Shadowing Practice: Dot Plots: How to Actually See What Your Users Are Doing - Learn English Speaking with Video
Ders oluşturuluyor...
1
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.
2
In my last video, I talked about cohort retention curves
3
and how you can use those to separate groups of users and track what they do over time throughout using your product.
4
And I think that's the best tool that you've got to figure out if people keep using your product.
5
But what you don't know is how are they using your product?
6
How are they interacting?
7
What features are they using?
8
What's the frequency of use?
9
What's the pacing of how they use the product?
10
And most founders just like ignore this.
11
But I think it's the most important signal to figure out if you've built something that people want.
12
So you want to be able to look at what individual users are doing.
13
But that's a lot, right?
14
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.
15
So with aggregate data, the graphs that we're all used to talking about, things like DAUs or MAUs,
16
these lump all of your users together and you can't really get a sense of what any individual user is doing.
17
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.
18
So today I want to tell you about a tool
19
that we came to in my startup
20
that allows you to understand what's going on with individual users
21
while also giving you a big picture view of how your entire product is performing.
22
And we call it the dot plot.
23
So let me show you what a dot plot looks like.
24
Based on the name you can figure out it probably involves dots.
25
What you basically do is just make a two-dimensional grid like a spreadsheet
26
where there are a bunch of columns and a bunch of rows.
27
Each row represents one individual user.
28
If I'm one of the users, I'll write my name here, Dave, I'm one of the users.
29
And every other user of your product gets their own row.
30
And then every column represents a time period.
31
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.
32
So let's just draw in the days.
33
I'll just do Monday, Tuesday, Wednesday, Thursday, Friday.
34
And you can make this as big or as small as you want.
35
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.
36
And then the idea, it's called a dot plot, is you put some dots in each of the cells.
37
You want to pick an event
38
that your user does in the process of using your product that you think represents value in the product.
39
Maybe it's
40
It's sharing a photo if you're building a photo app
41
or listening to a song if you're building a music app
42
or processing an invoice if you're building a B2B invoice processing product.
43
And you can just put a dot for each day that each user uses the product.
44
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.
45
Let's pick the event that we're going to chart here being listen to a song.
46
So anytime a user listens to a song during a day, we're going to put a dot.
47
So for me, let's say I listen to Spotify song on Monday and Tuesday and not on Wednesday, but Thursday and Friday again.
48
And then maybe again on Monday and Wednesday.
49
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.
50
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.
51
And what you'll eventually start seeing is a pretty high density visualization of individual users and their usage over time.
52
What's really cool about this is it lets you figure out patterns
53
that you probably would not have seen with your human brain just looking at aggregate charts or looking at individual user logs.
54
Okay, so let's look at this example I've just drawn.
55
For our Spotify app, what do we see?
56
What patterns have emerged now that we can see individual users and their own behavior?
57
Well, one thing I see is it seems like there's a set of people who use the product on weekdays, right?
58
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.
59
And there's a couple of users who seem to kind of only use it on the weekends.
60
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.
61
Do I want the weekday work time listeners or do I want the weekend users?
62
We would have no idea about this if we didn't have a dot plot visualization like this.
63
Another thing I can see is a measure of retention.
64
Like, do we see a lot of users like user 4
65
that try the app on one day and then never come back?
66
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.
67
As you get more sophisticated with dot plots, you can make them as intricate as you want.
68
At Bump, we had different symbols that we would put into these cells.
69
So we knew whether you shared your contact information using Bump or if you shared a photo.
70
And it gives you a lot more granularity and you can kind of go as deep as you want on this.
71
This idea of dot plots might be familiar to some of you.
72
You've probably seen it at the top of GitHub pages.
73
This is basically what a GitHub graph looks like.
74
They've just wrapped the days around per week.
75
Another thing you can do is instead of just tracking user actions, you can track user state.
76
So was this user using an iPhone or an Android phone?
77
Were they on the web?
78
Was this user coming from the United States or a different country?
79
Sometimes you have demographic information about your users.
80
Is this a user that makes a lot of money?
81
Or is this a college kid that you just got on Reddit or something?
82
You can encode those states with other symbols or shading the cells, different colors.
83
You can write things over here.
84
So like I might say, you know, this is a iOS user and this is an iOS user, but these ones are Androids.
85
And another thing you can do then is sort your rows based on whatever attributes you want to sort them by.
86
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.
87
So let's resort so we only see people that have rings around their first day.
88
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
89
that you would never have figured out on your own a priori.
90
This is actually an idea that I remember hearing about 10 years ago from Max Levchin, one of the founders of PayPal.
91
They had a big fraud problem at PayPal when they first launched, but they didn't know the patterns to look for.
92
So what they did instead is build a visualization, a graph of all the transactions that were happening on PayPal.
93
And they just had humans sit and stare at screens of these drawings and graphs.
94
And while the humans didn't know what exactly was going on, they were able to look at the screen and say,
95
that thing happening there, that's different and probably fraud.
96
And then they would go and dig into that.
