Patterns of DoorDash Customers

I’m doing this project as a part of the Data Analytics Accelerator program, where I was challenged to use Excel to analyze data from Doordash.

Doordash is a popular app, where users can order food from a variety of restaurants in their area for delivery. In order to optimize advertisements, it is important to understand the types of consumers that are ordering on the app. There are plenty of patterns that may surprise you, such as when you group customers into age categories there is not much of a difference when you compare the average percentage of total income spent on the app between the age groups.

The dataset was provided by the creators of Data Analytics Accelerator. The data analyzed was consumer data from the company iFood, the Brazilian equivalent of Doordash. The goal of the analysis was to establish trends in the data that would provide insight to the management of the company.

In order to do the analysis of this data set, Microsoft Excel was chosen due to the versatility of the app to conduct data analysis. Also, it was chosen due to the popularity of the software, so that anyone else who is interest in looking at the spreadsheet will be able to understand how the analysis was conducted. Right off the bat, there was one question to see if there was any correlation with the total amount spent on Doordash and the yearly income of the customer.

As one can see there is a correlation between to total amount spent on Doordash and the yearly income of the individual. Provided with this insight, one could recommend the marketing team at Doordash to target advertisements. Next, customers were split into different age categories. These categories were ages 24-35, 36-50, 51-65, and 65+. After the categories were established, the percentage of income spent on Doordash was calculated. This was done by dividing the total amount spent on the application and dividing it by the customer's total annual income. The mean of the percentage of incomes spent was calculated for each group, and plotted resulting in this graph:

All age groups spend relatively the same amount of their total income. This shows that companies should focus income more than age because regardless of age customers spent about the same percentage of their income. One factor that does diminish the disposable income of a customer is having children. A pivot table was used to analyze the effect of having children on the average amount spent on the application. The average amount spent on the application plotted for having 0, 1, and 2 children. The graph below shows the there is a significant correlation between the two variables.

With this information, it would be advised to advertise more to higher earners, with no children. One step further; what would those types of consumers be interested in buying? The data set provided several different types of product revenue. The most popular category among customers with no children was by far alcoholic beverages with meat products coming in second.

With this insight, one would advise the marketing team to perhaps advertise their alcoholic beverage more (assuming that it is legal).

In summary, income and the amount spent on Doordash are directly correlated. Customers of all age groups spent relatively the same percentage of their annual income on the app. While income was a predictor of how much a customer would spend on the app, it was important to consider another factor on disposable income. That being the number of children they have. Most of the revenue came from customers with no children, and the most popular category of purchases was alcoholic beverages.

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