DEV Community: Dorcas Chebet

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

Dorcas Chebet — Wed, 09 Sep 2026 11:06:44 +0000

Introduction

The case study is about jumia products which has generated large amount of data that include a certain number of products, prices, discounts, ratings and customer review .It contains information on 113 products .It also include their current prices, old prices, discount percentage s,number of customer review and ratings .Informations help the business to know the product performance and identify areas that requires attention.
The project involves transforming raw data into a structured dataset ,performing explolatory analysis and presenting the results through an interactive dashboard.

Project Objective.

The main project objective of this project is to analyze data from jumia.
the specific objectives are to analyze products by comparing current prices with the old prices.

-To identify the discount offered in different products

To identify products with high,medium and low discounts

To determine whether highly rated products receive more customer reviews

-To calculate descriptive statistics such as average,current price,average old price,average discount,total discount,total reviews and number of products.

which project generated more reviews

To use excel formulas to organize, categorise and interpret the Jumia product data.

Dataset and Business Queastions

The questions guiding the analysis were:

Does a higher discount correspond to more reviews?

2.Do highly rated products receive more reviews?

3.Is there a relationship between current price and ratings?

4.Which products have the highest rating?

5.Which products have the lowest discounts?

6.Which products have the highest number of reviews?

7.Are there products with high discount but relatively weak ratings or engagements?
The questions helped determine which calculations,pivot tables and visualization were needed.

Initial Data-Quality Audit

Before cleaning i identified the problems before making the changes or cleaning the dataset inorder to understand whether the data was complete ,consistent ,accurate and suitable for analysis.
For the jumia project the audit included:

1.Identifying if there was duplicate
2.Identify if there was missing values in important field such as price ,rating ,discount or reviews.
3.incorrect values :values that do not add up, such as current price being the same as the old price while a discount is still recorded.
4.Data type errors :to ensure prices are stored as numbers ,discounts as percentages/numbers, ratings as numbers and products names as text.

Excel techniques ,formulas and analysis performed.

Calculating the Discount amount

I calculated the discount amount by subtracting the current price from the old price.
formula:
=old price-current price
use of IF statements to classify products into different categories based on their ratings, discounts and prices.
I classified into categories such as:

Excellent
Average
Poor
Missing
Discounts were categorized as:
High discount
Medium discount
Low discount

Analysis performed

Rating analysis I analyzed and group product ratings into categories to determine which products have excellent, average, poor or missing ratings to help evaluate overall customer satisfaction.
Discount analysis I analyzed and categorized into high, medium and low discounts .I also compared the old prices with the current price to determine the amount customers could save.
Trend analysis The project was used to examine relationships between: -Discount percentage and number of customer review -Product rating and number of customer reviews -Product price and product rating. The analysis indicates that a higher discount percentage did not result in more customer reviews and highly rated products did not necessarily receive more reviews. #Dashboard creation process The final stage of the project was to transform the analysis into an interactive dashboard .Top most of the Dashboard was the heading written in bold. The key performance indicators were

Total products
Average Current price KSh 1,097
Average Discount 48%
Average rating 5 -Total reviews 723 Beneath this KPIs the slicers used were:

price category-it categorized data into High price ,Low price and Medium price. Discount category- it categorized data into High discount ,Low discount and medium discount. Rating Category-Categorized data into average, excellent ,poor and missing for missing values.

key insights and business recommendation.

1.Higher discounts do not necessarily lead to higher customer engagement .Products with higher discount percentages doo not consistently receive more customer review.
2.Customer reviews vary across products
3.Discounting should not be the only strategy
4.Products performance should consider both ratings and reviews.
5.Highly rated products do not necessarily receive more reviews.it does not guarantee strong customer engagement.

Recommendations

The sellers should focus on customer satisfaction by improving quality and customer service to maintain good ratings.
the sellers should use data to make pricing decisions
Review products with low engagement.
Use target discounts .Strategically on products where they are likely to improve sales or engagement.

Screenshot of original data, cleaned data ,pivot tables and dashboards

The Original Data

2.Cleaned data

3.Pivot tables

3.Dashboard

ASSIGNMENT WEEK 2

Dorcas Chebet — Sun, 30 Aug 2026 07:49:35 +0000

Getting Started with Excel For Data Analysis

Introduction

Excel is one of the tools commonly used for working data .It is used to enter, organize ,clean, calculate and analyze information using spreadsheets .learning excel gives good foundation on how data is handled before other advanced tools such as python ,SQL and Power BI are used.
In data analysis ,raw data can have errors, duplicates, missing values or unorganized information.

