शैडोइंग अभ्यास: Dot Plots: How to Actually See What Your Users Are Doing - वीडियो के साथ अंग्रेजी बोलना सीखें
पाठ बनाया जा रहा है...
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One of the biggest mistakes I see founders make is relying on aggregate user metrics, instead of understanding how any individual users use their product.
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In my last video, I talked about cohort retention curves
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and how you can use those to separate groups of users and track what they do over time throughout using your product.
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And I think that's the best tool that you've got to figure out if people keep using your product.
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But what you don't know is how are they using your product?
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How are they interacting?
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What features are they using?
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What's the frequency of use?
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What's the pacing of how they use the product?
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And most founders just like ignore this.
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But I think it's the most important signal to figure out if you've built something that people want.
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So you want to be able to look at what individual users are doing.
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But that's a lot, right?
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If you even have like 10 or 20 users, it's pretty challenging to just tail the logs and watch every event that every user is doing.
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So with aggregate data, the graphs that we're all used to talking about, things like DAUs or MAUs,
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these lump all of your users together and you can't really get a sense of what any individual user is doing.
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And if you have any amount of growth, those graphs tend to be going up and to the right, even if users aren't actually enjoying using your product.
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So today I want to tell you about a tool
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that we came to in my startup
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that allows you to understand what's going on with individual users
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while also giving you a big picture view of how your entire product is performing.
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And we call it the dot plot.
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So let me show you what a dot plot looks like.
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Based on the name you can figure out it probably involves dots.
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What you basically do is just make a two-dimensional grid like a spreadsheet
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where there are a bunch of columns and a bunch of rows.
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Each row represents one individual user.
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If I'm one of the users, I'll write my name here, Dave, I'm one of the users.
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And every other user of your product gets their own row.
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And then every column represents a time period.
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I think days are usually the right thing to use for your product, but it probably depends a bit on the nature of your product.
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So let's just draw in the days.
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I'll just do Monday, Tuesday, Wednesday, Thursday, Friday.
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And you can make this as big or as small as you want.
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For the sake of this example, I'll just do like a week or two of days just to show you what's going on here.
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And then the idea, it's called a dot plot, is you put some dots in each of the cells.
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You want to pick an event
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that your user does in the process of using your product that you think represents value in the product.
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Maybe it's
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It's sharing a photo if you're building a photo app
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or listening to a song if you're building a music app
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or processing an invoice if you're building a B2B invoice processing product.
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And you can just put a dot for each day that each user uses the product.
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Let's say we're Spotify and we're building a music streaming app and we want to see how our users are using it.
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Let's pick the event that we're going to chart here being listen to a song.
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So anytime a user listens to a song during a day, we're going to put a dot.
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So for me, let's say I listen to Spotify song on Monday and Tuesday and not on Wednesday, but Thursday and Friday again.
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And then maybe again on Monday and Wednesday.
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Another thing you can do to make a record of the first day that a user used the product, the day that they onboarded, you can put another symbol.
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Like let's say on a user's first day, we'll just draw a little ring around the dot like that just to give us a little bit more signal.
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And what you'll eventually start seeing is a pretty high density visualization of individual users and their usage over time.
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What's really cool about this is it lets you figure out patterns
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that you probably would not have seen with your human brain just looking at aggregate charts or looking at individual user logs.
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Okay, so let's look at this example I've just drawn.
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For our Spotify app, what do we see?
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What patterns have emerged now that we can see individual users and their own behavior?
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Well, one thing I see is it seems like there's a set of people who use the product on weekdays, right?
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We've got myself, we've got user number three here, user four used it on a Monday, user six used it during the week.
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And there's a couple of users who seem to kind of only use it on the weekends.
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That's an interesting observation that might help me redesign my product in a different way or target different users, maybe understand which users are the most valuable ones to me.
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Do I want the weekday work time listeners or do I want the weekend users?
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We would have no idea about this if we didn't have a dot plot visualization like this.
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Another thing I can see is a measure of retention.
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Like, do we see a lot of users like user 4
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that try the app on one day and then never come back?
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If we see that on a bunch of our rows, we have an idea of a potential problem that we've got in our onboarding or other things.
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As you get more sophisticated with dot plots, you can make them as intricate as you want.
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At Bump, we had different symbols that we would put into these cells.
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So we knew whether you shared your contact information using Bump or if you shared a photo.
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And it gives you a lot more granularity and you can kind of go as deep as you want on this.
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This idea of dot plots might be familiar to some of you.
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You've probably seen it at the top of GitHub pages.
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This is basically what a GitHub graph looks like.
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They've just wrapped the days around per week.
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Another thing you can do is instead of just tracking user actions, you can track user state.
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So was this user using an iPhone or an Android phone?
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Were they on the web?
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Was this user coming from the United States or a different country?
