The worst YouTube advice is to tell someone that mass production is the only way to succeed.
You don't need to create 50 thumbnails per video.
You don't need to come up with 100 video ideas before picking which one to go for.
You don't need to write all those title variants to ge your video.
All you need to do is spend some time analyzing what worked and figure out how to apply this to your own content.
All this mass production advice assumes that everybody has an unlimited amount of time and resources to test things.
All this mass production advice assumes that the data you get back from those tests comes without any bias.
Here's what I do with most top creators.
We pick 1 thumbnail, and we've got one backup. We may even create the backup only when the video underperforms.
We put our time towards figuring out a format that we can replicate multiple times, so 1 ideation session can turn into your next 12 ideas.
And we pick one title, often based on a previous upload.
Just because you can create variants doesn't mean it's a good use of your time. You're better off spending time learning what works rather than constantly testing and never focusing on getting practical insights from those tests.
More isn't always better.
You don't need to create 50 thumbnails per video.
You don't need to come up with 100 video ideas before picking which one to go for.
You don't need to write all those title variants to ge your video.
All you need to do is spend some time analyzing what worked and figure out how to apply this to your own content.
All this mass production advice assumes that everybody has an unlimited amount of time and resources to test things.
All this mass production advice assumes that the data you get back from those tests comes without any bias.
Here's what I do with most top creators.
We pick 1 thumbnail, and we've got one backup. We may even create the backup only when the video underperforms.
We put our time towards figuring out a format that we can replicate multiple times, so 1 ideation session can turn into your next 12 ideas.
And we pick one title, often based on a previous upload.
Just because you can create variants doesn't mean it's a good use of your time. You're better off spending time learning what works rather than constantly testing and never focusing on getting practical insights from those tests.
More isn't always better.
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They're willing to pay for advice, so we're giving it for free.
A few hours ago, I was tagged in a message asking for advice about their YouTube channel. The hosts of a YouTube channel called “The Adventure Crews” would go to ask for more information about how to solve their , we took a look at the message they sent. The first thing that the host shared was some data from their last video.
73% of their viewers are 45+
Roughly 75% male / 25% female
7:01 average view duration on their latest video
32.7% average percentage viewed
But there's something else they share right after this, which is the problem they are struggling to solve.
They say “We're losing more than HALF of our viewers in the first 60 seconds. Our retention drops from roughly 93% to 49% in that first minute. And here's what's interesting: Once viewers make it through our intro, the retention curve levels out pretty consistently across our videos. We just need to figure out how to get more of them past that first minute.”
So, let's solve this for them.
First, let's look at the data they shared and add some nuance to understand which metrics are actually useful.
The first piece of data is their demographics. Knowing that the majority of their audience is over 45 years old, with a split favoring males, is useful information when figuring out why certain things are working and why not. However, it doesn't necessarily solve our problem, so we're putting this information to the side for the actual analysis.
Next, we have their average view duration and average percentage viewed. Their AVD and APV. The problem with these metrics is that they're heavily influenced by selection bias. We don't know who YouTube showed the video to, whether these were colder audiences, or how the audience differs from their other videos. On top of that, AVD is influenced by video length, making comparisons even more difficult.
So while these numbers tell us something about viewer behavior, they don't necessarily tell us whether the video performed well or not. There are simply too many factors influencing them, so let's put these metrics to the side as well.
And this brings us to their actual problem: losing more than half their viewers in the first 60 seconds.
The same reasoning applies here. What matters isn't the actual percentage, but the behavior of the retention chart, which isn't something they shared.
I don't really care what percentage we start with and end up with after 60 seconds. What I care about is: what were the moments where we lost people, and what were the moments where people kept on watching?
Now, the intro isn't just about engagement. It's also the second decision point viewers use to decide whether they want to watch the video.
In this case, it seems like YouTube is serving the video to people who are likely to click, but many of them are making their decision during the intro rather than before clicking. This could explain why the initial drop looks so aggressive.
Later in their message, they ask whether the issue is storytelling, pacing, the way they're opening the video, or something they're completely overlooking.
So, after looking at the first 60 seconds of their video, here are the five things I've noticed.
1. Make sure the intro follows a logical structure.
Right now, we're giving the viewer a lot of information, but it's not presented in a logical order. We're jumping between teaser clips, context, narration, and different statements without really connecting them. This makes it difficult for the viewer to understand what we're trying to communicate. I would focus on restructuring the intro so that every piece of information builds on what we've previously established.
2. Make sure the visuals and narration support each other.
There are several moments where the visuals don't really connect with what we're saying. For example, we're showing lobster fishing while talking about spending 48 hours on the island. We're also including statements from locals that don't necessarily add anything to the context we've already provided. I would make sure that every visual and statement has a clear purpose and helps the viewer understand the story.
3. Start the story closer to where the actual experience begins.
After the intro, we're spending quite some time sitting in a car, talking about the island and explaining how we're getting there. The problem is that the story starts way too early. I would consider opening the actual video when we're standing in front of the boat or arriving at the isolated island. This immediately puts the viewer closer to the experience we're promising, rather than making them sit through information that isn't necessarily important.
4. Be more selective about the information we're giving the viewer.
We're spending quite some time explaining how to get to the island, the different transportation options, and when the ferry operates. But we should ask ourselves whether the viewer actually needs this information to follow the story. Just because something happened during the experience doesn't mean we need to include it. I would focus on information that helps establish the situation.
5. Create more tension and make the tone match the experience.
I think one of the biggest problems with the first minute is that everything feels extremely happy. We're presenting this remote, isolated island as somewhere difficult to live, but the music, narration, and overall presentation don't really communicate that. Everything feels quite relaxed. I would focus on creating more tension, emphasizing the isolation, and making the viewer curious about what we're going to discover. We need to establish why this experience is interesting and give the viewer something to look forward to.
A few hours ago, I was tagged in a message asking for advice about their YouTube channel. The hosts of a YouTube channel called “The Adventure Crews” would go to ask for more information about how to solve their , we took a look at the message they sent. The first thing that the host shared was some data from their last video.
73% of their viewers are 45+
Roughly 75% male / 25% female
7:01 average view duration on their latest video
32.7% average percentage viewed
But there's something else they share right after this, which is the problem they are struggling to solve.
They say “We're losing more than HALF of our viewers in the first 60 seconds. Our retention drops from roughly 93% to 49% in that first minute. And here's what's interesting: Once viewers make it through our intro, the retention curve levels out pretty consistently across our videos. We just need to figure out how to get more of them past that first minute.”
So, let's solve this for them.
