🚲 Bike Sales Data Analysis
Overview:
In this project, I stepped into the role of a data analyst for a fictional bike retail company, with a mission to uncover hidden insights from sales data collected over several years. The goal was to identify key trends in customer behavior, regional performance, and product success to support data-driven decision-making.
🧠 The Problem
The company had been collecting thousands of sales transactions from different regions across the globe, but the data was messy and overwhelming. Leadership wanted a clear picture:
Who are our best customers?
What products drive the most revenue?
Which markets are most profitable?
🛠️ My Approach & Tools Used
1. Data Cleaning (Excel):
The raw dataset included inconsistencies in country names, missing values, and disorganized fields. I used Excel to:
Standardize column values (e.g., fixing typos like “United States”).
Ensure date fields were properly formatted.
Categorize customers into meaningful age groups for segmentation.
2. Data Analysis with Pivot Tables:
Once clean, I used pivot tables to:
Aggregate revenue and profit by year, region, and product category.
Analyze customer demographics by age group and gender.
Summarize performance by country, revealing market strengths.
3. Data Visualization (Excel Dashboards):
To communicate findings visually, I created dynamic dashboards that:
Displayed year-over-year growth in profit and revenue.
Compared product performance across categories (e.g., Bikes vs Accessories).
Highlighted regional differences with bar charts and summary tables.
📈 Key Insights
Adults aged 35–64 generated the highest revenue, contributing over $47M.
Australia emerged as the top market, with Bikes as the dominant product category.
From 2017 to 2019, both revenue and profit showed consistent upward growth, indicating strong market performance and potential for expansion.
🧰 Technical Skills Applied
Excel: Advanced use of pivot tables, VLOOKUP, sorting/filtering, conditional formatting.
Data Cleaning: Manual and formula-based approaches to standardize data.
Data Visualization: Created charts and dashboards to present insights clearly and interactively.
Analytical Thinking: Segmented data to reveal actionable insights for different business questions.
🤝 Soft Skills Demonstrated
Problem Solving: Took messy, raw data and transformed it into a clean, analyzable dataset that answered real business questions.
Attention to Detail: Ensured data integrity through meticulous review and corrections.
Communication: Presented complex findings in a simple, visual format for non-technical stakeholders.
Time Management: Worked efficiently to produce a complete end-to-end analysis within a structured timeline.
🎯 Outcome
The final deliverable offered clear recommendations for strategic focus such as targeting marketing campaigns at 35–64 year olds in high-performing countries like Australia and demonstrated how even basic tools like Excel can drive powerful, data-informed decisions.
🧾Raw Dataset (Before Cleaning) and 🧹 Cleaned Dataset Sample
"Initial raw dataset with inconsistent formatting and typographical errors. Required thorough cleaning before analysis could begin."
"Cleaned dataset using Excel formulas and categorization techniques. Added 'Age Group' segmentation for demographic analysis."
📊Pivot Tables
"Pivot table used to summarize profit and revenue across different markets and customer groups, enabling high-level business insights."
📈Visual Dashboards
"Year-over-year growth visualized to track financial performance."
"Identified Australia as a top-performing market across all product categories."
"Adults aged 35–64 shown as the largest revenue-generating group."