DoorDash Analytics

Why DoorDash?

I moved back to Pennsylvania in 2021. Since then, I have been a user of DoorDash for take out and delivery.

As someone who struggles with finding time to go to the grocery store or make meals, DoorDash has helped me find food near me.

I became curious about who uses apps like DoorDash and why. Where are they from? How old are they? Do they have family or live alone?

The chance to look at the data has not only given me insights into the type of customer uses DoorDash but it has given me a new and interesting viewpoint on my own use of food delivery services.

What’s interesting about DoorDash?

There is a wide range of customer insights from the data found online.

• The oldest customer is 80 years old, while the youngest customer is 24 yeas old
• The total spent was $1,240,896.00
• The average spent was $562.76
• The customer use average is 6.88 years

Data background

This dataset is modified from the interview process for iFood, Brazil’s DoorDash equivalent.

It contains:

• 2,205 rows of customers and their information
• 36 columns of customer attributes

Each row contains information for one customer. For instance income, age, and date joined.

Those columns include:

• Income: Customer's Yearly Income
• MntTotal: Total Amount Spent by Customer
• Kidhome: Number of Young Kids in Home
• Teenhome: Number of Teenagers in Home
• Recency: Number of Days Since Last Purchase
• AcceptedCmp6: Did The Customer Accept Offer in 6th Campaign
• Age: Age of Customer
• Customer_Days: How Many Days Has Customer Been a Customer
• DateJoined: The date the customer first became a customer

Examining Age Demographics

I find myself wondering what the age range is for most apps I use. For some, it’s easier to hypothesize based on trends. For others, like DoorDash, it’s harder to infer. In these circumstances, I find a query into the data creates clearer results.

It was fairly easy to find the oldest and youngest DoorDash customers. Using a MAX and MIN aggregate function on the age column, I was able to determine that the age of DoorDash customers range from 24 years old to 80 years old.

In addition, I created an IF function that categorizes each customers’ age into a group. This allowed me to answer the question of what age group makes up most of the customer base. As shown below, ages 36-50 make up 42% of the customer base, followed by ages 51-65 at 34%.

Analysis of Influencing Factors on Total Spent

The total spent on DoorDash orders was $1.2 million. With an R squared value of 0.67, 67% of the change in amount spent can be explained by a change in income.

There are two outliers. One has a low income and a high spend value. The other has a high income and a low spend value. These might be accounted for by an error in data collection. Alternatively, the former outlier could be explained by a customer with a low income taking advantage of deals that lower order fees. The latter outlier could be explained by a customer with a high income who only orders from DoorDash once a week for certain meetings. Or a college student using their parents account.

Using an IF function, I categorized the time since last order value into groups of 1 week, 1 month, 2 months, 3 months, and 4 months. Using these last order groups, I calculated the total spent in each category using the SUM aggregate function.

The most spent was 3 months ago, with a total of $380,536. A campaign or reduction of fees might account for this increase in the total spent. The least amount spent, at $102,241 was in the 1 week category. This might be accounted for by the smaller category size. 1 week vs 1 month is a big difference in time. However, at 1 month, the total spent was $292,622. A difference of $63,475 between 1 month and $356,097 at 2 months is still a significant change. Again, this might be accounted for by a campaign or deal on the app, or that month might have had a big holiday or school event, such as finals week.

Examination of the Average Spent Amount

Finding the average amount spent was pretty simple. The AVERAGE aggregate function calculated $562.76 as the average. 40% of customers spent over the average amount. The other 60% spent under the average amount.

The age group that spent the most was customers who are 66 years old and above, followed by those 51-65 years old. This might be explained by customers getting a promotion and increase in salary or reaching retirement age. Likewise, it could be inferred that those who are older order more because of health problems that prevent them from cooking regularly.

Exploration of Average Customer Use

Using an AVERAGE aggregate function, the average customer use of DoorDash was calculated to be 6.88 years. This is supported by the above graph showing the average spent by age group. Each group is at least 6 years and the amount spent continuously increases since 36 years of age.

1,108 customers have used DoorDash longer than the average, while 1,097 customers have used DoorDash less than the average amount of time.

The age group with the most customers over the average use time is 36-50 years old. This age group also have the most customers under the average use time. This means most of the customers in that age group started using DoorDash when they were 24-35 years of age.

Key Insights for DoorDash Marketing

Marketing insights for DoorDash to increase their customer base and revenue.

• Campaigns should focus on customers age 36-50 and 51-60 as they are the largest age demographics in the customer base.
• Future campaigns should target customers who's last order was 2 months ago with the intent for them to order again before reaching 4 months.
• Milestone deals should be implemented to reward customer use.
• New customer advertisements should target 24-35 year olds to encourage them to start using the app.

To reiterate, DoorDash has a large variety of customers. Age ranges from 24 years old to 80 years old. Customers spent $562.76 on average, resulting in a total of $1.2 million, being spent on food delivery. The average time a customer used the app was 6.88 years.

In addition, marketing insights are provided to increase DoorDash customers and company revenue. Certain age groups should be targeted through specific advertising campaigns.

Thanks for reading! If you have any questions, please email me at the email below. Also, I am looking for a full-time remote job as a data analyst, so if you have any recommendations, please message me on LinkedIn or email me at xaviertquinn@gmail.com. Any information would be very helpful!

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