First, let's look at the data they shared and add some nuance to understand which metrics are actually useful.
The first piece of data is their demographics. Knowing that the majority of their audience is over 45 years old, with a split favoring males, is useful information when figuring out why certain things are working and why not. However, it doesn't necessarily solve our problem, so we're putting this information to the side for the actual analysis.
Next, we have their average view duration and average percentage viewed. Their AVD and APV. The problem with these metrics is that they're heavily influenced by selection bias. We don't know who YouTube showed the video to, whether these were colder audiences, or how the audience differs from their other videos. On top of that, AVD is influenced by video length, making comparisons even more difficult.
So while these numbers tell us something about viewer behavior, they don't necessarily tell us whether the video performed well or not. There are simply too many factors influencing them, so let's put these metrics to the side as well.
And this brings us to their actual problem: losing more than half their viewers in the first 60 seconds.
The same reasoning applies here. What matters isn't the actual percentage, but the behavior of the retention chart, which isn't something they shared.
I don't really care what percentage we start with and end up with after 60 seconds. What I care about is: what were the moments where we lost people, and what were the moments where people kept on watching?
Now, the intro isn't just about engagement. It's also the second decision point viewers use to decide whether they want to watch the video.
In this case, it seems like YouTube is serving the video to people who are likely to click, but many of them are making their decision during the intro rather than before clicking. This could explain why the initial drop looks so aggressive.
Later in their message, they ask whether the issue is storytelling, pacing, the way they're opening the video, or something they're completely overlooking.
So, after looking at the first 60 seconds of their video, here are the five things I've noticed.
1. Make sure the intro follows a logical structure.
Right now, we're giving the viewer a lot of information, but it's not presented in a logical order. We're jumping between teaser clips, context, narration, and different statements without really connecting them. This makes it difficult for the viewer to understand what we're trying to communicate. I would focus on restructuring the intro so that every piece of information builds on what we've previously established.
2. Make sure the visuals and narration support each other.
There are several moments where the visuals don't really connect with what we're saying. For example, we're showing lobster fishing while talking about spending 48 hours on the island. We're also including statements from locals that don't necessarily add anything to the context we've already provided. I would make sure that every visual and statement has a clear purpose and helps the viewer understand the story.
3. Start the story closer to where the actual experience begins.
After the intro, we're spending quite some time sitting in a car, talking about the island and explaining how we're getting there. The problem is that the story starts way too early. I would consider opening the actual video when we're standing in front of the boat or arriving at the isolated island. This immediately puts the viewer closer to the experience we're promising, rather than making them sit through information that isn't necessarily important.
4. Be more selective about the information we're giving the viewer.
We're spending quite some time explaining how to get to the island, the different transportation options, and when the ferry operates. But we should ask ourselves whether the viewer actually needs this information to follow the story. Just because something happened during the experience doesn't mean we need to include it. I would focus on information that helps establish the situation.
5. Create more tension and make the tone match the experience.
I think one of the biggest problems with the first minute is that everything feels extremely happy. We're presenting this remote, isolated island as somewhere difficult to live, but the music, narration, and overall presentation don't really communicate that. Everything feels quite relaxed. I would focus on creating more tension, emphasizing the isolation, and making the viewer curious about what we're going to discover. We need to establish why this experience is interesting and give the viewer something to look forward to.
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My 22-page in-depth public review of a MrBeast intro. x.com/i/article/2116…
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A Comprehensive Introduction To YouTube Dubbing (Including Results & First Languages)
If you want 3 to 4x the amount of views on your video, one of the easiest ways to do this is through dubbing. But there are a few things you need to look out for.
In this post, I want to give you dubbing, what it is, how it works, what pricing you should expect and the possible results you’re looking at.
What is dubbing?
First of all, let’s talk about what dubbing is.
Dubbing is the strategy where you add extra language tracks to your video that would alter the video’s audio when you’re from another country or when you prefer to watch something in a different language. Many creators gain up to 300-400% extra views after dubbing into the most important languages, so there’s a lot to gain here. (Check the image I’ve added)
But it’s not as simple as it seems.
The different types of dubbing
First, let’s talk about the different types of dubbing. I always position them under 4 different categories.
You have non-dubbed videos, which are videos without dubbing. So let’s say you speak English, everyone will see your video in this language.
You have auto-dubbed videos, which is where you use YouTube’s free auto-dubbing features to let them use AI to transform your voice into any language. So let’s say that a user only watches Spanish content, they may see your video in Spanish.
You have privatized AI dubbing. This is similar to YouTube’s auto-dubbing. However, you’re using custom models, paying a fee and having to upload individual tracks yourself.
You have human dubbing. This is where you’ll hire a studio to find voice actors to dub your videos and become your voice.
Now let’s talk about each of them, the positives and the negatives.
1. No Dubbing
The biggest advantage of non-dubbed videos is that everyone sees your videos as you intended them to be. The way you make your jokes, the balance of your tracks, the intonation of your voice and so much more. However, the biggest downside is that you’re locking yourself out of the rest of the world.
Now, most of the world speaks English fairly well, so for many creators this isn’t a problem. However, many people don’t speak English, so you’re also missing out on a huge portion of your potential audience.
So when would you even choose to go for no dubbing?
Well, this is mostly when you either want to avoid people having a different experience, or when you’re very personality-focused. Personality-driven channels don’t perform as well when dubbed as educational channels. You may only see a 30-50% increase in viewership in some cases if you’d dub, but then there’s the direct cost that comes with dubbing, and it doesn’t make sense.
So generally, I don’t advise people to dub until they’re a little bigger.
2. Auto-dubbing
Imma be completely honest, just turn that shit off. I don’t know why YouTube chose this company they’re currently working with, but it’s ass at best.
Yes, it may have some positive impact on some channels, but for the most part, the quality doesn’t just hurt the content, it also hurts your image towards a potential future audience member. The only positive thing that I can say about this is that it’s free.
Maybe by the time you see this video, it may have gotten better, but unless you’re lucky, it’s just garbage. Now, here’s the worst part.
YouTube auto-enabled this feature for your channel, so make sure to go to your channel settings and turn it off.
3. AI dubbing
Now, AI dubbing is very interesting because it uses AI models to clone your actual voice, and it’s pretty good at that. The cost is also relatively cheap, coming in at around $1.80-$2 per minute per language.
And I’ve honestly seen a lot of people have success with this. It’s a budget variant that actually does a pretty good job.
Now, most people tend to use ElevenLabs or Dittodub. Each has its own benefits, and I’d just try out which works best for you.
