What should you do before you answer an Analytical Interview Question ?
- Summarize the question
- Make sure you understand the objective
- Ask clarifying questions
- Share your problem solving roadmap
- Be courteous and don’t leave the interviewer behind
- Engage the interviewer
- Don’t be afraid to ask the interviewer for an assumption, especially when you don’t have a reasonable guesstimate
- Use your own assumption, but use reasonable justification
Sizing Framework: Using a top-down approach
- First think of the number of people in a population.
- From that population, how many are target customers?
- How often do these customers purchase the product?
- How many do they purchase? Just one (e.g. iPhone) or several (e.g. 4 car tires).
- What market share does the product have?
- Use market share estimation multiplied by price per unit to estimate revenue.
Draw the sizing framework
- # of people in pop.
- # target costs in the pop
- purchase freq
- Quantity per purchase period
- Market share
- Price per unit
What is the interviewer looking for in the estimation questions?
Listening skills Communication skills Teamwork skills Problem solving skills Analytical skills
How many Android smartphones are sold in the US each year?
Assumptions & Calculations:
There are approximately 330 million people in the United States
Of the 330 million individuals, approximately 90% of people have a cell phone.
Given that some cell phone owners may not be as tech-savvy, we will assume that about 80% of cell phone owners have a smartphone. Therefore, the total number of smartphone users is:
(330 million people in the United States) x (90% of population with a cell phone) x (80% of cell phone population with a smartphone) = ~240 million
While Android smartphones are more prevalently used on an international scale, both Android and Apple devices are equally competitive in the United States. Therefore, we’ll assume that Android has about 50% market share in the US.
With about 50% of the market being Android users, we then arrive at:
(240 million smartphone users in the US) x (50% Android marketshare) = 120 million Android users in the US.
Historically, in the US, smartphones are locked with contracts, meaning that smartphones can only be purchased or replaced when starting a new contract. With a typical contract lasting about two years long, we’ll assume that an average smart phone owner will replace his or her smartphone every 2 years.
Finally, calculating the number of Android smartphones sold in a year in the US:
(120 million Android users) x (50% buying a new phone this year) = 60 million Android devices sold in the US in a year.
How much data does YouTube store on a daily basis?
ASSUMPTIONS
*YouTube has roughly 1B users.
- Of its total users, 5% of its users upload videos on a consistent basis.
- A YouTube creator, on average, uploads approximately 10 videos in a year.
- An average video is 10 minutes long. With compression, a 10 minute long video typically using about 4GB of space will instead take only 40MB.
- Each video has a thumbnail (taking up 20KB) in addition to an average of five comments (100 bytes per comment).
CALCULATIONS
We now take the figures from the assumptions and multiply them to calculate the answer to the question:
Given that 5% of YouTubers uploads videos, we know that YouTube has approximately 50M creators (1B * 0.05 = 50M).
If every creator uploads an average of 10 videos annually, then the total number of videos uploaded annually would be (50M * 10) 500M. Divide 500M by the number of days in a year, gives us about 1.3M videos uploaded a day.
In addition to each video requiring 40MB, after factoring in the storage space required for the thumbnail image and five comments per video (20KB + 100 bytes * 5 comments), we find that each uploaded video requires ~40MB of space. Finally, multiplying this with 1.3M videos uploaded daily (40MB * 1.3M), we find that YouTube will require approximately 52TB of storage on a given day.
Can you estimate the number of vacuums sold in the US (market sizing)?
- First think of the number of people in a population.
- From that population, how many are target customers?
- How often do these customers purchase the product?
- How many do they purchase? Just one (e.g. iPhone) or several (e.g. 4 car tires).
- What market share does the product have?
- Use market share estimation multiplied by price per unit to estimate revenue.
How many queries per second does Gmail get?
ASSUMPTIONS
Define a query as a Gmail operation: read, write, or search
There are approx. 7 billion people in the world
40% of them use the Internet
20% use Gmail as their primary account
On average they log into their Gmail account 4 times a week
Each time they login, they read on average 8 emails, compose 2 emails, and search for 1 email
CALCULATIONS
The number of Gmail users is: (7 billion people in the world) x (40% use Internet) x (20% use Gmail as primary account) = 560 MM Gmail users
The number of queries per week is: (4 Gmail logins per week) x ([8 read email operations per login] + [2 compose email operations per login] + [1 search email operation]) = 44 Gmail queries per week per user
The number of Gmail queries per second = (560 MM Gmail users) * (44 Gmail operations per week per user) * (1 week per 604,800 seconds) = 40.7k queries per second
How many iPhones are sold in the US each year?
ASSUMPTIONS
- There are approximately 315 million people in the United States.
- 90% of people have a cell phone.
- Cell phone users are locked into contracts. That is, they cannot get a new phone unless they pay a penalty to break a contract. The contract is normally two years, so cell phones get replaced once every two years.
- Each person buys on average 1 cell phone.
- Smartphones comprise of 60% of new cell phone sales.
- Apple iPhone has 40% market share of the smart phone market.
