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

Introduction

Data analytics play a vital role in decision-making processes for e-commerce. Online market platforms generate massive amounts of data and information about products, their prices, discounts, customer reviews, offers available and, ratings. When properly analysed, this information can be of great use to the sellers, and institutions in understanding customer behaviour, identify products that are performing highly, improve pricing strategies, and come up with better promotional decisions.
In this particular project, I will focus on the analysis of Jumia product dataset using Microsoft Excel, which is an important tool that can be used for data analysis, to get some essential indications. The major objective of the analysis is to transform raw data into cleaned, structured, analysed, and interactive dashboard that provides meaningful business insights.

Project Objective

The primary objective of this project was to create an interactive Excel dashboard for analysing the performance of products listed on Jumia.
The final dashboard provides an overview of product performance using key performance indicators (KPIs), charts, product rankings, and category breakdowns.
The dashboard is intended to help Jumia sellers and decision-makers understand how pricing, discounts, ratings and customer engagement interact.

The Dataset

The dataset contains information and data about Jumia products.
The original dataset was made of 115 product records and 6 main columns

1. Product : Name of the product
2. Current Price : Current selling price (In Ksh)
3. Old Price : Original price before discount
4. Discount : Percentage discount offered
5. Review : Number of customer reviews
6. Rating : Average customer rating out of 5

Data Cleaning

The original dataset had several quality issues that I had to address before analysis such as:

• Duplicate records.
• Prices were stored as text containing the KSh currency symbol
• Ratings were stored in text such as 4.5 out of 5
• Review values appeared as negative numbers.
• Missing values in the Review and Rating fields

Action Taken

I started by checking for duplicates in the Original dataset and deleted them.
I did this by selecting the whole data set, and under the Data tab in Excel, I clicked on Remove Duplicates.

This step reduced the number of products to 111

Secondly, I changed the values in Original Price field,which were in Text form, into Numbers.
Similarly, the rating field contained values such as 4.5 out of 5 which is in Text format, and need to be in Numerical values

Checking for Missing Values

Missing values can interfere with calculations and visualizations. The original dataset had missing values in the Review and Rating Columns.

Cleaning the Review Column

The Review column had its values written as negative such as -2, -4, -14, -7.
Logically, the number of customer reviews cannot be negative. I treated the negative signs as erroneous and removed the negatives.

Creating the Discount Amount Column

One of the calculated fields required by the project was the absolute discount amount.
The formula is:
=Old Price - Current Price

Creating the Rating Category

I created the Rating Category to group the products according to their customer ratings as follows:

• Poor: Rating below 3
• Average: Rating between 3 and 4
• Excellent: Rating above 4.5 The Excel formula is: =IF(F2<3,"Poor",IF(F2<=4.4,"Average","Excellent"))

Creating the Discount Category

I created the discount category in three groups:

• Low Discount: Below 20%
• Medium Discount: 20%–40%
• High Discount: Above 40% The Excel formula is: =IF(D2<20%,"Low Discount",IF(D2<=40%,"Medium Discount","High Discount"))

Descriptive Statistics

After the data cleaning and transformation, I calculated the descriptive statistics as follows, with the excel functions:

• Total Products (=COUNTA(A2:A113)):111
• Avg Current Price (=AVERAGE(B2:B113)): 1181.37
• Avg Old Price(=AVERAGE(C2:C113)):1803.10
• Avg Discount(=AVERAGE(D2:D113)): 37%
• Avg Rating (=AVERAGE(F2:F113)): 3.88
• Total Reviews(=SUM(E2:E113)): 721

Correlation Analysis

I investigated three major relationships:

1. Discount and reviews
2. Rating and reviews
3. Price and rating

1. Discount Vs Customer Reviews

The first relationship examined was whether products with higher discounts receive more customer reviews.
The correlation between discount percentage and number of reviews, of the final 111 products was -0.139.
This indicates a very weak negative relationship in the dataset.
The results, therefore, suggests that higher discounts are not associated with substantially higher customer engagement in this dataset.
Consequently, it is important, for the sellers not to assume that increasing the discount will automatically generate more customer reviews.

Rating Vs Customer Reviews

The correlation for this relationship was 0.066.
This indicates a weak positive relationship.
Therfore, highly rated products do not necessarily receive significantly more reviews.
It is, therefore, important to note that Customer satisfaction and customer engagement are different dimensions of product performance.

Price Vs Rating

The correlation between price and rating is 0.104.
This represents a very weak positive relationship.
Therefore, more expensive products are not necessarily rated substantially higher than cheaper products.
The result suggests that price alone does not appear to determine customer satisfaction in this dataset.

Identifying Top 10 Products by Customer Reviews

The strongest observation is the first product, 120W Cordless Vacuum Cleaner. It has the highest number of reviews but only a 2.8/5 rating.
This translates into high customer engagement but low customer satisfaction.

