As part of an study class I analyzed iFood a online grocery shopping app from international company as they try to understand insights in their customer base and their shopping patterns as they get ready to launch their new campaign aiming to generate maximum revenue and for that they have employed my services to do data analysis and provide insights that will have data backing rather than trial and error. Gaining insights has become and important testimonial before making huge investments and waiting for the results rather be sure of what to expect based on the customer base and their patterns and create imaginative campaigns based on the understanding of demographic.
It is expected to exhibit a compound annual growth rate (CAGR 2024-2028) of 10.06%, resulting in a projected market volume of US$1.79tn by 2028. In the Grocery Delivery market, a revenue growth of 20.3% is anticipated in 2025. The online food delivery market worldwide was valued at around USD 130.8 billion in 2023. It is expected to grow at a CAGR of 13.2% between 2024 and 2032 to reach approximately USD 275.22 billion by 2032.
It was assessed have used a multi-restaurant delivery website or app service in the past 90 days, followed by 51% for those 30 to 44 years old, 29% for those 45 to 60, and just 14% for those 60 and over.
Here is the analysis for iFood grocery shopping to understand the trends and patterns from a sample dataset from GitHub that can be found here:https://github.com/nailson/ifood-data-business-analyst-test/blob/master/ifood_df.csv
This data considers a well-established company operating in the retail food sector. Presently they have around several hundred thousand of registered customers and serve almost one million consumers a year.
With the help of EXCEL, I imported a sample dataset that had 2200 rows and around 41 columns and following insights were uncovered as the highlights:
· $1.24Million was spent on iFood in 2018
· 67% of spend variance can be explained by income levels
· Growth thoughtful the year was mostly constant at 183 new customers per month with November & December being the low and January being the high
Analysis:
A peek into the demographic of the dataset showed that the number of customers being considered here is 2205 and the oldest customer is 80yrs, the average spent of the group was $562 and the max revenue collected was $1.24 Million with a median percent spend being around 70%. This is aligns with the observations on the popular website google.com as mentioned in the introduction.
Further analysis of Income vs Total amount spent by customers showed R^2 value of 0.67 which indicates that about 67% of spent variance can be explained by income levels as income increases the spent increases. I was able to plot this relationship on a scatter plot and a draw a trend line that explains with about 70% accuracy we can predict how much a customer will spend on iFood in the coming years. There were 2 outliers observed on the plot I was able to analyze them and attribute them to data quality error as the first point shows excessive spend with minimal income and the second point shows customer having max income and no spend which can both be attributed data input errors.
Another observation showed most customers spent in the range of $4 - $419 at max customers falling in this histogram bin and the most amount spent was in the range of $2077-$2491 and was shown by only 19 customers which attributes to less than 1% of the population under consideration
Very Important observation was seen in the Average spend per Age group line chart where I observed that max spend was done in the age group of 66+years customers and which at time exceeded over 100% followed by 51-65yrs at 93.23% overall average %Spend was 90.57%
This was further segregated into Ave spend by Singles and it was seen that the customers in age group of 24-35yrs and single spent the most at 41.55% and 66+yrs group spent the least at 15.71%. Average spend of single customers came to 21.63% then the Married couples group showed that 36-50yrs showed spend of 42.87% which was max for that groups and Average of married couples spend was 38.73% proving amazing insights that the Singles, Seniors and married couples are the customers iFood should be the customer to focus on newer campaigns for most revenue generation.
Customer age group analysis was done by plotting a histogram and getting customers in predetermined age group bins and it was identified that most customer were in the 36-50yrs age group at 926 customers and the most profitable groups as determined by earlier observation the age group 24-35 yrs had 219 customers and 66+yrs had 312 customers making it important for iFood to create promotions and deals to attract the demographic that is high revenue producing to make the future campaigns a great success.
Furthermore, the households that had no kids spent the highest average amount of $841.26 and the number of customers was the max at 1276 at 57.86% further it was observed that the second group that spent was household with 1 kid however, the amount spent was significantly lower at average amount of $183.43 and the number of customers in this group made up 40% of the customer base.
To round off the study I did analysis of the purchase patterns of the customer base and it was identified that the max store visits were done by the age group 36-50yrs along with web visits and they also used the most deals that were being offered. This was followed by the next group 51-65yrs and this group also was ahead in new web purchases but not not in comparison to the in-store purchases that was all time high. This explained that customer checks out the offerings and deals online and comes to the store for making purchases. If iFood wants to target more online sales they will have to come up deals and coupons specially for the online purchases.
I hope you have had a good time understanding my work and there are more data point that can be further identified and more studies can be employed however, I will leave that for the next campaign to analyze the results after iFood implements all the recommendations and use the insights to gain higher revenue and business growth.
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Wow amazing work!