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After Google: Should SEOs Jump Ship?

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After Google: Should SEOs Jump Ship?

The author’s views are entirely his or her own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz.

There was a pre-search-engine age. It’s hard to conceive of now, but there was. Even in the early days of search engines, when Ask Jeeves, Yahoo, and Excite still competed for the crown, I can remember web portals. Pages that I’d start at, in the “computer room” at school, to navigate and explore the web not by searching, but by clicking on organized links.

In the beginning, there were web portals. The internet was without form and void, and darkness was over the face of the deep.

These were already the death throes of a previous internet age. Search engine dominance, and specifically Google dominance, has been the norm for this kind of journey for decades now. It’s all that many SEOs have ever known.

But what comes next?

People have talked for a long time about existential threats to Google’s dominance, and often, implicitly, by extension, SEO. You’ll have heard the claims that Amazon or YouTube are now preferred engines for certain kinds of search, or that Google is going to struggle against the unique technological advantages of Apple, AI chatbots, the unique regional advantages of Baidu, or the unique format advantages of TikTok. Or maybe you’ve even heard that people prefer to restrict their searches only to Reddit. Even mainstream outlets are suggesting that Google search quality may be in decline.

This post is not about the health of Google search as a product, or about the implications of improving AI products for your SEO strategy right now. (Although, I know of at least one post for this blog being written on that topic!) Instead, this post is about which of these threats, if any, actually stand a chance of unseating Google’s dominance.

In what capacity?

To ask what might take Google’s role, we must first ask what role it is that we’re interested in. Google is many things, and possibly part of the reason Google’s doom is so often predicted is that we’re not always talking about the same specific things.

What exactly is it that search engines as a genre, and then Google, have dominated? Perhaps we might mean:

  • The place you’d start to find a web page on a site you’ve not yet discovered? For example, you might not know yet what the best site is for a given topic.

  • The place you’d start to find a web page on a site you’re already familiar with? Perhaps you’re searching on Google hoping to see a result from Reddit, or from Wikipedia.

  • The place you’d start to answer a given question? So maybe you’d be happy with a non-web result as long as it answered your question.

  • The place you’d start to complete a task? So, again, the best answer might not be a web page at all.

The truth is that the present reality blurs these use cases to the point of it not being useful to separate them. But for Google to be replaced by something that maintains this close alignment, it’d have to be a close peer competitor.

The obvious pretenders

There are two that come to mind, as similarly resourced companies trying similar things via a similar method (a web index): Bing and Apple.

I don’t want to be dismissive of Bing, or of the value of someone — anyone — else maintaining a similar enough competitor to keep Google somewhat honest. Although it’s often mocked in SEO circles, Bing in reality is not so many years behind Google at any given point. But, really, it’s hard to see the events that could lead to Bing supplanting Google at its own game. It’s just too similar for people to make the switch. One possibility based on recent news is for Bing to become less similar, pursuing one of the precise alternatives I’ll cover below – but more on that when we get to it.

Apple, on the other hand, is doing something similar, but with some unique advantages. I must credit my former colleague (and 2023 Mozcon speaker) Tom Anthony who has been very prescient around Apple’s moves in this space, going so far as to backward-engineer Apple search results that weren’t supposed to be publicly available. Apple can do things that Bing can’t, leveraging Apple’s app ecosystem and device integration to provide search results that skip certain steps of a user journey in ways that Google cannot, or will not.

The trouble with Apple as a Google search competitor is obvious, though. The unique advantages, as I said, are to do with apps and hardware. Apple devices are expensive — prohibitively so. (This varies by market – in the US, with the base price of a phone contract being so high, iPhones are more palatable and have a notably higher market share than in Europe, for example. But, that’s a topic for another day – either way…). There is a fairly hard cap on the market share of a search engine that is only superior on high-end devices, and not only that, but ones from a specific brand.

So could Apple take a big chunk out of Google? Yes, it may already quietly have done so with various iOS changes pushing the prevalence of Apple’s own search results. But totally replace Google? Very unlikely.

You can say the same for regional competitors like Baidu, Yandex, or Naver. These may well consistently beat out Google in their own backyards, and perhaps even spread to nearby countries and regions, but it’s hard to see them beating Google in its own backyard(s).

Revolution, not evolution

So what about competitors that replace Google by doing something totally different, to solve the same problems? The reality is that a lot of the problems we solve right now with web search, are not actually well suited to web search. The fact that something like a Google Home will often answer your questions by essentially reading out a featured snippet is a symptom of Google’s dominance, not a symptom of web search being well suited to that use case. Even Google themselves recognize this, and betray that in tools like Google Translate, clocks, calculators, and so on, embedded in SERPs. So who might the more disruptive threats be?

One name that came up a lot in 2022 is TikTok, and I’d point you to this excellent post by Lidia Infante on this very blog. To sum up her argument, TikTok can take market share from Google, but it can’t replace Google entirely. TikTok is too specialized (in video format and certain topic areas), and the quality assurance is too weak. So, again, we have a competitor that chips away at Google without replacing it.

