JUMIA PRODUCT PERFORMANCE DASHBOARD ANALYSIS

Introduction

This project analyzes a sample of product listings from Jumia, an e-commerce marketplace, to understand how price, discount depth, ratings, and customer engagement (review counts) relate to one another and the users, insights that a seller or marketplace team could take. The entire workflow data cleaning, threshold definition, flagging, ranking, correlation analysis, and visualization was built in Microsoft Excel, using table-based formulas, named ranges, quartile-based thresholds, and native charts.

What we are to consider is do price and discounting actually drive customer engagement and satisfaction on this platform, or are other factors at play?

Dataset Overview

• Source: Product listings exported from Jumia (Kenya), covering household, electronics, and lifestyle categories.
• Size: 115 products, 6 original columns: Product, Current price, old price, Discount, Review, Ratingd.
• Price range: KSh 38 – KSh 3,750.
• Discount range: 1% – 64%.
• Data completeness: Price and discount data are complete for all 115 products. Review count and rating are missing for 58 of 115 products (50%) — this gap is preserved and clearly labeled throughout the analysis rather than treated as zero.

Raw data issues found and corrected during cleaning:

• Prices stored as text with currency symbols and thousands separators like "KSh 1,980".
• One product listed as a price range rather than a single value like "KSh 1,620 - KSh 1,980".
• Ratings stored as text "4.5 out of 5" instead of numeric values.
• Review counts stored as negative numbers like -14, which appears to be a data extraction artifact rather than a true negative quantity.
• Discount stored as text with a % sign.

Data Enrichment

Each raw data issue was resolved with a specific understanding of data:

Field	Issue	Resolution
Current price / old price	Text with KSh and commas; one row was a price range	Stripped currency text and commas, converted to numeric; the single range row was resolved to its midpoint
Ratingd	Text like "4.5 out of 5"	Extracted the numeric portion, converted to a decimal number
Discount	Text with %	Stripped the % and converted to numeric
Review	Stored as negative integers	Derived a new Engagement field = ABS(Review), since review counts cannot be negative; the true engagement measure is this absolute value

Threshold definition (quartile-based, not arbitrary):

All "high/low/strong/weak" categories in this project are defined using the first (Q1) and third (Q3) quartiles of the actual dataset, calculated with QUARTILE.INC, so the thresholds adapt to the data rather than relying on a fixed guess:

Metric	Q1	Q3
Price	KSh 500	KSh 1,663
Discount	27%	49%
Rating	3.0	4.6
Engagement (reviews)	5	14

Derived flags (each computed with an Excel formula referencing these named threshold cells):

• Price Tier: Low / Medium / High
• High Discount & Low Rating: Discount ≥ 49% AND Rating ≤ 3.0
• High Discount & Low Engagement: Discount ≥ 49% AND Reviews ≤ 5
• Many Reviews & Average Rating: Reviews ≥ 14 AND Rating between 3.0–4.6
• Excellent Rating: Reviews ≥ 14 AND Rating ≥ 4.6

Every flag return "Missing" instead of 0 or blank, When the underlying rating or review data doesn't exist, so the dashboard never silently misrepresents a data gap as poor performance.

Key Insights

1. Demand is concentrated, not broad-based. Only 16 of 57 rated products (28%) meet the "Strong Engagement" threshold (≥14 reviews).
2. Discounting does not reliably drive demand. 13 of the highest-discount products (≥49%) still fall below the engagement threshold.
3. A handful of high-discount products carry real reputation risk. 6 products combine deep discounts with weak ratings (≤3.0); one of them also has the highest review count in the whole dataset.
4. Price, discount, and rating are essentially uncorrelated. All three Pearson correlations tested came out near zero (|r| < 0.15, R² < 0.02).
5. The catalog has a solid base of "reliable, average" sellers 10 products combine strong engagement with a respectable but not exceptional rating, representing the best low-effort improvement opportunity.

Discount Analysis

Products were split into Low (≤27%), Medium (27–49%), and High (>49%) discount tiers using the quartile thresholds above.

