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

Introduction

This is my first major data analysis project, so this process has been heavily about learning how to work with real world data. 
For this project, I used Microsoft Excel to analyse product data from Jumia, focusing on pricing, discounts, ratings, and customer reviews. The goal was to take a raw dataset, clean and prepare it, analyse the information, and turn the results into an interactive dashboard that could communicate the findings clearly.

Questions of the day

I wanted the analysis to answer questions that could tell us something about product performance.
The main questions were:

1. Do higher discounts result in greater customer engagement?
2. Do products with more reviews also receive higher ratings?
3. Is there a relationship between product price and rating?
4. Which products stand out based on customer reviews and ratings?
5. How much can we actually conclude when some products have missing customer feedback? One important consideration was that the dataset did not contain sales or revenue figures. Because of this, I used review count as a proxy for customer engagement

Raw_Data

The original dataset contained product information including:

• Product name
• Current price
• Old price
• Discount percentage
• Number of customer reviews
• Product rating The original data contained several issues that needed to be addressed before analysis could be done. ### Some of the problems I encountered included:
• Duplicate product records
• Incorrect or inconsistent data types
• Negative values in the review column
• Missing ratings and review information
• Price formatting issues
• A misspelled rating column
• Discount values that needed to be checked against the listed prices This was probably one of the biggest lessons from the project, having to understand the data and what it represents before analysing it.

Cleaning the Data

I kept the original data separate in a Raw_Data worksheet. I then created a Cleaned_Data worksheet where the data could be standardized and prepared for analysis.

Removing duplicates

I identified and removed 3 duplicate records.

Correcting data types

Prices, discounts, ratings, and review counts needed to be stored as numerical values.

Handling negative reviews

The review column contained negative values. Since a product cannot logically have a negative number of customer reviews, I treated the negative signs as a data-formatting or scraping issue and converted those values to positive numbers.

Handling missing values

Not every product had rating or review information. Instead of replacing missing values with zeros or averages, I left the information missing and categorized it as "Not Provided" where appropriate. This is important because no recorded review is not the same thing as zero reviews. Replacing missing information with zero can create a misleading picture of customer engagement.

Checking discounts

I also compared the discount information against the listed and current prices to make sure the data was consistent.

Creating Categories for Analysis

Once the basic cleaning was complete, I created additional fields to make it easier to analyze the products. These included categories for:

• Rating
• Discount
• Price This made it possible to use PivotTables and slicers to look at the dataset from different perspectives rather than relying only on the original numerical values.

Key Figures

After cleaning the dataset, I calculated several key metrics.

Total products - 113
Average current price - KES 1,181.37
Average discount - 37%
Average rating - 3.9 / 5
Total reviews - 723
Highest current price - KES 3,750
Lowest current price - KES 38

These figures are what I use for the KPI section of my dashboard.

Questions to Analysis

With the data cleaned, I could finally start testing the questions I had at the beginning. One of the most interesting parts was looking at the relationships between discounts, reviews, ratings, and prices.
I calculated the correlation values for three relationships:
Discount vs Reviews - -0.14
Rating vs Reviews - 0.06
Current Price vs Rating - 0.10
The values show weak relationships between the variables.

Discount vs Reviews: -0.14

The relationship between discount and reviews was slightly negative, but very weak. This means that in this dataset, products with larger discounts did not consistently have more customer reviews.

Rating vs Reviews: 0.06

The relationship between ratings and review counts was almost zero. This can suggest that having more customer reviews did not automatically mean that a product had a higher rating. A product can be popular enough to receive many reviews without being among the highest-rated products.

Current Price vs Rating: 0.10

The relationship between current price and rating was also very weak. More expensive products did not consistently receive better ratings. This may mean that within this dataset, price alone was not a strong indicator of perceived product quality.

Creating PivotTables

After completing the basic analysis, I used PivotTables to summarize the dataset and identify patterns that would be difficult to see from the raw rows alone.
The PivotTables helped me examine areas such as:

• Rating categories
• Discount categories
• Price categories
• Customer review counts
• Top-rated products
• Most-reviewed products
• Highly discounted products

These summaries then became the basis for the charts used in the dashboard.