Identifying Top 10 Products by Rating

The products showed to genarally have relatively small review counts.
This is why a high rating should not automatically be interpreted as strong market demand.

Identifying Top 10 Products by Discount

Identifying High Discounts with Low Ratings

One of the most useful analyses was to identify products that combine:
High discount + Low rating
These products are a bit troublesome since they may be heavily promoted but still receive poor customer feedback.
One clear example is the 5-PCS Stainless Steel Cooking Pot Set with:

• Discount: 55%
• Rating: 2.1/5

• Reviews: 13
  This data suggests that the problem is not in the price. Reducing the price further, may increase sales temporarily, but may not solve the root cause of underlying customer dissatisfaction.
  Potential causes may include:

• Product quality

• Product description

• Product expectations

• Packaging

• Delivery experience

• Product durability

Creating KPI cards for the Dashboard

The Key Performance Indicators for my dashboard included:

`- TOTAL PRODUCTS

• AVERAGE PRICE
• AVERAGE DISCOUNT
• AVERAGE RATING
• TOTAL REVIEWS`

Dashboard Layout

I organised the dashboard into several sections, keeping the most important information visible, while also providing detailed physical views.

Pivot Tables

Rating Category

This pivot table was to provide a breakdown of products into:

• Poor
• Average
• Excellent

Discount Category

Key Business Findings

The analysis produced several important findings as follows:

1.High Discounts Do Not Guarantee High Engagement

The correlation between discount percentage and reviews was approximately -0.139, indicating a weak negative relationship.
Therefore, increasing discounts does not automatically result in more customer reviews.

2.High Ratings Do Not Guarantee High Demand

The relationship between ratings and review gave a correlation of 0.066, indicating almost no linear relationship. Some highly rated products have very few reviews.
Sellers should, therefore, consider both Rating and Rating Volume to evaluate performance.

3.Price Does Not Strongly Determine Rating

The correlation between price and rating of 0.104 indicates a very weak positive relationship.
Therefore, premium pricing does not automatically result in higher customer ratings.

4.Some Products Have Strong Engagement but Poor Satisfaction

A clear example in this category is the 120W Cordless Vacuum Cleaner. with
29 Reviews
2.8 Rating
This product attracts considerable customer engagement but has poor satisfaction. It needs to be analysed what could be the cause for this trend.

Some Products Are Heavily Discounted Despite Poor Ratings

A good example in this category is the 5-PCS Stainless Steel Cooking Pot Set that has:
55% Discount, 2.1 Rating, 13 Reviews
This indicates that high discounting does not necessarily solve customer satisfaction problems.

Recommendations

1. Avoid excessive reliance on discounts

The sellers should not assume that increasing discounts will automatically increase customer engagement.
Promotional startegies should involve improvements of product quality and better customer experienvces

2. Investigate products with high reviews, and low ratings

Products with large numbers of reviews but low ratings should receive urgent attention. A clear example is the 120W Cordless Vacuum Cleaner

3. Analyse customer feedback

The sellers should examine the content of negative reviews to identify recurring problems that may include:

• Quality
• Durability
• Size
• Functionality

4. Use ratings and the number of reviews together

A 5.0 rating based on one review should not be treated in the same way as a 4.7 rating based on dozens of reviews.
A better performance framework should consider:

Customer Rating + Review Volume + Discount + Price

5. Reconsider high discounts on poorly rated products

When a product has high discount and poor rating, the selllers need to examine the underlying problem before enhancing the discount.

Limitations

• Missing data in ratings and review counts of some products
• The database does not contain a dedicated product-category field. Adding categories would make it possible to compare performance across product groups
• The sales data contains reviews but does not have actual sales quantities
• The revenue and profit-margin data are not available

Conclusion

This project demonstrated how Microsoft Excel can be used to transform raw e-commerce data into a practical business intelligence dashboard.
The final cleaned dataset contains 111 products after removing three duplicate records and the problematic sofa-cover record.
The analysis demonstrates the importance of cleaning and validating data before creating a dashboard.
The final findings show that:

• Higher discounts do not necessarily generate higher customer engagement.
• Highly rated products do not necessarily have more reviews.
• Expensive products are not necessarily rated higher.
• Some products have high customer engagement but poor ratings.
• Some products have substantial discounts but poor customer satisfaction.

The most important lesson from this project is that a dashboard is only as reliable as the data behind it. Careful data cleaning, appropriate Excel formulas, meaningful visualizations and thoughtful interpretation are all necessary to turn raw e-commerce data into actionable business intelligence.

Github Link

Below is the link for Github Repository.
https://github.com/Expertwriter006/Jumia-Product-Performance-Dashboard