MARKETING
AI and the Future of SEO
Artificial intelligence is taking the SEO industry by storm.
With the emergence of smart chatbots that can perform all kinds of tasks – seemingly – as well as humans can, and the integration of AI-powered features into the two prominent search engines, Bing and Google, there are a lot of questions on how this may change SEO and whether it is going to survive.
The most popular chatbot, ChatGPT, has no doubts as to whether SEO is going to be just fine:
And no, Artificial Intelligence doesn’t think it will ever replace web search if that’s your concern:
People will need search engines to access more diverse and advanced information and to find content that fits their personal tastes.
With that being said, SEO is going to change dramatically under the impact of quickly developing AI technologies.
And here’s what may happen:
Search Engines into Conversational Answer Engines
Google has been pioneering the idea of an “answer engine” for many years now. Instead of generating search results in response to a user’s query, Google has been providing quick answers and follow-up questions, suggesting related news and videos and guessing the context behind each query.
With the emergence of AI technology, this trend has become even more obvious. Bing has become the trend setter at integrating AI into online search, for a change. They were the first to introduce AI-powered conversational features into their search engines. The AI bot called Bart answers search queries in real time and even suggests follow-up questions to continue the conversation:
The bot interacts with searchers conversationally just like a human does but it does provide clickable links to allow them to access the articles it used to give the answers.
Google announced its AI features a few days later: Their bot is still in private beta, so we could only see the GIF giving a glimpse into how it would work.
Google’s bot responds to a user’s query in real time typing answers right in front of their eyes. Unlike Bing, it doesn’t look like the chatbot is willing to reveal its sources which seemed concerning for two reasons:
- Just like Bard, Google’s bot is scanning billions of documents across the internet to come up with answers. Not giving access to the sources seems very unfair to the publishers who created those documents.
- The fact that the bots are using third-party information means there can be human errors in the answers. Without an ability to access two or more sources, it will be hard to verify the information provided by the AI technology.
Google is claiming that they are still working and welcoming feedback, so there’s hope they will rethink the way it is set-up now and get more transparent with their sources.
In both ways, so far AI-powered search engines look an extension to what they were previously: Answer engines that strive to instantly help their users with additional information. If they cite the sources, optimizing for these kinds of real-time answers won’t be much different from optimizing for featured snippets. All you need is to do your best to answer your target audience’s questions, and the chatbots will likely cite you.
The only difference is that it is not quite clear how to measure your progress: We don’t know yet how those clicks from the bots’ answers will be recorded and tracked. But I suppose, search engines will be transparent in that respect allowing you to see which of your pages got cited by search chatbots most. Google already offers this kind of reports for its highly personalized Discover section.
AI and Its Impact on SEO Profession
With ChatGPT being able to perform all kinds of traditional SEO tasks – including content creation and optimization, writing emails, and even creating Schema code – there have been a lot of concerns as to how many SEO professions are going to become obsolete.
Well, at its current phase ChatGPT is not a threat to any profession. It can eliminate some outsourcing needs enabling SEO teams to handle more tasks at home, including basic coding needs.
But with its current possibilities most of those performed tasks will require a lot of human interference. Its written content is basic and detectable, so it cannot be used for content creation but it can turn useful in creating content outlines and briefs. Its code usually requires fine-tuning. Its keyword research is basic.
So far ChatGPT is simply a little helper that can make your work faster rather than replacing you in any task.
It may change in the future as it becomes more advanced but an SEO profession is so much more than simply performing tasks. It involves planning and strategizing. It is all about relationship building and collaboration. But more importantly, it requires understanding of unpredictable human reactions which AI will hardly ever learn to relate to.
The Key Lies in Mutual Benefits
So in the above section, we’ve seen how AI can actually make an SEO’s job easier. It is clear that the technology can benefit us all – users and web publishers. The key is to start using AI technology to understand how we can safely co-exist without threatening each other.
If you have been in the SEO industry for at least a couple of years, you’ve surely seen how SEO always thrives with the emergence of new technology. Tools that make SEOs more informed and productive have never killed SEO. Instead they made the industry more advanced and integrated.
When SEO was born, we were merely focusing on identifying keywords and integrating them into the web document for Google to match them with the exact query. We’ve come a long way since them. We’ve gone through years of Google learning to use semantic analysis to understand intent behind search queries, penalizing and filtering out low-quality backlinks and giving direct answers to their users in the form of featured snippets and People Also Ask results.
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A decade or so ago, SEOs needed considerable development skills to put together sites and publish digital content but with the emergence of advanced site building platforms, anyone was able to create websites and design landing pages. Web analytics has also become accessible and easy to understand to just about anyone, whether they have any SEO expertise or not.
None of those changes has killed SEO but made it stronger.
AI technology is not brand new to the digital marketing industry. It has been integrated into customer support tools (IVR and chatbots) for years. There are AI-driven accessibility tools, AI-powered on-page engagement tools, AI-driven social media listening tools, etc. We have all learned to use multiple AI tools to improve digital marketing strategies.
New technology has been helping SEOs reverse engineer Google’s algorithm, analyze competitors, improve your keyword strategy and build web pages for users to easily get exactly what they came for. SEO is now integrated into product development, user experience testing and niche relationship building. It has become more than creating content for search engines and letting search crawlers access it easily. SEO is getting harder and harder to kill because it got smarter and smarter.
If AI technology seems to threaten you, get to know it better. Learn to benefit from it, and you will see that it can benefit everyone, including your users.
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
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!
MARKETING
Why We Are Always ‘Clicking to Buy’, According to Psychologists
Amazon pillows.
MARKETING
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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