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Hello everyone, welcome to this Netflix product manager interview walkthrough.

We have Michael here with us today.

He was an ex Netflix PM.

Thanks for joining us Michael.

Thanks for having me Tom.

I'm honored to be here.

Yeah, it's great to have you on.

Before we get started, do you want to just quickly give people an overview of your experience as a PM?

Absolutely.

Yeah.

So I've been a product senior product manager at Netflix and Meta and Roku,

where I've kind of owned and scaled these customer -facing solutions across web and mobile and hardware device ecosystems as well.

I think my work has focused more on driving measurable impact

and leading these large -scale platform transformations

and overall kind of improving the customer experience as I launched products at all these companies.

Great.

And yeah, obviously now you're a coach on I Got an Offer.

and until recently you were running PM interviews at Netflix, right?

So what better person to show us how to give a really strong answer to a typical Netflix question?

I've got a Product Sense question for you today.

So yeah, when you're ready, you can start showing people exactly how to tackle this question.

Let's do it.

Let's dive in.

Okay.

It's a very typical one.

Michael, how would you improve Netflix's recommendations?

Oh, wow.

This is, I think this is one of the most common questions in PM interviews.

And a lot of candidates kind of jump straight into, you know, solutioning of this, right?

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 for me, I think what makes my response a little bit different is

we're not just going to focus on the typical product management frameworks, which I think you guys are all kind of familiar with.

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 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 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.

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.

When we say improved recommendations, are we focused on a specific goal?

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 regions 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 initiative that the company may be running.

So an example of this, I would say, great.

So before I jump into solutions, let me quickly ground us on Netflix's vision and goals.

I think any improvement needs to ultimately align.

back with those.

Netflix's mission, as I understand, is to 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.

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.

So I think the real question

that we need to ask ourselves is how can we help users find what they want to watch faster

and feel more satisfied with what they chose, 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.

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 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, I would kind of narrow this down a little more, right?

So I would break this up into the user segmentation.

Here I would, you know, identify our user groups and then make a deliberate choice,

justified choice, on who to focus on for this interview.

Because right now, it's still a very large scope.

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.

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And I think that's the empathy layer that really transforms a competent answer into something really memorable for the interviewers as well.

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So I'll give you an example.

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Okay, so let me think about who actually uses Netflix recommendations.

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I'm going to segment out the users to make sure that I'm solving for the right person here.

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When I think about Netflix and the users, obviously there's a solo binge watchers, right?

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These are generally people who watch alone.

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They know what genres they like,

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but sometimes they may struggle to find a new series or new show once they've finished what they really like.

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So the recommendations here is helping them find new shows to watch once they finish the series.

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We also have family viewers who want to count.

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They could have multiple profiles, but generally they're spending time watching together.

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for them i think it's more the recommendations are more around

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what everyone will enjoy right i think this is always a challenge everyone in the family has a different taste

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so it's kind of a challenge for them to find what

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they want to watch we also have casual browsers people who

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are subscribed they don't watch a whole lot they may open netflix

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and scroll for 10 to 20 minutes and then they can't decide

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and then they just close the app

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so i think the casual browsers is our biggest churn risk

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that we have um and then lastly i think about lapsed users people who were once active

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but then they haven't watched in a while so the recommendations

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that are surfaced to them may feel a little irrelevant or stale

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because their tastes may have changed um for the purpose of

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today's interview i want to focus on the casual browser because i think this user demographic represents our highest churn risk.

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I think they're already on the platform, so their acquisition cost is sunk.

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And improving their recommendation and discovery experience, I think, has a direct and measurable impact on retention.

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And the ROI potential is the highest of.. any of these segments.

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So let me bring this casual user to life because I think it helps to understand who we're solving for.

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So imagine this.

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Let's say it's a weeknight.

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Let's say it's a Wednesday night, 9 .30.

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Our user, I'll name her Maya, she just put her kids to bed and she has like 45 minutes before she needs to sleep.

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So she opens Netflix.

