Netflix product manager mock interview and tips (with ex-Netflix Lead PM)

Hello everyone, welcome to this Netflix product manager interview walk-through. Um, we have Michael here with us today. Um, he was a ex-Netflix PM.

Thanks for joining us, Michael. >> Thanks for having me, Tim. I'm honored to be here.

>> Yeah, um, it's great to have you on. Um, before we get started, do you want to just quickly give people an overview of of your experience as a PM? >> Absolutely, yeah.

So, I've been uh a product senior product manager at Netflix and Meta and Roku, where I've kind of owned and scaled these customer-facing solutions across uh web and mobile and hardware device ecosystems as well. Um, I think my work work is focused more on driving uh measurable impact and leading these large-scale platform transformations and overall kind of improving the customer experience as I, you know, launched products at all these companies. >> Great.

And uh yeah, obviously now you're a coach on I got an offer. Um, and yeah, until recently you were you were running PM interviews at Netflix, right? So, what what better person to to show us how to give a really strong answer to a typical Netflix question.

Um, I've got a product sense question for you today. Um, so yeah, when you're ready, uh you can start showing people exactly how how to tackle this this question. Um, >> Let's do it.

Let's dive in. >> Okay. It's a very typical one.

Um, Michael, how would you improve Netflix's recommendations? >> Oh, wow. This is um, I think this is one of the most common questions in PM interviews.

And um, a lot of candidates kind of jump straight into, you know, solutioning of this, right? They they get excited about creating new solutions. Like, oh, I would build better filters.

I would create AI recommendations. And that's not exactly what interviewers and hiring managers are looking for when they ask a question like this. What we're really trying to understand is, you know, your thought process and your ability to comfortably work through, you know, ambiguity.

Right? This is such a vague question, but today I'm going to show you exactly how top candidates are able to structure their answer step by step. So, you can kind of stand out from your competition and hopefully leave that lasting impression to the interviewer.

So, um for me, I think what makes my response a little bit different is we're not just going to focus on this the typical product management frameworks, which I think you guys are all kind of familiar with. Um I want to kind of incorporate this human-centered design thinking. So, your answer doesn't just sound smart, but it actually sounds like you sat with the people that you're designing this for.

And that combination of that um structure and rigor and empathy is kind of what separates a good PM candidate from a great one. So, the first thing I would do, um before I say a single word about Netflix, I want to pause and kind of ask a couple of focused questions, right? I think this for the interviewer signals a sense of PM maturity.

Um it shows that, you know, you're not going to rush into solutions. So, I would say like, "So, Tom, before we dive in, is I would love to kind of clarify a few things so that I'm making sure that we're solving for the right problem here. " Um when we say improve recommendations, are we focused on a specific goal?

Like, are we trying to reduce churn or are we trying to increase the session time for users or, you know, improve content discovery for a particular audience? >> Yeah, I think we're trying to trying to improve all three of those. >> Got it.

Got it. That makes sense. Okay.

Um is there a specific platform or device type that we're prioritizing, whether it's for mobile users or TV, or are we thinking about all platforms? >> All platforms. >> Got it.

Okay. Um lastly, are there any constraints that I should keep in mind in terms of like budget or timeline or specific reason? >> Yeah, you have a tight budget and you have a tight timeline.

>> Okay, that helps a little bit. So, notice these answers Tom's responses were very very vague, still pretty ambiguous. Um Tom, I'd like to take some time to kind of think about my solution here um before we kind of move on.

Step two is kind of anchoring yourself, right? Right now we still have this ambiguity, but it's it helps to kind of anchor yourself um with the company mission before you begin to brainstorm. I think top candidates you'll notice frame their answers within the company's either mission or North Star or any um initiative that the company maybe running.

So, um an example of this is I would say, "Great. So, before I jump into solutions, let me quickly ground us on, you know, Netflix's mission and goals. I think any improvement needs to ultimately align back with those.

