iFood Excel Explanatory Data Analysis Project

Introduction:

I remember when I first heard about the food delivery business model and thought it was a very interesting and creative idea that allowed customers to pay for food on an app and have it delivered to their front doors.

Initially, I recall it being very successful for fast-food chains. However, during the pandemic, I saw these apps reach their full potential as people started ordering more from casual sit-down restaurants and fine dining establishments.

This dataset was obtained from iFood, a Brazilian food delivery service located in Brazil.

The dataset contains over 2,000 rows of data, covering various factors such as the total amount spent by a customer, the number of children in a household, the customers' education levels, and much more.

Data Analysis and Findings

As shown in the chart, just over 50% of the total expenditure is spent on wines, making it the dominant category. This is followed by meats, gold, fish, sweets, and finally, fruits, which represent the smallest portion of spending.

I must admit, I was quite surprised by how much customers spent on wines through this app. I wouldn’t have guessed this just by looking at the raw data alone.

Recommendations: If I were part of the iFood marketing team, I would strongly recommend launching a marketing campaign aimed at increasing the sales of fruits and sweets. This could be achieved by offering special discounts or promotions within the app to entice customers to purchase these items more frequently.

Now let’s take a closer look to better understand the purchasing behavior of different age demographics and see which age groups are buying what. One consistent pattern across all age groups is the significant consumption of wines and meats. This indicates that the iFood delivery service is particularly popular for its wine offerings and meat-based dishes.

This graph highlights a positive, linear relationship. The more disposable income people have, the more they tend to spend on this app. The R-squared value, which measures the proportion of variance explained by the model, is 0.6711. This indicates that approximately 67% of the variation in total spending can be attributed to increases in income levels.

The months of January, March, July, and August show the highest frequency of users for this food delivery service. Summer months are particularly popular as the hot weather encourages people to order takeout rather than dine in at restaurants. The notable decline from September to December could indicate that customers prefer dining out during the holiday season due to hectic travel schedules or may be spending more time with their families during this period.

This figure is one of my favorites as it effectively highlights how customer spending behavior varies depending on whether they have children. Households with no children are significantly more likely to spend money on this service compared to those with children. This may be because parents with children often prioritize experience-based lifestyles, preferring outdoor activities for their kids rather than staying indoors.

This graph reveals that individuals in the 24-35 age range are significantly more conservative with their spending compared to those aged 36-65. Notably, the 51-65 age group spends the highest amount, with a total of $418,695, followed closely by the 36-50 age group, which spends $411,557 on this food delivery service.

Recommendations: If I were part of the marketing department, I would recommend launching targeted campaigns to increase spending among the 24-35 age bracket. Since many individuals in this age group may be enrolled in graduate school or busy with work, offering discounts or a reward points system could incentivize them to use the service more frequently and help us boost spending in this demographic.

Those with a bachelor’s degree are the most frequent users of this service. This suggests that many individuals in this group have recently entered the workforce and rely on the convenience of this service for their meals. In contrast, those with a master’s or PhD degree are often older, may have families, and are more likely to have the skills and time to cook meals at home.

Conclusion:

In conclusion, this dataset was both fascinating and insightful, highlighting the importance of data visualizations in uncovering trends. The marketing team at iFood should consider developing more creative strategies to target young professionals while also focusing on increasing the sales of fruits and sweets. These efforts could help expand their customer base and improve overall revenue.

If you enjoyed this article and want to see more of my explanatory data analyses, please like, comment, and connect!