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and she doesn't want anything heavy at the moment.

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She's pretty tired, middle of the week, but also doesn't want to commit to the first episode of a new series

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if she's not sure she's going to be able to finish it, right?

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So she scrolls and scrolls more.

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She watches some trailers, reads some descriptions, and backs out, tries another row, same thing.

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Nothing really feels quite right at the moment.

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So before she knows it, 20 minutes go by.

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And then she decides, oh, I can't, I can't decide.

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I'm just going to hop over to YouTube.

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That moment, that forget it moment for Maya, I think that's the moment that we need to be designing against.

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So Maya isn't broken, right?

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She, for her, the product isn't understanding her.

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So the product experience at that moment for her is what's broken.

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So here, when I was defining all the user segments, notice how I named this user Maya, and it really helped them kind of come to life, hopefully.

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Interviewers are humans too, right?

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I think we've had that same experience.

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I've had this experience yesterday.

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And the moment we recognize ourselves in your narrative,

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I think this is where you make the emotional case for why this problem actually matters, right?

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It's not just the logical one.

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So it's really important to frame this demographic and bring this person to life.

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This shows that you're really thinking about the user experience and you're thinking not just about the user, but also the company outcomes as well.

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So I think that's the key thing to highlight there.

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From there, I'll move on to some of the pain points.

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I'll frame the pain points through the lens of our user Maya. For someone like Maya,

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she has this limited amount of time right she wants to

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it's the end of the day she is exhausted she wants

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to switch off she wants to mentally just switch off feel entertained

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so she can go to bed feeling like she's had this moment for herself right so with

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that in mind i think this is where um her pain

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points lie i think this is where the product failed her

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the first thing obviously it's decision fatigue right the home page right now it shows

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so many titles with dozens of rows

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and for someone like her who came to the product to kind of relax

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and unwind these choices often creates this decision paralysis right i

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think it's designed currently for someone who has a little more bandwidth

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and not someone with this limited amount of time the second pain point i think is around situational context blindness.

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I think Netflix recommends kind of the same content regardless of time or your mood or energy level.

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So if you think about Maya, this 9 .30 p .m.

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Maya might have completely different needs than like a Saturday afternoon Maya, but the product itself doesn't really know that difference.

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A third pain point, I think there's no way for her to express what she wants at that moment, right?

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There's no way for her to express her intent.

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She can't like talk to Netflix and say, hey, Netflix, show me something funny that won't make me think.

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She can only kind of scroll and hope that the algorithm infers what she wants.

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So there's no way for her to be heard.

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And then lastly, I think there's also this content uncertainty, right?

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I think even if Maya stumbles upon something that may be a good match for her,

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there aren't any real signals that help her feel confident that she made the right choice.

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So she keeps on browsing.

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So all of these factors are actually what drives this like 20 minute through scrolling and abandonment behavior.

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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.

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I think both of these are direct causes of that.

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scroll and abandon behavior, which ultimately puts our subscription at risk.

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So here I kind of showed the interviewer that, you know, I didn't just list out my problems,

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but I also picked out the ones that I wanted to build against, right?

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I think this helps you kind of narrow down your solution path a little bit.

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And for such an ambiguous question like this, pain points are kind of communicated best when you articulate them through the lens of your user.

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It kind of shows the interviewer

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that your solution is going to be grounded in a real user need and not just product assumptions.

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Okay, from there, I would kind of move on to the fun part, which is the solutioning.

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I call this the fun part because there's no real right or wrong answer.

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But this is where you get to ideate your creative solutions.

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You're coming up with these on the fly,

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but it's important to kind of lead with that human experience first instead of just describing the features.

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I would kind of encourage you to describe the moment of experience that it creates for the user.

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Right.

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And then you can kind of go into how it works under the hood

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and make sure your solutions vary from across a spectrum of complexity levels.

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So we'll take it from there.

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Okay, so I thought about some ideas and came up with a few different solutions.