However, there are also a few other ones coming to the market, so just see what’s out there, but I honestly would just avoid paying more than $2 per minute per language, especially as AI models are getting cheaper and cheaper anyways.
The biggest upside is that you can quickly do a lot of languages for a fraction of the cost and reach a wider audience.
The biggest downside is that you’ll still notice tons of mistakes and mismatches with your voice, so it’s not perfect yet.
If you’re really fancy, you could even use your own models.
4. Human dubbing
Lastly, you have manual or human dubbing. This is where you work together with a studio to get an actual human to become you.
The biggest upside is that these voice actors will be better at translating the jokes and tone you’re trying to convey. And this often results in good outcomes. However, that’s also where the biggest issues come into play.
The average price for a language is about $25-$35 per minute. Some languages are higher, some are lower.
Another downside is that when you change studios, the voice also changes.
Another issue is that many of these studios aren’t publicly transparent about their pricing, so you’ll notice them trying to quote you a higher price than they charge others, so I highly encourage you to negotiate.
And lastly, the turnaround times are much slower than AI dubbing.
Currently, human dubbing is still superior to the other types, but the hassle and pricing make it often only worth it if you’re already pulling lots of views or if your revenue stream is different from AdSense.
Which languages should you dub into?
You’ve now gotten yourself an understanding of what’s possible, what fits your budget range and some of the positives and negatives behind each type of dubbing. So, what’s next?
Next are the languages to go for. There’s a big complexity in choosing what languages to go for, but I’m going to give you the typical list and order of which languages typically work well.
Two notes. First, based on the content type, etc., you may find better or worse results by language, and certain types of channels may even be able to consider countries that aren’t on this list. For example, if you play a certain sport that’s also popular in Denmark, but Danish isn’t on this list, you may still want to consider Danish.
Another note is that this list was created years ago, when I did a deep analysis into population size, English proficiency, dubbing culture, internet adoption, market size, potential earnings and so much more. These days, I use a simple tool to help me predict dubbing potential, but the findings are extremely overlapping.
So there are about 10 languages you typically go for. And this counts for Shorts and long-form.
1. Spanish
2. Indonesian
3. Arabic
4. Turkish
5. Portuguese
6. Bangla
7. Thai
8. Vietnamese
9. Hindi
10. Russian
If you want to be experimental and add extras, some people tend to go for:
11. Italian
12. German
13. Tamil
But honestly, I’d start with the first 10 languages. You could even make some of them AI, some of them human dubbing.
Important practical note!
When you add languages like Spanish, make sure you add them as Spanish, not Spanish (Mexico). It’s a common mistake people make.
Or you could do all if you want to play it safe. Or, if you’ve gone as far as picking regional accents.
Final things to keep in mind
Make sure you’re able to add custom tracks before you start paying for anything. And also note that results aren’t always guaranteed for anyone. I don’t want anybody to come after me, making it seem like I guaranteed a certain result.
And don’t forget to also update your title into these languages and even your thumbnails (if you have access to this feature).
Hope this breakdown was helpful.
If you want 3 to 4x the amount of views on your video, one of the easiest ways to do this is through dubbing. But there are a few things you need to look out for.
In this post, I want to give you dubbing, what it is, how it works, what pricing you should expect and the possible results you’re looking at.
What is dubbing?
First of all, let’s talk about what dubbing is.
Dubbing is the strategy where you add extra language tracks to your video that would alter the video’s audio when you’re from another country or when you prefer to watch something in a different language. Many creators gain up to 300-400% extra views after dubbing into the most important languages, so there’s a lot to gain here. (Check the image I’ve added)
But it’s not as simple as it seems.
The different types of dubbing
First, let’s talk about the different types of dubbing. I always position them under 4 different categories.
You have non-dubbed videos, which are videos without dubbing. So let’s say you speak English, everyone will see your video in this language.
You have auto-dubbed videos, which is where you use YouTube’s free auto-dubbing features to let them use AI to transform your voice into any language. So let’s say that a user only watches Spanish content, they may see your video in Spanish.
You have privatized AI dubbing. This is similar to YouTube’s auto-dubbing. However, you’re using custom models, paying a fee and having to upload individual tracks yourself.
You have human dubbing. This is where you’ll hire a studio to find voice actors to dub your videos and become your voice.
Now let’s talk about each of them, the positives and the negatives.
1. No Dubbing
The biggest advantage of non-dubbed videos is that everyone sees your videos as you intended them to be. The way you make your jokes, the balance of your tracks, the intonation of your voice and so much more. However, the biggest downside is that you’re locking yourself out of the rest of the world.
Now, most of the world speaks English fairly well, so for many creators this isn’t a problem. However, many people don’t speak English, so you’re also missing out on a huge portion of your potential audience.
So when would you even choose to go for no dubbing?
Well, this is mostly when you either want to avoid people having a different experience, or when you’re very personality-focused. Personality-driven channels don’t perform as well when dubbed as educational channels. You may only see a 30-50% increase in viewership in some cases if you’d dub, but then there’s the direct cost that comes with dubbing, and it doesn’t make sense.
So generally, I don’t advise people to dub until they’re a little bigger.
2. Auto-dubbing
Imma be completely honest, just turn that shit off. I don’t know why YouTube chose this company they’re currently working with, but it’s ass at best.
Yes, it may have some positive impact on some channels, but for the most part, the quality doesn’t just hurt the content, it also hurts your image towards a potential future audience member. The only positive thing that I can say about this is that it’s free.
Maybe by the time you see this video, it may have gotten better, but unless you’re lucky, it’s just garbage. Now, here’s the worst part.
YouTube auto-enabled this feature for your channel, so make sure to go to your channel settings and turn it off.
3. AI dubbing
Now, AI dubbing is very interesting because it uses AI models to clone your actual voice, and it’s pretty good at that. The cost is also relatively cheap, coming in at around $1.80-$2 per minute per language.
And I’ve honestly seen a lot of people have success with this. It’s a budget variant that actually does a pretty good job.
Now, most people tend to use ElevenLabs or Dittodub. Each has its own benefits, and I’d just try out which works best for you.
However, there are also a few other ones coming to the market, so just see what’s out there, but I honestly would just avoid paying more than $2 per minute per language, especially as AI models are getting cheaper and cheaper anyways.
The biggest upside is that you can quickly do a lot of languages for a fraction of the cost and reach a wider audience.
The biggest downside is that you’ll still notice tons of mistakes and mismatches with your voice, so it’s not perfect yet.
If you’re really fancy, you could even use your own models.
