Excel Sales Data Analysis Project

This project was completed as part of a Data Technician Bootcamp to develop practical skills in data analysis using Microsoft Excel. The goal of the project was to explore retail and sales datasets, apply Excel formulas for analysis, summarise insights using PivotTables, and present findings through charts and visualisations.

The project demonstrates how Excel can be used to clean, organise, analyse, and visualise real-world business data.

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Project Overview

The analysis focuses on exploring sales datasets to identify patterns in:

• Customer demographics
• Product sales performance
• Regional sales trends
• Market opportunities

The project involves transforming raw sales data into meaningful insights using a range of Excel features.

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Skills Demonstrated

Excel Formulas & Functions

Several core Excel functions were used to perform calculations and extract insights from the dataset:

• SUM – Calculate totals such as sales and commission
• SUMIF – Conditional totals based on specific criteria
• AVERAGE – Calculate mean values
• AVERAGEIF – Conditional averages
• DATE, MONTH, YEAR – Extract and manipulate date information
• UNIQUE – Identify unique values within datasets
• VLOOKUP – Retrieve related information from tables
• SWITCH – Categorise numerical values into groups

Example formula used for categorising sales volume:

=SWITCH(TRUE, C2 > 600, "High", C2 >= 300, "Medium", "Low")

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Data Analysis Techniques

The project also demonstrates fundamental data analysis techniques in Excel:

• Filtering datasets to isolate relevant records
• Sorting data to identify trends and patterns
• Aggregating metrics such as totals and averages
• Organising structured datasets for analysis

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Pivot Table Analysis

PivotTables were used to summarise and explore the data across multiple dimensions, including:

• Country
• Age group
• Gender
• Product category
• Sales volume

This allowed key insights to be identified quickly from large datasets.

Example Insights

Some observations from the analysis included:

• The United States generated the highest adult sales
• Canada had the lowest overall sales performance
• Female customers had higher sales across all age groups
• Certain markets showed no youth sales, highlighting potential market gaps

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Data Categorisation

Sales values were categorised into performance groups using the SWITCH function.

Sales Volume	Category
> 600	High
300–600	Medium
< 300	Low

This helped simplify analysis and made the results easier to interpret.

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Data Visualisation

Charts were created to communicate insights from the data, including:

• Sales by country
• Product sales comparisons
• Demographic sales patterns
• PivotTable summaries

These visualisations help present the findings clearly and make trends easier to understand.

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Tools Used

• Microsoft Excel
• Excel Formulas & Functions
• Pivot Tables
• Data Filtering & Sorting
• Charts & Visualisations

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Repository Structure

excel-sales-analysis/
│
├── datasets/
│   ├── retail_sales_dataset.xlsx
│   └── bike_sales_dataset.xlsx
│
├── analysis/
│   └── excel_analysis_workbook.xlsx
│
└── README.md

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Key Learning Outcomes

Through this project I developed practical skills in:

• Data exploration and analysis using Excel
• Writing formulas to automate calculations
• Using PivotTables to summarise complex datasets
• Categorising and transforming data
• Presenting insights through visualisations

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About This Project

This repository is part of my learning journey in data analytics and data technician training, where I am developing practical skills in data analysis, spreadsheets, and data visualisation.

Future projects will expand on these skills using tools such as Python, SQL, and Power BI.