97
it's kind of the same idea with dot plots you can look at these these charts
98
and figure out huh there's something going on with users i see this pattern emerging
99
and then you can go dig into it a lot deeper
100
so to illustrate the point i was talking about where dot
101
plots give you a lot more granularity about what's going on
102
with the users let's draw the dau graph for these users
103
so what you would have seen had you only been looking
104
at your dau graph i'll just draw it on top of here to illustrate
105
so again like imagine each of these days is the same
106
day above the da you graph here looks like this on
107
day one it's two on day two it's three on day three it's two 2,
108
2, 2, 2, 2, 1, 0, 1.
109
So if you were just looking at DAUs, this is the graph you would see.
110
And it really doesn't tell you all that much.
111
It basically tells you, yeah, we're not growing.
112
We have some users.
113
Instead, looking at the dot plot, we have a much richer understanding of our users.
114
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,
115
probably they're doing it at the office
116
or in some other place where they can listen to music every single day of the work week.
117
And again, you can go a lot deeper on this.
118
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.
119
Let's say if a user uses search in Spotify, we'll put a little S next to it.
120
Or if they use maybe a playlist, they join a public playlist, let's say we could put a P there.
121
And you might start to see patterns where specific features maybe drive behaviors in the product that you actually want.
122
Let's just say for the sake of argument that we see this one user here that joined a public playlist.
123
They then have a string of many, many consecutive days of using the product.
124
We could then infer like, oh, maybe the playlist feature is really causal to having people be really active in our product.
125
This is the sort of stuff that you can learn with dot plots.
126
So what's really great for most founders, you have a very small number of users at the beginning.
127
And so you can literally look at every single user of your product on every single day they've ever used it.
128
And it all fits on one screen on your monitor.
129
That's really great.
130
but it actually does scale to when you have thousands or millions or billions of users.
131
This is a tool that we used at Google Photos when we had well more than a billion users.
132
And the idea is you can just choose to sample your users and represent them on a dot plot however you want.
133
So we would have days where we print out dozens of
134
these pieces of paper with dot plots on them for different samples of our user base.
135
I would print out a piece of paper and hand one of our team members, like, here's the iOS users in France.
136
I want you to understand what they're doing.
137
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.
138
Let's see what they're up to.
139
And we would have days where we just sit in the office
140
and look at these dot plots and try to draw conclusions about what's going on with our users.
141
So you might be thinking to yourself, this is cool, Dave, but we're a B2B product
142
and we just sell seats to businesses and pay for it
143
and so i that's all that matters right turns out
144
that dot plots could be really useful to you too let
145
me give you a specific example i worked with a company in the most recent yc batch
146
that had a very name brand customer that signed up
147
and bought their product i think it was like a 80
148
000 a year contract they onboarded the company the company said they wanted 10 seats
149
and later the company chert let me show you what they
150
could have figured out had they been using dot plots So this is what it actually looked like.
151
The company bought 10 seats, but only three seats ever activated.
152
Only three of those people ever tried the product.
153
And if you look at their usage, they weren't getting a lot of value from it.
154
Nobody used it more than two days per week.
155
And it looks like pretty sporadic usage.
156
And it turns out what happened is the company was in the state.
157
The champion had gotten excited about this product and bought it.
158
And then the champion left the company.
159
And as soon as the champion left, a new person came in and they said, why are we using this software?
160
We're going to churn.
161
And so they opted out of a renewal clause at the last moment.
162
The company could have known that this contract was in jeopardy by looking at the dot plot.
163
So there's a few ways you can misuse dot plots.
164
The number one thing is to just chart the wrong event.
165
A lot of founders might want to populate their dot plot with the easiest way to populate
166
so it feels good and you see a lot of dots.
167
Maybe you'll pick like opened the app or signed into the product.
168
Those are pretty bad events to choose because they don't really measure whether the user is getting real value.
169
So I suggest you pick something that actually represents value being created for the user.
170
Listen to a song, shared a photo, something like that that's a real event.
171
The other mistake you can make is picking a time period that's too wide.
172
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
173
or maybe even like sub-day granularity.
174
So I would go so far as to say until you have hundreds of users, the dot plot could be your only dashboard.
175
What's great about dot plots is they're just a logs visualization tool.
176
There's no fancy computations happening here.
177
You basically just need to parse your logs and put them into a 2D grid.
178
This is a thing that modern AI coding tools can whip up in like 10 minutes.
179
These are best used in conjunction with cohort retention curves.
180
Cohort retention curves teach you in aggregate whether groups of users that you acquire stick with you over time.
181
That's very important.
182
You should definitely be measuring that.
183
But the dot plot shows you how those users are actually using your product.
184
And they give you the color to go ask the right questions of your users, to go build the right features,
185
to fix things that are broken in your product that you would never learn by looking at aggregate metrics.
186
So cohort retention curves and dot plots are in my experience two of the most important tools
187
that you've got to understand your users.
188
Good luck!
