Retail · Beginner
Jumia Product Performance Dashboard
Turn a raw export of Jumia listings into an Excel dashboard on pricing, discounts and customer reviews.
The scenario
The scenarioYou are a data analyst supporting Jumia's marketplace team. Jumia wants to understand how pricing strategies, discounts and customer feedback influence product performance. You have been handed a raw export of home, kitchen and tools listings exactly as it came off the site: prices are text with a currency prefix, some listings carry a price range, review counts are stored as negatives and ratings read "4.5 out of 5". Your job is to clean it, analyse it and build an interactive Excel dashboard that helps Jumia and its sellers make better decisions about pricing, promotions and customer engagement.
Your role
Data Analyst, Jumia marketplace team
The business problem
Jumia and its sellers set discounts and prices without knowing whether they move customer engagement. Nobody can currently say whether higher discounts bring more reviews, whether well-rated products cost more or less than the rest, which listings are performing well, or which need a different pricing or marketing strategy.
Objectives
Objectives- 1Clean the raw export so every price, discount, review count and rating is a usable number.
- 2Enrich the data with discount amount, rating category, discount category and price category.
- 3Establish descriptive statistics: average prices, average discount, average rating, totals and price extremes.
- 4Test whether discounts, ratings and prices relate to customer reviews.
- 5Identify the top and bottom performers by rating, reviews and discount.
- 6Build an interactive Excel dashboard with KPIs, top-10 tables, trend charts, category breakdowns and slicers.
- 7Write findings and recommendations that Jumia sellers can act on.
Tasks and business questions
Tasks and business questions- 1
Understand the brief
Read the scenario and the dataset overview. Write down the decisions Jumia and its sellers want to make and the questions the dashboard must answer before you open the data.
- What decisions do Jumia and its sellers want to make with this dashboard?
- Which columns will need transforming before any analysis is possible?
- 2
Clean and prepare the data
Keep the raw sheet untouched and build a cleaned copy. Check for missing values, remove duplicate rows, strip the currency prefix and thousands separators from both price columns, turn the discount into a number, make review counts positive whole numbers, and standardise the rating into a numeric value. Record every decision in a cleaning log.
- Which rows are exact duplicates, and how did you confirm that before deleting them?
- How did you convert values such as "KSh 1,525" and "KSh 1,620 - KSh 1,980" into numbers?
- What did you do with negative review counts, missing ratings and the "out of 5" text?
- Which other inconsistencies did you find, and how did you correct them?
- 3
Enrich the data
Add calculated columns: Discount Amount (old price minus current price), a rating category (Poor below 3, Average from 3 to 4, Excellent above 4.5), a discount category (Low below 20%, Medium 20% to 40%, High above 40%) and a price category with thresholds you choose and justify.
- What is the discount amount for each product, and does it agree with the discount percentage?
- How did you set the price category thresholds, and why?
- The rating bands in the brief leave a gap between 4 and 4.5. How did you handle it?
- 4
Calculate descriptive statistics
Use formulas or a pivot table to establish the baseline figures: average current price, average old price, average discount percentage, average rating, total number of products, total number of reviews, and the most and least expensive products.
- What are the average current price, old price, discount and rating?
- How many products and how many reviews are in the cleaned data?
- Which products are the most and least expensive?
- 5
Analyse trends and product performance
Test the relationships in the brief and rank the products. Use scatter or column charts and pivot tables to compare discount against reviews, rating against reviews and price against rating. Produce the top-10 lists and flag the outliers.
- Does a higher discount percentage result in more customer reviews?
- Do highly rated products receive more reviews, and are expensive products rated higher than cheaper ones?
- Which are the top 10 products by discount, by number of reviews and by rating, and the 5 lowest-rated?
- Which products have high discounts but low ratings, and which have high discounts but little engagement?
- Which products show strong customer demand, and which receive many reviews but only average ratings?
- 6
Build the interactive dashboard
Lay out a dashboard sheet with an overview of KPIs (total products, average price, average discount, average rating, total reviews), a product performance section with the three top-10 tables, a trend section with the three relationship charts, and a categories section for rating and discount bands. Add slicers for rating category, discount category and price category, and use conditional formatting to highlight the extremes.
- Which five KPIs sit at the top of the dashboard, and where does each number come from?
- Do all charts and tables respond to the rating, discount and price slicers?
- Can a seller with no Excel skill read the dashboard unaided?
- 7
Write up, publish and submit
Write the README with your process, key findings, business insights and recommendations. Publish the Dev.to article with screenshots of each stage, push the workbook and README to a GitHub repository, then submit the repository and article links here.
- Are higher discounts leading to higher customer engagement?
- Do highly rated products have higher or lower prices?
- Which products are performing best, and which need a better pricing strategy?
- What three recommendations would you give Jumia sellers, and which figures support each one?
Dataset and data dictionary
Dataset and data dictionaryA single table of 115 Jumia product listings with six columns: product name, current price, old price, discount percentage, number of reviews and rating. The export is deliberately raw. Prices are text with a "KSh" prefix and thousands separators, a few listings show a price range rather than a single price, review counts are stored as negative numbers, ratings are text such as "4.5 out of 5", the rating column header is misspelt, and there are missing values and duplicate rows to find.
Jumia product listings (raw export)jumia_products
115 listings exactly as exported from Jumia: text prices, negative review counts, ratings as text, duplicates and blanks included. · 115 rows
| Column | Type | Description | Example |
|---|---|---|---|
Product | text | Listing title as shown on Jumia. Some titles repeat, which is how duplicate rows are spotted. | Portable Mini Cordless Car Vacuum Cleaner - Blue |
Current price | text (KSh) | Selling price after discount, stored as text with a "KSh" prefix and thousands separator. A few listings show a range. | KSh 2,199 |
old price | text (KSh) | Price before the discount, in the same text format as the current price. | KSh 2,923 |
Discount | text (percentage) | Percentage discount on the listing, stored as text with a percent sign. | 25% |
Review | integer (stored negative) | Number of customer reviews, exported as a negative number. Blank where the listing has no reviews. | -24 |
Ratingd | text (out of 5) | Average customer rating stored as "x out of 5". The header is misspelt in the export. Blank where there are no reviews. | 4.6 out of 5 |
Required deliverables
- Excel workbook (.xlsx) containing the original data, the cleaned data, all pivot tables, pivot charts and the interactive dashboard with slicers
- README.md explaining the project, the cleaning and analysis process, key findings, business insights and recommendations
- GitHub repository holding the workbook and README, properly structured and documented
- Dev.to article titled "Building an Interactive Excel Dashboard for E-commerce Product Analysis: A Case Study of Jumia Products", with screenshots of the raw data, cleaned data, formulas, pivot tables, charts and final dashboard
Excel workbook (.xlsx) containing the original data, the cleaned data, all pivot tables, pivot charts and the interactive dashboard with slicersHow it is marked
- Data cleaning15%
- Technical accuracy20%
- Analysis quality20%
- Visualisation and dashboard design15%
- Business insights15%
- Recommendations10%
- Documentation and presentation5%
- Pass mark60%
Every price, review and rating converted to a correct number with the cleaning steps documented; duplicates and missing values handled deliberately rather than silently; category thresholds stated and applied consistently; pivot tables and charts that answer the brief's questions; a dashboard a seller can filter with slicers and read unaided; and findings that quote specific figures from the data rather than general statements.