Decision-making questions force you to make actual product calls with incomplete information and significant trade-offs.
I find these to be the most useful interview questions for improving PM skills. Most of the scenarios here—from launching features to navigating trade-offs—are things you encounter often in PM life. The framework I'll share is almost identical to what I use in real life, which is rare for interview prep. There's usually some artificiality in interview frameworks. In product design, we push for wild creativity, though day-to-day you might just iterate on existing features. In A/B testing interviews, you propose bold experiments, while in reality you're often just tweaking colors and fonts.
Having operated at both startups and BigTech, I found it interesting that in large organizations (where most of you will be interviewing): how you make decisions is often more important than what you decide. Why? Because the impact of your decisions won't be clear for 2+ years—by which time you'll likely be promoted or in a different role. If you make the wrong call, it takes quarters to correct.
Contrast this with startups where you're on high velocity daily, making rapid decisions, and if you're wrong, you pivot next week. Making mistakes isn't the end of the world there—you can improve while you still can. But in BigTech, with complex dependencies and dozens of stakeholders, the process becomes critical. You need buy-in, escalation paths, structured frameworks—all that "process" that feels like overhead but actually prevents million-dollar mistakes.
Today's case is as real as it gets. In 2022, Netflix faced this exact decision, and the stakes couldn't have been higher.
The setup: Netflix collects $15-20 billion annually from users who value the ad-free experience. That's their brand. That's their differentiation.
The pressure: Growth has slowed from 20%+ to just 4%. Every competitor has launched cheaper ad-supported tiers. Wall Street is demanding answers. YouTube with its ad-supported model is a behemoth to fight against.
The dilemma: Launch an ad tier and risk cannibalizing your premium brand? Or hold firm and risk becoming the expensive outlier in a commoditizing market?
Today, we're diving deep into this decision: Should Netflix launch an ad-supported tier?
Before we dive in, let me share three things I've learned about decision-making as a PM that guides my approach to this question type.
1. Decide How to Decide
As we touched on earlier, many product decisions fail not because of the choice made, but because of how it was made. At Google, I saw three approaches:
Escalation: Once, my team and our partner team hit a pricing deadlock—any change hurt someone's revenue. We could've debated forever. Instead, we quickly escalated to our shared VP. Was it perfect? No. But everyone understood there was no "right" answer, and having someone we both reported to make the call got us unstuck in days, not weeks.
Collaborative Framework (The Default): When launching features with multiple design trade-offs, I'd set up weekly decision syncs. We'd review options, find alignment, make the call, and move on. This built buy-in even when people disagreed with specific choices. The predictable cadence prevented decision fatigue and "let's revisit this" loops.
Domain Expert Empowerment: Need to price a new product? Find someone who's done it three times before. Give them context, let them recommend, and move fast. I've seen months of analysis compressed into one conversation with the right expert. The key is being clear they're deciding, not just advising.
The trap is using escalation too often (looks weak), forcing collaboration when you need speed (analysis paralysis), or empowering experts without context (creates silos). Sometimes spending 10 minutes agreeing on how to decide saves hours of circular debate.
The Netflix ad decision is perfect for approach #2—high stakes, multiple stakeholders, no clear expert.
2. Start with Goals, Not Solutions
One of my mentors at Google taught me that product decisions are always better done with breadth-first thinking vs depth-first. When making engineering decisions, you can dive deep into edge cases and implementation details. But product and business decisions are different—if you jump straight into the details, you'll find endless disagreements and miss the opportunity to align on what actually matters.
Instead, start broad. First align on the goal—are we optimizing for user growth, revenue, or brand perception? Then agree on principles—what constraints must we respect? Only after this alignment should you dive into specific solutions. This approach transforms contentious debates into productive discussions because everyone's working from the same foundation.
The biggest mistake PMs make is jumping to solutions before agreeing on what they're optimizing for. Different goals lead to entirely different decisions. Be explicit about your north star before evaluating any options.
3. Embrace the AND, Not Just the OR
There's a scene in Suits where Harvey Specter asks what you'd do with a gun to your head. Most people say "whatever they want." Harvey's response? "Wrong. There are 146 other things you could do." (Or was it 780? The number doesn't matter—the mindset does.)
This captures a fundamental bias we all have: framing problems as binary choices. Whenever someone presents you with "either A or B," that's your signal that you're missing something. The best product decisions often find creative third options that capture multiple benefits while minimizing trade-offs.
Instead of accepting the dichotomy, ask: How might we get both? What if we phased it? Could we segment? What's the crazy option that, if it worked, would be better than either choice?
