Building an Interactive Excel Dashboard.

Introduction

The dataset: 112 products scraped from Jumia Kenya, with fields for current price, old price, discount percentage, review count, and customer rating. The goal was to clean it, analyze it, and turn it into an interactive Excel dashboard — the kind with slicers and pivot charts, not just static tables.

Cleaning

Before any analysis, I had to fix:

• Prices stored as text — Excel treats "KSh 1,499" as a string, not a number, so no math works until you strip the formatting
• Ratings buried in descriptive text — extracting a clean numeric rating from messy scraped fields
• Invalid negative review counts — clearly a scraping artifact, not real data
• 55 of 112 products (49%) with no review or rating. I could have dropped those 55 rows,Instead labeled them e — "No reviews" and "Not Rated" — rather than treating them as blank. That turned out to matter a lot, because **half the catalog having zero customer feedback is itself one of the most important findings in the whole project, did need cleaning.

Building Categories for Pivot Analysis

To make pivot tables and slicers actually useful, I added four calculated columns:

• Review_Category — Low / Medium / High / No reviews
• Rating_category — Poor / Average / Excellent / Not Rated
• Discount_category — Low / Medium / High Discount
• price_category — Low / Medium / High / Premium (quartile-based bands)

Building the Dashboard

I built 8 pivot tables from a single shared table (tblCleaned) every pivot table needs to come from the same source table for slicers to control all of them simultaneously. Miss this, and you end up with a slicer that only filters one chart instead of your whole dashboard — a mistake I made and had to fix along the way.

The dashboard includes:

• 5 KPI cards (total products, average price, average discount, average rating, total reviews)
• 3 top-10 bar charts (by rating, by reviews, by discount)
• 3 trend charts (discount vs. reviews, rating vs. reviews, price vs. rating)
• 2 pie charts (rating breakdown, discount breakdown)
• 3 slicers (rating category, discount category, price category) connected across all 8 pivots

What the Data Actually Said

Discount % vs. Review count: correlation ≈ -0.14
Higher discounts do not lead to more reviews. If anything, there's a very weak negative relationship.

Rating vs. Review count: correlation ≈ +0.06
Essentially zero relationship. Highly-rated products don't get meaningfully more reviews than mediocre ones.

Price vs. Rating: correlation ≈ +0.15
Barely positive. Expensive products are not reliably rated higher than cheap ones — though mid-to-upper-priced items (KSh 1,190–1,820) did show the best average rating (~4.2) of any price tier.

• Only 16 of 112 products (14%) hit "strong engagement" (14+ reviews)
• 90% of heavily discounted products (40%+ off) show low or no engagement
• 49% of the entire catalog has zero reviews, regardless of discount or price

The product that stuck with me most: a 120W Cordless Vacuum Cleaner with 69 reviews — the most of any product in the dataset — sitting at just 2.8★. High visibility, low satisfaction. That's a much bigger red flag than a discount that didn't move the needle.

Repo & Full Breakdown

Full project, including the cleaned dataset, the Excel workbook (raw data → cleaned data → pivot tables → dashboard), and a detailed README with all findings:https://github.com/kimutaikoros/jumia-product-performance-dashboard