From Orders to Insights: Exploring DoorDash Consumer Sales Trends

Why Analyze DoorDash Order Data?

This project allowed me to step into a real-world scenario where I could engage with data that mattered. The idea of working with company data instead of hypothetical examples was thrilling. Additionally, I wanted to push myself to learn features in Excel that I usually skimmed over. It was my chance to turn theory into practice and gain insights that could be valuable for a business.

In this article, you’ll learn about the surprising findings from my analysis of iFood customer data. I’ll share what I discovered about spending trends, the demographics of our customers, and how tools like Pivot Charts can make data analysis easier.

Key Takeaways

• Customers without children tend to spend more than those with children.
• Pivot Charts significantly simplify data aggregation and visualization.
• The average customer income is around $40,000.
• Customer membership declines during the holiday season.

My analysis journey began with cleaning the dataset to ensure accuracy. I transformed the data into a format suitable for analysis and then visualized it using various Excel tools. The real eye-opener was learning how to use Pivot Tables effectively, which made aggregating and displaying the data a breeze! I expected to see some interesting trends, but what I found truly surprised me. I’ll also add the link to the original dataset below so you can check it out yourself!

Link to dataset

Analysis

This scatterplot illustrates the relationship between income and spending. It clearly shows a direct correlation: as income increases, so does the amount spent in a single visit. This makes sense — more disposable income usually leads to more spending.

The histogram showcases income distribution among customers. Interestingly, it reveals that most of our shoppers are average consumers with an income around $40,000. However, higher-income individuals shop less frequently, which was unexpected.

My pivot table analyzed family spending habits. To no surprise, customers with children spent less compared to those without. This finding prompts deeper reflections about shopping habits among parents, perhaps due to budget constraints or prioritizing necessities. One thing to note is that this dataset had no date range which is why the Average Amount Spent is so large.

An analysis of the average salary difference between individuals with a Master’s degree and those with only an undergraduate degree revealed a smaller gap than expected. Given the limited dataset, this raises an interesting question: does the perceived value of pursuing a Master’s degree justify the financial investment, or are there other factors influencing this outcome?

Main Takeaways

Through this project, I learned that Excel is far more powerful than it appears at first glance. While it’s great for basic analysis, diving deeper reveals its full potential for advanced data manipulation and visualization. Understanding these tools can provide clearer insights and support better decision-making.

Reflecting on this experience, I encountered a few challenges, especially while cleaning the data. However, overcoming these hurdles reinforced my problem-solving skills and made the final results even more rewarding. This project not only broadened my technical skills but has also shaped my perspective on data analysis and its importance in the business landscape. I’m excited to carry these lessons into future endeavors.

Let’s Connect!

Follow me on LinkedIn! I’d love to hear your thoughts on my findings or any questions you might have about data analysis.