" Netflix's mission, as I understand, is to um entertain the world, right? Their business model is subscription-based, which means ultimately I think their North Star is around retention, right? Making sure that subscribers are engaged and enjoy what they watch so they don't consider canceling their subscription.

Um so, when I think about this recommendation system, I think this is probably the most important or one of the most important product levers that Netflix has, right? I think they've even said publicly that poor recommendations have this direct impact on subscriber churn. So, Netflix improving recommendations, I think the feature itself, I wouldn't say is broken by any means, but I think currently the users might be facing more of a decision problem, right?

I think it's not necessarily optimized for how customers are choosing what to watch. Um so, I think the real question that we need to ask ourselves is how can we help users um find what they want to watch faster and feel more satisfied with what they chose, which would which would lead to them kind of staying subscribed longer. So, I think the goal here is to help users confidently decide what to watch as quickly as possible.

Okay. So, here notice I kind of quoted the company mission and initiative. It was pretty vague, right?

It was pretty rough, but I think even this kind of shows the interviewer your level of, you know, business acumen and your preparation, right? You don't need to be exact. You don't need like specific details, but I think what matters more is showing that you understand the business as well as the product.

So, now that we've done that, um I would kind of narrow this down a little more, right? So, I would break this up into um the user segmentation. Um here I would, you know, identify our user groups um and then make a deliberate choice, justify choice on who to focus on for this interview, cuz right now it's still a very large scope.

Um I think a key to defining this user segment is to really bring them to life. Once you have identified a segment of users, bring them to life with a short narrative. And I think that's the empathy layer that really transforms a competent answer into something really memorable for the interviewers as well.

So, I'll give you an example. Um okay, so let me think about who actually uses Netflix recommendations. I'm going to segment out the users um to make sure that I'm solving for the right person here.

When I think about Netflix and the users, um obviously there's a solo binge watchers, right? They These are generally people who watch alone. They know what genres they like.

Um but sometimes they may you know, struggle to find a new series or new show once they finished what they really like. Um so, the recommendations here is helping them find new shows to watch once they finish the series. Um we also have like family viewers who, you know, one account, they could have multiple profiles, but generally they're spending time watching together.

Um for them, I think it's more the recommendations are more around what everyone will enjoy, right? I think this is always a challenge. Everyone in the family has a different taste.

So, it's kind of a challenge for them to find what they want to watch. We also have casual browsers, um people who are subscribed, they don't watch a whole lot. Um they may open Netflix and scroll for 10 to 20 minutes and then they can't decide and then they just close the app.

So, I think the casual browsers is our biggest churn risk that we have. Um and then lastly, I think about lapsed users, people who were once active, but then they haven't watched in a while. So, the recommendations that are surfaced to them may feel a little irrelevant or stale because their tastes may have changed.

Um for the purpose of today's interview, I want to focus on the casual browser cuz I think this user demographic represents our highest churn risk. Um, I think they're already on the platform, so their acquisition cost is sunk. Um, and improving their recommendation and discovery experience, I think has a direct and measurable impact on retention and the ROI potential is the highest of any of these segments.

So, let me bring this casual user to life cuz I think it helps to understand who we're solving for, okay? So, imagine this. Let's say it's uh, a weeknight.

Let's say it's a Wednesday night, 9:30. Um, our user, I'll name her Maya. Um, she just put her kids to bed and she has like 45 minutes before she needs to sleep.

So, she opens Netflix and she doesn't want anything heavy at the moment. She's pretty tired, middle of the week. Um, but also doesn't want to commit to the first episode of a new series if she's not sure she's going to be able to finish it, right?

So, she scrolls and scrolls more. She watches some trailers, reads some descriptions, and backs out. Trys another row, same thing.

Nothing really feels quite right at the moment. So, before she knows it, 20 minutes go by. And then, she decides, "Oh, I can't I can't decide.

I'm just going to hop over to YouTube. " That moment, that forget it moment for Maya, I think that's the moment that we need to be designing against. So, Maya isn't broken, right?

