Product Management — Guesstimate | The number of orders placed through Food ordering Apps in Mumbai per day
The problem statement given is what is the average number of orders placed on Food ordering applications in Mumbai in a day, we will consider these applications to be Swiggy and Zomato.
To solve this problem first we need to clarify certain questions that we would be assuming, they are as follows.
Are we considering the orders placed during peak festive seasons?
[Re:] No, consider this day to be a normal working day
The number of orders placed in a day will be affected by any promotional offers running on Swiggy and Zomato, should we consider such promotional offers?
[Re: ] No, no promotional offers are going on at the given time.
Are we considering any particular AOV?
[Re:] No, consider it to be minimum.
Is this Pre-COVID or Post-COVID?
[Re:] Consider it to be post-COVID.
Now that we have the answer to our questions we can work on deriving the formulation with the help of a Top-down approach.
The idea is to put the target market into different buckets.
The total population of Mumbai is ~10Mn for simplicity purposes
We will divide this into a target bucket audience as follows:
Family (Considering the avg size is of 4 members): 55% = 5.5Mn
Couple: 30% = 3Mn
Students/Bachelors: 15% = 1.5 Mn
Since we have put the total population into different categories we will find out the actual no of active users of the food ordering applications.
Family: Since it is a family of 4 members assuming a couple and their children, the max no of active users would be 1, for a very optimistic approach, hence the total no of active app users = 25% of 5.5Mn → ~1.4Mn
Couple: The couple consists of both the people working, both of them would be active users of the applications to place food orders through the application, hence the no of active users would be 2 i.e., 3Mn active users.
Students/bachelors: Since bachelors or students are on their own, the total number of active users comes to 1.5 Mn.
The total no of active Swiggy and Zomato Users comes out to 1.4(family) + 3(couple) + 1.5(Students/Bachelors) = 5.9 Mn.
Now we will categorize the orders into 4 meals as follows:
Breakfast
Lunch
Snacks
Dinner
We will calculate the actual no of orders placed into these 4 meal categories according to the user types.
Breakfast
Family [B(F)]: Since the family is active and has everything planned out in terms of food item stock etc, there are approx. 10% of users ordering for breakfast on occasion, which comes out to 0.14Mn orders.
A couple [B(C)]: Since it is a couple and working couple, breakfast ordering is very less or close to now considering they are busy commuting to the office and also have a morning routine, hence we will assume no orders are placed for breakfast by a couple.
Students/Bachelors[B(S/B)]: Since This category of users is on a budget and are only relied on food from outside their chances of ordering food are high but considering the budget, the users ordering food would be approx. 20% which comes out to 0.3Mn.
Lunch
Family[L(F)]: Since it is a family of 4 having children taking education, it requires a lunch box to be given to them and also with this the working individual will have his/her lunch box, hence the lunch orders would be very less considering only 5% orders would be placed, which is 0.07Mn.
A couple [L(C)]: Since this category is office-going or busy professionals, there are high chances for lunch orders at the office or home in case of WFH, considering it to be approx. 30% which is 0.9Mn.
Students/Bachelors[L(S/B)]: This category is either working or attending education, and considering the budget, they would be on the number of orders placed would be approx. 10% which is 0.15Mn.
Snacks/Binge ordering/Cravings :
Family[S(F)]: This category of meal ordering from a family is much less since as described they have everything planned, considering the children and junk food or outside would be harmful to their health, we will consider it to be 5% which is 0.07Mn.
A couple [S(C)]: This category would be ordering the most for the snacks category because of their busy schedule they get very less amount of time to go out physically and shop for groceries etc. hence it would be 40% which is 0.12Mn
Students/Bachelors[S(S/B)]: These users would not be ordering snacks much since considering their lifestyle they would be with their friends and having food outside, still we will consider it to be approx. 5% 0.075Mn
Dinner:
Family[D(F)]: Since it is a family sitting any kind of occasion/celebration like birthdays, or anniversaries everything would be celebrated in the evening since all the family members would be available at that time the % would be high, 30% i.e., 0.42Mn.
A couple [D(C)]: Similar to the above assumption for celebrations these users would prefer to go out to spend some quality time considering they can manage their schedule for the next day and also because of the busy work-life schedule, hence the ordering would be less to be approx. 10% = 0.3Mn.
Students/Bachelors[D(S/B)]: As mentioned earlier these people are on a budget to manage their expenses we will consider it to be approx. 10% which is 0.15Mn.
Hence the total no of orders placed in a day would be
= F[B+L+S+D] + C[B+L+S+D] + S/B[B+L+S+D]
=[0.14Mn + 0.07Mn + 0.07Mn + 0.42Mn] + [0+ 0.9Mn +1.2Mn + 0.3Mn] + [0.3Mn + 0.15Mn + 0.075Mn + 0.15Mn]
= [0.7Mn] + [2.4Mn] +[0.67Mn]
= ~3.7Mn orders in a day.
Post Covid Scenario
Considering the post covid scenario the habit of people ordering food has drastically changed be it any user type, so we will consider the orders from the user category couples and family to be increased for meal type dinners and snacks by an approx increase of 10 % assuming their habit of going out would be dangerous for health and rather should order at home.
Apart from this, we can consider decreasing in orders from the students/bachelors category since post covid they would have migrated back to their hometown with their family for work/education purposes, considering a decrease of approx 40%
Total no of orders/day post covid is:
= [0.14Mn + 0.07Mn + {0.07Mn +0.14Mn} + {0.42Mn + 0.14Mn}] + [0+ 0.9Mn + {1.2Mn + 0.3Mn} + {0.3Mn + 0.3Mn}] + [{0.3Mn + 0.15Mn + 0.075Mn + 0.15Mn} — 40%]
= ~4.3Mn orders/day.
P.S. We can also further drill a level deeper into the earnings of each user category and their feasibility to order food online, each user bucket would be categorised into 4 buckets of earnings and then the Avg orders placed in a day would be calculated.
