When people hear data analysis, they often imagine complex Python scripts, SQL queries, or advanced BI tools. However, in reality, the foundation of data analysis across most organizations—from global corporations to local startups—is Microsoft Excel.
If you're starting your journey into data, Excel is not optional—it is a core skill required across operations, finance, marketing, and analytics roles.
In this article, I’ll explain how Excel is used in real-world scenarios using a product performance dataset from Jumia, one of Africa’s largest e-commerce platforms.
what is excel
Microsoft Excel is far more than a spreadsheet tool. It is a multi-functional analytical platform that serves as:
A database – storing large volumes of structured data
A calculation engine – performing simple to complex computations
A data transformation tool – cleaning and reshaping messy datasets
A visualization platform – building dashboards and reports
Real-World Applications of Excel
1.Marketing
: Evaluating Pricing Strategy
A marketing team needs to determine whether discounts drive engagement.
Task: Analyze the relationship between discount percentage and customer reviews
Solution: Using PivotTables and scatter plots to identify trends.
Insight: Moderate-to-high discounts significantly increased customer engagement.
2. Merchandising
: Identifying Underperforming Products
The goal is to flag products that are discounted but poorly rated.
Task: Detect products with high discounts and low ratings
Solution: Conditional Formatting to highlight risk products instantly
3. Operations
: Data Cleaning
Raw data is rarely usable in its original form.
Task: Convert text-based values into numeric format
Solution:
Find & Replace to clean currency values
TEXTSPLIT to extract ratings
Key Excel Features Used
1. Data Cleaning
Removed “KSh” and commas from price fields
Extracted numeric ratings using TEXTSPLIT
Used ABS() to correct negative values
2. Calculated Columns
Discount Amount = Old Price – Current Price
Rating Classification using nested IF statements
Discount categorization (Low, Medium, High)
3. PivotTables
Used to summarize:
Average ratings by discount category
Discount distribution across products
4. XLOOKUP
Used to map:
Top-performing products
Associated prices and names
5. Conditional Formatting
Highlighted:
High-discount, low-rating products
Immediate visual alerts for decision-making
6. Dashboard Creation
Built an interactive dashboard with:
Charts linked to PivotTables
Slicers for filtering data dynamically
Scatter plots to reveal pricing patterns
Key Insights from the Analysis
Discounts increase engagement: High-discount products generated significantly more reviews
Discounts don’t guarantee quality: Some heavily discounted products had poor ratings
Optimal pricing range: Products discounted between 20–40% achieved the highest ratings
Strong customer satisfaction: Majority of products had ratings above 4.5
The Excel Mindset
Effective Excel usage goes beyond formulas:
Maintain a clean workflow (Raw → Working → Dashboard)
Use Excel Tables for dynamic updates
Document formulas for future reference
Focus on clarity and reproducibility
Conclusion
Excel remains one of the most powerful and accessible tools for data analysis. It bridges the gap between raw data and actionable business insights.
From cleaning messy datasets to building dashboards and uncovering trends, Excel enables end-to-end analysis without requiring advanced programming skills.
For anyone entering data-related fields, mastering Excel is one of the highest ROI skills you can develop.
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