs, and presentation.
  • /data: Original and clean databases.

Data Cleaning and Preparation

The dataset needed an extensive cleaning process to ensure data integrity:

1. Corrupted Database: Identified and addressed issues that affected the overall integrity of the database.

2. Incongruent Data: Resolved inconsistencies, mismatched, and/or duplicate data.

3. Double Dots Numbers: Rectified anomalies in numerical values, focusing on duplicated or irregular decimal points.

4. Data Quality Concerns: Mitigated issues affecting the reliability and completeness of the dataset.

Analysis Summary

The analysis revealed key insights into the impact of discounts on Eniac's sales performance. The graphs illustrating the key findings were plotted with the Seaborn library and then transferred to a short presentation.

1. Monthly Revenue and Average Discount Rate: There is a positive correlation between monthly revenue and the average discount rate.Specifically, periods with higher average discounts show an increment in sales performance.

2. Seasonal Shifts and Special Events: Discounts significantly influence sales during seasonal shifts and special events. Applying discounts during these periods increases the likelihood of generating sales.

3. Discounts Across Product Categories: Discounts vary based on the pricing and quantity of different product categories. Lower-priced items tend to have higher discounts, and products with higher quantities sold also attract higher discounts.

4. Effect on Revenue per Day by Category: Discounts impact the revenue per day, with variations observed across different product categories, especially those with the highest discounts and most sold products.

This project was made possible by the collaborative efforts of our team:

Me, Hanne Prüfer, Irene da Cruz, and Roberto Cavotti.