4. Human dubbing
Lastly, you have manual or human dubbing. This is where you work together with a studio to get an actual human to become you.
The biggest upside is that these voice actors will be better at translating the jokes and tone you’re trying to convey. And this often results in good outcomes. However, that’s also where the biggest issues come into play.
The average price for a language is about $25-$35 per minute. Some languages are higher, some are lower.
Another downside is that when you change studios, the voice also changes.
Another issue is that many of these studios aren’t publicly transparent about their pricing, so you’ll notice them trying to quote you a higher price than they charge others, so I highly encourage you to negotiate.
And lastly, the turnaround times are much slower than AI dubbing.
Currently, human dubbing is still superior to the other types, but the hassle and pricing make it often only worth it if you’re already pulling lots of views or if your revenue stream is different from AdSense.
Which languages should you dub into?
You’ve now gotten yourself an understanding of what’s possible, what fits your budget range and some of the positives and negatives behind each type of dubbing. So, what’s next?
Next are the languages to go for. There’s a big complexity in choosing what languages to go for, but I’m going to give you the typical list and order of which languages typically work well.
Two notes. First, based on the content type, etc., you may find better or worse results by language, and certain types of channels may even be able to consider countries that aren’t on this list. For example, if you play a certain sport that’s also popular in Denmark, but Danish isn’t on this list, you may still want to consider Danish.
Another note is that this list was created years ago, when I did a deep analysis into population size, English proficiency, dubbing culture, internet adoption, market size, potential earnings and so much more. These days, I use a simple tool to help me predict dubbing potential, but the findings are extremely overlapping.
So there are about 10 languages you typically go for. And this counts for Shorts and long-form.
1. Spanish
2. Indonesian
3. Arabic
4. Turkish
5. Portuguese
6. Bangla
7. Thai
8. Vietnamese
9. Hindi
10. Russian
If you want to be experimental and add extras, some people tend to go for:
11. Italian
12. German
13. Tamil
But honestly, I’d start with the first 10 languages. You could even make some of them AI, some of them human dubbing.
Important practical note!
When you add languages like Spanish, make sure you add them as Spanish, not Spanish (Mexico). It’s a common mistake people make.
Or you could do all if you want to play it safe. Or, if you’ve gone as far as picking regional accents.
Final things to keep in mind
Make sure you’re able to add custom tracks before you start paying for anything. And also note that results aren’t always guaranteed for anyone. I don’t want anybody to come after me, making it seem like I guaranteed a certain result.
And don’t forget to also update your title into these languages and even your thumbnails (if you have access to this feature).
Hope this breakdown was helpful.
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🚨 YouTube is finally changing its termination policy! (+nuance)
As of today, YouTube has decided to start experimenting with a new way of keeping a person’s YouTube channel alive. They’ve started testing a new option for creators facing immediate termination due to certain spam lf-correct and save their channel.
Now, it's important to state that they're specifically targeting channels that are violating the spam policies. We're not talking about channels that violate other types of policies. And if you're not 100% sure of what spam policies are, here are 3 of the most common practices that violate this policy:
1. Automated or synthetic mass-production: Using automated tools or AI to churn out high volumes of similar content with minimal changes. Example: Channels that use the exact same background music and repetitive AI-generated imagery across many videos, with each video reading out an AI-generated script.
2. Scraped content: Re-posting material from other websites or platforms, or other videos, without adding anything of your own, such as original commentary, educational value, or unique editing. Example: A channel that takes popular clips and re-uploads them in their entirety without any transformative editing, reaction, or educational breakdown.
3. Malicious clickbait: Using maliciously misleading titles, thumbnails, descriptions, or imagery to trick users into clicking on a video that does not deliver what was promised. Example: Uploading a video for which the title offers a full sports match, but the video shows only a clip.
These are direct quotes from YouTube’s own policy page.
How it works is as follows:
If you’re part of the experiment (not everyone currently is), and your channel is up for termination, you’ll have a 30-day window to rectify the situation and sort out your channel. You’ll be able to do this through one of YouTube’s 3 options:
Option 1: Fix or remove specific videos. You’ll be able to review your content and use YouTube Studio tools (like the video editor) to edit or delete individual videos that violate their policies.
Option 2: Remove all videos. Delete all uploaded videos on your channel to immediately unlock it.
Option 3: Submit an appeal. If you believe a mistake was made and your channel complies with their policies, you can file an appeal.
Once the violative content is removed and you pass a review, your channel will be unlocked. If, for some reason, you get rejected, you can still use the manual “Remove All Videos” option as long as you are still within the 30-day window.
From my perspective, I believe this experiment is great and helps protect genuine creators, even if I’m already seeing the many ways through which people will abuse this system, especially to build a channel to high subs and then just sell that channel. It’s against the terms of service, but I’m already smelling the abusers from a mile away. Regardless, this experiment is a fantastic step in the right direction for protecting creators.
As of today, YouTube has decided to start experimenting with a new way of keeping a person’s YouTube channel alive. They’ve started testing a new option for creators facing immediate termination due to certain spam lf-correct and save their channel.
Now, it's important to state that they're specifically targeting channels that are violating the spam policies. We're not talking about channels that violate other types of policies. And if you're not 100% sure of what spam policies are, here are 3 of the most common practices that violate this policy:
1. Automated or synthetic mass-production: Using automated tools or AI to churn out high volumes of similar content with minimal changes. Example: Channels that use the exact same background music and repetitive AI-generated imagery across many videos, with each video reading out an AI-generated script.
2. Scraped content: Re-posting material from other websites or platforms, or other videos, without adding anything of your own, such as original commentary, educational value, or unique editing. Example: A channel that takes popular clips and re-uploads them in their entirety without any transformative editing, reaction, or educational breakdown.
3. Malicious clickbait: Using maliciously misleading titles, thumbnails, descriptions, or imagery to trick users into clicking on a video that does not deliver what was promised. Example: Uploading a video for which the title offers a full sports match, but the video shows only a clip.
These are direct quotes from YouTube’s own policy page.
How it works is as follows:
If you’re part of the experiment (not everyone currently is), and your channel is up for termination, you’ll have a 30-day window to rectify the situation and sort out your channel. You’ll be able to do this through one of YouTube’s 3 options:
Option 1: Fix or remove specific videos. You’ll be able to review your content and use YouTube Studio tools (like the video editor) to edit or delete individual videos that violate their policies.
Option 2: Remove all videos. Delete all uploaded videos on your channel to immediately unlock it.
Option 3: Submit an appeal. If you believe a mistake was made and your channel complies with their policies, you can file an appeal.