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I think the first one I would call a mood prompt

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so imagine Maya opens Netflix

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and instead of these dozens of rows she just sees a single you know warm question

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that asks like hey what are you feeling tonight with some simple responses of like I'm feeling something light -hearted

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or something frightening or something funny and she just taps on light -hearted

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and immediately the entire interface can kind of reorganize itself around that specific intent.

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So she taps into one show and then she can begin watching.

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So here, this, I think, can transform this whole browsing experience into more of a guided decision -making process for Maya.

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Under the hood, I would say this is a pretty strong indicator for us.

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I think it gives Netflix a real -time intent signal for that session.

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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.

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I think this is a quick win for Netflix because it's a relatively low complexity build from what I'm thinking.

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There's no new machine learning that's needed.

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All we're doing is just adding a new UI prompt and passing this parameter into the existing ranking API model.

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I think Netflix already kind of tags all of their titles by genre and tone and content duration.

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So all we're doing is kind of surfacing that up, surfacing those tags to the user for the first time.

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I think the heavier lift will be on the UI side, the UX UI side to make sure the prompt feels kind of warm

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and not like a mandatory form that they have to go through.

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A second idea that I had was creating a time -based filter.

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So Netflix already has content duration length for all of the titles.

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And I think adding this filter, which is a time selector, would kind of filter the catalog accordingly.

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So there's no model changes here, no new infrastructure.

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And the backend filters by runtime, which already exists for every title.

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So it's simple, it's fast, and I would say immediately useful for time -constrained users like Maya.

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A third solution that I thought of was something I would call social signal.

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So this is kind of tapping into human behavior, a human motivator, right?

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Which is if you think about our desire to do what other people like us are doing.

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I think this would be more of a personalized signal based

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on watch histories of other users who have a similar watch history as Maya

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so we could surface up some simple one -liners um

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so for maya she could see like oh hey 92 percent of users like you finished this show

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or this is trending amongst parents so now she's not guessing anymore

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so to speak and she feels a lot more confident with you know the the recommendations

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that are giving to her and i think Thank you.

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that's the difference between recommendation and just raw popularity.

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In terms of engineering complexity, I think this one is obviously a lot more extensive.

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We need to build out this people like you model,

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cohort model, and kind of cluster the users based on taste and generate these social signals that way,

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not just based on the raw popularity of the show, which already exists.

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So I think this would be a longer term kind of solution, but it could also prove to be very valuable for us.

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Lastly, another kind of lower effort item that I thought of was just creating this dislike signal.

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Netflix already has the thumbs up, thumbs down system, which lives kind of a couple layers deep on the content detail page.

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But I think if we surface this up, this could be pretty powerful for casual browsers like Maya who don't have a rich watch history.

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I think these negative signals, so Maya could click on this is not for me or thumbs down.

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And then the algorithm would kind of improve instantly for her.

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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

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but if she is able to click a this is not for me I don't like this

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she can absolutely this is kind of a shortcut to the algorithm right it gives her the feeling

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that she's also kind of being her so from here I would kind of prioritize I provided some solutions

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you'll notice that you know there wasn't like a moonshot crazy solution

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that was out of this world I kept my examples kind of, I guess, realistic.

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Not that creative, honestly.

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But from here, you can narrow down your choices, right?

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And come up with a clear rationale behind it.

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You can connect the story directly back to Maya's pain points and her business and our business goals.

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But don't just say what you will build.

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You also need to state what you would want to validate first.

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And bonus points if you're, you know, amazingly creative and able to kind of figure out a way to build out a roadmap with all of your solutions.

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So I'll kind of provide that example for us. So four solutions.

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Let me tell you what I would build first and why.

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So I would kind of think about this in a couple different phases.

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for phase one I would want to start with our first solution

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which was the mood prompt and immediately follow

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that up with our second solution

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which was time -based filtering I think both of these are relatively low complexity items

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and they both directly attack Maya's biggest pain points

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which were decision fatigue and situational context blindness right I think it's a simple UI change with some lightweight API parameters.