Excel Basics

Dataset- It is a collection of related information organized into rows and columns .Example,

Employee ID	Name	Department	Age	Salary
1102	Marion	Finance	28	45000
1103	Peter	IT	30	70000
1104	June	HR	34	60000
1105	Irene	Finance	32	80000
1106	Dennis	IT	29	65000

Explanation
_column-is a verticle line of data that runs straight from top to bottom
_row-it is a horizontal line of data that runs from left to right.
_cell-it is an intersection of a row and column
_Header-The name of a column.
entering data into Excel
for the case of our dataset, it is entered as,
A1:Employees ID
B2:Name
C1:Department
D1:Age
E1:Salary
It is important to organize data rather than putting two different pieces of information in the same column. For example:
Employee ID
1102,Marion,finance
instead;
Employee ID Name Department
1102 Marion Finance
Hence the data will be easier to analyze.

Formatting in Excel

Formatting in excel involves,

Bold headings
Number of formatting
Alignment
Adjusting column width
Dates
Borders
Percentages
Currency
formatting makes easy to interpret data but it doesn't clean the data.

Formulas applied in Excel.

A formula directs on how to perform a calculation .For example when you enter
=60+85
Excel calculates as 145
To calculate average salary the formula is
=AVERAGE(range criteria)
=AVERAGE(D2:D5)
To calculate the highest salary
=MAX(D2:D5)
To calculate the lowest salary
=MIN(D1:D5)
To count the number of employees
=COUNTA(A1:A5)

Sorting

sorting is organizing data in a particular order.
Example :Employees can be sort according to their departments,
Names Departments
Marion Finance
Peter IT
June HR
Irene Finance
Dennis IT
This sorted data enables the analyst to quickly identify an employee which department the employees comes from.

Filtering of data

Filtering hides data that does not meet a particular condition.
For example, since our dataset contains employees from different departments, we can filter the column to only show employees in IT departments ,filtering does not delete the other records .It only hides since it does not match the selected condition.

Data cleaning

It is the process of finding and correcting errors in a dataset.
Common errors in a dataset involves,
Missing values
Duplicate values
Inconsistent information
In data cleaning ,excel functions are:
UPPER
=UPPER(A2) It helps changes the text to uppercase
LOWER
=lOWER it help changes the text to lowercase
TRIM
=TRIM(A2) It help removes unnecessary spaces from text.

Preparation of data for analysis.

It is the process of cleaning, organizing raw data to be accurately ready for analysist avoid misleading results.
The analyst should ensure that the dataset is well structured, complete ,consistent ,accurate and identify missing values.

GitHub Repository

You can find my Excel data analysis project on GitHub:

[view my GitHub Repository

ASSIGNMENT WEEK 1

Dorcas Chebet — Sun, 23 Aug 2026 11:37:33 +0000

Understanding the Git workflow.

Introduction

Git is a system that is used incase one needs to track their projects or check if there was changes or need to get back to the initial project.
Git is important since it simplify the work of re-writting the project over again by keeping track on the initial project through tracking the changes in the file or checking on what has changed or under certain changes.
A complete work flow involves,

working directory
staging area
commit
push

working directory.

It is a folder in your laptop or computer where you work on your project .Working directory is where you:
create files
edit files
delete files
add data and make changes to your project.
The commands that are used in the working directory include,
pwd-it shows your current location
ls_it is used for listing files and a folder.
cd folder-it moves into a folder.
cd..-it is used if one needs to move back to the previous folder.
mkdir-it creates a new file folder.
touch file-wit creates a new empty folder.
git status-command used in tracking a folder.
git add-moves changes to staging area.

staging area.

It is where you put the changes you have chosen to save in your next commit in git.

The commands that are used in staging area:

git add- stage all the changes
git status- check what is staged
git diff--staged -sees what is exactly staged
git restore--staged-it removes a file from staging
git commit -m -it ensure you run git add before committing

Commit

It is a back up of the changes that have been placed in the staging area.
Good commit messages can tell what happened.

Push

Git push is a command that takes the commits made in a computer and send them to a repository such as Github.It is the final stage in working directory that sends the project to remote or local repository.

assignment

Dorcas Chebet — Thu, 20 Aug 2026 10:15:21 +0000

assinment week 1 article submission

DEV Community: Dorcas Chebet

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

Introduction

Project Objective.

Dataset and Business Queastions

Initial Data-Quality Audit

Excel techniques ,formulas and analysis performed.

Calculating the Discount amount

Analysis performed

ASSIGNMENT WEEK 2

Getting Started with Excel For Data Analysis

Introduction

Excel Basics

Formatting in Excel

Formulas applied in Excel.

Sorting

Filtering of data

Data cleaning

Preparation of data for analysis.

GitHub Repository

ASSIGNMENT WEEK 1

Understanding the Git workflow.

Introduction

working directory.

staging area.

The commands that are used in staging area:

Commit

Push

assignment