Once the violative content is removed and you pass a review, your channel will be unlocked. If, for some reason, you get rejected, you can still use the manual “Remove All Videos” option as long as you are still within the 30-day window.
From my perspective, I believe this experiment is great and helps protect genuine creators, even if I’m already seeing the many ways through which people will abuse this system, especially to build a channel to high subs and then just sell that channel. It’s against the terms of service, but I’m already smelling the abusers from a mile away. Regardless, this experiment is a fantastic step in the right direction for protecting creators.
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I disagree. Let me add some important nuance to this post.
If you're trying to figure out whether your video has pacing issues, don't look for individual drop-off moments in your retention chart.
Drop-off moments typically indicate that a significant number of viewers are losing interest at a particular point in the video. However, pacing issues don't typically cause viewers to lose interest at the exact same moment.
Instead, if you want to identify pacing issues, you should be looking at the broader patterns in your retention chart. You're not looking for a sudden drop, you're looking for a gradual decline in viewer retention over a longer period of time.
This may be seen as overly critical. But it's important that nuance is spread, otherwise people think they're just going to find all of their issues within a small little drop in their retention charts.
Some insights in pacing issues
Pacing issues often lead to boredom. And, if you're wondering how to solve pacing issue, it typically comes from a lack of variety, too much repetition, or not enough new relevant information being introduced.
If you're trying to figure out whether your video has pacing issues, don't look for individual drop-off moments in your retention chart.
Drop-off moments typically indicate that a significant number of viewers are losing interest at a particular point in the video. However, pacing issues don't typically cause viewers to lose interest at the exact same moment.
Instead, if you want to identify pacing issues, you should be looking at the broader patterns in your retention chart. You're not looking for a sudden drop, you're looking for a gradual decline in viewer retention over a longer period of time.
This may be seen as overly critical. But it's important that nuance is spread, otherwise people think they're just going to find all of their issues within a small little drop in their retention charts.
Some insights in pacing issues
Pacing issues often lead to boredom. And, if you're wondering how to solve pacing issue, it typically comes from a lack of variety, too much repetition, or not enough new relevant information being introduced.
use your audience retention graph in YouTube Studio to pinpoint drop-off moments and adjust your pacing for future uploads
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YouTube just released a 43-minute video discussing Dynamic Thumbnails, A/B testing, and YouTube’s algorithm. Here are the 7 most interesting insights so you don't need to watch it yourself:
1. Thumbnail A/B Testing
YouTube doubles down on not showing CTR in A/B tests, as they be optimizing for the click, as it may hurt the overall watch experience of the video.
2. Shorts A/B Testing
YouTube is going to allow us to pick 2 to 3 Shorts to test against each other. They will then show each of them to a different segment of the audience. You will then get retention data like watch time, but also the retention graphs for those videos. From a past interview, we already learned that you'll have up to 7 days to select which version you want. (Even though this wasn't mentioned in this interview, I wanted to add this here because it's an interesting insight that I don't think a lot of people are aware of.)
3. Dynamic Thumbnails
When touching on dynamic thumbnails, Todd highlights that they are designing their system to dynamically adjust who and how many people get to see which thumbnail, which is different from the current thumbnail A/B testing (where it's pretty much an even split randomly across who sees it).
He says that the way it works is that there will be some randomness introduced to make sure they learn new things, but ultimately, they want to start shifting traffic towards the direction that is working, or, in this case, the thumbnail that is working.
If a particular thumbnail works well, YouTube is going to put more impressions towards that thumbnail, and by doing so, YouTube reduces the risk of messing up a thumbnail test. Over time, the test will get better at learning from failures and reducing impressions on thumbnails that fail by leaning more towards what's already working.
On top of this, dynamic thumbnails will also consider audience segments and see which thumbnails work better for which segments. They will use factors such as location and whether somebody has watched the channel before. These factors are something that Todd refers to as segment features.
But the conversation also kind of hinted towards the fact that it's not as rigorous as we would want it to be (for example, segment features it's based on interest). It seems to be hinting that most of the factors are going to be towards the more measurable things that YouTube already has.
4. Back Catalog Optimization
In the interview, they briefly touch on back catalog optimization, but they don't go too deep into this one. We already know from a previous announcement that this has everything to do with their Ask Studio features, which allow creators to have AI involved in optimizing their back catalog for them by creating new thumbnails, etc.
5. Hyperpersonalization
In the interview, they touch on another thing that they also touched on before: YouTube is actually becoming a hyper-personalized platform where different things are weighted differently for different people in the algorithm.
What this means in simple terms is that, for one viewer, it may be more important how clickable a video is, while for another, it's much more important how satisfying a video is. This is something that YouTube has recently been pushing a lot more in their interviews.
To translate this even further, this means that there's no single unifying metric that people should be looking at to understand what worked. Other than views ofcourse.
6. Custom Feeds
Later in the video, Todd talks about custom feeds and how the algorithm will work for those custom feeds. As we currently already know a lot about how the home feed works and the other recommendations, he says that it uses similar technology to what we use for the regular home feed, but it's a lot more language-based.
What this means is that when a user sets up the custom feeds, they type out or use their voice to say what they're looking for, and it can be pretty abstract. The algorithm will do an interpretation by using language modeling to understand and generalize into a vibe of what the person may actually be looking for.
It's important to know here (and he mentions this as well) that this isn't just strictly keyword matching, but it is more about the interpretation of what the user is looking for.
And if the user wants to see more popular stuff in their custom feeds, they can highlight this one, but it will also make it easier to match viewers with less popular stuff.
7. New Creator Discovery
At some point, the conversation shifts towards how new creators can get discovered today. The first thing Todd highlights is that new creators should stop focusing on metrics, and they should focus on finding purpose and figuring out what kind of content they want to make.
This hints towards the idea that metrics in Studio are not as valuable once you start out as a creator, which I do agree with.
Then Todd talks about discoverability for new creators. He says that parts of the recommendation system are looking for videos that they don't know much about because maybe the channel they're uploaded to has zero subscribers. There's not really anybody who has watched the channel before. And they know that.
In order to give these videos an on-ramp to success on YouTube, they need to give these videos a chance. They have something called exploration.
An exploration is when viewers show up on the home feed. Every once in a while, they're going to show that person a video that YouTube themselves don't really know much about. Then, when this person likes the video, it's going to show it to more people like this person. This is how on-ramps work.
YouTube is always trying to balance exploring new stuff that they don't know much about and serving stuff that has already been successful for years.