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It has a low engineering cost, which is fast to ship given our tight timeline and budget.

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And it's easy to A -B test within a few weeks.

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As a fast follow item, I would also want to build our fourth solution, which was the dislike signal.

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I think we could test this in a short amount of time as well at almost no additional cost.

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So for users like Maya who have sparse watch histories, I think these negative signals could provide a lot more value

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and ultimately teach our algorithm faster than any positive signals ever could, right?

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Solution three, which was the social proof concept, I would classify this as a phase two.

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It definitely has a higher impact ceiling, but also comes with a high level of complexity and longer timelines.

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So I would first want to validate that the data

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that we receive from the signals from phase one are trending

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the right direction as expected before we kind of invest into this cohort bottling phase.

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So I think taken together, this whole roadmap moves from,

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you know, helping casual users narrow down their recommendations and decision time to really helping them trust the recommendations.

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And I think that progression maps directly to the stages of Maya's frustrations.

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So here I presented the four solutions and I prioritized them into a phased roadmap, which is a pretty senior product manager move.

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I think it shows that you can brainstorm and think sequentially and not just pick a favorite, right?

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Notice how the prioritization logic kind of mapped back to Maya

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and her pain points and the engineering complexity all in one breath.

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And I think that's what makes an answer kind of architecturally sound.

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and not just improvised.

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After that, I would go into the metrics.

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I would lightly want to touch on some metrics to build out some success criteria.

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So when I think about metrics, I think the obvious primary metric here is looking at the time to play,

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which is the elapsed time from when a user opens Netflix to pressing play on a given title.

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I think this is our most direct measure of whether we're solving Maya's problem

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and our target should be to kind of move this time to play from our current baseline

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which for casual users I'm assuming to be you know 10

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to 20 minutes to something under five minutes within the first 60 days of launch.

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I would also want to look at session completion rate, which is the percentage of users who use our new solutions, making sure they watch at least 70 % of a title.

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I think this kind of tells us that we're not just getting users to press play faster,

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but we're also matching them successfully with the content that they will actually enjoy.

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Another metric, I think, is around churn data, right?

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We need to make sure that even a one to two percentage reduction in cancellation rate,

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I think this would be monumental for Netflix.

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It's a huge, huge number if you think about Netflix's subscriber scale.

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So those are kind of the metrics that I would kind of monitor.

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Also, there are some guardrail metrics as well, things that we don't want to break, right?

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There's obviously the content diversity index,

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which kind of shows the number of unique genres of a user watches within the first within a 30 day period.

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And if this drops significantly, then we'll know that users are kind of being funneled into their just comfort zone genre.

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And that's going to hurt our long term engagement.

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And then I would also think about the prompt skip rate right

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if we see that over 60 of our users are just dismissing this prompt entirely i think

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it's clear that it's just adding friction and not really adding much value

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so i would rethink the ui of this so

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if we're able to achieve all of our metrics target here

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that would be a clear indicator for us to move from this a b test experiment to a full rollout

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and hopefully a phase two approach so now

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that we talked about some of the metrics um i also want to touch on some trade -offs here

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so um you know phase one and phase two

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obviously with the prompt this is

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you know adding friction at session start right i think adding

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this extra step can be pretty annoying for power users right who know exactly what they want to watch

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i think a mitigation to this is to make sure

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that it is optional and dismissible we want to make this feel like a a nudge

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and not like a required form right another risk i would say is um i would call,

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I guess, suppression of organic discovery.

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So if this prompt continuously just narrows users into the same genres, it could ultimately hurt Netflix's product content offerings, right?

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I think Netflix spends a ton of money on marketing their new content.

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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.

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Alongside of that, I think if we think about the dislike thumbs down button,

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this could also lead to some false preference data.

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So, you know, if we, you know, if users click this dislike button just so

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that they could get rid of that content because they've already watched it, then it kind of incorrectly infers the user's taste preference.

