How Would You Measure Success for YouTube Shorts? - Google PM Interview Question
Google PM Interview: Step-by step walkthrough for measuring success for YouTube Shorts.
Check answers for more Product Metrics Questions: https://www.crackpminterview.com/t/product-metrics-questions
“How would you measure success for YouTube Shorts?”
This question a perfect test case that reveals your product sense, business acumen, and ability to think systematically about success.
In this post, I’ll walk you through a complete, interview-ready answer to this question. You’ll see exactly how to apply the 5-step approach to deliver a structured response that demonstrates strategic thinking and deep product understanding:
Clarify the Context: What questions to ask before diving into metrics
Define the North Star Metric: How to choose and articulate the most important metric
Build the Metrics Hierarchy: Primary, secondary, and guardrail metrics with clear reasoning
Map to User Journey: Applying the AARRR framework to cover the complete funnel
Acknowledge Trade-offs: Demonstrating mature thinking about complexity
By the end, you’ll have a complete template for answering this question and understand how to adapt this approach to any product metrics question.
For a deep-dive on “How to answer Product Metrics questions in PM Interview?” - read here
Now, let’s dive in and answer this question.
Step 1: Clarify the Context
Before jumping into metrics, always start by clarifying the context. This demonstrates thoughtfulness and gives you critical information to shape your answer.
Questions to Ask the Interviewer
Here are the essential questions you should ask:
Question 1: What’s the product stage we’re evaluating?
“Should I think about Shorts as it exists today - a relatively mature feature competing with TikTok and Instagram Reels - or should I imagine we’re at launch and defining initial success metrics?”
Why this matters: New products need to prove value and find product-market fit (focus on activation and initial engagement), while mature products need to optimize and scale (focus on retention and monetization). The metrics framework stays similar, but priorities and targets shift dramatically.
Question 2: How does Shorts monetize?
“I know YouTube’s primary business model is advertising. Should I assume Shorts monetizes the same way through ads, or is there a different monetization strategy like creator subscriptions or tipping?”
Why this matters: Different business models require completely different metrics. Ad-supported products focus on time spent and impressions; subscription products focus on retention and churn; transaction-based products focus on volume and frequency. The monetization model fundamentally shapes what success looks like.
Question 3: Which stakeholders should I prioritize?
“YouTube has two key user groups: viewers and creators. Should I consider success from both perspectives equally, or should I focus primarily on viewers with creators as secondary?”
Why this matters: Two-sided marketplaces require metrics for both sides. If creators aren’t successful, content supply dries up and the product fails regardless of viewer metrics. Understanding which stakeholder to prioritize helps structure the metrics hierarchy appropriately.
Question 4: How should Shorts relate to long-form content?
“How should I think about Shorts in relation to YouTube’s core long-form content? Is the goal for Shorts to be complementary and drive incremental engagement, or is some cannibalization of long-form content acceptable?”
Why this matters: Features within larger platforms must be evaluated on their impact to the overall ecosystem, not just in isolation. Cannibalization concerns significantly affect which metrics matter most. If Shorts must be purely incremental, guardrails around long-form content health become critical.
Question 5: Any other constraints or context I should know?
“Are there any technical constraints, competitive pressures, or strategic priorities I should be aware of when defining success metrics?”
Why this matters: Real-world context shapes what’s feasible and what matters most. Understanding if there are regulatory concerns, technical limitations, or specific competitive benchmarks helps you propose realistic and relevant metrics.
Example Clarifying Conversation
You: “Great question! Before I dive into metrics, let me make sure I understand the context correctly. First, should I think about Shorts as it exists today - a relatively mature feature competing with TikTok and Instagram Reels - or should I imagine we’re at launch?”
Interviewer: “Good question. Let’s assume Shorts has been live for about a year, so we’re past initial launch but still optimizing.”
You: “Got it. Second, I know YouTube’s primary business model is advertising. Should I assume Shorts monetizes the same way through ads?”
Interviewer: “Yes, it’s ad-supported like the rest of YouTube.”
You: “Perfect. Third, YouTube has two key user groups: viewers and creators. Should I consider success from both perspectives?”
Interviewer: “Definitely consider both. Both matter for ecosystem health.”
You: “Great. And finally, how should I think about Shorts in relation to YouTube’s core long-form content? Is the goal for Shorts to drive incremental engagement, or is some cannibalization acceptable?”
Interviewer: “We want Shorts to drive incremental engagement rather than replace long-form viewing, but we’re realistic that some redistribution might happen.”
What You Learned and Why It Matters
From this conversation, you’ve established:
Product maturity: One year in, so we need metrics that show sustainable engagement, not just novelty
Business model: Ad-supported, so time spent and ad impressions matter
Dual stakeholders: Both creators and viewers need to be successful
Platform relationship: Incremental growth preferred; cannibalization is a key risk to monitor
This context will directly inform your metric choices. You now know to include guardrails around long-form content health, metrics for both creators and viewers, and focus on sustainable engagement patterns rather than just acquisition.