Now, this breakdown was an interpretation mixed with exact things that they have referred to, but I wanted to highlight this one, as there were some interesting things in this video. I wanted to keep everybody up to date with what's going on.
1. Thumbnail A/B Testing
YouTube doubles down on not showing CTR in A/B tests, as they be optimizing for the click, as it may hurt the overall watch experience of the video.
2. Shorts A/B Testing
YouTube is going to allow us to pick 2 to 3 Shorts to test against each other. They will then show each of them to a different segment of the audience. You will then get retention data like watch time, but also the retention graphs for those videos. From a past interview, we already learned that you'll have up to 7 days to select which version you want. (Even though this wasn't mentioned in this interview, I wanted to add this here because it's an interesting insight that I don't think a lot of people are aware of.)
3. Dynamic Thumbnails
When touching on dynamic thumbnails, Todd highlights that they are designing their system to dynamically adjust who and how many people get to see which thumbnail, which is different from the current thumbnail A/B testing (where it's pretty much an even split randomly across who sees it).
He says that the way it works is that there will be some randomness introduced to make sure they learn new things, but ultimately, they want to start shifting traffic towards the direction that is working, or, in this case, the thumbnail that is working.
If a particular thumbnail works well, YouTube is going to put more impressions towards that thumbnail, and by doing so, YouTube reduces the risk of messing up a thumbnail test. Over time, the test will get better at learning from failures and reducing impressions on thumbnails that fail by leaning more towards what's already working.
On top of this, dynamic thumbnails will also consider audience segments and see which thumbnails work better for which segments. They will use factors such as location and whether somebody has watched the channel before. These factors are something that Todd refers to as segment features.
But the conversation also kind of hinted towards the fact that it's not as rigorous as we would want it to be (for example, segment features it's based on interest). It seems to be hinting that most of the factors are going to be towards the more measurable things that YouTube already has.
4. Back Catalog Optimization
In the interview, they briefly touch on back catalog optimization, but they don't go too deep into this one. We already know from a previous announcement that this has everything to do with their Ask Studio features, which allow creators to have AI involved in optimizing their back catalog for them by creating new thumbnails, etc.
5. Hyperpersonalization
In the interview, they touch on another thing that they also touched on before: YouTube is actually becoming a hyper-personalized platform where different things are weighted differently for different people in the algorithm.
What this means in simple terms is that, for one viewer, it may be more important how clickable a video is, while for another, it's much more important how satisfying a video is. This is something that YouTube has recently been pushing a lot more in their interviews.
To translate this even further, this means that there's no single unifying metric that people should be looking at to understand what worked. Other than views ofcourse.
6. Custom Feeds
Later in the video, Todd talks about custom feeds and how the algorithm will work for those custom feeds. As we currently already know a lot about how the home feed works and the other recommendations, he says that it uses similar technology to what we use for the regular home feed, but it's a lot more language-based.
What this means is that when a user sets up the custom feeds, they type out or use their voice to say what they're looking for, and it can be pretty abstract. The algorithm will do an interpretation by using language modeling to understand and generalize into a vibe of what the person may actually be looking for.
It's important to know here (and he mentions this as well) that this isn't just strictly keyword matching, but it is more about the interpretation of what the user is looking for.
And if the user wants to see more popular stuff in their custom feeds, they can highlight this one, but it will also make it easier to match viewers with less popular stuff.
7. New Creator Discovery
At some point, the conversation shifts towards how new creators can get discovered today. The first thing Todd highlights is that new creators should stop focusing on metrics, and they should focus on finding purpose and figuring out what kind of content they want to make.
This hints towards the idea that metrics in Studio are not as valuable once you start out as a creator, which I do agree with.
Then Todd talks about discoverability for new creators. He says that parts of the recommendation system are looking for videos that they don't know much about because maybe the channel they're uploaded to has zero subscribers. There's not really anybody who has watched the channel before. And they know that.
In order to give these videos an on-ramp to success on YouTube, they need to give these videos a chance. They have something called exploration.
An exploration is when viewers show up on the home feed. Every once in a while, they're going to show that person a video that YouTube themselves don't really know much about. Then, when this person likes the video, it's going to show it to more people like this person. This is how on-ramps work.
YouTube is always trying to balance exploring new stuff that they don't know much about and serving stuff that has already been successful for years.
Now, this breakdown was an interpretation mixed with exact things that they have referred to, but I wanted to highlight this one, as there were some interesting things in this video. I wanted to keep everybody up to date with what's going on.
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For me, the most interesting part was to learn about which segment features they're going to look at
Also, Credit to @chucky for doing this interview with them and asking some interesting questions
Also, Credit to @chucky for doing this interview with them and asking some interesting questions
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The biggest lie in YouTube strategy: You should aim for 70% retention.
It's a stupid rule that completely ignores how the algorithm works.
Your retention percentage is heavily influenced by:
(1) The type of video you make
(2) The length of your video
(3) Who was shown your n your video
(5) The structure of your video
(6) The traffic source that brought viewers to your video
(7) The device used to watch your video
(8) The viewer segment against which retention is measured
Yeah... this is why I keep saying that the only things worth looking at are your views and the behavior shown in your retention chart.
It's a stupid rule that completely ignores how the algorithm works.
Your retention percentage is heavily influenced by:
(1) The type of video you make
(2) The length of your video
(3) Who was shown your n your video
(5) The structure of your video
(6) The traffic source that brought viewers to your video
(7) The device used to watch your video
(8) The viewer segment against which retention is measured
Yeah... this is why I keep saying that the only things worth looking at are your views and the behavior shown in your retention chart.
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Appreciate all the birthday wishes! To make it personal, I've been spending the last few weeks thinking about how I can provide the most amount of value in the upcoming year! Hope every single one of you will like it, got some wild ideas
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🚨 UPDATE: YouTube is finally allowing you to create up to 8 custom feeds! (+2 more updates)
Around 30 minutes ago, YouTube started rolling out a new update to its recommendations feed. You can now customize your YouTube Home page by describing the exact ideas you’re looking for In a nutshell, if you want a feed filled with chess videos, you’re able to do this. If you just want a bunch of podcast channels, you can do this too.
YouTube also announced that regular accounts get up to 3 custom feeds, while Premium accounts get up to 8.
Here’s what I’m thinking.
Custom feeds allow us to create something within YouTube where we can easily look at our competitors and see what’s trending. Nothing massive, but definitely something worth exploring. My only concern is, “How granular can I go with this?” Either way, we’ll be able to test it soon.
