DoorDash Market Analysis (Excel)

Introduction

DoorDash is one of the leading food delivery options in the United States. This case study was intended to simulate the activity of the data analytics team at DoorDash and provide business insight towards developing and/or improving the company's marketing strategy.

My goal for this project was the complete the analysis using Microsoft Excel. I've used Excel for years, starting in school and now using the tool daily in my current role. I'm very comfortable using Excel and consider myself a proficient user, however it is always great to practice!

The Data

The data and data dictionary was provided via the Data Analytics Accelerator program in a .csv format. The data is a modified version of the sample data set that DoorDash provides as part of the interview process for a data analyst role and although fictional, is representative of actual data that the food-delivery company utilizes to develop marketing campaigns.

Analysis

The first step in the analysis for this project was to create a new "Customer ID" column to easily identify and distinguish unique customers. Using various aggregate functions in Excel (MIN, MAX, SUM, etc.) I answered some key questions that give potential stakeholders a better understanding of their customers.

Next, I created a couple plots related to the total amount spent by customers. The first of which was a scatter plot that visualizes total amount spent on DoorDash vs customer income. The plot clearly shows that the higher the customer's income, the more they tend to spend on food delivery.

The second plot was a histogram that displayed the range of total amount spent per customer split into 6 "buckets" and the number of customers that fall into each range. The chart shows that an overwhelming majority of customers spend $419 or less on food delivery.

Next, I wanted to take a look at monthly data to see if there was a trend on when customers used (or didn't use) food delivery. To do so, I changed the "date joined" column via MONTH and TEXT functions and plotted how many customers joined per month. January saw the most new customers, whereas November and December saw the least. It is difficult to identify the reason why different months have higher new customers, however the lower numbers in Nov. and Dec. may be due to holidays/family gatherings that consists of more cooking done at home rather than delivery.

Another metric I was curious about was how having kids affected the customers. I created a quick pivot table to see how the total and average money spent on DoorDash, as well as the number of customers, varied depending on whether the household had 0, 1, or 2 kids in the home. Customers without kids absolutely spent more in total and on average, with numbers drastically dropping off if there were 2 kids in the home. These numbers would suggest that a marketing strategy that targets people without kids would lead towards more revenue.

Finally, I used the VLOOKUP function to create a tool for future users to quickly search for a specific customer to identify how much that customer spent in total. This would prove a useful tool for any user or stakeholder in being able to easily target individual customers rather then endlessly filtering or scrolling through the raw data.

Conclusion

Using Excel, I summarized the data and identified key metrics that help give an understanding of the DoorDash customer base, including total customers, total and average customer spending habits. Through a variety of plots that give a better sense of the customer data, I would recommend a marketing campaign that targets customers without children with an income of ~$60,000 that is more aggressively geared towards the beginning months of the year and the end of summer/early fall. This strategy would target DoorDash's key customer group that orders more frequently.

Thank you taking the time to read my DoorDash market analysis! If you have any feedback, questions, or comments please reach out on LinkedIn.