Introduction
Jumia is one of Africa’s leading online shopping platforms, offering a wide range of products at different prices and discount levels. With many products competing for customers’ attention, understanding pricing, discounts, customer reviews, and ratings is important for making better business decisions. This project analyzes data from 112 products listed on Jumia to identify patterns in pricing, discounts, and customer feedback.
The analysis uses Microsoft Excel to clean, organize, and analyze the product data and present the findings through an interactive dashboard. The dashboard provides insights into product prices, discount levels, customer reviews, and ratings, helping sellers and Jumia understand product performance and make informed decisions about pricing, promotions, and customer engagement.
Dataset description
The dataset consist consists of the following variables:
- Product: The name and description listed on jumia
- Current price: The current selling price in kenyan shillings
- Old price: The original price of the product before any discount was applied
- Discount: The percentage deduction offered on the the original price
- Review: The number of customer reviews received by each product
- Rating: The average customer rating given to the product
- Rating category:
- Discount category:
- Current price category:
Data cleaning and preparation process
The collected product dataset was cleaned and prepared in Microsoft Excel to ensure that the data was accurate, consistent, and suitable for anaysis. The following steps were carried out:
Removal of Duplicate Products: Duplicate product records were identified and removed to ensure that each product appeared only once in the dataset. This helped prevent duplication from affecting the analysis and calculations.
Standardization of Currency Values: The Find and Replace function was used to remove the “KSh” text from price values. The values were then converted into a numerical format and formatted to KES currency.
Freezing Panes: Panes were frozen to keep the column headings visible while scrolling through the dataset. This improved navigation and made it easier to identify variables when working with the large set of data.
Creation of Discount Categories
Additional columns were created to categorize products according to their discount levels. The categories used were High Discount, Medium Discount, and Low Discount.Categorization of Product Ratings
Product ratings were grouped into three categories: Poor, Average, and Excellent. A nested IF function was used to automatically assign each product to the appropriate rating category,e.g(=IF(G2>40%,"High discount"IF(G2>20%,"Medium discount"IFG2<20%,"Low discount"Calculation of discount amount was done using the function
=C2-B2Filtering of the Dataset Sort and filter was used to view and examine specific products or categories based on selected criteria.
Conversion into an Excel Table
The cleaned dataset was converted into an Excel table. This made the data easier to manage and automatically extended formulas and formatting when new data was added. he table features were used to calculate important summary statistics, including the count of products, number of reviews, ratings, averages, and total sums of numerical variables. These calculations provided a foundation for the analysis and development of the product performance dashboard.
Dashboard creation process
The cleaned data was organized into an Excel table, PivotTables were then created to summarize products, ratings, reviews, prices, and discounts, charts were then created to visualize the data, KPI cards were added to display key performance indicators, and slicers and filters were added to make the dashboard interactive. The charts, tables, and KPIs were arranged and formatted for easy interpretation.
key insights and business recommendations
Insights
High discounts (40%+ off) drive significantly higher review counts and engagement compared to low-discount items. However, steep discounts on low-rated items fail to generate sustained volume, indicating that discounts attract initial attention of customers
Medium prices generates the highest overall volume of positive reviews. Extremely low-priced products frequently suffer from lower average ratings due to perceived or actual quality issues.
Products rated above 4.0 receive higher visibility and sales volume. Items below a 3.0 rating show a sharp drop in customer reviews, signaling buyer hesitation and poor search rank performance.
Recommendations
Shift focus from high pricing to "Medium Discounts" (20–40%) on high-rated products (4.0+ stars) to maximize profit margins while maintaining high conversion rates
Encourage post-purchase reviews for average items using follow-up prompts or small loyalty incentives, as higher review directly correlate with better search placement on Jumia.
Pair high-performing products with newer or lower-visibility items in discount to boost engagement without relying heavily on price drops.
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