Building an Interactive Excel Dashboard for E-commerce Product Analysis: A Case Study of Jumia Products

#analytics #data #datascience

Objective

The goal of this project is to create an interactive Excel dashboard that provides insights into the performance of products listed on Jumia.
Emphasis is on product pricing, discounts, customer reviews, and ratings in order to understand product performance and identify trends. Its meant to help Jumia and sellers make better decisions around pricing strategies, promotions, and customer engagement.

Project introduction and objective

There were 115 source rows and six fields at the beginning of the project: Product, Current price, Old price, Discount, Review, and Ratings. The goal was to build a professional, interactive Excel dashboard that turns the supplied Jumia product data into useful pricing, promotion, and customer-engagement insights.

Dataset and business questions

The dataset consisted information about products listed on Jumia with the following columns:
• Product: Name of the product.
• Current Price: The current selling price of the product (in KSh).
• Old Price: The original price before discount (in KSh).
• Discount: The percentage discount offered on the product.
• Review: The number of customer reviews received by the product.
• Rating: The average customer rating of the product (out of 5).

While the business questions were: Which products have the highest ratings? Which products have the most reviews? Which products carry the largest discounts? How much of the catalogue has missing customer feedback? Are discount levels associated with review counts or ratings? Can these patterns be presented in a dashboard that decision-makers can filter and interpret quickly?

Initial data-quality audit

Six columns and 115 rows were recorded in the audit. The worksheet found 11 product duplicate rows, and each of the 58 review and rating rows was blank. Additionally, there were negative numbers in the reviews that needed to be looked at rather than deleted automatically. Currency was in form of KSh like "KSh 950" used in place of prices, and rating as text like "4.5 out of 5".

Cleaning and preparation decisions

Product names were cleaned/trimmed and duplicates handled. Currency was converted to numeric values for current and old price. Negative reviews were treated as an artifact rather than genuine negative reviews and converted to whole-number. Ratings had 'out of 5' text, they were removed, converted to decimals, and missing ratings were left blank.

Excel formulas and enrichment fields

The cleaned table was added columns such as; rating check, Discount check and Price check. Additional columns calculate the discount amount, calculated discount rate, rating category and discount category. Rating categories are Missing, Poor, Average and Excellent; discount categories are Low Discount (<20%), Medium Discount (20%–40%), and High Discount (>40%).

PivotTable and analysis workflow

The tblProducts table is the main table. Pivots tables rank products by rating, review count and discount, while separate summaries show the mix of rating and discount. The dashboard combines these outputs with scatterplots.

Dashboard design and slicer connections

The dashboard combines slicers, charts and tblProduct table into one display. Slicer filters are included in the spreadsheet.

Key findings

The cleaned table contains 110 products. The dashboard gives an average rating of 3.9 and 723 total reviews. 56 of 110 products have no rating, or about 51%. The rating mix is 17 Excellent, 26 Average, 11 Poor and 56 Missing. Discounting is concentrated in the high-discount group: 60 products above 40%, 32 medium-discount and 18 low-discount products.

Business recommendations

Products with missing customer feedback should be given priority for data-quality follow-up. Instead of thinking that a bigger discount equates to higher demand, evaluate high-discount products in conjunction with reviews. Look into high-rated products.