Excel Dashboards: Product Analysis for Jumia Kenya

Introduction

E-commerce platforms like Jumia collect large amounts of data daily on prices, discounts, reviews, and ratings. However, this data is only useful when it is broken down and analyzed to identify trends and insights that can help improve pricing, promotions, and customer engagement.

For this project, I analyzed a dataset of 112 products listed on Jumia, to understand how pricing and discounting relate to customer reviews and ratings. The goal was to move from a raw dataset to a clean, interactive Excel dashboard that surfaces real business insights to be used by the company's managers and directors to make desions.

This article walks you through my process: cleaning the data, enriching it with calculated fields, running the analysis, building the dashboard, and the insights that came out of it.

Dataset Description

The dataset contained six fields per product:

• Product — the product name
• Current Price — the current selling price, in KSh
• Old Price — the price before any discount
• Discount — the discount percentage applied
• Review — the number of customer reviews
• Rating — the average customer rating, out of 5

At a glance, the raw data had a few problems typical of scraped or exported e-commerce data: prices stored as text with currency symbols and commas, ratings embedded in strings like "4.5 out of 5" instead of numbers, and some review counts stored as negative values. None of this was usable for analysis until it was cleaned.

Data Cleaning and Preparation

Before any analysis could happen, I worked through a standard data-cleaning checklist:

• Numeric conversion — stripped KSh, commas, and other non-numeric characters from Current Price and Old Price, converting both to clean numeric columns.
• Rating extraction — parsed the numeric rating out of the "X out of 5" text format.
• Fixing sign errors — corrected review counts that had been stored as negative numbers.
• Missing values — checked every column for gaps. Notably, 55 of the 112 products (about 49%) had no rating at all. Rather than dropping these rows (which would have thrown away real pricing and discount data), I labeled them "Not Provided" in a rating category column so they could be handled explicitly instead of silently distorting averages.
• Duplicates and inconsistencies — scanned for duplicate or near-duplicate product entries and inconsistent formatting.

Enrichment: New Calculated Columns

Once the base data was clean, I added several derived columns to make deeper analysis possible:

• Discount Amount = Old Price − Current Price
• Rating Category: Poor (below 3), Average (3–4), Excellent (above 4.5)

Formula used:
=IF(ISBLANK(I13),"",IF(I13<3,"Poor",IF(I13>=4.5,"Excellent","Average")))

• Discount Category: Low (below 20%), Medium (20–40%), High (above 40%)

• Formula used:
  =IF(F2<20%,"Low Discount",IF(F2<=40%,"Medium Discount", "High Discount"))

• Price Category: Low, Medium, High — based on price quartiles (roughly KSh 493 and KSh 1,670 as breakpoints)

• Formula used:
  =IF([@[Current Price]]="","Missing",IF([@[Current Price]]<Data_Analysis!$B$21,"Low Price",IF([@[Current Price]]<Data_Analysis!$B$22,"Medium Price","High Price")))

Excel Techniques and Analysis

With clean, enriched data in place, I moved to analysis using core Excel tools: formulas (AVERAGE, MAX, MIN, CORREL, COUNTIFS), PivotTables, and Pivot Charts.

Descriptive statistics across the 112 products:

Metric	Value
Total products	112
Total reviews	723
Average current price	KSh 1,186.89
Average old price	KSh 1,811.11
Average discount	36.8%
Average rating (where available)	3.89 / 5
Most expensive product	32PCS Portable Cordless Drill Set — KSh 3,750
Least expensive product	3PCS Single Head Knitting Crochet Sweater Needle Set — KSh 38

Correlation analysis using CORREL() to test three relationships:

Relationship	Correlation	Reading
Discount % vs. Reviews	−0.14	No relationship — discounts don't drive more reviews
Rating vs. Reviews	+0.06	Essentially no relationship
Price vs. Rating	+0.11	Weak — expensive products aren't rated meaningfully higher

These weak-to-negligible correlations were one of the more interesting findings of the project — the intuitive assumption that "bigger discount = more engagement" simply didn't hold up in this dataset.

Category breakdowns (via PivotTables):

• Rating: 23 Excellent, 22 Average, 12 Poor, 55 Not Provided
• Discount: 62 High, 32 Medium, 18 Low
• Price: 88 High, 7 Medium, 17 Low

Seller-performance flags — I built rule-based flags with COUNTIFS to segment products by behavior pattern:

Flag	Count	Rule
Strong customer engagement	27	Reviews ≥ 8
High discount + low engagement	50	Discount > 40% AND Reviews < 8
Many reviews + average rating	14	Reviews ≥ 8 AND Rating 3.0–4.5
High discount + low rating	10	Discount > 40% AND Rating < 3

Dashboard Creation Process

The final step was pulling everything into a single interactive dashboard sheet. I used:

• KPI cards for total products, average price, average discount, average rating, and total reviews
• PivotCharts for top 10 products by rating, by reviews, and by discount
• Scatter/column charts to visualize the discount-vs-review, rating-vs-review, and price-vs-rating relationships
• Pie and column charts for the rating, discount, and price category breakdowns
• Conditional formatting to visually flag high-discount/low-rating and other risk products directly in the tables
• Slicers connected to all PivotTables/charts for Rating Category, Discount Category, and Price Category, so the whole dashboard can be filtered interactively

(Screenshot: final dashboard with slicers)

Key Insights and Business Recommendations

A few findings stood out as genuinely useful for decision-making, not just descriptive trivia:

1. Discounting alone doesn't drive engagement. Nearly half the catalog (50 of 112 products) pairs a discount above 40% with fewer than 8 reviews. Sellers relying on discount depth to generate interest aren't seeing it pay off.
2. Popularity ≠ quality. The single most-reviewed product (a cordless vacuum cleaner, 69 reviews) has a "Poor" rating of 2.8/5 — visibility without customer satisfaction is a churn risk.
3. Price doesn't buy trust. The weak price–rating correlation shows customers aren't rating expensive items more favorably just because they cost more.
4. Rating data has a real gap. With 49% of products unrated, both buyers and analysts are working with an incomplete picture of product quality.
5. High discount + low rating (10 products) is a quality issue, not a pricing issue — these listings are already discounted heavily and still underperforming, so deeper discounts are unlikely to fix the underlying problem.

Recommendations for Jumia sellers:

• Don't lean on discount depth as an engagement strategy — invest in better listings (photos, descriptions, search visibility) instead.
• Encourage post-purchase reviews, especially for high-selling but unrated products, to close the 49% rating gap.
• Treat high-review/low-rating products as a quality-control priority, not a marketing one.
• Be skeptical of "perfect" 5.0 ratings built on only 1–2 reviews — they're not statistically meaningful yet.
• Escalate the 10 "high discount + low rating" products for product-level review rather than further discounting.

Closing Thoughts

This project reinforced something that comes up in almost every real-world dataset: the obvious assumption (discounts drive engagement, price signals quality) often doesn't survive contact with the actual numbers. Cleaning the data properly — especially handling the missing ratings honestly instead of hiding them — was what made the correlation analysis trustworthy enough to act on.

The full workbook, including the raw data, cleaned data, analysis sheets, and interactive dashboard, is available in the GitHub repository linked below.

GitHub repo: https://github.com/zenamuhoro-dot/jumia-product-performance/tree/main