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Sometimes you have demographic information about your users.
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Is this a user that makes a lot of money?
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Or is this a college kid that you just got on Reddit or something?
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You can encode those states with other symbols or shading the cells, different colors.
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You can write things over here.
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So like I might say, you know, this is a iOS user and this is an iOS user, but these ones are Androids.
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And another thing you can do then is sort your rows based on whatever attributes you want to sort them by.
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So you might say, I only want to look at iOS users first, or I only want to look at users whose first time using the app was this Monday.
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So let's resort so we only see people that have rings around their first day.
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What you find when you look at this in aggregate, you can then kind of zoom out and see an entire page of these, is your brain will start to notice these patterns in a way
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that you would never have figured out on your own a priori.
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This is actually an idea that I remember hearing about 10 years ago from Max Levchin, one of the founders of PayPal.
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They had a big fraud problem at PayPal when they first launched, but they didn't know the patterns to look for.
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So what they did instead is build a visualization, a graph of all the transactions that were happening on PayPal.
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And they just had humans sit and stare at screens of these drawings and graphs.
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And while the humans didn't know what exactly was going on, they were able to look at the screen and say,
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that thing happening there, that's different and probably fraud.
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And then they would go and dig into that.
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it's kind of the same idea with dot plots you can look at these these charts
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and figure out huh there's something going on with users i see this pattern emerging
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and then you can go dig into it a lot deeper
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so to illustrate the point i was talking about where dot
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plots give you a lot more granularity about what's going on
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with the users let's draw the dau graph for these users
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so what you would have seen had you only been looking
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at your dau graph i'll just draw it on top of here to illustrate
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so again like imagine each of these days is the same
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day above the da you graph here looks like this on
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day one it's two on day two it's three on day three it's two 2,
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2, 2, 2, 2, 1, 0, 1.
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So if you were just looking at DAUs, this is the graph you would see.
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And it really doesn't tell you all that much.
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It basically tells you, yeah, we're not growing.
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We have some users.
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Instead, looking at the dot plot, we have a much richer understanding of our users.
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We know something about their behavior, maybe something about their lives we probably have inferred from this that these people that use it during the week,
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probably they're doing it at the office
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or in some other place where they can listen to music every single day of the work week.
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And again, you can go a lot deeper on this.
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And if you change the dots to be different symbols, for example, in our Spotify example, we could choose to represent different features of the product.
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Let's say if a user uses search in Spotify, we'll put a little S next to it.
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Or if they use maybe a playlist, they join a public playlist, let's say we could put a P there.
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And you might start to see patterns where specific features maybe drive behaviors in the product that you actually want.
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Let's just say for the sake of argument that we see this one user here that joined a public playlist.
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They then have a string of many, many consecutive days of using the product.
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We could then infer like, oh, maybe the playlist feature is really causal to having people be really active in our product.
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This is the sort of stuff that you can learn with dot plots.
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So what's really great for most founders, you have a very small number of users at the beginning.
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And so you can literally look at every single user of your product on every single day they've ever used it.
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And it all fits on one screen on your monitor.
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That's really great.
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but it actually does scale to when you have thousands or millions or billions of users.
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This is a tool that we used at Google Photos when we had well more than a billion users.
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And the idea is you can just choose to sample your users and represent them on a dot plot however you want.
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So we would have days where we print out dozens of
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these pieces of paper with dot plots on them for different samples of our user base.
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I would print out a piece of paper and hand one of our team members, like, here's the iOS users in France.
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I want you to understand what they're doing.
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And I would hand another piece of paper to somebody else and say, these are the users on web in the United States who make more than $80,000 a year.
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Let's see what they're up to.
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And we would have days where we just sit in the office
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and look at these dot plots and try to draw conclusions about what's going on with our users.
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So you might be thinking to yourself, this is cool, Dave, but we're a B2B product
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and we just sell seats to businesses and pay for it
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and so i that's all that matters right turns out
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that dot plots could be really useful to you too let
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me give you a specific example i worked with a company in the most recent yc batch
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that had a very name brand customer that signed up
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and bought their product i think it was like a 80
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000 a year contract they onboarded the company the company said they wanted 10 seats
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and later the company chert let me show you what they
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could have figured out had they been using dot plots So this is what it actually looked like.
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The company bought 10 seats, but only three seats ever activated.
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Only three of those people ever tried the product.
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And if you look at their usage, they weren't getting a lot of value from it.
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Nobody used it more than two days per week.
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And it looks like pretty sporadic usage.
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And it turns out what happened is the company was in the state.
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The champion had gotten excited about this product and bought it.
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And then the champion left the company.
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And as soon as the champion left, a new person came in and they said, why are we using this software?
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We're going to churn.
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And so they opted out of a renewal clause at the last moment.
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The company could have known that this contract was in jeopardy by looking at the dot plot.