Step 2: Define the North Star Metric
After clarifying context, the next step is identifying the single most important metric that captures the product’s core value. This is your North Star Metric.
Proposed North Star: Weekly Active Shorts Viewers
I propose Weekly Active Shorts Viewers as the North Star Metric - specifically, the number of unique users who watch at least one Short per week.
Why This Metric?
1) It reflects real user value (breadth of engagement)
Unlike total views, which could be driven by a small number of power users binge-watching hundreds of Shorts, Weekly Active Shorts Viewers ensures we’re creating value for a broad user base. Each person counted represents someone finding genuine value in Shorts.
If we have 10 million Shorts views, that could be:
100,000 users watching 100 Shorts each (narrow, potentially unhealthy)
5 million users watching 2 Shorts each (broad, healthier distribution)
The North Star makes this distinction clear.
2) It indicates habit formation (weekly vs daily)
Weekly usage suggests Shorts has become part of users’ routines, not just a one-time curiosity. This is critical for sustainable engagement and long-term product success.
We’re choosing “weekly” rather than “daily” because:
Not too strict: Daily might exclude users who value Shorts but don’t use YouTube every day
Not too loose: Monthly would be too forgiving and wouldn’t indicate strong habits
Realistic for content consumption: People might watch Shorts a few times per week, which is healthy
3) It predicts business success (ad revenue potential)
More weekly active users directly translates to more ad inventory and revenue potential. If users return weekly, YouTube has recurring opportunities to monetize without relying on unsustainable binge behavior.
The math: 10 million weekly active users × 15 Shorts per week × ad impressions = predictable, scalable revenue
4) It’s balanced and actionable
The metric strikes the right balance:
High enough to indicate meaningful engagement
Achievable enough that product teams can influence it
Simple enough that anyone in the company can understand it
Measurable through standard analytics
5) It complements YouTube’s broader goals
Growing weekly actives on Shorts likely expands YouTube’s overall user base and engagement. Some users might discover YouTube through Shorts, while existing users get another reason to return to the platform.
Alternatives Considered and Rejected
1) Time spent on Shorts - Why not?
While time spent seems appealing (more time = more ads = more revenue), it has serious downsides:
Incentivizes addictive content: Optimizing purely for time spent might lead to promoting clickbait or endless-scroll content that keeps users watching but doesn’t create real value
Could harm user well-being: Extended passive scrolling might lead to user regret and negative sentiment
Might cannibalize long-form: If users spend hours on Shorts, they’re not watching longer videos, which typically have higher RPM (revenue per thousand impressions)
Time spent is a valuable secondary metric, but it’s dangerous as a North Star without guardrails.
2) Total Shorts views - Why not?
This is a classic vanity metric:
Doesn’t indicate breadth: 100 million views from 1 million users is very different from 100 million views from 50 million users
Doesn’t predict retention: Users might watch one Short once and never return
Easily gamed: Auto-play and algorithmic manipulation can inflate views without delivering value
Total views belongs in the secondary metrics, not as the North Star.
3) Daily Active Shorts Users - Why not?
Daily might seem better than weekly for showing strong engagement, but:
Too strict for content consumption: Unlike messaging apps (Slack, WhatsApp) where daily usage makes sense, content consumption is naturally more sporadic
Could create pressure: Requiring daily usage might make the experience feel like an obligation rather than entertainment
Excludes valuable users: Someone who watches Shorts 3x per week is a highly engaged user but wouldn’t count in DAU
Weekly strikes the better balance for this type of product.
Step 3: Build the Metrics Hierarchy
Your North Star tells you where you’re going, but you need supporting metrics to understand how you’re getting there, what’s driving success, and what might go wrong.
Think of this as a pyramid with the North Star at the top, supported by layers of increasingly granular metrics.
Primary Metrics
These are the 1-2 key metrics that directly drive the North Star. If these go up, Weekly Active Shorts Viewers will likely increase.
1. Average Shorts Watched per Weekly Active User
What it measures: Depth of engagement - how many Shorts does each weekly active user consume?
Why it matters:
If users are coming back weekly but only watching 1-2 Shorts, that’s very different from watching 15-20 Shorts. This metric tells us whether Shorts is becoming a destination activity or just a brief curiosity.
Low numbers suggest we’re not capturing sustained attention - users might check in but not find enough value to engage deeply. High numbers indicate Shorts is becoming a core part of the YouTube experience.
Target benchmark:
We’d want to see this trending upward over time, ideally reaching 15-20+ Shorts per week as users develop the habit and find the format compelling. This would put us in the range of competitive short-form video platforms.