She for her, it the product isn't understanding her. So, the product experience at that moment for her is what's broken. So, here, when I was defining all the user segments, um, notice how I named this user Maya and it really helped them kind of come to life, hopefully.

Um interviewers are humans, too, right? I think we've we've had that ex- same experience. I've had this experience yesterday.

Um and the moment we recognize ourselves in your narrative, I think this is the this is where you make the emotional case for why this problem actually matters, right? It's not just the logical one. So, it's really important to frame this demographic and bring this person to life.

Um this shows that you're really thinking about um the user experience, and you're thinking not just about the user, but also the company outcomes as well. So, I think that's the key thing to highlight there. From there, I'll move on to some of the pain points.

Um I'll I'll frame the pain points through the lens of our user, Maya. Okay? For someone like Maya, she has this limited amount of time, right?

She wants to It's the end of the day, she is exhausted. She wants to switch off. She wants to mentally just switch off, feel entertained, so she can go to bed feeling like she's had this moment for herself.

Right? So, with that in mind, I think this is where um her pain points lie. I think this is where the product failed her.

The first thing, obviously, decision fatigue, right? The homepage right now, it shows so many titles um with dozens of rows. Um and for someone like her who came to the product to kind of relax and unwind, uh these choices often creates this decision paralysis, right?

I think it's designed currently for someone who has a little more bandwidth, um and not someone with this limited amount amount of time. Um the second pain point I think is around situational context blindness. I think Netflix recommends kind of the same content regardless of time or your mood or energy level.

Um so if you think about Maya, this 9:30 p. m. Maya might have complete completely different needs than like a Saturday afternoon Maya.

But the product itself doesn't really know that difference. Um a third pain point, I think there's no way for her to express what she wants at that moment, right? There's no way for her to express her intent.

She can't like talk to Netflix and say, "Hey, Netflix, show me something funny that won't make me think. " Um she can only kind of scroll and hope that the algorithm infers what she wants. So there's no way for her to be heard.

Um and then lastly, I think there's also this content uncertainty. Right? I think even if Maya stumbles um upon something that may be a good match for her, there aren't any real signals that help her feel confident that she made the right choice.

So she keeps on browsing. So all of these factors are actually what drives this like 20-minute doom scrolling and abandoned behavior. Um if I had to rank these, I think decision fatigue and situational context blindness do the most damage when we think about retention and churn.

I think both of these are direct causes of that scrolling and abandoned behavior, which ultimately puts our subscription at risk. So here I kind of showed the interviewer that, you know, I didn't just list out my problems, but I also picked out the ones that I wanted to build against, right? I think this helps you kind of narrow down your solution path a little bit.

Um and for such an ambiguous question like this, pain points that are kind of communicated best when you articulate them through the lens of your user. Um it it kind of shows the interviewer that your solution is going to be grounded in a real user need and not just product assumptions. Okay, from there I would kind of move on to the fun part, which is the solutioning.

Um I I call this the fun part cuz uh there's no real right or wrong answer. Um but this is where you get to ideate your creative solutions, right? You're coming up with these um on the fly, but it's important to kind of lead with that human experience first um instead of just describing the features, right?

Um I would kind of encourage you to describe the moment uh of experience that it creates for the user. Right? Um and then you can kind of go into how it works under the hood and make sure your solutions vary from uh uh across a spectrum of complexity levels.

Um so we'll take it from there. Um okay, so I thought about some of some ideas um and came up with a few different solutions. Um I think the first one I would call a a mood prompt.

Um so imagine Maya opens Netflix and instead of these dozens of rows, she just sees a single, you know, warm question that asks like, "Hey, what are you feeling tonight? " with some simple responses of like, "I'm feeling something lighthearted" or something frightening or something uh funny. And she just taps on lighthearted and immediately the entire interface can kind of reorganize itself um around that specific intent.