📺 Aynı Kanal
C11
Garry Tan: Own Your Intelligence
Business EnglishUniversity StudentsWorking Adults
B25
How Pitching Investors is Different Than Pitching Customers - Michael Seibel
Business EnglishInterview EnglishUniversity Students
C14
Jensen Huang: The Mindset That Built NVIDIA
Business EnglishUniversity StudentsWorking Adults
B2How to Get Your First 10 Customers
Business EnglishWorking Adults
C1How to Build an AI-Native Services Company
Business EnglishUniversity StudentsWorking Adults
✨ Video Öner
B21
157 cümleler
The Weird Design Playbook of 6 App Outliers
Business EnglishUniversity StudentsWorking Adults
B21
364 cümleler
Claude Cowork Tutorial: Build Monthly Reports in Minutes
Business EnglishUniversity StudentsWorking Adults
B14
83 cümleler
What are KPIs and Metrics? | Data Fundamental for Beginners
Business EnglishUniversity StudentsWorking Adults
B2271 cümleler
I Challenge You: Commit to YouTube for 3 months (exact plan inside!)
Business EnglishUniversity StudentsWorking Adults
B21
49 cümleler
Learn UX Storyboarding in 5 Minutes: w/ Steps and Examples | UXtweak
Business EnglishUniversity StudentsWorking Adults
B280 cümleler
How to Build An Investor Pitch Deck For Y Combinator
Business EnglishInterview EnglishUniversity Students
B21
119 cümleler
Basic Opt Flw regimes
Business EnglishUniversity StudentsWorking Adults
B21
583 cümleler
Episode 531: Shallow feedback and my coworker is a huge sloperator
Business EnglishUniversity StudentsWorking Adults
B21
220 cümleler
How Modern Data Teams Work (Engineer, Analyst, Scientist, Architect, ML)
Business EnglishUniversity StudentsWorking Adults
B21
282 cümleler
What Stanford students actually use ChatGPT for
Business EnglishUniversity StudentsWorking Adults
B22
286 cümleler
I Tested OpenAI's Dots vs. Meta's Muse. What You Need to Know.
Business EnglishUniversity StudentsWorking Adults
B2171 cümleler
I built an app and no one's using it
Business EnglishUniversity StudentsWorking Adults
Bu dersin kelimeleri ve konuşma notları
Bu videoda gölgeleme çalışması için 187 cümle ve 2680 kelime var. Konuşma bölümü 13:31 sürüyor. Konuşmacı hızlı konuşuyor, dakikada yaklaşık 198 kelime; bu yüzden birbirine bağlanan ve zayıflayan sesler duyacaksınız. Kelimelerin %86’i İngilizcede en sık kullanılan 3.000 kelime arasında; geri kalanına başlamadan önce bakmakta fayda var.
Bu videodaki önemli kelimeler
Videodan öğrenmeye değer 15 kelime, telaffuzu ve anlamıyla:
| Kelime | Telaffuz | Anlam |
|---|---|---|
| graph isim | /ɡɹæf/ | grafik |
| founder isim | /ˈfaʊ̯n.dəː/ | kurucu |
| aggregate isim | /ˈæɡ.ɹɪ.ɡət/ | yığın, kitle |
| log isim | /lɔɡ/ | kütük |
| symbol isim | /ˈsɪmbəl/ | simge, sembol |
| curve isim | /kɚv/ | kıvrım |
| playlist isim | /ˈpleɪˌlɪst/ | çalma listesi, oynatma listesi |
| sake isim | /ˈseɪ̯k/ | gaye, kast |
| fraud isim | /ˈfɹɔːd/ | dolandırıcılık |
| grid isim | /ɡɹɪd/ | şebeke |
| ignore fiil | /ɪɡˈnɔː/ | görmezden gelmek |
| conclusion isim | /kənˈkluːʒən/ | son |
| string isim | /stɹɪŋ/ | ip, tel |
| monitor isim | /ˈmɑ.nɪ.tɚ/ | ekran |
| resort isim | /ɹɪˈzɔɹt/ | tatil köyü |
Duyacağınız deyimsel fiiller
| Kelime | Anlam |
|---|---|
| figure out fiil | açığa çıkarmak |
Bu videodaki dil bilgisi
Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:
| Yapı | Videoda |
|---|---|
| Koşul cümleleri if + cümle, will/would + fiil — bir koşul ve sonucu | If I'm one of the users, I'll write |
| Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem | you've built · I've just drawn · have emerged |
| Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli | being created · are broken |
Dikkat edilecek telaffuzlar
Konuşmacı we're, I'll, they're gibi kısaltılmış biçimleri 44 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “sh” ve “zh” sesleri: retention /ɹɪˈtɛnʃən/, conclusion /kənˈkluːʒən/, transaction /tɹænˈzækʃən/, shade /ʃeɪd/, observation /ˌɑbzɚˈveɪʃn̩/
- Uzun kelimeler — vurguyu doğru yere koyun: consecutive /kənˈsɛkjʊtɪv/, dimensional /daɪˈmɛn.ʃə.nəl/, demographic /ˌdɛm.əˈɡɹæf.ɪk/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, granularity /ˌɡɹæn.jəˈlɝ.ə.ti/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- 0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; graph, founder, aggregate gibi kelimelere özellikle dikkat edin.
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.