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So there's a few ways you can misuse dot plots.
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The number one thing is to just chart the wrong event.
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A lot of founders might want to populate their dot plot with the easiest way to populate
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so it feels good and you see a lot of dots.
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Maybe you'll pick like opened the app or signed into the product.
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Those are pretty bad events to choose because they don't really measure whether the user is getting real value.
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So I suggest you pick something that actually represents value being created for the user.
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Listen to a song, shared a photo, something like that that's a real event.
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The other mistake you can make is picking a time period that's too wide.
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Sometimes founders want to make it look better and they pick weeks, like week one, week two, week three it's way harder to figure out what's actually going on unless you look at it at the day
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or maybe even like sub-day granularity.
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So I would go so far as to say until you have hundreds of users, the dot plot could be your only dashboard.
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What's great about dot plots is they're just a logs visualization tool.
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There's no fancy computations happening here.
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You basically just need to parse your logs and put them into a 2D grid.
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This is a thing that modern AI coding tools can whip up in like 10 minutes.
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These are best used in conjunction with cohort retention curves.
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Cohort retention curves teach you in aggregate whether groups of users that you acquire stick with you over time.
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That's very important.
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You should definitely be measuring that.
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But the dot plot shows you how those users are actually using your product.
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And they give you the color to go ask the right questions of your users, to go build the right features,
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to fix things that are broken in your product that you would never learn by looking at aggregate metrics.
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So cohort retention curves and dot plots are in my experience two of the most important tools
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that you've got to understand your users.
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Good luck!
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पहली छापें
इस वीडियो में दी गई जानकारी और संवाद बहुत प्रभावशाली हैं। यह न केवल उपयोगकर्ताओं के अनुभव को समझने की आवश्यकता पर जोर देता है, बल्कि यह भी बताता है कि व्यक्तिगत उपयोगकर्ता डेटा को समझना कितना आवश्यक है। जब आप यह सुनते हैं कि "उपयोगकर्ता अपने उत्पाद का कैसे उपयोग कर रहे हैं," तो यह आपके मन में एक स्पष्ट चित्र बनाता है। यह संवाद उच्चारण और भाषा के प्रवाह में सुधार के लिए एक बेहतरीन सामग्री है, विशेषकर "अंग्रेजी शैडोइंग" तकनीक का उपयोग करते समय।
इसे तोड़ें
वीडियो में कुछ वाक्य हैं जो प्राकृतिक रूप से सुनाई देते हैं और जिन्हें हम बेहतर ढंग से समझ सकते हैं:
- "मैंने पिछले वीडियो में समूह रखरखाव वक्रों के बारे में बात की थी" - इस वाक्य में वाक्य संरचना सरल है, और यह स्पष्टता के साथ जानकारी प्रस्तुत करता है।
- "आपको यह देखने की आवश्यकता है कि व्यक्तिगत उपयोगकर्ता क्या कर रहे हैं" - यह वाक्य प्रभावी ढंग से दर्शाता है कि व्यक्तिगत डेटा का महत्व क्या है।
- "अगर आपके पास कोई भी वृद्धि है, तो ये ग्राफ ऊपर और दाईं ओर बढ़ते हैं" - यह वाक्य उपयोगकर्ता डेटा की वास्तविकता को उजागर करता है।
इन वाक्यों का उच्चारण करते समय, आप "shadow speak" तकनीक का उपयोग कर सकते हैं। यह आपको उच्चारण में सुधार और भाषा के प्रवाह को बेहतर बनाने में मदद करेगा।
अभ्यास दिनचर्या
इस वीडियो से अभ्यास करने के लिए, आप निम्नलिखित क्रम का पालन कर सकते हैं:
- वीडियो को ध्यान से सुनें और इसके संवाद का अनुसरण करें।
- प्रत्येक वाक्य को सुनें और उसे दोहराएं। इसे "shadowspeaks" के रूप में जानें।
- अपना रिकॉर्डिंग उपकरण लें और अपने उच्चारण को सुनें।
- अपने उच्चारण में सुधार के लिए आवश्यकतानुसार दोहराएं।
इस अभ्यास के माध्यम से, आप "अंग्रेजी उच्चारण में सुधार" कर सकते हैं और अपनी भाषा कौशल को निखार सकते हैं। महत्वपूर्ण यह है कि आप निरंतरता बनाए रखें और नियमित रूप से इस प्रक्रिया को दोहराएं। इससे आपकी अंग्रेजी में धाराप्रवाह और आत्मविश्वास दोनों में वृद्धि होगी।
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| शर्त वाले वाक्य if + उपवाक्य, will/would + क्रिया — शर्त और उसका नतीजा | If I'm one of the users, I'll write |
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | you've built · I've just drawn · have emerged |
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | being created · are broken |
शैडोइंग तकनीक क्या है?
शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।