Discount Tier	Avg. Reviews	Avg. Rating	Count
Low (≤27%)	16.7	4.19	13
Medium (27–49%)	12.8	3.89	33
High (>49%)	7.6	3.53	11

The pattern runs opposite to what a simple "discounts drive sales" assumption would predict: the least-discounted products average more than double the review count of the most-discounted products, and also carry a higher average rating. The Pearson correlation between discount and review count is r = -0.14 (R² = 0.02) a very weak negative relationship, not a strong one, but consistent with the tier breakdown.

13 products sit in the "Promotion Inefficiency" flag (discount ≥49%, reviews <14) nearly half of the High discount tier. Six products combine a high discount with a low rating (≤3.0), the clearest quality-risk segment in the dataset.

Ratings and Reviews Analysis

Rating distribution across the 57 products with data:

Rating Category	Count	% of Rated Products
Low (≤3.0)	17	30%
Average (3.0–4.6)	26	46%
Excellent (>4.6)	14	25%

Average review count by rating band tells an important story: Average-rated products (16.0 avg. reviews) actually out-engage Excellent-rated products (8.0 avg. reviews). The correlation between rating and review count is essentially flat (r = 0.06), meaning higher-rated products are not systematically the most-reviewed ones on this platform. This suggests visibility and quality are currently decoupled a real opportunity, since improving quality on already-visible "Average" products is likely a faster win than trying to generate visibility for untested high-quality products.

Price and rating also show no meaningful relationship (r = 0.11, R² = 0.01) higher-priced items are not rated meaningfully better or worse than lower-priced ones.

Products

A few individual products stand out from the ranked analysis:

• Highest risk: 120W Cordless Vacuum Cleaner the single highest review count in the dataset (69) paired with a weak 2.8 rating and a 49% discount. High visibility with a poor quality signal makes this the top candidate for a quality/fulfillment investigation.
• Most expensive: 32PCS Portable Cordless Drill Set KSh 3,750, the top of the Price tier, but with very limited engagement (5 reviews), raising a visibility question rather than a quality one.
• Strong performers (Strong Engagement + Excellent Rating): 6 products meet both criteria the smallest, most valuable segment in the catalog, and the best candidates for promotional placement since they carry the lowest risk of disappointing a new customer.
• Reliable, improvable sellers: 10 products combine high engagement with an "Average" rating proven demand, with room to move into the "Excellent" tier through targeted quality or listing improvements. Below is representation of the dashboard used to show visuals:

Conclusion

Across 115 Jumia product listings, this analysis found no meaningful linear relationship between price, discount depth, and either customer rating or engagement the assumption that deeper discounts drive more attention or that higher prices signal higher quality is not supported by this dataset. Instead, the data points to two more specific, actionable patterns:

1. A small set of high-discount, low-rating products (6 items) most notably a cordless vacuum cleaner with the platform's highest review count represent a concentrated reputation risk that deserves direct quality investigation rather than further discounting.
2. A larger set of high-engagement, average-rated products (10 items) represents the most efficient improvement opportunity in the catalog: these products already have proven demand, so incremental quality or listing improvements are likely to yield a better return than trying to generate new demand from scratch or discounting further.

Recommended next steps, in priority order:

1. Investigate the flagged high-discount/low-rating products for quality, fulfillment, or listing-accuracy issues, starting with the highest-review-count item.
2. Audit the 13 "promotion inefficiency" products' listing content and targeting before applying further discounts.
3. Invest in incremental quality/content improvements on the 10 high-engagement, average-rated products.
4. Push for complete review and rating data collection half the catalog is currently invisible to this kind of analysis.
5. Treat price and discount as secondary levers; they are not shown to reliably move engagement or rating in this dataset.

Limitations: This dataset does not include sales volume, conversion rate, listing age, product category, or return/complaint data. The patterns described above are correlational, drawn from a single snapshot in time, and should be tested (e.g. via controlled discount experiments or content A/B tests) before being treated as causal drivers of performance. Additionally, 50% of products have no rating or review data at all, so every rating/engagement-based finding in this report applies only to the 57 products where that data exists.