So she taps into one show and then she can begin watching. So here, this I think can transform this whole browsing experience into more of a a guided decision-making process for Maya. Um under the hood, I would say this is a pretty um strong indicator for us.

I think it's uh it gives Netflix a real-time intent signal for that session. Um and it doesn't have to be a permanent change to her long-term profile at all, but it's just for that session duration. Um I think this is a quick win for Netflix cuz it's a relatively low complexity build from what I'm thinking.

Um there's no new machine learning that's needed. All we're doing is just adding a new UI prompt and passing this parameter into the existing ranking API model. I think Netflix already kind of tags all of their titles by, you know, genre and tone and content duration.

So all we're doing is kind surfacing that up. Um surfacing those tags to the user for the first time. Um I think the heavier lift will be on the UI side, the UX UI side, to make sure the prompt feels kind of warm and not like a mandatory form that they have to go through.

Um a second idea that I had was creating a time-based filter. So Netflix already has, you know, content duration length for all of the titles and I think adding this filter, uh which is a time selector would kind of filter the catalog accordingly. So there's no model changes here, no new infrastructure, and the back-end filters kind of the back-end filters by runtime, um which already exists for every title.

So it's simple, it's fast, and um I would say immediately useful for time-constrained users like Maya. Um a third solution that I thought of was something I would call social signal. So, this is kind of tapping into human behavior, um a human motivator, right?

Which is uh if you think about our desire to do what other people like us are doing. I think this would be more of a personalized signal based on watch histories of other users who have a similar watch history as Maya. So, we could surface up some simple one-liners.

Um so, for Maya, she could see like, "Oh, hey, 92% of users like you finished this show. " Or this is trending amongst parents. So, now she's not guessing anymore, so to speak, and she feels a lot more confident with, you know, the the recommendations that are giving to her.

And I think that's the difference between recommendation and just raw popularity. Um in terms of engineering complexity, I think this one is obviously a lot more extensive. We need to build out this um people like you model cohort model and kind of cluster the users based on taste um and generate these social signals that way, um not just based on the raw popularity of the show, which already exists.

So, I think this would be a longer-term kind of solution, but it could also prove to be very valuable for us. Lastly, um another kind of lower-effort item that I thought of was just creating this dislike signal. Netflix already has the thumbs up, thumbs down system, um which lives kind of a couple layers deep um on the content detail page, but I think if we surface this up, um this could be pretty powerful for casual browsers like Maya who don't have a rich watch history, right?

I think these negative signals, so Maya could click on this is not for me or thumbs down, and then the algorithm would kind of improve instantly for her. Um I think for her, users like Maya who you know, Netflix can't really She can't teach Netflix what she loves if she never really finishes anything. But, if she is able to click a this is not for me, I don't like this, um, she can absolutely This is kind of a shortcut to the algorithm, right?

It gives her the feeling that she's also kind of being heard. So, from here, I would kind of prioritize. Um, I provided some solutions.

Um, you'll notice that, you know, there wasn't like a moonshot crazy solution that was out of this world. But, I my I kept my examples kind of, I guess, realistic. Um, not that creative, honestly.

But, um, from here, you can narrow down your choices, right? And um, come up with a clear rationale behind it. You can connect the story directly back to Maya's pain points and her business and our business goals.

Um, but don't just say what you will build. Um, you also need to state what you would want to validate first. And bonus points if you're, you know, um, amazingly creative and able to kind of figure out a way to build out a road map with all of your solutions.

So, I'll kind of provide that example for us. So, for solutions, let me for Let me tell you what I would build first and and why. So, I would kind of think about this in a couple of different phases.

Um, for phase one, I would want to start with our first solution, which was the mood prompt, and immediately follow that up with our second solution, which was time-based filtering. I think both of these are relatively low complexity items. Um, and they both directly attack Maya's biggest pain points, which were decision fatigue and situational context blindness, right?

I think it's a simple UI change with some lightweight API parameters. It has a low engineering cost, which and is fast to ship given our tight timeline and budget. Um and it's easy to AB test within a few weeks.