2. Shorts Completion Rate
What it measures: What percentage of started Shorts do users watch to completion?
Why it matters:
Completion rate is a powerful quality signal. High completion rates indicate content relevance and satisfaction - users are finding Shorts worth finishing. If users consistently abandon Shorts after 2-3 seconds, we’re not serving relevant content, no matter how many they start watching.
This metric prevents us from gaming the system. We could boost “Shorts viewed” by auto-playing videos or using clickbait thumbnails, but completion rate reveals whether users are actually engaged.
Target benchmark:
60%+ completion rate would indicate strong content-market fit. Below 40% would signal serious quality issues - either poor recommendations, misleading thumbnails, or content that doesn’t deliver on its promise.
Secondary/Supporting Metrics
These are leading indicators that drive the primary metrics. They help diagnose why primary metrics are moving and give early signals of future performance.
Discovery & Activation
Shorts discovery rate
What percentage of YouTube users discover the Shorts feature each week? This could be via the Shorts tab, home feed placement, or creator promotion.
Low discovery means we have an awareness problem before we even get to engagement. If only 30% of YouTube users have discovered Shorts, we have massive growth potential through better placement and promotion.
First-session engagement
For users discovering Shorts for the first time, how many do they watch in their first session?
Research suggests that 3+ Shorts in the first session typically indicates an “aha moment” - users understand the format and find value. This is a leading indicator of retention. Users who watch 5+ Shorts in their first session likely have 3x higher Week 1 retention than those who watch only 1.
Time to first watch
How long does it take a user to start their first Short after discovering the feature?
If users discover the Shorts tab but take 30 minutes or several sessions to actually watch one, we have a UX or value proposition clarity issue. Immediate engagement suggests intuitive design and clear value.
Content & Engagement
Average watch time per Short
Measures content engagement quality beyond just completion. Are users watching the full 60 seconds or dropping off after 10 seconds?
This helps us understand content quality and length optimization. If average watch time is 45 seconds but completion rate is low, it suggests Shorts are too long for the content they contain.
Shorts scroll velocity
How many Shorts do users scroll past before finding one to watch? Lower is better.
This is a key recommendation quality metric. If users scroll past 20 Shorts before watching one, our algorithm isn’t serving relevant content. If they watch every 3rd Short they see, recommendations are working well.
Scroll velocity is a leading indicator of engagement depth and satisfaction.
Like/share rate
What percentage of watched Shorts receive a like or share?
Strong signals of content resonance. When users actively engage beyond watching, it indicates the content sparked emotion or provided value worth spreading. This drives the referral flywheel.
Sound-on percentage
What percentage of Shorts are watched with sound enabled?
This indicates intentional, active viewing vs. passive scrolling in environments where sound isn’t appropriate (work, public transit). Higher sound-on percentages suggest users are choosing to actively engage rather than mindlessly scrolling.
Content Supply (Creator Metrics)
Weekly active Shorts creators
Number of creators uploading at least one Short per week.
Healthy content supply is critical for sustainable user engagement. If creator numbers stagnate or decline, we’ll eventually run out of fresh content, killing viewer engagement.
Shorts upload frequency
Average number of Shorts uploaded per active creator per week.
We need creators producing content consistently, not just trying Shorts once. High frequency (3+ Shorts per week) indicates creators see value and are building Shorts into their content strategy.
Creator retention rate
Percentage of creators still actively uploading Shorts 30, 60, and 90 days after their first Short.
High creator churn suggests creators aren’t seeing value - either insufficient reach, poor monetization, or negative audience response. Sustainable growth requires creator retention, not just acquisition.
Guardrail/Counter Metrics
These are your safety nets - metrics that ensure you’re not optimizing the wrong things or creating unintended negative consequences.
User Well-being
Session length distribution
Are Shorts sessions healthy (5-15 minutes) or problematically long (45-60+ minutes suggesting compulsive use)?
We want engagement, not addiction. If we see a large percentage of users in very long sessions (60+ minutes), that’s a warning sign that we might be optimizing for addictive behavior rather than value delivery.
User sentiment surveys
Monthly pulse surveys asking: “Do you feel good about your time spent on Shorts?”
This catches issues that behavioral metrics might miss. Users might watch lots of Shorts (good behavioral metrics) but feel regret afterward (poor sentiment). This disconnect indicates optimization problems.
Problematic content report rate
Rate of reports for misleading, harmful, or inappropriate content.
This ensures we’re not optimizing for engagement at the expense of platform safety and quality. If problematic content reports spike, our recommendation algorithm might be favoring sensational or harmful content.
Ecosystem Health
Long-form watch time among Shorts users
This is absolutely critical and directly addresses the interviewer’s concern about cannibalization.
We need to monitor whether users who adopt Shorts maintain or increase their long-form viewing. If we see significant cannibalization, Shorts users reducing long-form consumption by 20%+, that’s a major red flag because long-form likely has higher monetization.