Netflix demonstrated this perfectly—instead of "ads OR no ads," they chose "ad-supported tier AND ad-free tier," capturing both markets without forcing a single choice on all users. The binary framing was the trap, not the problem.
As discussed earlier, I follow Goal-Oriented Decision-Making—because it's easier to gain agreement on high-level goals than specific decisions.
It has five steps:
Clarify the Context: Understand the situation and constraints
Define Goals and Success Criteria: What are we optimizing for?
Generate Options: Think beyond binary choices
Evaluate Systematically: Assess each option against your criteria
Recommend and Next Steps: Make the call and address risks
Note: Steps 1 and 5 are common across most PM interview questions. The core part thus translates into: define goals → generate options → evaluate.
What makes this powerful is that it mirrors how decisions actually get made at companies like Google. You align on objectives first, then let the data guide you to the best option for those objectives.
Interviewer: "You're a PM at Netflix. Leadership is evaluating whether to introduce an ad-supported lower-priced subscription plan. The streaming market has matured—growth has slowed from 20% to 4%, and most competitors now offer ad-supported tiers. What's your recommendation?"
Let me understand the situation better:
Current state: Netflix is the market leader with premium positioning, but we're operating in a mature industry where growth is hard to come by. Most traditional media houses now have online subscriptions competing for the same wallet share.
Market pressure: Every major competitor—Disney+, HBO Max, Paramount+—has launched ad tiers. They're typically priced 40-50% below standard plans. We're now the expensive outlier.
The revenue equation: This is critical for our decision. Revenue = (Avg price per subscriber × Number of subscribers) + Ad revenue
Understanding the drivers:
Average price: Influenced by competitive dynamics (price war risk) and cannibalization
Subscribers: New customer acquisition potential
Ad revenue: Multiple factors but mainly execution risk
Key clarifying questions:
"What's driving this consideration now?" → Slowing growth from 20%+ to 4%, competitive pressure
"Any non-negotiables?" → Must protect brand premium-ness
"Timeline?" → Decision needed this quarter
(Interviewer: "Exactly. Monetization is now a key concern alongside growth, but we can't destroy what makes Netflix special.")
Let me establish what we're optimizing for:
Primary Goal: Maximize long-term revenue growth
Not just subscriber count (that could mean racing to the bottom)
Not just ARPU (that could mean losing market share)
The full equation: total revenue with sustainable growth
Key Constraints:
Protect Netflix's premium brand position
Avoid triggering industry-wide price war
Maintain quality user experience
Success Metrics:
Net revenue impact (positive after cannibalization)
New subscriber acquisition (incremental, not just shifted)
Market share defense
Brand perception scores
Time Horizon: 3-year outlook—this is strategic, not tactical
Here's where we avoid binary thinking. Let me lay out four distinct options:
Option A: Status Quo
Maintain premium-only positioning
No ad tier, continue competing on content and experience
Focus on pricing power in existing base
Option B: Limited Ad Tier
Restricted ad-supported option
Fewer features (single device, 720p, no downloads)
Limited content (no newest releases)
Clear differentiation from premium
Option C: Full Ad Tier
Comprehensive ad-supported offering
Minimal restrictions beyond ads
Aggressive pricing to gain share
Option D: Test and Learn
Regional pilot program (Canada, UK)
Gather data before global decision
Partner with ad tech company for capabilities
Let me work through each option against our criteria:
Option A: Status Quo
Impact on Revenue:
Continued 4% growth trajectory
Risk of accelerating market share loss as price gap widens
Miss price-sensitive subscribers
Feasibility: Simple—do nothing
Key Risk: Becomes the "expensive" option in consumer minds
Option B: Limited Ad Tier ⭐
Competitive Dynamics (Avoiding Price War):
As industry leader, we have most to lose in a price war
This is defensive positioning—matching competitors, not undercutting
Won't trigger destructive competition since others already have ad tiers
New Customer Acquisition:
Target customers willing to tolerate ads for lower price
Customer attitudes have evolved—Netflix's original ad-free value prop less critical now
Other services normalized ad-supported streaming
Could capture 15-20M new subscribers
Cannibalization Risk (Managed Through Design):
Risk: 10% of existing subscribers might downgrade
Mitigation: "Damage the ad tier a bit" through restrictions:
Single device only (families need multiple)
720p quality (noticeable on large TVs)
No downloads (travelers need this)
Week delay for new content
Ensure combined value (subscription + ad revenue) exceeds current ARPU
Execution Risk (Partnership Strategy):
Netflix lacks ads industry experience
Building internally risks poor targeting, privacy issues, brand damage
Solution: Partner with Microsoft/Google for ad tech
Accelerated execution while we learn the business
Revenue Analysis:
New subs: 15-20M × $7/month = $1.26B annual
Cannibalization: 10% downgrades × $3 loss = -$200M
Ad revenue: $3 per sub/month = $540M
Net impact: +$1.6B annually
Option C: Full Ad Tier
All factors point to high risk:
Could trigger price war (we have most to lose)
High cannibalization without restrictions (30%+)
Same execution complexity as Option B
Net revenue uncertain
Option D: Test and Learn
Mixed signals:
Reduces execution risk through learning
But competitors gain ground while we test
Delays revenue opportunity
My Recommendation: Option B - Launch a Limited Ad Tier
The Decision: "I recommend launching a restricted ad-supported tier to maximize long-term revenue. This approach attracts new subscribers without triggering a price war. While cannibalization is a concern, it can be managed through strategic restrictions. The most significant risk—execution—is mitigated through partnership."
