Analytics Case Study – 1

Analytics Case Study – In this space, we will try to cover case studies that are actually asked in Analytics interviews. Every company asks for case studies in their interview process and it is one of the important rounds where you show your business acumen to the interviewer.
If you are reading this question then do try to attempt it in the comment section
Let’s start Analytics Case Study part 1

Question – Punjabi By Nature is going through a rough phase and is wasting 30-40 Kgs of biryani on some days of the week.Management is thinking to find a method to sell the left over biryani or reduce the amount of biryani prepared.

You are the manager and you are supposed to come up with a methodology for the same

Approach 1

Assumptions:-
– PBN is a restaurant and not a home kitchen.
– They cook biryani only once a day.

Here is kind of a mindmap I created for this problems with my idea’s and suggestions

– Reduce the amount cooked/ Sell leftover

– Selling Leftovers
– Leveraging food delivery apps
– Exclusive Partnership with food delivery apps with good offers during closing hours
– Free testers with other orders of delivery app
– Leveraging footfall in restaurant
– x% off for customers on donating food for the needy
– Exclusive buy 1 get 1 on specific days of weeks where wastage is more historically
– Marketing
– selling them nearby competitor stores with publicity pamphlets

– Estimating the cooking amount
– Based on data
– Reduced quality/ quantity of food/ Prices
– Quality
– Recent change in the cook for biryani
– Customer reviews on popular food critic website
– Quantity
– Change in portion size
– Prices
– Change in prices from history
– Competitors nearby
– New competitors nearby or on delivery apps
– Prices and offers compared to competitors
– Change in customers eating habits
– Footfall and ordering trend for all the competitors

Historic data for the following columns can be asked for further analysis – (Order history, Cook information,
Customer feedback from various websites, portion sizes per plate, price per plate, Competitors order history)

Leveraging above columns, a suitable model can be trained giving a probabilistic estimate for the amount of biryani
to be cooked everyday.

– Non data based
– X% off on reserving table or an order a day before
– Cooking biryani twice a day instead of once allows more control on quantity cooked

Approach 2

Restaurants usually predict how much food will be needed by estimation. I would like to use a data-driven approach to refine the estimation.
1) As a manager, I would like to take a look at historical data and see how the trend has been over the years. Has PBN grown? Is there an increase in the number of customers and/or number of branches?
2) Are there two branches opened nearby resulting in incorrect estimation and hence, the wastage? Or has a new competitor restaurant opened up nearby?
3) I would see if I can observe a seasonality. The sales might be going up on days like public holidays or Sundays. The food quantity should be greater on these occasions. However, on Tuesdays, the quantity of non-veg biryani needed would be less (demographic info)
4) When we have left-over biryani of a particular kind and the day is coming to an end, do we offer more discounts compared to our competitors to increase the sales? We can offer discounts on food delivery apps at the end of the day and this could influence people to place orders online
5) On certain days when we have too much leftover food, we can keep our restaurant open for deliveries at night as there are limited good options available for employees working at night
6) Can we use the biryani as a substitute to plain rice in some kind of thalis?
7) We can put up a stall at snacks or dinner time in mess of some companies if permission is granted to finish the extra food
8) If I do have some time and resources, I would like to plot a time-series graph of quantity required and forecast values for better results!
9) If there is still extra biryani left I would like to offer it to my employees, cleaners, cooks and some poor people rather than wasting it

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Important Resources to crack interviews (Mostly Free)

There are a few things that might be very useful for your preparation

The Data Monk Youtube channel – Here you will get only those videos that are asked in interviews with Data Analysts, Data Scientists, Machine Learning Engineers, Business Intelligence Engineers, Analytics managers, etc.
Go through the watchlist which makes you uncomfortable:-

All the list of 200 videos
Complete Python Playlist for Data Science
Company-wise Data Science Interview Questions – Must Watch
All important Machine Learning Algorithm with code in Python
Complete Python Numpy Playlist
Complete Python Pandas Playlist
SQL Complete Playlist
Case Study and Guesstimates Complete Playlist
Complete Playlist of Statistics

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