Unlocking the Power of DoorDash Sales Analytics with Excel

Introduction

Picture this: you've just returned home after a long day...

After a year of residing in Australia ????, I found DoorDash to be my go-to delivery service whenever the urge for a delicious meal without leaving home struck. Have you ever wondered how many others share this convenience-driven dining approach?

As a graduate student studying abroad, my usage of the platform was somewhat limited. This led me to ponder: what's the experience like for working adults who rely more heavily on delivery services? Do they spend as few as I did, or do they balance it with home-cooked meals?

?? What You'll Learn Here

1. Annual Spending: Discover the astonishing truth that people collectively spend approximately $1.14 million on delivery services each year.
2. Income & Spending: Delve into the data and uncover a fascinating 67% positive correlation between customers' income levels and their total expenditure on delivery services.
3. Monthly Sign-Ups: Explore the trends that reveal an average of 84 new sign-ups each month during the two-year period.
4. Seasonal Patterns: Examine the data to find out when customers are most inclined to sign up for delivery services. January and March emerge as peak months, with July to September following closely behind.

?? The Dataset

Fortuitously, my questions found answers in a dataset from the Data Analytics Bootcamp, spearheaded by Avery Smith . This dataset, adapted from a Brazilian delivery service akin to DoorDash, not only mirrors its purpose but also offers a treasure trove of around 2000 data points for me to delve into! ??????

This dataset encompasses several key attributes, including:

• Personal information
• Expenditure breakdown by product category
• Customer channel preferences for product purchases
• Campaign acceptance data
• Dates and periods of customer sign-ups

(For those interested in delving deeper into the dataset, you can access it here!)

Now, let's delve into the analysis!

Analysis

??? Data Preparation

In the initial phase of data preparation, we took steps to ensure the dataset's integrity and utility. Duplicate rows, which could potentially distort our analysis, were meticulously removed. This curation process resulted in a refined dataset containing 2021 data points.

To enhance the dataset's organization and analytical capabilities, we introduced a new column labeled "CustomerID." This addition serves the purpose of uniquely identifying each data point, facilitating more granular insights and precise analysis.

?? Annual Spending

Now that we've meticulously cleaned our dataset, let's embark on our journey to unveil intriguing insights. Our first stop is to examine the total expenditure made by customers on the delivery service during the period from December 2014 to November 2016.

Our analysis reveals that in this two-year timeframe, customers collectively spent a staggering sum of approximately $1.14 million. Breaking it down, on average, each customer's expenditure amounts to approximately $563.

While this might not initially strike as a substantial sum, it's important to consider that this figure represents expenditures made within two years. When contextualized as roughly 0.46% of each customer's annual income, it appears quite reasonable and manageable.

?? Income & Spending

Now that we've gained insights into overall spending patterns, let's delve into the fascinating correlation between income and expenditure.

The scatter plot above tells a compelling story – a robust 67% positive correlation exists between these two variables. While a few outliers exist, such as those in the upper left and lower right corners, the majority of customers tend to increase their spending on delivery services as their annual income rises. This correlation underscores the dynamic relationship between personal income and dining preferences.

?? Monthly Sign-Ups

Now, let's explore the status of each month with a focus on customer sign-ups.

This insightful pivot table reveals an average of 168 new customers signing up each month throughout the two-year period. To put it in perspective, that's an impressive 84 new customers embracing the delivery service every month.

Diving deeper, we find that January boasts the highest sign-up count with a remarkable 204 new members. On the flip side, December exhibits the lowest, with just 125 new sign-ups.

Now, let's shift our focus to uncovering seasonal patterns. ?????

??? Seasonal Patterns

Take a look at the bar chart below, and you'll notice a clear trend. January and March consistently stand out with higher member counts, closely followed by July to September.

Now, let's dive a bit deeper into this observation. These months align with the summer and winter seasons.

Keep in mind that this dataset originates from a Brazilian delivery service. Brazil, situated in the southern hemisphere, experiences opposite seasons compared to the northern hemisphere.

It appears we've unearthed some intriguing insights from this analysis! ???????

Conclusion & Future Directions

The data reveals some intriguing insights about the behavior of food delivery platform users in Brazil. They collectively spend approximately $1.14 million annually on this delivery service, with an average spend of $563 per person during the two-year period. Surprisingly, this is significantly higher than my own spending habits in Australia. No more guilt about the occasional splurge due to laziness! ????

Another interesting finding is the positive correlation between income and spending. It seems that those with higher incomes tend to use the service more frequently. Does this mean I'll join the ranks of higher spenders in the future?

?? Only time will tell.

When it comes to monthly sign-ups, the average of 84 new members each month appears relatively low compared to DoorDash and similar services worldwide. Many of my friends sign up for such services at least once (and end it after a month). Could it be related to the seasons? After all, we're analyzing a dataset from Brazil, where the climate differs from the northern hemisphere. Perhaps people prefer delivery during extreme weather conditions.

While this analysis primarily focuses on spending patterns and time periods, there are still unexplored aspects such as personal information and campaign details that could provide further insights into customer behavior. I hope this article gives you a better understanding of how DoorDash operates in terms of marketing and how people's spending habits contribute to its success! ??????

Feel free to reach out if you'd like to discuss these findings further or explore potential collaborations! ????

Call to Action

Feeling inspired to explore more about data analysis, or have any questions? I'd love to connect with you! Feel free to reach out on LinkedIn or drop me an email at ?? [email protected]. Let's continue this data-driven journey together! ??????

Thanks for reading this article again! ??