We’d measure this through controlled comparisons: Shorts users vs. non-Shorts users, controlling for other factors.
Platform total time
Is Shorts adding incremental time on YouTube, or just redistributing existing time?
Ideally, we’d see: Total YouTube time = Long-form time (maintained or slight decrease) + Shorts time (new). If total time is flat while Shorts grows, we’re just moving engagement around.
Creator satisfaction score
Survey data from creators about their satisfaction with Shorts reach, monetization, audience building, and platform support.
Unhappy creators will stop uploading, killing our supply. This leading indicator tells us about ecosystem health before we see creator retention drop.
Content Quality
Repeat creator rate
Do creators who upload one Short continue uploading more?
High one-and-done rates suggest creators tried Shorts but didn’t see value (poor reach, negative response, effort not worth it). Sustainable growth requires repeat creators, not just trial.
Original content percentage
What percentage of Shorts are original vs. reposts from TikTok or Instagram?
Too many reposts suggest we’re not building a differentiated content ecosystem - we’re just becoming a dumping ground for content created elsewhere. Original content indicates creators view Shorts as a primary platform.
Business Metrics
Ad load per Short
How many ads are we showing per Short or per session?
There’s a balance between maximizing revenue and maintaining user experience. If we show too many ads (one every 2 Shorts), we’ll destroy engagement and drive users to competitors.
Revenue per thousand Shorts views (RPM)
Is monetization efficient? Are advertisers willing to pay for Shorts inventory?
This tells us about advertiser demand and ad quality. If Shorts RPM is 10% of long-form RPM, we have a monetization problem that needs addressing.
Segmentation Strategy
Metrics often behave very differently across segments, and aggregate numbers can hide critical insights.
User Segments
New vs. returning Shorts users
New users need quick activation and clear value delivery. Returning users need sustained engagement and fresh content. Metrics like completion rate and session length mean different things for these groups.
A new user watching 1 Short with 30% completion might be exploring. A returning user with the same behavior suggests declining interest.
Age cohorts
Gen Z (primary TikTok audience) likely engages very differently than Millennials or Gen X. We need to see if Shorts resonates across demographics or only appeals to younger users.
If we’re only successful with 18-24 year-olds, we have a narrow product. If we succeed across 18-45, we have broader appeal than TikTok.
Creators vs. pure viewers
Fundamentally different motivations and success metrics.
Creators care about: reach, audience growth, monetization, content performance Viewers care about: content quality, discovery, entertainment value
These groups need separate metric dashboards.
Platform
Mobile vs. desktop
Shorts is mobile-first (vertical video, swipe interface), but desktop behavior matters for understanding comprehensive usage and potential growth areas.
Mobile metrics should dominate, but if desktop shows strong engagement, there might be use cases we haven’t considered.
iOS vs. Android
Different user demographics, potentially different features, different monetization characteristics.
App vs. mobile web
Different experiences and capabilities. Mobile web is more limited but also more accessible (no download required).
Geographic
High vs. low bandwidth regions
Content consumption differs dramatically based on connectivity.
In low-bandwidth areas, Shorts might be the primary way users consume YouTube (because long-form video buffers too much). This could drive disproportionate growth in emerging markets.
Cultural regions
Content preferences, engagement patterns, and social sharing behaviors vary significantly by culture and geography.
What works in the US might not work in India or Brazil. We need market-specific insights to optimize globally.
Step 4: Map to User Journey (AARRR Framework)
Now let’s map our metrics to the complete user journey using the AARRR (Pirate Metrics) framework. This ensures we’re covering all stages from discovery through monetization.
Why AARRR Works for YouTube Shorts
AARRR is ideal for YouTube Shorts because:
It’s a consumer product: AARRR was designed for consumer apps and fits naturally
It’s within a growth-focused platform: YouTube prioritizes growth and engagement, making AARRR’s growth-oriented structure appropriate
It has a clear funnel: Users progress through defined stages from discovery to habitual usage to monetization
It addresses the full lifecycle: From initial awareness through sustainable revenue generation
Let’s walk through each stage:
I) Acquisition: Discovering Shorts
Unlike traditional acquisition where we’d focus on external channels bringing new users to a platform, Shorts acquisition is primarily about existing YouTube users discovering the feature.
