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YouTube Explains Some Common Algorithm and Video Distribution Queries

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youtube explains some common algorithm and video distribution queries

YouTube has sought to answer some common questions about how its algorithm works when highlighting certain content to users – and why your video metrics may not always reflect performance.

In a new video on the Creator Insider channel, YouTube product managers Patricia and Rachel focus specifically on the impact of click-through rate (CTR) and average viewer duration (AVD), and how YouTube’s algorithm factors these into video distribution and performance.

Recently, YouTube outlined its coming analytics insights display, which will show creators their average CTR and AVD, helping to provide more insight into how their content is performing.

YouTube analytics

But as YouTube notes, that’s not always indicative – first off, the managers discuss the discrepancy that can occur when videos which have a low CTR still have high views.

As per YouTube:

“Click-through rate is a really tricky metric to understand. So, for a lot of creators, if you go in you look at your most successful videos the videos with the most views those are actually the most likely to have the lowest click-through rate.”

YouTube explains that this is because high distribution videos end up being shown to a much broader audience, which means that your content will be shown to a lot more people who are less familiar with you and your work. That, inevitably, means your CTR will be lower. So in some ways, it’s a consequence of success – the broader your distribution, based on how your video is performing, the lower your CTR will be.

“On the other hand, some of your smaller videos that were shown to a really relevant small targeted audience are the most likely to click, so those might have a really high click-through rate.”

So it makes sense that there will be a discrepancy between performance when your CTR is low on high performing clips, even if these data points seem like they should, generally, correlate.

But that can make it hard to measure your overall performance, or even track relevant trends. On this, YouTube recommends that creators take into account all of the available metrics, including these new insights, in order to get a better comparative view – i.e. if one metric seems out of whack, the other may better qualify it.

In isolation, any single metric could, theoretically, be confusing.

“In the long term, we hope to have A/B thumbnail testing eventually, which will help give you even more concrete answers.”

YouTube also addresses creator concerns around the impact of lower average view time resulting from external links, which could impact reach and distribution in the algorithm.

“So, in discovery, we actually look at how a video performs in the context it’s shown. So when a video is shown on Home, how does it perform there? And Home and Watch Next each have their own ranking models.”

In other words, both these discovery surfaces have different ways of deciding which videos to show to users – which, of course, makes sense, as your Home recommendations will be based on your overall viewing history, and your ‘Watch Next’ listing will be influence by what you just viewed.

With respect to the impact of lower AVD, YouTube says that this is one of several factors which will influence what’s displayed in a users’ home feed.

“So overall click-through rate and average duration are pretty good indicators of how your video is doing in general, but they definitely don’t cover all cases. […] Having more traffic from external is not going to hurt your discovery, or how your video is being recommended in Home or Watch Next. Sometimes you have to do deep-dives into individual traffic sources.”

YouTube also notes here that the new analytics display actually filters out CTR and AVD for both ‘Home‘ and ‘Watch Next’, making it easier to understand the impacts of external sources on those two metrics.

YouTube also addresses the concern that its algorithm unfairly compares average view duration for videos of different lengths.

“So, in discovery, we actually look at both relative and absolute watch time, those are both meaningful signals and how your videos are going to be recommended. You would still need to do some cross-referencing, even if we swapped out ‘average view duration’ for ‘average percentage viewed’, because it’s easier for short videos to hit a really high amount of average percentage watched. We can only focus creators on so many metrics, and we chose average view duration because how much time somebody spends with you in your content is a really strong indicator of interest. That being said, we want videos of all lengths to succeed on YouTube, and get discovered.”

YouTube also seeks to explain another common issue, where a video’s CTR will be good, and the AVD will also be good, yet the video will still underperform, making it difficult to understand what went wrong.

“CTR and AVD are among dozens of signals that we use for search and discovery – but there are also a lot of other factors that are going to influence how many impressions your videos will get, and how many people watch them.”

