DoorDash Delivery Analysis
This project analyzes trends based on a fictitious version of a food delivery service. All of the information was taken from sales in the year 2018.
ITake-Out Analysis for
Doordash Sales in 2018
​
Do you remember the first time you ordered from Doordash? For me, it was after a family member had undergone a specialized surgery two states away. Once the wave of relief that everything had gone well washed over us, we realized how hungry we all were! Of course the hotel had closed its evening buffet & we were too tired to drive anywhere.
So one of the teenagers in the family suggested we get food delivered from Doordash. It was convenient and delicious, but a little spendy which made me curious about their customer base and the business model in general.
So when I had the chance to explore a dataset online, I jumped at the opportunity to uncover some important details regarding DoorDash demographics and sales.
​
Using Excel, I imported the 2205 rows of data and discovered:
-
Household income highly correlates to the amount spent users spend on DoorDash, by 67% in fact
-
In 2018, users spent $1.24 million with DoorDash in total
-
Wine sales is the largest category, comprising about half of all sales
-
Campaign 6 was the most successful, as measured by customers who joined
-
Monthly growth of new customers was relatively consistent throughout the year with January outperforming all other months and November and December the lowest.
First, the Data!
​
This dataset is one that has been used as a part of an interview process and was utilized by myself and my classmates in the Data Analytics Accelerator Bootcamp by Avery Smith.
​
The link to the dataset is: Doordash Case Study
Of the thirty-eight columns, some of the most important are the: customer income, amount spent per customer, the six categories of sales, the number of customers who joined after each of six campaigns, the age of customers, their marital status, their level of education and how many new customers joined each month during 2018.
The Analysis
Since this is a service of convenience, my first question was to understand if household income is related to the the amount of money spent on Doordash. This scatterplot shows that 67% of the spend variance can be attributed to household income level.
​
​
​
​
​
​
​
​
​
​
A simple SUM function revealed that the 2205 customers spent a total of $1.24 million on DoorDash in 2018.
This lead me to wonder about the six categories of sales and which comprised the most and the least of the total sales. As noted below, wine sales account for about half of all DoorDash sales!
​
​
​
​
​
​
​
​
​
Next, I wanted to explore the effectiveness of the six campaigns that DoorDash ran to attract customers. While campaign 2 was (by far) the least successful, campaign 6 generated almost twice the results of the other campaigns, which were more or less about equally successful.
​
​
​
​
​
​
​
​
​
​
​
My final line of inquiry was to investigate how the amount of new customers changed throughout the year. Interestingly, the amount was relatively consistent from month to month with two exceptions. First, the amount of new customers dipped in November and December. Also, January was the month when the most new customers joined DoorDash.
​
​
​
​
​
​
​
​
​
​
Time to Recap
​
We live in an age of convenience and the huge success of DoorDash is just one example of this. Taking a closer look at the business model provided some insights.
-
Income levels can be used to explain 67% of the spend variance
-
Customers spent a cumulative $1.24 million using the DoorDash service in 2018
-
Wine sales comprised about half of all total purchases, followed by the meat category
-
All campaigns to gain customers were of roughly equal success, except campaign 6, which yielded twice as many customers, and campaign 2, which was a bomb
-
Throughout the year, new customers joined at a relatively constant rate, except for the November & December dip, followed by the January spike
Action!
​
This is my first LinkedIn article. I thank you for reading and welcome your feedback! Please consider following me or connecting on LinkedIn at Carly Jocson. And keep me in mind for any remote positions as a data analyst!