CALCULATIONS
- The number of people looking to buy a new phone each year is:
- (315 million people in the US) x (90% people have a cell phone) x (50% will be buying a new phone this year) = 142 million
- The number of people that will buy the iPhone each year:
- (142 million people that will be buying a new phone this year) x (60% will get a smartphone) x (40% of the smartphone buyers will buy an iPhone) = 34 million
As the PM for Google Glass ‘Enterprise Edition’ PM, which metrics would you track? How do you know if the product is successful?
I didn’t own or test one by based on my readings reasons that the explorer edition didn’t see much success were: The high price, not enough apps available to work with it, and that viewing information inside the glass was difficult (placing wasn’t great and most visuals were too small). However, there were still many industries and companies that tested the glass and had good results. If the Glass comes back in an Enterprise Edition I will be looking at the following to check success:
- Most clear one is number of companies buying the glass and the growth rate within the company. Ex. If company X buys the glass for a team of two, do they expand to more members of that team or other teams within 6 months.
- Assuming there are a group of apps to use with the Glass, counting new downloads per app.
- If accessible, percentage of increase in productivity and job satisfaction in companies/teams using the Glass.
- Tracking number of live footage recorded with the Glass and stored or shared. Not sure of the technicals as I’m not in that team obv. but there should be a way to track videos uploaded to at least the main sites (Vimeo, YouTube) and footage users store in Google Drive/Cloud.
- What industries have the best adoption, why. What industries are the slowest adopters and why.
- There should be a support team for an Enterprise Glass! Looking at number of support issues, time to close, what problem or question has the highest support requests, etc.
- What features are used the most and least.
- Are most new Glass users come from existing Google Enterprise Customers or they are N2N? What’s the percentage.
And many more!
Calculate the number of queries answered by Google per second.
Clarifications:
- Mode of searches is Manual via mobile, desktop, app, web, home device. Ignoring any robo/auto API based searches.
- Geographically, we need to look at global population?
Analysis: We’ll do a top-down approach.
Global Population = 7.5B
Less, Geographies excluded: China, Half of Russia (people here don’t use Google as their primary search engine); Less 1.5B (remain 6B)
Less, Only 50% people are connected to internet (remain 3B)
Less, Google market share with Bing, AOL, other regional engines: Let’s assume it to be about 70-80% = 75% (remain 2.25B)
We are left with 2.2B people who are potentially using Google search as their primary search.
Usage Frequency
We can segment them in their frequency of usage. I would say there are 3 types of searchers: Aggressive, Active, Passive
Passive searchers are those who don’t really use internet to search on a daily basis (like old people, kids, our mom/dads, majority of rural population). Assuming this type represents the majority = 50% = 1.1B people
Active people would search once a day. Assumed to be 25% of population = 550M people
Aggressive searchers are super active searchers who might search 4-8 times a day. Assumed to be 25% of the total population = 550M people
Total searches per day= 5501 + 5506 = 3.8B searches per day
= 3.8B / 24 hours / 60 min / 60 sec = ~ 44,000 searches per second
You are the PM for a B2C product that has an advertisement-based monetization model with significant and steady daily revenues. One day, there are no ads served and the revenues plummet to zero. What would be your strategy, as a Product Manager, to deal with this crisis?
Clarification
No ads served - was there an abrupt drop (sharp cut off in a few hours) or did it taper off over a period of time (say 1 month) and finally there were no ads that were served one fine day
Was there any geographic focus of the product? Did this drop happen specific to a geographic region?
Is it a web based product? Or Mobile product? Or both?
Assumptions
This was a sharp drop (as otherwise as PM i should have noticed the steady decline)
This was an overall drop
This is both web and mobile environment.
Advertising ecosystems are 2 sided
Client traffic (demand)
Advertisers (supply)
System
Root cause analysis
Client
Was there any sharp drop in client traffic on one fine day? If yes, need to explore why
Was there any significant event externally for e.g. US presidential elections which tuned users away from the app or blacklisting the product by Gov etc.
Was there a recent product release for e.g. login changes which locked out a large no of users
Did the product depend on traffic from external partners significantly say FB and there was a drop due to algo change or some other issue on their part.
Advertisers
Analyze if the campaigns inadvertently ended on the one fine day
This could be similar to Y2K problem
If bulk of the ads are from Third party Supply Side platforms, check if there are issues from their end (blacklisted , contract changes, technical changes etc)
Analyze if bulk of the revenue came from 1 or 2 specific segments of advertisers which were affected by environmental changes,
Analyze if there are external factors which lead to advertiser cancellation
Pricing increases
Policy changes
Key features inadvertently dropped
System Issues
Release which caused ad systems to be unavailable
Releases which caused key DBs to be unavailable
Release/ Software changes which caused ads to no longer called from client
Release/ software changes which caused key attributes to not send (which causes ads to be not matched)
Considering the above scenarios, based on the root cause analysis the following actions can be taken
Client
Traffic drop due to login or release issues
Hot release to fix it (considering mobile environment, cannot roll back if there have already been installs)
External issues:
If the product is left redundant due to external factors, check what other services/ content/ features which can be provided
If it is a temporary drop,then wait it out for a couple of hours to see if traffic comes back
Advertisers
Check for campaign statuses with last updates made (to see if advertisers ended the campaigns)
Work with BD teams to resolve issue with ad networks/ products
Work with Sales to reach out to the big customers to see who & why cancelled?
System
Work with engineering teams to identify any technical issues