YouTube specifically highlights three other considerations to keep in mind:

  • Competition – With so much content available, there’s inevitably a level of competition for audience, and at times, even if your video ticks all the boxes, it simply won’t gain significant traction.
  • Topic interest – YouTube also notes that some topics are simply more popular. “For example, soccer – there are more people in the world interested in soccer than there are in golf. Sometimes soccer videos will get more views than golf, and that’s not because our algorithm has a preference for soccer videos, it just has a larger potential audience size for that kind of topic.”
  • Seasonality – YouTube also notes that different topics will see higher levels of interest at different times of the year. That also, conversely, means that some topics will also generate less interest at the same time, which sort of points to the competition factor once again.

YouTube also notes that it’s working on new tools to provide more insight, and help creators maximize their video views. As noted, A/B thumbnail testing is one element, which will provide more specific insight into how your thumbnails are grabbing attention (or not), while YouTube’s also looking to add a new comparison tool for CTR and AVD, which would show how your channel is performing on these elements when matched against other channels similar to your own. This would be similar to the Page performance comparison tools available on Facebook or LinkedIn.

There are some interesting, valuable – though technical – insights here, which address some common YouTube algorithm pain points. If you’ve been trying to get your head around why your YouTube clips are performing, or not, these notes may help better contextualize YouTube’s internal processes.

You can watch the full Creator Insider video here.

Socialmediatoday.com

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EU Officials Launch Investigation into TikTok Over Potential DSA Violations

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EU Officials Launch Investigation into TikTok Over Potential DSA Violations

EU officials are wasting no time enacting their new powers under the Digital Services Act (DSA), with the European Commission announcing a new investigation into whether TikTok is currently in violation of DSA rules in relation to the protection of minors in the app.

Concerns have actually been raised around TikTok’s compliance on several fronts, including systemic risks related to app addiction, its age verification processes, its security measures for minors, data transparency, and more.

As per the European Commission:

On the basis of the preliminary investigation conducted so far, including on the basis of an analysis of the risk assessment report sent by TikTok in September 2023, as well as TikTok’s replies to the Commission’s formal Requests for Information (on illegal contentprotection of minors, and data access), the Commission has decided to open formal proceedings against TikTok under the Digital Services Act.

It’s the second major probe under the new DSA laws, with X also currently under EU investigation over its efforts in restricting illegal content, and stopping the spread of misinformation in the app.

TikTok will now need to provide further information to EU investigators to assess its efforts, with a maximum penalty of up to 6% of its global earnings on the cards if it is found to be in violation.

Though that’s probably unlikely, given that the DSA also includes clauses that enable investigators to “accept any commitment made by TikTok to remedy on the matters subject to the proceeding”.

Given that the DSA has only recently been initiated, this will probably be the outcome of these early investigations, though EU officials may also want to send a strong message early, in order to underline the seriousness of the new rules.

Though there’s also this:

The duration of an in-depth investigation depends on several factors, including the complexity of the case, the extent to which the company concerned cooperates with the Commission and the exercise of the rights of defence.

So any investigation could carry on for some time, meaning we won’t know the outcome for a while yet. But again, potentially, TikTok could face big fines if it is found to be in breach, and it fails to take action to address any highlighted concerns.

It’ll be interesting to see how EU officials look to enact the regulations, and keep each platform in line with these more restrictive processes. That could get especially complex with the DSA, given the variable interpretations around what constitutes adequate action on certain fronts.

As such, these early cases could play a key role in establishing precedent, which could indeed see big fines coming, and could even force apps to reassess their operations in the region as a result.  

I mean, Meta has threatened that before, and depending on how EU officials approach these new laws, there could be further concerns on this front.

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Josh Brolin Summarizes Dune 2 in Greatest Instagram Caption of All Time

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Josh Brolin Summarizes Dune 2 in Greatest Instagram Caption of All Time

The Dune: Part Two star took a unique approach to marketing the movie. Dune: Part Two is so close to hitting theaters, and no one is more excited than …

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Reddit’s Exclusive Data Sharing Deal with an Unnamed AI Company Could Mark a Key Industry Shift

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Reddit’s Exclusive Data Sharing Deal with an Unnamed AI Company Could Mark a Key Industry Shift

Is Reddit’s actually data worth $60 million per year?

