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Interview Case Studies

Whenever you are interview for a Data Science/Business Analyst role, there is bound to have a roundwhere you will be provided with a case and you need to show your thought process. Basically, there are two types of case studies in an interview:-
1. Business case
2. Guess estimate case

In a Business case study, you will be provided with a live or solved project and your thought process and approach will be checked. Whereas a Guess estimate case is where your ability to think all the possible cases will be examined. Let’s see how to approach these questions which were asked in real interviews

Company Name – Myntra 
Round – 2 Case Study
Topic:- Suppose you have a restaurant and there is a crunch of sitting space, apart from that you have one kitchen door which is generally crowded with the waiters and food delivery guys which leads to delay in serving the food. Would you like to create room for one more small window to expand the kitchen and make the service quicker?
What will be the impact on the already small sitting space?
How to optimize the above scenario?

P.S. – The approach shared below is not the best which you can think of. You may add your points and think through the problem
Take a stand and always talk about your solution with the mindset of a data scientist. Throw numbers wherever possible 
If I were the owner of the restaurant, I would have added a small room for some selected food items. Looking at the data, we can conclude on a few food items which are served most often to the dining customers as well as to the delivery customers.
Suppose it’s a Punjabi restaurant and the best sellers are Naan, Butter Chicken, and Punjabi Biryani. Once we have the data, we can have these items prepared in the main kitchen, but the delivery/serving should be done on the small room/window which is newly created. What about the space crunch? The waiters will have a hard time going back and forth?
The space crunch needs to be compromised and in return, we will be providing quicker service which should compensate for space. The serving time and sitting time of the customer will decrease which might counter the space crunch as more number of customers can be entertained. There is one more problem with the restaurant and that is the wastage of raw materials as the shelf life of a few ingredients are very low. How to counter it?
Looking at the data we can come up with a few nodes of this problem. We can ask these questions to the data:-
1. Which all items are sold the most?
2. What is the key ingredient of these items?
3. Day-Food item pair, with this we can get which all items are sold on which day
We can predict the amount of demand in the coming week and can act accordingly. But prediction might not work the best in every case, right?
I agree, to counter this we can have a variable price menu, so if the shelf life of a particular item is low, we can give a discount on these items or can give a combo offer to clear the stock at the very end of the day. There were questions on the formula which you will use to determine the new price in the above situation. Basically, you need to come up with parameters and you have to decide the importance of the parameters by either giving them multiplicative or additive importance in the formula or you can club your answer with any other offer. Following were the discussion points:-
1. We can take those items which will get wasted by the end of the day(looking at the stock near the closing time) and can set a variable price. Suppose, you realize at 9 pm that at least 100 kgs of Biryani will be wasted tonight(by 12 am), then you can set a variable price on Biryani
Cost Price of 1 kg of Biryani – 100
Selling Price of 1 kg of Biryani – 200
Stock left – 100 Kgs
Time left – 3 hours
Aim – To minimize the loss Revised Price = ((180 – x)/180)*Selling Price
where x is the number of minutes after 9
So, at 9:30 pm, the price of the Biryani will be = ((180-30)/180)*200 = Rs. 166

This is one way where we can help the restaurant in clearing the stock.

We can also give the customers a Mega Offer, where if they buy a Biryani today, then they will get some y% discount tomorrow. This will also help in retaining the customers.

Apart from that, we can also give them more Biryani at a lesser price. Suppose Rs. 166 is the price at 9:30 pm, then we can offer them 1.5 kgs of Biryani at Rs.210. This will clear the stock easily.
Keep thinking about more points 🙂

Company Name – Sapient
Round – 2 (Case Study)
Topic – Recommendation of food items to new customer in a restaurant Punjabi By Nature, a restaurant in Bangalore, delivers and serves food to its customer. It has been in the business for the last 10 years. Recently the owner heard about Data Science and they want to leverage the opportunity in order to boost its revenue. The restaurant has been collecting the following data since the last 10 years:-
1. Name of Customer
2. Sex of Customer
3. Age of Customer
4. Food item code

Your job is to recommend 2 food items to a customer new to the restaurant. Answer the following questions:-

a. Looking at the data, provide 3 findings for the restaurant to boost their performance
A. i. Food item combination can help you recommend a particular item. For example, if people prefer curd with Parathas, then you can recommend it
ii. A list of the most popular food item
iii. Looking at the customer’s age and sex you can decide what to offer to a new customer. If the data suggests that a girl in the age group 20-25 likes chocolate ice cream then you can recommend this ice cream to the new girl customer b. Think of 4 more data points which might help you with the analysis
A. i. Pincode of delivery
ii. Time of order
iii. Phone Number
iv. Date and Day of service c. Now, what else can you find out from this data?
A. i. Day of service can get you the popular food item on each day and weekend
ii. Pincode can help you identify if there is a demand for some specific food items in a particular area
iii. The phone number to inform about new offers
iv. Time of order can get you the time at which the restaurant should shoot a particular offer for a specific food item

This was more like a discussion round and you will be grilled on your points 😛