Then of course, most recently, SEOs of Twitter have been right to point out that for many queries, ChatGPT produces better responses than Google. Take this example, “excel query for extraction the domain name from a url”:

1674474876 177 After Google Should SEOs Jump Ship
1674474876 874 After Google Should SEOs Jump Ship

The ChatGPT result above is far more informative and easy to follow. However, like TikTok, this only works for certain things. ChatGPT is not a web search engine:

1674474876 627 After Google Should SEOs Jump Ship
1674474876 949 After Google Should SEOs Jump Ship

So you have to be willing to abandon the premise that your result should be a web page. Which, in this context, comes down to: do you trust an answer if you don’t know who wrote it? ChatGPT and similar technologies have access to “knowledge” sourced from the web, like Google, but they don’t cite a source. Indeed, it would be immensely difficult to trace the source of their various claims, some of which seem quite… odd.

Similar to TikTok, then, this is something I might prefer to Google for a specific kind of query. In this particular case, the kind of query that previously took me to StackOverflow. But I’m not going to ask it for mortgage advice.

I noted above that Bing is rumored to be integrating ChatGPT with its own search product. This enlarges the threat to Google in that it makes this technology more accessible, but really, the same qualms apply – there are many, many queries for which this is not helpful. Even if Bing can hybridize these technologies into a “best of both” of traditional web search and NLP, well – that’s already the road Google is going down.

The other challenge with this “ChatAI as search” model is an economic one. Google and Amazon have both already come to the conclusion that the type of queries asked of their personal assistant devices are barely, if at all, economic to run – because of the limited monetization opportunities for purely informational queries. Perhaps my distinction above, about what we mean by replacing Google, is very relevant here – some of our use cases of Google as a search engine are actually just a loss leader for others. As such, perhaps this bundling of disparate uses is necessary.

The King is dea… wait, wait, he’s still breathing

1674474877 369 After Google Should SEOs Jump Ship

Number of explicit core search queries powered by search engines in the United States as of January 2022 – via Statista

Ultimately, these threats look set to chip away at Google, not replace it. At worst, a broad monopoly will be sliced up and shrunk, and that doesn’t feel like any great evil. For SEOs, we should be aware of these new search engines, and these new “search engines”, and of the risks attached to being locked into the Google ecosystem. But don’t forget the chart above: the original pie is not going anywhere. The Google SEO game is still not a bad game to be playing.



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YouTube Ad Specs, Sizes, and Examples [2024 Update]

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YouTube Ad Specs, Sizes, and Examples

Introduction

With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.

Types of YouTube Ads

Video Ads

  • Description: These play before, during, or after a YouTube video on computers or mobile devices.
  • Types:
    • In-stream ads: Can be skippable or non-skippable.
    • Bumper ads: Non-skippable, short ads that play before, during, or after a video.

Display Ads

  • Description: These appear in different spots on YouTube and usually use text or static images.
  • Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).

Companion Banners

  • Description: Appears to the right of the YouTube player on desktop.
  • Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.

In-feed Ads

  • Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.

Outstream Ads

  • Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.

Masthead Ads

  • Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.

YouTube Ad Specs by Type

Skippable In-stream Video Ads

  • Placement: Before, during, or after a YouTube video.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
    • Action: 15-20 seconds

Non-skippable In-stream Video Ads

  • Description: Must be watched completely before the main video.
  • Length: 15 seconds (or 20 seconds in certain markets).
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1

Bumper Ads

  • Length: Maximum 6 seconds.
  • File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
  • Resolution:
    • Horizontal: 640 x 360px
    • Vertical: 480 x 360px

In-feed Ads

  • Description: Show alongside YouTube content, like search results or the Home feed.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
  • Headline/Description:
    • Headline: Up to 2 lines, 40 characters per line
    • Description: Up to 2 lines, 35 characters per line

Display Ads

  • Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
  • Image Size: 300×60 pixels.
  • File Type: GIF, JPG, PNG.
  • File Size: Max 150KB.
  • Max Animation Length: 30 seconds.

Outstream Ads

  • Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
  • Logo Specs:
    • Square: 1:1 (200 x 200px).
    • File Type: JPG, GIF, PNG.
    • Max Size: 200KB.

Masthead Ads

  • Description: High-visibility ads at the top of the YouTube homepage.
  • Resolution: 1920 x 1080 or higher.
  • File Type: JPG or PNG (without transparency).

Conclusion

YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!

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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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A deeper dive into data, personalization and Copilots

Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.

To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.

Dig deeper: Salesforce piles on the Einstein Copilots

Salesforce’s evolving architecture

It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?

“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”

Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”

That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.

“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.

Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”

Let’s learn more about Einstein Copilot

“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.

For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”

Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”

It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”

What’s new about Einstein Personalization

Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?

“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”

Finally, trust

One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.

“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”

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