Next, they’re moving the Subscriptions feed to the top bar. Now, I’m not expecting this to do too much since subscriptions were already pretty prominent before. But it’s still interesting that they’ve decided to put it there.
Lastly, they’re also creating a standardized feed across that top bar, which includes live content. This is bigger than it looks because this seems to be a move by YouTube to focus on suggesting content formats rather than interests. I believe streamers may benefit from this one.
Around 30 minutes ago, YouTube started rolling out a new update to its recommendations feed. You can now customize your YouTube Home page by describing the exact ideas you’re looking for In a nutshell, if you want a feed filled with chess videos, you’re able to do this. If you just want a bunch of podcast channels, you can do this too.
YouTube also announced that regular accounts get up to 3 custom feeds, while Premium accounts get up to 8.
Here’s what I’m thinking.
Custom feeds allow us to create something within YouTube where we can easily look at our competitors and see what’s trending. Nothing massive, but definitely something worth exploring. My only concern is, “How granular can I go with this?” Either way, we’ll be able to test it soon.
Next, they’re moving the Subscriptions feed to the top bar. Now, I’m not expecting this to do too much since subscriptions were already pretty prominent before. But it’s still interesting that they’ve decided to put it there.
Lastly, they’re also creating a standardized feed across that top bar, which includes live content. This is bigger than it looks because this seems to be a move by YouTube to focus on suggesting content formats rather than interests. I believe streamers may benefit from this one.
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There's a little bit of bad news: they're currently only rolling this out to signed-in viewers aged 13 and older in the U.S. using English language settings on mobile and desktop, with watch and search history turned on (rip for many of us)
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🤏 Small YouTube Studio Update just arrived.
A few minutes ago, I noticed that YouTube made a small update to its YouTube Studio dashboard overlay, at least for some creators. It has added the Real-time analytics directly onto your YouTube Studio dashboard, instead of only ick on Analytics.
It’s a little odd why you’d have it in two places at the same time, because it’s still accessible in your Analytics tab, with the lovely blue bars. But my assumption is that they want to make analytics more accessible for many creators who don’t want to be overwhelmed or distracted by all of the other information.
So far, I've only found a few accounts that have this update, so it doesn't seem to be available to everyone yet.
A small update, but I just saw it, so I wanted to share it quickly. Nothing too impactful.
A few minutes ago, I noticed that YouTube made a small update to its YouTube Studio dashboard overlay, at least for some creators. It has added the Real-time analytics directly onto your YouTube Studio dashboard, instead of only ick on Analytics.
It’s a little odd why you’d have it in two places at the same time, because it’s still accessible in your Analytics tab, with the lovely blue bars. But my assumption is that they want to make analytics more accessible for many creators who don’t want to be overwhelmed or distracted by all of the other information.
So far, I've only found a few accounts that have this update, so it doesn't seem to be available to everyone yet.
A small update, but I just saw it, so I wanted to share it quickly. Nothing too impactful.
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First time publishing an article instead of putting it in a simple post, we'll see how this goes
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Reminder that you don’t need to match your thumbnail to the first frame of your video. This narrative has been going around recently because people found some creators who do this.
Is it an interesting way to connect your thumbnail to your autoplay seconds? Sure.
Is it the sole reason for a video’s success? Definitely not.
Is it an interesting way to connect your thumbnail to your autoplay seconds? Sure.
Is it the sole reason for a video’s success? Definitely not.
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🚨 New information on Video Retention A/B Testing & Ask Studio (24-Minute Video Summary)
Earlier this month, YouTube released a video interview with Amjad Hanit (VP of Creator Products) on video retention A/B testing and Ask Studio. In this interview, a lot of new information was coming features.
So, to save you 24 minutes of watching and having to consider what’s important, I wanted to summarize this video into its most important points, together with a brief opinion about this.
First, retention A/B testing.
As I mentioned in my previous post, YouTube is working on a feature that will allow creators to test up to three different versions of the same video to see which one performs better.
But we now have a better understanding of what YouTube expects people to actually test, and we also have an understanding of the limitations to those tests.
The main three ways that YouTube expects people to use this tool are to test intros, different storylines, and even different endings. But from the way Amjad was talking about the tool, they seem very eager to find out how it’s actually going to get used.
All of this is quite interesting, but for me, I’m mostly curious about testing some of the smaller little differences. Different ways to visualize an explanation, different types of segment transitions, changing the tone in which we give a certain piece of information, changing whose perspective we’re following, seeing if a change in music improves or harms retention. Obviously, it’s too early to tell what will and won’t work, but that’s where my attention goes.
Now, the way it’ll work is that you’ll receive separate retention curves for the different versions you’re testing, allowing you to see exactly how the audience behaved differently. YouTube did seem to show an understanding that the curvature is more important than metrics such as AVD and APV. I was very happy to see them finally acknowledge this publicly.
However, there’s one thing that concerns me.
There was no mention of being able to see exactly who actually watched each version.
This is currently the biggest issue with thumbnail A/B testing as well. Was this mainly your regular audience? Were there many new viewers? What’s the variance in their composition, etc.? And I do believe that this is a pretty important missing piece of information I’m hoping to see when they release it. Else, we may draw a bunch of wrong conclusions from these tests.
Imagine one version of your video performing better with new viewers, while another performs better with your regular audience. Without these insights, you’d have no idea what caused the retention to behave a certain way. You could be thinking that you’ve just optimized your video, while in reality, you only made it better for your current audience rather than the audience you’re wishing for.
Another interesting detail is that creators will reportedly have seven days to decide which version they want to keep after the test.
YouTube wasn’t too clear on how that works, when you can test, how often, or whether it would only be possible during the first days of release. But the 7-day timeline was mentioned casually.
Second, dynamic thumbnails.
This is actually one of the features I’m most excited about.
YouTube is introducing dynamic thumbnails, which will allow you to have up to three different thumbnails that can be shown to different audience segments.
This is fundamentally different from their current thumbnail A/B testing, which I do not approve of due to the lack of granular data, process transparency, and accuracy over time.
The way it works is that YouTube will dynamically change which thumbnail to show to the viewer by predicting which of the three thumbnails you upload works best for that particular segment of viewers. So if a certain thumbnail works better for newer viewers, it’ll show that thumbnail to them. If a certain thumbnail works better for returning viewers, it’ll show that thumbnail.
The biggest question, which they still didn’t give an answer to, is how these segments are created. Will it detect based on interest or overall viewing behavior? Or will it only stick to simple segmentation techniques?
So, still many questions that we need answers to, but the potential is insane.
Third, Ask Studio is becoming much more than an analytics assistant.
