iFood User Data Analysis and Marketing Strategy

I traveled to Brazil in December and was astounded by the abundance of iFood delivery motorcycles I saw on the streets. Unlike in the United States, deliveries in Brazil are typically made by motorcycles due to the complexity of traffic and the flexibility of parking. When I lived in Sao Paulo in 2013, I don't recall seeing deliveries being made by a single company; usually, businesses had their own delivery employees. However, it appears that the landscape has changed, and iFood has taken the lead in food delivery in Brazil.

As a participant in Avery Smith's Data Analytics Bootcamp, I embarked on my first project: analyzing a dataset with 2.2k data points from iFood using Excel. You can access the dataset at this link. I decided to put on my marketing data analyst hat and delve into iFood's data to grasp:

What is the average spending per user on the iFood app?

Do individuals with children tend to spend more?

Is there a spending disparity between single and married individuals? What about age demographics?

What are the defining characteristics and spending habits of iFood users?

To begin, I've analyzed the spending habits of customers who invest the most in food delivery. By creating a pivot table, I've compared the average spent among divorced, married, single, living together partners, and widowed individuals. Curiously, while married customers constitute the majority of iFood users, widows emerge as the highest spenders on average. However, given their smaller representation in the dataset, the significance of this finding may be limited. Furthermore, marital status alone seems to have minimal impact on spending patterns:

When analyzing spending per household with children, it's apparent that households without kids tend to spend more on food delivery. Moreover, as the number of children increases, average spending decreases. This trend likely stems from the preference of families with children for home-cooked meals, a more cost-effective option for larger households:

In terms of age demographics, the majority of iFood users fall within the 36 to 50-year-old bracket:

Analyzing Spending Habits

Taking a look into product preferences, I've calculated the average spent per product category. Notably, wine products rank highest in spending, followed by meat products:

Marketing Strategy

Based on these analyses, I propose the following recommendations for the marketing team:

Promote Convenience to Families without Children: Highlight the convenience of food delivery for households without kids, as they tend to spend more on food delivery compared to those with children. Emphasize time-saving benefits and the luxury of having restaurant-quality meals at home, which might resonate more strongly with this demographic.

Explore Family Meal Bundles or Discounts: Recognizing that households with children tend to spend less on food delivery, consider introducing family meal bundles or special discounts tailored to larger households. These promotions could incentivize families with children to choose iFood for their meal solutions more frequently.

Target the 36-50 Age Bracket: Since the majority of iFood users fall within the 36 to 50-year-old age range, marketing efforts should be tailored to resonate with this demographic. Consider their lifestyle preferences, such as convenience, quality, and variety, when crafting marketing messages and promotions.

Promote High-Spending Product Categories: Given that wine products rank highest in spending followed by meat products, consider promoting these categories more prominently within the app. Highlight premium wine selections and high-quality meat options to encourage increased spending among users.

I utilized Excel for this analysis, which proved to be very convenient given the relatively small dataset. Typically, when doing analyses in Excel, I rely on pivot tables, VLOOKUP, and SUMIF formulas, and this analysis was no exception. I found exploring the iFood data to be enjoyable. Feel free to suggest any other topics you'd like me to analyze!