That’s reportedly how much an as-yet-unnamed AI development company has paid to gain exclusive access to Reddit’s full data set, which will see said AI company incorporate Reddit user responses into its large language model (LLM), with a view to the system providing more human-like answers and insight, and becoming a bigger challenger in online search.

As reported by Bloomberg, after working to restrict access to its data over the last year, in order to stop AI companies from profiting off its content, Reddit has now signed an exclusive contract with “an unnamed large AI company”, which will see that company integrate Reddit insights into its models.

Which is a high price tag, considering that the top tier of X’s API access (200 million posts per month) costs around $2.5 million per year.

So could Reddit’s data be worth significantly more than that, and if it is, does it then make sense for Reddit to provide such on an exclusive basis?

The value of Reddit data is that it provides actual, human usage insight, which can often be of more value than online reviews that can be gamed and skewed by paid responses. That’s getting even worse in the age of generative AI, with some companies now employing AI tools to create human-sounding reviews online, in order to boost their product ratings.

As a result, more and more people have been turning to Reddit to get honest product reviews and performance insight. They’re still using Google, but more people are using the “site:reddit.com” qualifier to glean more specific insights from Reddit communities.

For example, if you were looking for a new hair dryer, you can look up “best hair dryer” on Google to get this:

Or you can add “best hair dryer site:reddit.com” for this:

Google example

The Reddit forum links connect through to actual people’s experiences, and include solid, functional insight from those who’ve used each device. The Reddit responses are also up and downvoted, making it easier to find the best response to guide your search process.

The more specific, personal insight can add significant value to the answers provided, and many people have found that this is now a better, more valuable discovery process than trusting Google results within themselves.

And now, one AI company will get all of this insight exclusively to itself.

That could be a big boost to its business ambitions, with a view to making AI chatbots more of a rival for traditional search behavior. Already, more people are turning to conversational chatbots for online discovery, and with this, whichever LLM can access Reddit data will have an exclusive trove of valuable consumer insights, which it can repackage within its responses.

For example, using the same hair dryer prompt in ChatGPT, the system currently gives me a listing of technical considerations and recommendations based on top sellers. But with added Reddit commentary, it could also provide a more personalized addendum:

“According to users, the best hair dryer for curly hair is the Ella Bella Ionic hair dryer, while those with straight hair tend to prefer the Dyson Supersonic.”

The system could then provide more specific answers based on your requirements, by sourcing that info from subreddit communities.

It’s a significant value-add, which will make whichever company gets this info a far more viable option as a search consideration, though the $60 million per year ongoing price tag is high, and is also at least somewhat reliant on Reddit continuing to grow, in order to maximize its value and utility.

And Reddit is growing. Reddit’s added 20 million more users over the past three years, and it continues to see strong engagement in over 100,000 active communities. The company’s been working to highlight its business value, ahead of a planned IPO, which could come next month, and this deal will now be factored into the valuation of the platform moving forward.

In some ways, it’s possible that Reddit could be limiting its opportunities by signing an exclusive data contract. But that’s why the price tag is so high, and it’ll be interesting to see which chatbot comes out with “Reddit exclusive insights” as a value add sometime soon.

I mean, it seems likely that it’ll be OpenAI, with the backing of Microsoft, as it looks to take on Google’s Search dominance. With the rise of conversational searches, that does seem like a logical investment, and with another data source taken out of the mix, that could also lead to more differentiation in the market.

It could also point to similar exclusivity deals in future, as each company tries to differentiate and dominate with their chatbot tools. Current AI chatbots have been able to scrape vast amounts of data from across the web, which means that their initial models will all be relatively similar as a result, but in future, as information evolves, and new data is required to match search intent, fresh sources will also be required to maintain relevance, and audience interest.

Meta claims to have an advantage in this respect, because it has all of the insights published to Facebook and Instagram to work with, while Elon Musk will view xAI as holding a lead, due to his platform being the leading real-time news discussion app.

But maybe, considering broader trends, Reddit insight is actually the real leader in terms of refining search queries.

And maybe, that will prove to be more important than most think.  



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