Discovery channels:
Direct discovery:
Users clicking the Shorts tab in main navigation
Active search for short-form content
Passive discovery:
Shorts appearing in the home feed as users scroll
Shorts suggested after watching long-form videos
Shorts from subscribed creators
External discovery:
Shared links from other platforms (TikTok, Instagram, WhatsApp)
Social media mentions and viral moments
Key metrics:
Shorts tab click-through rate: What percentage of YouTube sessions include a Shorts tab click? This tells us if the placement and branding are working. Target: 15-20% of sessions
Home feed Shorts impression-to-view rate: When Shorts appear in users’ home feeds, do they engage? Low rates suggest poor recommendation relevance or unclear value prop. Target: 8-12%
External referral traffic: How much traffic comes from Shorts shared outside YouTube? High external traffic suggests organic virality. Target: Growing 20%+ month-over-month
New-to-YouTube users via Shorts: Is Shorts bringing in users who wouldn’t otherwise use YouTube? This would be highly valuable for platform-level growth
What success looks like:
Within the first month of a user being on YouTube, they should discover Shorts. We’d want 60%+ of active YouTube users discovering Shorts within their first 30 days, with a growing percentage coming from external shares (indicating viral growth).
II) Activation: First Shorts Experience
This is the critical “aha moment” when users first experience why Shorts is valuable.
Defining the “aha moment”:
Based on user research and data analysis, the aha moment for Shorts is likely watching 3-5 Shorts in the first session, which is enough to:
Understand the vertical video format
Experience the swipe/scroll mechanism
Find at least some content that resonates
See the variety of content available
Users who watch 5+ Shorts in their first session show 3-4x higher Week 1 retention than those who watch only 1.
Key metrics:
First-session Shorts watched (distribution): How many users watch 0, 1-2, 3-5, 6+ Shorts in their first session? This distribution tells us how well we’re converting curiosity into engagement. Target: 40%+ reaching 3+ Shorts
Time to first watch after Shorts discovery: If users discover Shorts but take hours to actually watch one, we have friction in the activation flow. Target: <5 minutes for 60% of users
First-session completion rate: Are first impressions positive? If users skip through all their first Shorts without completing any, they’re not finding value. Target: 50%+ (slightly lower than steady-state because users are still learning)
Percentage reaching ‘aha moment’: What percentage of users who discover Shorts watch 3+ in their first session? This is our activation rate. Target: 35-40%
What success looks like:
Of users who discover Shorts, 60%+ should watch at least one, and 35%+ should reach the aha moment (3+ Shorts in first session). Those who reach the aha moment should show 40%+ Day 1 retention.
III) Retention: Coming Back to Shorts
This is where our North Star metric lives. We need repeated engagement, not one-time visits. Retention is the single best predictor of product-market fit.
Key metrics:
Day 1, Day 7, Day 30 retention: Standard cohort retention analysis. What percentage of users who watch Shorts today come back tomorrow, in a week, in a month?
Target Day 1: 40-45%
Target Day 7: 25-30%
Target Day 30: 15-20%
Weekly Shorts session frequency: For users who are weekly active, how many separate sessions do they have with Shorts? Higher frequency indicates stronger habits. Target: 3-4 sessions per week
Cohort retention curves: Comparing different user cohorts (January acquirers vs. March acquirers) shows if product improvements are working. We want to see newer cohorts having better retention curves
Churn reasons: Why do users stop watching Shorts? This requires surveys, user research, or behavioral analysis to understand:
Ran out of relevant content
Found it too addictive/regretted time spent
Competitors (TikTok, Instagram Reels) are better
Content quality declined
Technical issues
What success looks like:
Retention curves that flatten after the first week (indicating we’ve found our core audience), with 15-20% of activated users still watching Shorts monthly. Cohort-over-cohort improvements showing product iterations are working.
IV) Revenue: Monetizing Shorts
Since YouTube is ad-supported, revenue comes from ad impressions during Shorts viewing. The challenge is that short-form content typically has lower CPMs (cost per thousand impressions) than long-form.
Key metrics:
Ad impressions per Weekly Active Shorts User: How many ads does each engaged user see per week? This balances monetization with user experience. Target: 15-25 impressions per week (roughly 1 ad per 5-7 Shorts)
Revenue per thousand Shorts views (RPM): Unit economics of Shorts. How much revenue does each thousand Shorts views generate? Target: At least 40% of long-form RPM (acknowledging shorter watch time per view)
Shorts revenue as % of total YouTube revenue: Portfolio contribution. Is Shorts becoming a meaningful revenue driver or remaining marginal? Target: Growing toward 10-15% of total revenue within 2-3 years
Creator monetization satisfaction: Can creators make money from Shorts? This is critical for supply. Target: 60%+ of active creators satisfied with Shorts monetization (measured via surveys)
Ad engagement rate: Are users watching ads, or skipping immediately? High skip rates might indicate we’re over-loading ads or placing them poorly. Target: <30% skip rate on skippable ads
What success looks like:
Shorts RPM should be at least 40% of long-form RPM initially, growing toward 60% as we optimize ad formats and prove value to advertisers. Creators should be earning enough to justify continued investment in Shorts content.
The key is sustainable monetization that doesn’t destroy user experience - better to have slightly lower short-term revenue with strong retention than maximize ads and kill engagement.