Designing the Dashboard

I designed the dashboard around the following:

KPI Cards

The main KPIs provide a quick overview of the dataset:
-Total products

• Average current price
• Average discount
• Average rating
• Total reviews

Product Performance

I included visualizations that highlight products based on measures such as ratings, reviews, and discounts.

Relationship Analysis

The dashboard also includes visualizations exploring the relationships between:

• Discounts and reviews
• Ratings and reviews
• Current prices and ratings ### Category Analysis I used charts to compare products across rating, discount, and price categories. ### Slicers To make the dashboard interactive, I added slicers for:
• Rating Category
• Discount Category
• Price Category These allow the user to filter the dashboard and explore specific groups of products. So one can filter for a particular discount or rating category and see how the other dashboard elements respond.

Key Insights

1. Discounts do not equal engagement

The discount-versus-review correlation was -0.14, showing a very weak negative relationship. This suggests that simply offering a larger discount does not guarantee greater customer engagement.
For sellers, this could mean that other factors  such as product visibility, quality, brand reputation, listing information, or customer experience may matter more than discount alone.

2. Popularity and rating are not the same 

The correlation between rating and reviews was 0.06. This means that a product can attract many reviews without having an exceptionally high rating.

3. Price does not appear to determine rating

The correlation between current price and rating was 0.10. The relationship is weak enough that I can conclude that customers do not appear to be rewarding more expensive products with consistently higher ratings.

4. Some products do stand out

Although the overall relationships were weak, individual products still stood out. Some highly reviewed products also maintained strong ratings, making them interesting examples of products that combine customer engagement with positive feedback. These products may be worth examining more closely to understand what is working for them.

5. Missing feedback creates a blind spot

A significant portion of the listings did not have recorded rating or review information. That makes it harder to draw conclusions about customer satisfaction and engagement across the entire dataset.

Recommendations

Based on the analysis, I would recommend the following:

1. Large discounts should not automatically be treated as a strategy for increasing customer engagement.Sellers could instead test discounts alongside other factors such as product presentation, visibility, and customer experience.
2. Because review counts provide a useful engagement signal, encouraging customers to leave ratings and reviews could provide sellers with more useful feedback while also improving the amount of information available to future customers.
3. A product with many reviews but an average or low rating deserves attention. High engagement combined with weaker customer satisfaction could mean there are issues with product quality, expectations, delivery, or the overall customer experience.
4. Products that combine high review counts with strong ratings are worth studying further. Understanding what these products have in common could help identify practices that could be applied to other listings.

What I Personally Learnt

This project taught me a lot. I learned how easily a small issue in raw data can affect the results of an entire analysis. 
I also became more comfortable with:

• Cleaning real-world datasets
• Working with Excel tables
• Using formulas to create calculated fields
• Building PivotTables
• Creating PivotCharts
• Using slicers for interactive filtering
• Designing dashboards
• Interpreting basic correlations
• Turning analytical findings into practical recommendations This project gave me a foundation for working with larger and more complex datasets in future projects.

Limitations

There are several limitations to this analysis.
First, the dataset represents a snapshot of product information. Prices, discounts, ratings, and review counts can change over time.
Second, the dataset does not contain sales, revenue, product visibility, or listing-age information. Because of this, review count can only be used as a proxy for engagement and cannot be treated as a direct measure of sales.
Third, missing rating and review information reduces the amount of customer-feedback data available for analysis.
Finally, correlation does not establish causation. The relationships observed in this dataset should therefore be interpreted as patterns rather than proof that one variable causes another.

And finally

This project gave me practical experience with the full data-analysis process. From figuring out what needed cleaning, to deciding how missing information should be handled, to building visualizations and communicating what the results actually mean.
And for a first Excel data-analysis project, I'm calling that a win.

Conclusion

The Jumia Product Performance Dashboard demonstrates how Excel can be used to take raw e-commerce data through the full analysis process including cleaning, preparation, exploration, visualisation, and communication.
While the analysis did not find strong relationships between discounts, prices, ratings, and reviews, that result is itself useful. Sometimes the most valuable finding is discovering that the assumption you started with does not hold up in the data.