Summary of Why This Works:
In essence, introducing an ad-supported tier appears favorable because it addresses all four critical factors:
Competitive Dynamics: As the industry leader, we avoid starting a price war by matching (not undercutting) existing ad tiers
New Customer Acquisition: We capture price-sensitive customers who've become accustomed to ads through other services
Cannibalization Management: Strategic restrictions create natural friction preventing mass downgrades
Execution Risk: Partnership approach avoids the pitfalls of building ads capabilities from scratch
If I Had More Time and Resources:
To make this decision with even more confidence, I would:
Conduct research to estimate how many new subscribers Netflix could realistically acquire
Explore partnership feasibility with Microsoft, Google, or Amazon for ad technology
Run limited regional rollout to gather real-world data on cannibalization and user behavior
What Actually Happened (Historical Context): Netflix launched "Basic with Ads" in November 2022 at $6.99/month. Results:
15M+ subscribers in first 6 months
Minimal cannibalization (<10%)
Microsoft partnership proved wise
Stock recovered significantly
The restricted tier approach worked pretty well!
Don't get stuck in endless evaluation. Set a decision deadline and work backward.
Avoid presenting only two options. There's usually a third way that captures benefits of both while minimizing downsides. Netflix didn't choose "ads OR no ads"—they found a middle path with restricted tiers.
A mediocre strategy well-executed beats a perfect strategy poorly executed. Always consider execution capability. Netflix wisely partnered rather than trying to build ad tech from scratch.
First-order: Direct impact of your decision (Netflix gets ad revenue)
Second-order: How others react (competitors might lower prices)
Third-order: Market dynamics that emerge (race to the bottom)
Try to think a couple of moves ahead.
Want to practice this decision-making case with an AI interviewer that challenges your assumptions and tests your framework?
Practice here: PM Interview: Netflix Ad-Supported Tier Decision
The AI interviewer will push you on your trade-offs, challenge your financial models, and test whether you can make complex decisions under pressure—just like a real Netflix interviewer would.
Explore more decision-making question resources I've created:
How AWS Started - Especially the part about decision criteria
Try the complete PM Interview Collection: Consists of different PM interview question types including decision-making
As PMs, we constantly share screenshots—marking up designs, highlighting issues, proposing changes. This week, I'm sharing Shots—a tool that transforms basic screenshots into professional mockups.
Here's why it's become essential for my PM workflow:
Instant polish: Paste any screenshot and add depth, shadows, and 3D perspective in seconds
Dead simple: 1 click interface and everything right there on one page
Active development: Keeps dropping new animations and features
I've been using Shots for months now and love what it does to my screeshots.
Found a tool that helps with better product decisions? Hit reply—I'd love to hear about it!
How would you approach the Netflix ad tier decision? What factors would weigh heaviest for you? Reply and let me know—some of the best insights come from readers!
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— Ajitesh
CEO & Co-founder, Tough Tongue AI
Ex-Google PM (Gemini)
P.S. The Netflix ad tier decision is fascinating because it actually happened, and we can see the results. Netflix was "six months away" from launching ads for about five years—until suddenly they weren't. What changed? The market forced their hand. Growth slowed, the stock crashed 70%, and maintaining the status quo became riskier than changing.
Often the best decision isn't the perfect decision—it's the one you can execute well at the right time. Netflix partnering with Microsoft seemed odd to many (why not Google or Amazon?), but it was brilliant: Microsoft had the tech but wasn't a competitor.
The next time you're in an interview (or a real product decision), remember: it's about navigating trade-offs wisely with the information you have. That's the real job.
pps: this blog was first published on Tough Tongue AI