Um as a fast follow item, I would also want to build our fourth solution, which was the dislike signal. I think we could test this in a short amount of time as well at almost no additional cost. So, for users like Maya who have sparse sparse watch histories, I think these negative signals could provide a lot more value and ultimately teach our algorithm faster than any positive signals ever could, right?

Um solution three, which was the social proof concept, um I I would classify this as a phase two. It definitely has a higher impact ceiling, um but also comes with a high level of complexity um and longer timeline. So, I would first want to validate that the data that we receive from the signals from phase one um are trending the right direction as expected before we kind of invest into this cohort modeling phase.

So, I think taken together, this whole roadmap moves from, you know, helping casual users narrow down their recommendations um and decision time to really helping them trust the recommendations. And I think that progression maps directly to the stages of Maya's frustrations. So, here I presented the four solutions and I prioritized them into a phased roadmap, which is a pretty senior product manager move.

I think it shows that you can brainstorm and um think sequentially um and not just pick a favorite, right? Notice how the prioritization logic kind of mapped back to Maya and her pain points and the engineering complexity all in one breath. And I think that's what makes an answer um kind of architecturally sound and not just improvised.

After that, I would go into the metrics. I would likely want to touch on some metrics to build out some success criteria. So, when I think about metrics, um I think the obvious primary metric here is looking at the time to play, which is the elapsed time from when a user opens Netflix to pressing play on a given title.

I think this is our most direct measure of whether we're solving Maya's problem. And our target should be to kind of move this time to play from our current baseline, which for casual users I'm assuming to be, you know, 10 to 20 minutes to something under 5 minutes within the first 60 days of launch. Um I would also want to look at, you know, session completion rate, which is the percentage of users who use our new solutions, making sure they watch at least 70% of a title.

I think this kind of tells us that we're not just getting users to press play faster, but we're also matching them successfully with the content that they will actually enjoy. Um another metric I think is around churn data, right? We need to make sure that um even a 1 to 2% reduction in cancellation rate, um I think this would be monumental for Netflix.

It's it's a huge huge number if you think about Netflix's subscriber scale. So, um those are kind of the metrics that I um that I would kind of wanted to write. Also, there are some guardrail metrics as well, things that we don't want to break, right?

There's obviously the content diversity index, which kind of shows the number of unique genres of a user watches within the first within a 30-day period. And if this drops significantly, then we'll know that users are kind of being funneled into their just comfort zone genre. Um and and that's kind of going to hurt our long-term engagement.

Um and then I would also think about the prompt skip rate, right? If we see that over 60% of our users are just dismissing this prompt entirely, I think it's clear that it's just adding friction and not really adding much value. So, I would rethink the UI of this.

So, if we're able to achieve all of our metrics target here, that would be a clear indicator for us to move from this AB test experiment to a full rollout and hopefully a phase two approach. So, now that we talked about some of the metrics, um I also want to touch on some tradeoffs here. So, um you know, phase one and phase two.

Obviously, with the prompt, this is, you know, adding friction at session start, right? I think adding this extra step can be pretty annoying for power users, right? If you know exactly what they want to watch.

Um I think a mitigation to this is to make sure that it is optional and dismissible. We want to make this feel like a a nudge and not like a required form, right? Um another risk, I would say, is um I would call, I guess, suppression of organic discovery.

So, if this prompt continuously just narrows users into the same genres, it could ultimately hurt Netflix's product content offerings, right? I think we Netflix spends a ton of money on marketing their new content. Um, if they're but if users are just being funneled into their own comfort zone, I think this hurts our long-term engagement and the ability to surface new content.

Um, alongside of that, I think if we think about the dislike thumbs down button, um, this could also lead to some uh, false preference data. So, you know, if we you know, if users click this dislike button just so that they could get rid of that content cuz they've already watched it, um, then it kind of incorrectly infers the user's taste preference. So, I think a mitigation to this is to kind of add a, um, already seen data point, um, as a separate signal category in the back end.