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So I think a mitigation to this is to kind of

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add a already seen data point as a separate signal category in the back end.

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For phase two, the social signal, I think building out this cohort of people like you,

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it's pretty challenging to define well.

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I think it takes several iteration.

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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.

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So I think...

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you know it's it's especially vulnerable for new subscribers as well it could lead to some misleading percentages early on

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so we need to be mindful of that

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so here i named all of my solutions

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and then i also came up with the risks and trade -offs

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and mitigations so when you think about all of that this shows

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that you kind of have that full lifespan of your proposed solutions And then at the end,

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this is where I would kind of go into the whole summary, right?

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I talked a lot.

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So it's important to kind of recap on what I said and clean, don't trail off, and kind of invite dialogue.

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So I'll say, hey, let's quickly summarize where I landed, right?

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Throughout my thinking, I kind of try to stay grounded in Maya's point of view, her actual experience, and not so much what the algorithm does,

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but what it really feels like to be her in a given moment.

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I think I focused on the casual browser demographic because this segment had the biggest impact on retention,

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which is ultimately the core business goal for a subscription model like Netflix.

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The primary points that I wanted to solve, pain points, were around decision fatigue and situational context blindness,

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both of which are direct causes of that scroll and abandoned behavior, which puts our subscriptions at risk.

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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,

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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.

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We also talked about, you know, the metrics in terms of tracking the time to play, session completion rates and churn rate deltas,

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along with some guardrail metrics around content diversity and looking at the prompt skip rate.

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So we also talked about the risks and trade -offs and mitigations around each.

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But ultimately, I think the whole solution was really designed to reduce the effort

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that Maya has to spend on what to watch and increase that feeling that Netflix truly understands her.

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And that's ultimately what great recommendations should feel like, right?

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It should be relevant and intuitive.

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Does that framing kind of make sense?

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And I'm happy to go in further on any of the solutions.

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Wow, that's really helpful.

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That makes so much sense.

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I actually have a follow -up question for you.

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Because you mentioned you kind of created a bit of a roadmap there.

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So how would you get Netflix leadership to actually approve this roadmap?

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Oh, yeah.

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That is a very important question.

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I think this is a stakeholder management and influence kind of discussion.

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I would frame this as a business case around Netflix,

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the metric that Netflix cares about the most, subscriber retention.

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I would lead with the problem size.

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How many casual browser sessions are ending without a play today?

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And what percentage of these users churn within 30 days?

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If we could get an estimate or a rough dollar value on reducing that churn rate by one to two percentage points,

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I think the investment in a phase one, which is a fast, low risk experiment becomes an obvious yes for leadership.

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And they would more than likely approve a six week pilot

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with a clear success plan than showing them a three quarter roadmap.

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So I would let kind of the data from phase one make the cause for phase two and beyond.

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Okay, great.

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Great answer.

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Okay, Michael, I think that brings us to the end of that interview and interview walkthrough.

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That was really useful, I think, for people watching.

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They're going to get loads out of that.

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And obviously, yeah, whether they're interviewing at Netflix or Meta, Google, any of these big, big top companies that have product sense interviews,

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I think that's going to be massively helpful. So amazing.

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Yeah.

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Thanks so much.

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Yeah.

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Thanks for having me.

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Yeah.

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No, we'd love to love to have you back on soon, if you will.

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Absolutely.

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Yeah.

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Remember, yeah, please like and subscribe if you found that useful.

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And obviously you can book a session with Michael if you have interviews coming up and you want some expert feedback.

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Cool.

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Thanks.

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

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Từ vựng và ghi chú luyện nói cho bài học này

Video này có 405 câu và 5234 từ để luyện shadowing. Phần lời nói dài 35:07. Người nói giữ tốc độ đều, khoảng 149 từ mỗi phút, vừa sức để nói đuổi theo. 82% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất; phần còn lại nên xem trước khi luyện.