V) Referral: Viral Growth and Sharing
Shorts has significant viral potential if users share content outside YouTube, driving acquisition through word-of-mouth and social networks.
Key metrics:
Share rate per Short: What percentage of Shorts viewed get shared? This indicates content quality and emotional resonance. Target: 5-8% share rate
Share destination analysis: Where are users sharing Shorts? WhatsApp, Instagram Stories, Twitter, SMS? Understanding channels helps us optimize share flows and track attribution. WhatsApp and Instagram likely dominate
New user acquisition via shared links: Traffic from shared Shorts links and conversion to viewing. What percentage of people who receive a shared Short actually watch it? Target: 30-40% conversion from click to view
Viral coefficient: On average, how many new Weekly Active Shorts Users does each existing user bring through sharing? Coefficient >1 means exponential growth. Target: 0.3-0.5 initially, growing toward 1.0+
Cross-platform attribution: Can we track users arriving from TikTok or Instagram because they saw a YouTube Short shared there? This is technically challenging but strategically valuable for understanding competitive dynamics
What success looks like:
5-8% of Shorts getting shared, with shared Shorts driving 10-15% of new user acquisition. Strong viral growth in specific content categories (comedy, life hacks, wow moments) that drive discovery.
How AARRR Helps Diagnose Issues
The framework’s real power is in diagnosis. If our North Star (Weekly Active Shorts Viewers) plateaus or declines, we can systematically investigate:
Is it an Acquisition problem?
Are fewer users discovering Shorts?
Check: Shorts tab CTR, home feed impression rate
If declining: Improve placement, increase promotion, test new discovery mechanisms
Is it an Activation problem?
Are users discovering Shorts but not engaging?
Check: First-session engagement distribution, time to first watch
If declining: Improve onboarding, optimize first-session recommendations, clarify value prop
Is it a Retention problem?
Are users trying Shorts but not coming back?
Check: Day 1/7/30 retention, cohort curves, churn reasons
If declining: Improve content quality, optimize recommendations for repeat users, address specific churn drivers
Is it a Revenue problem?
Are we monetizing but killing engagement?
Check: Ad load, RPM, correlation between ad exposure and retention
If problematic: Reduce ad frequency, improve ad relevance, test new ad formats
This systematic approach prevents random guessing and ensures you’re optimizing the right part of the funnel.
Step 5: Acknowledge Trade-offs and Considerations
The final step is acknowledging that real-world product management involves complexity, trade-offs, and imperfect information. This demonstrates mature thinking.
Competing Priorities
Shorts growth vs. long-form content health
There’s inherent tension between growing Shorts aggressively and maintaining the health of YouTube’s core long-form business.
If Shorts becomes too successful at capturing user attention and long-form viewing drops significantly, we could actually reduce total revenue since:
Long-form content typically has higher RPM (revenue per thousand impressions) due to pre-roll and mid-roll ads
Long-form content has higher advertiser demand
Long-form enables premium ad formats
This is why our guardrail on long-form watch time among Shorts users is absolutely critical. We need to be willing to constrain Shorts growth if it’s coming at the expense of long-form health.
Example decision: If data shows Shorts users reducing long-form viewing by 25%, we might need to limit Shorts recommendations in certain contexts or adjust the algorithm to promote more balance.
Engagement vs. user well-being
We could optimize purely for time spent and create an addictive, endless scroll experience like TikTok. This might maximize short-term engagement metrics, but could lead to:
User regret and negative sentiment
Bad press and regulatory scrutiny
Long-term backlash and user departure
This is why guardrails around session length distribution and user sentiment matter. We need to be willing to sacrifice some engagement if users report feeling bad about their time spent.
Example decision: If sentiment surveys show increasing user regret, we might introduce session time reminders or periodic “take a break” prompts, even if it reduces average session length.
Creator economics tension
If Shorts RPM is significantly lower than long-form (which it likely is), creators face a dilemma:
Shorts might get more reach and views (algorithmic promotion)
But Shorts earn less money per view
So creators might feel pressured to make Shorts for visibility but lose revenue
This creates misalignment where the platform wants Shorts growth but creators’ economic incentives point toward long-form.
Solution approaches:
Improve Shorts monetization (higher CPMs, better ad formats)
Create creator funds to subsidize Shorts while monetization improves
Demonstrate that Shorts drive subscriptions and long-form viewership (cross-promotion value)
Measurement Challenges
True engagement vs. passive viewing
Users might have a Short playing while not actually watching - phone is on the table, they’re doing something else, auto-play is running.
We’d need to combine multiple signals to understand genuine engagement:
Completion rate (are they watching to the end?)
Interaction signals (likes, comments, shares)
Scroll behavior (are they actively swiping or letting auto-play run?)
