Door Dash Market Analysis using Excel

Door Dash Market Analysis using Excel

By: Samantha Paul

Recently I’ve been working with Door Dash sales data provided by Data Analytics Accelerator. The data was provided and analyzed through Excel. I wanted to focus on answering these business questions:

* What is the average amount spent by each customer?

* What is the total amount spent by customers?

* Does age affect money spent?

* How old is the oldest customer?

* Who is the most recently acquired customer?

- Analysis -

• In Excel, I inserted a new column for Customer ID. From there I sorted and filtered data.
• A few of the business questions come from data aggregation. Using AVERAGE, SUM, MAX and MIN.
• I used different formula. I needed to find the percentage that customers spent versus their total income.
• I wanted to find out if age had something to do with the amount people spent. I inserted a new column that sorted customers into 4 different age groups (24-35, 36-50, 51-65, & 66+).
• From here I was able to use the data to start making some graphs. The first graph was a Scatter Plot showing the Income versus the Total Spent. I included the R-squared value which represents the percentage of variance. Or how well? “income” and “total spent” can be defined by R squared.

• Next, I created a Histogram of the Amount Total spent by customers.

• Needing to aggregate the data deeper, I created a pivot table that broke down customers in categories of age and if they had kids.

• After getting the data needed. I created an interactive tool using VLOOKUP. The tool would enable users to type any Customer ID and the amount that the customer spent gets returned.

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? - Insights and Recommendations? -

• The average spent by customers is? $562.76
• The total spent by customers is $1,240,896
• Ages between 51-65 years had spent the most amount of money.
• The oldest customer is 80 years old.
• The newest customer is 2195.

Looking at the data ages 36-50 held a higher population but it was the?51-65 year group who spent the most overall. On average, the higher the income the higher a customer would spend. This data point would lead me to believe that the app is more popular with the 36-50 age group, but 51-65 year olds are more financially sound.

A thing about data is that once you start exploring, the more insights you can make. One significant find was that the years 2015 and 2016 were when the majority of the customers joined. This would lead me to dig a bit further as to why there were such a significant increase in customers and bring up findings to stakeholders to create a similar scenario to increase numbers.

Thank you for taking the time to read my analysis! If you liked what you see, have pointers or just want to talk data, please connect with me! Click here! --> Samantha Paul