YouTube is also expanding their Ask Studio, which is an AI-powered tool designed to help you better understand your content and your analytics.
I am very skeptical about this, though. Mainly because of additions like “Draft Mode”. So rather than waiting for your video to be published and then figuring out what went wrong, you’ll be able to submit a draft and receive AI feedback on your video.
I’m not very hopeful because it’ll probably use a lot of generic storytelling knowledge that isn’t even aligned with what audiences want. I’m very curious about how they’re looking to solve this. It’ll probably be most helpful to the newest of the newest creators who barely know how to put together a logical storyline. Anybody with any experience will find this useless (my prediction).
However, something interesting that they’re also developing under Ask Studio is a feature where it analyzes your older videos and suggests updated thumbnails to improve their performance. This one is interesting because it may get it right sometimes, and you’ll get some extra views on those videos. Thumbnail styles that worked 3 years ago may not always work today, and having a system to look out for that is pretty interesting.
Another thing they announced with Ask Studio is a feature where it helps you better analyze your data. Currently, it’s pretty bad, but it’ll be able to recognize which title patterns generally score better views and so on. This will have the potential to replace a lot of the beginner strategists who don’t do anything more than just crunch numbers at a cost. So strategists will now be forced to move towards more explanatory consultations where the goal isn’t to point out a pattern, but rather to explain it.
So, what do I think about all of this?
Overall, I’m very excited. If I had to create a ranking, I’d say I’m most excited about dynamic thumbnails, then the A/B testing for retention, and then Ask Studio.
I believe Ask Studio has the most quality worries from my end. AI isn’t very good at understanding what audiences like. It still lacks a lot of that “intuitive” feel most top creators have.
I’ll keep you guys up to date when we know more.
Earlier this month, YouTube released a video interview with Amjad Hanit (VP of Creator Products) on video retention A/B testing and Ask Studio. In this interview, a lot of new information was coming features.
So, to save you 24 minutes of watching and having to consider what’s important, I wanted to summarize this video into its most important points, together with a brief opinion about this.
First, retention A/B testing.
As I mentioned in my previous post, YouTube is working on a feature that will allow creators to test up to three different versions of the same video to see which one performs better.
But we now have a better understanding of what YouTube expects people to actually test, and we also have an understanding of the limitations to those tests.
The main three ways that YouTube expects people to use this tool are to test intros, different storylines, and even different endings. But from the way Amjad was talking about the tool, they seem very eager to find out how it’s actually going to get used.
All of this is quite interesting, but for me, I’m mostly curious about testing some of the smaller little differences. Different ways to visualize an explanation, different types of segment transitions, changing the tone in which we give a certain piece of information, changing whose perspective we’re following, seeing if a change in music improves or harms retention. Obviously, it’s too early to tell what will and won’t work, but that’s where my attention goes.
Now, the way it’ll work is that you’ll receive separate retention curves for the different versions you’re testing, allowing you to see exactly how the audience behaved differently. YouTube did seem to show an understanding that the curvature is more important than metrics such as AVD and APV. I was very happy to see them finally acknowledge this publicly.
However, there’s one thing that concerns me.
There was no mention of being able to see exactly who actually watched each version.
This is currently the biggest issue with thumbnail A/B testing as well. Was this mainly your regular audience? Were there many new viewers? What’s the variance in their composition, etc.? And I do believe that this is a pretty important missing piece of information I’m hoping to see when they release it. Else, we may draw a bunch of wrong conclusions from these tests.
Imagine one version of your video performing better with new viewers, while another performs better with your regular audience. Without these insights, you’d have no idea what caused the retention to behave a certain way. You could be thinking that you’ve just optimized your video, while in reality, you only made it better for your current audience rather than the audience you’re wishing for.
Another interesting detail is that creators will reportedly have seven days to decide which version they want to keep after the test.
YouTube wasn’t too clear on how that works, when you can test, how often, or whether it would only be possible during the first days of release. But the 7-day timeline was mentioned casually.
Second, dynamic thumbnails.
This is actually one of the features I’m most excited about.
YouTube is introducing dynamic thumbnails, which will allow you to have up to three different thumbnails that can be shown to different audience segments.
This is fundamentally different from their current thumbnail A/B testing, which I do not approve of due to the lack of granular data, process transparency, and accuracy over time.
The way it works is that YouTube will dynamically change which thumbnail to show to the viewer by predicting which of the three thumbnails you upload works best for that particular segment of viewers. So if a certain thumbnail works better for newer viewers, it’ll show that thumbnail to them. If a certain thumbnail works better for returning viewers, it’ll show that thumbnail.
The biggest question, which they still didn’t give an answer to, is how these segments are created. Will it detect based on interest or overall viewing behavior? Or will it only stick to simple segmentation techniques?
So, still many questions that we need answers to, but the potential is insane.
Third, Ask Studio is becoming much more than an analytics assistant.
YouTube is also expanding their Ask Studio, which is an AI-powered tool designed to help you better understand your content and your analytics.
I am very skeptical about this, though. Mainly because of additions like “Draft Mode”. So rather than waiting for your video to be published and then figuring out what went wrong, you’ll be able to submit a draft and receive AI feedback on your video.
I’m not very hopeful because it’ll probably use a lot of generic storytelling knowledge that isn’t even aligned with what audiences want. I’m very curious about how they’re looking to solve this. It’ll probably be most helpful to the newest of the newest creators who barely know how to put together a logical storyline. Anybody with any experience will find this useless (my prediction).
However, something interesting that they’re also developing under Ask Studio is a feature where it analyzes your older videos and suggests updated thumbnails to improve their performance. This one is interesting because it may get it right sometimes, and you’ll get some extra views on those videos. Thumbnail styles that worked 3 years ago may not always work today, and having a system to look out for that is pretty interesting.
Another thing they announced with Ask Studio is a feature where it helps you better analyze your data. Currently, it’s pretty bad, but it’ll be able to recognize which title patterns generally score better views and so on. This will have the potential to replace a lot of the beginner strategists who don’t do anything more than just crunch numbers at a cost. So strategists will now be forced to move towards more explanatory consultations where the goal isn’t to point out a pattern, but rather to explain it.
So, what do I think about all of this?
Overall, I’m very excited. If I had to create a ranking, I’d say I’m most excited about dynamic thumbnails, then the A/B testing for retention, and then Ask Studio.
I believe Ask Studio has the most quality worries from my end. AI isn’t very good at understanding what audiences like. It still lacks a lot of that “intuitive” feel most top creators have.
I’ll keep you guys up to date when we know more.
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