"Goal:
To design a product that helps people keep track of their belongings and prevent loss.
Identifying users:
Individuals who frequently lose their belongings
Parents who want to keep track of their children's belongings
Tourists who want to secure their belongings while travelling
Selecting the right user:Given Google's expertise in technology, we will focus on designing a product for individuals who frequently lose their belongings.
Defining features and prioritizing:
Item tracki"
Anonymous Panda - "Goal:
To design a product that helps people keep track of their belongings and prevent loss.
Identifying users:
Individuals who frequently lose their belongings
Parents who want to keep track of their children's belongings
Tourists who want to secure their belongings while travelling
Selecting the right user:Given Google's expertise in technology, we will focus on designing a product for individuals who frequently lose their belongings.
Defining features and prioritizing:
Item tracki"See full answer
"Clarify:
who owns previous seasons? are we competing against them?
Cost based:
Cost >= Revenue generated
Revenue = subscribers + advertisements
subscribers = new subscribers (joining for the season) x avg spend on plan
advertisements = total viewors (new + existing) x total season duration x ads/unit time x rev/ ad display
Costs = pricing of the show + operational costs (data center cost + streaming cost)
"
Rev - "Clarify:
who owns previous seasons? are we competing against them?
Cost based:
Cost >= Revenue generated
Revenue = subscribers + advertisements
subscribers = new subscribers (joining for the season) x avg spend on plan
advertisements = total viewors (new + existing) x total season duration x ads/unit time x rev/ ad display
Costs = pricing of the show + operational costs (data center cost + streaming cost)
"See full answer
"Discuss the reason for the failure to deliver with the stakeholders: Was the target too ambitious? Is there new information that has created a material change in circumstances around the project? Work with the stakeholders to determine more realistic targets and determine a new timeframe for delivering the original feature."
Viren D. - "Discuss the reason for the failure to deliver with the stakeholders: Was the target too ambitious? Is there new information that has created a material change in circumstances around the project? Work with the stakeholders to determine more realistic targets and determine a new timeframe for delivering the original feature."See full answer
"problem clarification
how is FB Messenger measured right now?engagement
DAU, WAU
sessions duration & frequency
feature adoption rates
cross platform usage with other Meta apps
are we talking about feature additions, performance optimization, UI/UX, integrations (on platform or off)?
also is there a primary goal or business objective driving this improvement: engagement, retention, monetization, competitive positioning?
are we thinking about all 1BB+ users? or a segment?
timeframe? looking for"
Brett a M. - "problem clarification
how is FB Messenger measured right now?engagement
DAU, WAU
sessions duration & frequency
feature adoption rates
cross platform usage with other Meta apps
are we talking about feature additions, performance optimization, UI/UX, integrations (on platform or off)?
also is there a primary goal or business objective driving this improvement: engagement, retention, monetization, competitive positioning?
are we thinking about all 1BB+ users? or a segment?
timeframe? looking for"See full answer
"TF-IDF CONCEPT EXPLANATION AND INTUITION BUILDING:
TF-IDF is a measure that reflects the importance of a word in the document relative to a collection of documents. Its full form is Term Frequency - Inverse Document Frequency.
The term TF indicates how often a term occurs in a particular document. It is the ratio of count of a particular term in a document to the number of terms in that particular document. So, the intuition is that if a term occurs frequently in a single documen"
Satyam C. - "TF-IDF CONCEPT EXPLANATION AND INTUITION BUILDING:
TF-IDF is a measure that reflects the importance of a word in the document relative to a collection of documents. Its full form is Term Frequency - Inverse Document Frequency.
The term TF indicates how often a term occurs in a particular document. It is the ratio of count of a particular term in a document to the number of terms in that particular document. So, the intuition is that if a term occurs frequently in a single documen"See full answer
"Type I error (typically denoted by alpha) is the probability of mistakenly rejecting a true null hypothesis (i.e., We conclude that something significant is happening when there's nothing going on). Type II (typically denoted by beta) error is the probability of failing to reject a false null hypothesis (i.e., we conclude that there's nothing going on when there is something significant happening).
The difference is that type I error is a false positive and type II error is a false negative. T"
Lucas G. - "Type I error (typically denoted by alpha) is the probability of mistakenly rejecting a true null hypothesis (i.e., We conclude that something significant is happening when there's nothing going on). Type II (typically denoted by beta) error is the probability of failing to reject a false null hypothesis (i.e., we conclude that there's nothing going on when there is something significant happening).
The difference is that type I error is a false positive and type II error is a false negative. T"See full answer
"On the topic of personalisation the main complexity comes from stitching the data together so that you can create a curated and hopefully personal experience for the consumers (e.g. product offer that match user's interest).
Since the existing technology we use, especially on the app, do not support some of of the BE foundations needed to personalize omni-channel the main complexity is in integrating with the BE services especially creating connected data pipelines. My main contribution is in"
Delyan P. - "On the topic of personalisation the main complexity comes from stitching the data together so that you can create a curated and hopefully personal experience for the consumers (e.g. product offer that match user's interest).
Since the existing technology we use, especially on the app, do not support some of of the BE foundations needed to personalize omni-channel the main complexity is in integrating with the BE services especially creating connected data pipelines. My main contribution is in"See full answer
"Let's start by describing a time machine, which is a device that allows somebody to move backwards or forwards in time.
The movement could be physical movement, wherein the user gets physically transported to a different timeline, or it could be getting a glimpse into a different timeline, like wearing a VR headset and getting to experience a different timeline without physically being there. For the purpose of this exercise, I will assume, this time machine allows a person to physically trans"
Akshay R. - "Let's start by describing a time machine, which is a device that allows somebody to move backwards or forwards in time.
The movement could be physical movement, wherein the user gets physically transported to a different timeline, or it could be getting a glimpse into a different timeline, like wearing a VR headset and getting to experience a different timeline without physically being there. For the purpose of this exercise, I will assume, this time machine allows a person to physically trans"See full answer