Từ vựng quan trọng trong video

15 từ ít gặp trong video, kèm phiên âm và nghĩa:

recommendation /ˌɹɛkəmɛnˈdeɪʃən/ (danh từ) — sự giới thiệu, sự tiến cử. An act of recommending.
metric /ˈmɛt.ɹɪk/ (tính từ) — mét. Of or relating to the metric system of measurement.
filter /ˈfɪltɚ/ (danh từ) — máy lọc. A device which separates a suspended, dissolved, or particulate matter from a fluid, solution, or other substance; any device that separates one substance from…
solve /sɒlv/ (động từ) — giải quyết. To find an answer or solution to a problem or question; to work out.
algorithm /ˈælɡəɹɪðm̩/ (danh từ) — thuật toán. A collection of ordered steps that solve a mathematical problem. A precise step-by-step plan for a computational procedure that possibly begins with an input…
dislike /dɪsˈlaɪk/ (động từ) — không ưa, không thích. To have a feeling of aversion or antipathy towards; not to like.
percentage /pə(ɹ)ˈsɛntɪd͡ʒ/ (danh từ) — số phần trăm, tỷ lệ phần trăm. The amount, number or rate of something, regarded as part of a total of 100; a part of a whole.
mood /muːd/ (danh từ) — tâm trạng, tính khí. A mental or emotional state, composure.
narrow /ˈnæɹəʊ/ (tính từ) — hẹp, chật hẹp. Having a small width; not wide; having opposite edges or sides that are close, especially by comparison to length or depth.
thumb /ˈθʌm/ (danh từ) — ngón tay cái. The shortest and thickest digit of the hand that for humans has the most mobility and can be made to oppose (moved to touch) all of the other fingers.
fatigue /fəˈtiːɡ/ (danh từ) — mệt mỏi. A weariness caused by exertion; exhaustion.
scroll /skɹoʊl/ (danh từ) — cuộn, cuốn. A roll of paper or parchment; a writing formed into a roll.
discovery /dɪˈskʌv.ɹi/ (danh từ) — khám phá. Something discovered.
layer /ˈleɪ̯ɚ/ (danh từ) — lớp. A single thickness of some material covering a surface.
intent /ɪnˈtɛnt/ (danh từ) — ý đồ, ý định. Something that is intended.

Cụm động từ bạn sẽ nghe

switch off (động từ) — tắt. To stop or disable a device by turning a switch or a lever.
look for (động từ) — tìm, tìm kiếm. To seek.

Phát âm cần chú ý

Người nói dùng 85 dạng rút gọn, ví dụ we're, I'm, don't. Hãy nói theo dạng ngắn đúng như bạn nghe.

Âm “th”: algorithm /ˈælɡəɹɪðm̩/, thumb /ˈθʌm/, empathy /ˈɛmpəθi/
Âm “sh” và “zh”: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, casual /ˈkæʒ(u)əl/, genre /ˈ(d)ʒɑnɹə/, retention /ɹɪˈtɛnʃən/, subscription /səbˈskɹɪpʃən/
Từ dài — đặt trọng âm cho đúng: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, complexity /kəmˈplɛk.sɪ.ti/, demographic /ˌdɛm.əˈɡɹæf.ɪk/, indicator /ˈɪn.dɪˌkeɪ.tə(ɹ)/, experiment /ɪkˈspɛɹ.ə.mənt/

Cách luyện với video này

Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
Nói đuổi từng câu ở tốc độ bình thường, lặp lại mỗi câu đến khi nhịp của bạn khớp với người nói.
Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như recommendation, metric, filter.

Ngữ pháp trong video

Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:

Cấu trúc	Trong video
Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại	I've been · has focused · they've even said
Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm	is broken · are subscribed · are surfaced
Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật	based, which means · people who watch · people who were

Phương Pháp Shadowing Là Gì?

Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.

Phương pháp shadowing: đọc hướng dẫn từng bước đầy đủ →

Luyện nói tiếng Anh bằng Shadowing qua video: Netflix product manager mock interview and tips (with ex-Netflix Lead PM)

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