Analyzing DoorDash sales over a year using Excel

Why this project? 

This is the first project in Avery Smith’s Data Accelerator Bootcamp. That’s why I am doing this project now.  I’m also curious to know  about the analytics of Door Dash over the course of a year.

Here’s why you should keep reading ?

When people in the United States want to order something tasty  and not have to travel out to get it, but have it delivered right to their door,  they may  use the services of DoorDash.

Door dash is a very convenient though not inexpensive way to order your food. So, one might wonder who are the people using Door Dash? Also, how many are ordering and what are they ordering as well as how much they are spending.

What and Where of the Dataset?

With a Door Dash Case Study data set provided through the course (DoorDash_Case_Study.xlsx) I was able to learn some key facts about DoorDash sales and demographics. This includes their age, income, when they joined, and how much money they spent. The original data set can be found here: : https://github.com/nailson/ifood-data-business-analyst-test/blob/master/ifood_df.csv 

All Analysis

By creating a scatter plot of Income (x axis) vs total Spent (Y axis) I found that the amount of money spent by customer rose proportionally with income (see trend line). Note that the trend line does not intersect with 0 on the y axis. This implies that the customer had to have a certain level of income (roughly around 30,000) before he began using the services of DoorDash.

The r squared value of 0.6774 indicates that the variance  of the dependent variable (amount spent) is roughly explained by the independent variable (income).

The above bar chart shows the comparative number of customers who joined DoorDash in the given month. Most joined in January, possibly because they received a Christmas gift that started membership in January, with a sharp drop off after that. The least number of customers joined in November.

Main Takeaways

·        The amount of money customers spend on DoorDash services was roughly proportional to their income, i.e. the more money they made, the more they spent.

·        Most customers joined in January followed by fewer in subsequent months with the fewest joining in November.

·        The use of a pivot table and statistics showed that most customers are between 35 and 65 with the oldest being 80 years of age.

Call to Action

·        DoorDash might consider  and advertising campaign in mid-summer to boost sales for the second half of the year. Also, a discount program might  encourage lower income people to buy food more often.