Um, for phase two, the the social signal, I think this building out this cohort of people like you is it's pretty challenging to define well. I think it takes several iteration. And if this model is off, let's say Maya sees social signals from users who have completely different preferences, then this feature would ultimately backfire.

So, I think you know, it's it's especially vulnerable for new subscribers as well. Um, it could lead to some misleading percentages early on. So, we need to be mindful of that.

So, here I named all of my solutions and then I also came up with the risks and trade-offs and mitigations. So, when you think about all of that, this shows that you kind of have that um, full lifespan of your proposed solutions. And then at the end, this is where I would kind of go into the whole summary, right?

I talked a lot. So, it's important to kind of recap on what I what I said and and clean the trail off um and kind of invite dialogue. So, I'll say, "Hey, let's quickly summarize where I landed, right?

Um throughout my thinking, I kind of try to stay grounded in uh Maya's point of view, her actual experience, and not so much what the algorithm does, um but what it really feels like to be her in a given moment, right? I think I've focused on the casual browser demographic cuz this segment had the biggest impact on retention, which is ultimately the core business goal for a subscription model like Netflix. Um the primary points that I wanted to solve, pain points, were around decision fatigue and con uh situational context blindness, uh both of which are direct causes of, you know, that scroll and abandon behavior, which puts our subscriptions at risk.

So, my recommended roadmap was to break this out into a phased approach with phase one including the mood prompt, time-based filtering, and dislike signal, and phase two building out this social signal indicator layer, once we've kind of proven that users responded well to our signals from phase one. We also talked about, you know, the the metrics in terms of tracking the time to play, session completion rates, and churn rate date deltas, um along with some guardrail metrics around content diversity and looking at the prompt skip rate. So, we also um um talked about the risks and tradeoffs and mitigations around each, but ultimately, I think the whole solution was really designed to reduce the effort that Maya has to spend on what to watch and increase that feeling that Netflix truly understands her.

And that's ultimately what great recommendations should feel like, right? It's It should be relevant and intuitive. Does that framing kind of make sense?

And I'm happy to go in further on any of the solutions. >> Wow, that's really helpful. That That makes so much sense.

I actually have a a follow-up question for you. Cuz you mentioned, you know, you kind of created a bit of a road map there. So, how would you get Netflix leadership to actually approve this road map?

>> Oh, yeah. That's a That is a very important question. I think this is This is a stakeholder management and influence kind of discussion.

I would frame this as um a business case around Netflix the met- the metric that Netflix cares about the most, you know, subscriber retention, right? I would lead with you know, the problem size. How many casual browser sessions are ending without a play today?

And what percentage of these users churn within 30 days, right? If we could get an estimate or a rough dollar value on reducing that churn rate by 1 to 2 percentage points, I think the investment at in a phase one, which is a fast, low-risk experiment, becomes an obvious yes for leadership. And they would more more than likely approve a six-week pilot with a clear success plan than you know, showing them a three-quarter road map.

So, I would let kind of the data from phase one make the case for phase two and beyond. >> Okay. Great.

Right. Answer. Okay.

Michael, I think that brings us to the end of of that inter- interview and interview walk-through. That was really That was really useful, I think, for people watching. They're going to get loads out of that.

And obviously, yeah, whether they're interviewing at Netflix or Meta, Google, any of any of these big big top companies that that have product sense uh, interviews, I think that's going to be massively helpful. So, >> Amazing. Yeah.

>> Thanks so much. Yeah, no, we'd love to love to have you back on on soon if if you will. >> Absolutely.

Yeah. >> Remember, yeah, please like and subscribe if you found that useful and and obviously can book a session with with Michael if you have interviews coming up and and you want some expert feedback. Um, cool.

Thanks. >> If you want to get help in your career from someone who's done it at the very top, go to igot an offer. com, choose your coach, and book a session.