Potentially computer vision for attention detection (are they looking at the screen?)
Single metrics can be misleading. A user with 20 Shorts “viewed” but 5% completion rate and no interactions is probably not genuinely engaged.
Causation vs. correlation
If Weekly Active Shorts Users grows 30% month-over-month, what caused it?
Our product improvements (better recommendations, new features)?
Better content from creators (viral trends, higher quality)?
External factors (TikTok had outages, Instagram Reels declined)?
Seasonality (summer, holidays)?
Establishing causation requires:
Controlled experiments (A/B tests)
Historical pattern analysis (did this happen last year?)
Competitor monitoring (what’s happening on other platforms?)
Creator surveys (why are you posting more Shorts?)
We can’t just assume metric movements are due to our actions.
Creator satisfaction measurement
Unlike user metrics that we can track behaviorally, understanding creator satisfaction requires:
Regular surveys (prone to response bias)
Qualitative interviews (time-intensive, small sample)
Behavioral proxies (upload frequency, but doesn’t reveal sentiment)
This is lower-fidelity data than user metrics, but equally important. We need to invest in creator research programs to maintain reliable insights.
Short-term vs. Long-term
The metrics that indicate short-term success might conflict with long-term health.
Short-term optimization (Year 1):
Focus: Viral, attention-grabbing, somewhat sensationalist content
Why: Drives rapid user growth, beats TikTok in the attention economy
Metrics look great: High engagement, rapid DAU growth, lots of shares
Risk: Building habits around low-quality content
Long-term sustainability (Year 2-3):
Focus: Diverse, high-quality content that keeps users coming back for years
Why: Sustainable creator economics, differentiated content ecosystem, platform health
Requires: Investment in creator tools, monetization, community building
Trade-off: Might sacrifice some short-term growth
We’d need to explicitly balance these time horizons in our metric targets and product roadmap.
Example: In Year 1, we might tolerate high concentration in viral content categories (comedy, pranks). By Year 2, we’d want to see healthy diversity across educational, informational, artistic, and entertainment content.
How Metrics Would Evolve
Our metrics framework would remain consistent, but the relative weight and targets would shift based on product stage:
Year 1 (Months 1-12): Proving Value
Primary focus: Activation rate and initial retention
Goal: Do users “get it”? Is there product-market fit?
More forgiving on: Monetization efficiency, creator economics
Key question: Can we build a compelling short-form video experience on YouTube?
Year 2 (Months 13-24): Optimizing Quality
Primary focus: Engagement depth and creator satisfaction
Goal: Move from novelty-driven usage to sustainable habits
Metrics shift: Less weight on acquisition, more on retention and quality
Key question: Can we retain users long-term and build a healthy creator ecosystem?
Year 3+ (Months 25+): Scaling Business
Primary focus: Monetization efficiency and strategic differentiation
Goal: Make Shorts a meaningful revenue contributor and platform differentiator
Metrics shift: RPM, creator earnings, competitive positioning
Key question: Can Shorts be a sustainable, profitable business?
This evolution means being willing to sacrifice short-term metrics for long-term goals. For example, in Year 2 we might reduce certain types of viral content promotion to improve overall content quality, even if it temporarily slows user growth.
Complete Answer Synthesis
Let’s bring everything together into a cohesive summary.
Summary of the Full Answer
North Star Metric: Weekly Active Shorts Viewers
This metric captures sustainable user value (breadth of engagement), indicates habit formation (weekly cadence), predicts business success (ad revenue potential), and complements YouTube’s broader platform goals.
Primary Drivers:
Average Shorts watched per weekly active user: Measures depth of engagement - are users just checking in or truly engaging?
Shorts completion rate: Quality signal ensuring we’re serving relevant content, not just maximizing views
Supporting Metrics:
Comprehensive coverage across the user journey:
Discovery & Activation: Shorts discovery rate, first-session engagement, time to first watch
Content & Engagement: Watch time, scroll velocity, like/share rate, sound-on percentage
Content Supply: Weekly active creators, upload frequency, creator retention
Guardrails:
Critical safety nets preventing optimization failures:
User well-being: Session length distribution, user sentiment, problematic content reports
Ecosystem health: Long-form watch time (cannibalization check), platform total time, creator satisfaction
Content quality: Repeat creator rate, original content percentage
Business health: Ad load, RPM
Framework Application (AARRR):
Systematic coverage of the full funnel:
Acquisition: How users discover Shorts (tab CTR, feed impressions, external shares)
Activation: First Shorts experience (aha moment = 3-5 Shorts in first session)
Retention: Building habits (Day 1/7/30 retention, weekly session frequency)
Revenue: Sustainable monetization (ad impressions, RPM, creator earnings)
Referral: Viral growth (share rate, viral coefficient)
Key Trade-offs Acknowledged:
Mature understanding of complexity:
Shorts growth vs. long-form content health
Engagement vs. user well-being
Short-term viral growth vs. long-term quality
Creator economics alignment
Measurement challenges (causation, true engagement)
What Made This Answer Strong
Structured thinking: Clear 5-step framework applied systematically, making the answer easy to follow and comprehensive.
