Register Now

Login

Lost Password

Lost your password? Please enter your email address. You will receive a link and will create a new password via email.

Login

Register Now

It will take less than 1 minute to register for lifetime. Bonus Tip - We don't send OTP to your email id Make Sure to use your own email id for free books and giveaways

Guesstimate – Annual income of a beggar in Bangalore

Annual income of a beggar in Bangalore

Guesstimate is asked in an interview to understand the analytical understanding of the candidate as well as to check his thought/approach diversity.

Guesstimates can range from finding the number of ‘Red’ Cars in Delhi to the number of trees in Bangalore.

In one such interview, the candidate was asked to estimate the income of a beggar in Bangalore. We received 22 responses on this question, you can read their diverse approach or can write your own

Link to question – https://thedatamonk.com/question/how-much-is-the-annual-income-of-a-beggar-in-bangalore/

Annual income of a beggar in Bangalore

Link to the question with all the approach – https://thedatamonk.com/question/how-much-is-the-annual-income-of-a-beggar-in-bangalore/
If you have a different approach then please add your answer, else upvote the one which you like

Annual income of a beggar
Annual income of a beggar

First Approach – Ognish Banerjee

Assumption :
1. A beggar meets 100 people per day out of that chance of converting is 50%
2. The beggar knows where to search for people , let’s say near offices or tech park to have a better conversion rate
3. The beggar knows the peak time of the day as well as the peak time during the evening
4. The beggar starts his day from 10 am in the morning till 10 pm till night
5. Average income from a person is Rs 20

Now to go about this.

I’m categorizing the daily hour for wekkdays

10 am to 1 pm – peak time
1 pm to 5 pm – medium
5pm to 8 pm – peak
8pm to 10 pm – medium

Number of people he meets in these hours

10 am to 1 pm – 40 people
1 pm to 5pm – 10 people
5pm to 8pm – 40 people
8pm to 10pm – 10 people

On average expected income would be

(40*20) + (10*20) + (40*20) + (10*20) = 2000
5 days for weekdays = 10000
50% conversion rate = 5000

On weekdays in a month his income is = 5000 * 4 ( 4 weeks in a month) = 20000

Now taking the week ends. His strategy would be different. The beggar won’t be roaming near the offices, rather he would roam near the neighborhood during day and shopping mall/theatre during night.

If I go with similar calculation (not mentioned here) his evening income is way higher, peak time 6pm – 11pm

He meets 50 people with 20 rs each – 1000
50% conversion – 500
Number of weekends 8 – 500*8 = 4000

Total monthly income = 24000

Total annual income : 24000 * 12 around (3 lacs ) taking holidays, special events all around the year.

I’m a Mu Sigman also, during my first year I also earned the same like a beggar!!! Wow

Second Approach – Nilanjan Kumar

In the 24 hours time, let’s assume that a beggar sleeps 5 hours a day and also assume 5 hours he is busy doing other pieces of stuff like eating, playing cards on the roadside, etc. so that makes our time as: –

24-5-5 = 14 hours.

The beggar divided his time as below(5 days of working) : –
Time Interaction with People(per day) Conversion rate
6 a.m – 10 a.m 50 80%
12 p.m – 4 p.m 30 50%
6 p.m – 10 p.m 40 70%
11 p.m – 1 a.m 25 30%

Reasons for taking the conversion rate like this:
6 a.m. to 10 a.m – People in a good mood going to start their day by offering something to needy people.
12 p.m to 4 p.m. – People will be in a hurry as they have got limited time from the office.
6 p.m to 10 p.m – Back from work if a day went well they will offer to make each day good.
11 p.m to 1 a.m – End of the day.

If on average they get Rs. 5 from a single person, then: –

Time People offering money(per day) Amount(avg Rs 5 per person)
6 a.m – 10 a.m 40 200
12 p.m – 4 p.m 15 75
6 p.m – 10 p.m 28 140
11 p.m – 1 a.m 10 50
Total = Rs 465 per day

This 465 per day will be for 5 days a week as on weekends the time between 12 p.m. to 4 p.m and 6 p.m to 10 p.m will have conversion rates more. But on the contrary morning conversion rate will be less. So taking that situation into count, this Rs. 465 per day will increase to Rs. 500 per day (considering an average increase of Rs. 35 ).

Hence, Calculating for per week: –

(465*5)+(500*2) = Rs. 3325
A month has 4 weeks, hence income in 1 month = Rs. 3325*4 = Rs. 13,300
For annual income, 12 months, Hence, 12* 13300 = Rs. 1,59,600/-

The Data Monk Interview Books – Don’t Miss

Now we are also available on our website where you can directly download the PDF of the topic you are interested in. At Amazon, each book costs ~299, on our website we have put it at a 60-80% discount. There are ~4000 solved interview questions prepared for you.

10 e-book bundle with 1400 interview questions spread across SQL, Python, Statistics, Case Studies, and Machine Learning Algorithms – Ideal for 0-3 years experienced candidates

23 E-book with ~2000 interview questions spread across AWS, SQL, Python, 10+ ML algorithms, MS Excel, and Case Studies – Complete Package for someone between 0 to 8 years of experience (The above 10 e-book bundle has a completely different set of e-books)

12 E-books for 12 Machine Learning algorithms with 1000+ interview questions – For those candidates who want to include any Machine Learning Algorithm in their resume and to learn/revise the important concepts. These 12 e-books are a part of the 23 e-book package

Individual 50+ e-books on separate topics

Important Resources to crack interviews (Mostly Free)

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

The Data Monk Youtube channel – Here you will get only those videos that are asked in interviews for 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

About TheDataMonkGrand Master

I am the Co-Founder of The Data Monk. I have a total of 6+ years of analytics experience 3+ years at Mu Sigma 2 years at OYO 1 year and counting at The Data Monk I am an active trader and a logically sarcastic idiot :)

Follow Me