User empathy: Consistently connected metrics to actual user value and behavior, not just abstract numbers. Referenced real usage patterns and emotional responses.
Business acumen: Tied every metric choice to YouTube’s business model (ad-supported), competitive context (TikTok, Instagram Reels), and platform dynamics (two-sided marketplace).
Completeness: Covered the entire funnel from discovery through monetization, addressed both viewers and creators, included guardrails and segmentation.
Prioritization: Clear hierarchy from North Star (1 metric) through primary (2 metrics) to secondary (8-10 metrics) to guardrails (8-10 metrics). Shows ability to separate signal from noise.
Risk awareness: Identified multiple things that could go wrong (cannibalization, addiction, creator churn) and proposed specific guardrails for each.
Adaptability: Showed how metrics and priorities would evolve over the product lifecycle (Year 1 vs. Year 2 vs. Year 3+).
Communication: Used clear language, concrete examples, and explained the “why” behind every choice. Checked in with the interviewer at appropriate points.
Framework application: Used AARRR effectively with clear rationale for why it fits this specific product, not just generic framework recitation.
Intellectual honesty: Acknowledged measurement challenges, trade-offs, and areas of uncertainty rather than pretending everything is simple.
This combination demonstrates the complete package interviewers look for: strategic thinking, analytical rigor, user focus, business understanding, and communication skills.
Similar Product Metrics Questions to Practice
Social Media & Content Platforms
“How would you measure success for Instagram Reels?”
“What metrics would you track for Twitter Spaces?”
“How would you measure success for LinkedIn Newsletter feature?”
“What metrics would you track for Pinterest Idea Pins?”
“How would you measure success for Reddit’s community chat feature?”
E-commerce & Marketplace
“How would you measure success for Amazon Subscribe & Save?”
“What metrics would you track for Etsy’s personalized recommendations?”
“How would you measure success for Airbnb Experiences?”
“What metrics would you track for eBay’s auction feature?”
“How would you measure success for Shopify’s one-click checkout?”
Entertainment & Media
“How would you measure success for Spotify Podcasts?”
“What metrics would you track for Netflix’s ‘Top 10’ feature?”
“How would you measure success for Kindle Unlimited?”
“What metrics would you track for Twitch’s subscriber-only streams?”
“How would you measure success for Apple Music’s spatial audio?”
Productivity & Communication
“How would you measure success for Slack Huddles?”
“What metrics would you track for Notion’s AI writing assistant?”
“How would you measure success for Google Docs suggesting mode?”
“What metrics would you track for Zoom’s virtual backgrounds?”
“How would you measure success for Microsoft Teams channels?”
Health, Fitness & Education
“How would you measure success for Duolingo’s streak feature?”
“What metrics would you track for Peloton’s instructor follow feature?”
“How would you measure success for Headspace’s sleep stories?”
“What metrics would you track for MyFitnessPal’s barcode scanner?”
“How would you measure success for Khan Academy’s mastery challenges?”
Finance & Payments
“How would you measure success for Venmo’s social feed?”
“What metrics would you track for Robinhood’s crypto wallet?”
“How would you measure success for PayPal’s buy now, pay later feature?”
“What metrics would you track for Mint’s budget planning tool?”
“How would you measure success for Cash App’s Cash Card?”
Transportation & Travel
“How would you measure success for Uber Pool?”
“What metrics would you track for Google Maps’ eco-friendly routes?”
“How would you measure success for Expedia’s price tracking?”
“What metrics would you track for Lyft’s scheduled rides?”
“How would you measure success for Waze’s carpool feature?”
Food & Delivery
“How would you measure success for DoorDash’s DashPass subscription?”
“What metrics would you track for Yelp’s waitlist feature?”
“How would you measure success for Uber Eats’ group ordering?”
“What metrics would you track for Grubhub’s restaurant recommendations?”
“How would you measure success for Instacart’s same-day delivery?”
You now have a complete, interview-ready answer to one of the most common Product Metrics question asked in a PM interview. Use this as a template for approaching any product metrics question.
The framework stays the same. The specifics adapt to each product.
Practice this question out loud. Then practice with the similar questions above. Then apply the framework to products you use daily.
With enough practice, this structured thinking becomes second nature and you’ll walk into your PM interviews with confidence.
For the complete guide on Product Metrics questions, including 40+ practice questions across 8 product categories, read: How to Answer Product Metrics Questions in PM Interviews
Good luck! 🚀
Other Deep Dives on Product Management Interview Question Types
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very useful info!