MARKETING
AI For PPC Is Only Useful If You Use External Tools
Using AI (Artificial Intelligence) for PPC is no longer an option. It’s a necessity.
This was a topic that we ran with when we wrote for Search Engine Land. But generally, saying that AI is essential is only half the story.
When it comes to managing your PPC campaigns, you don’t just need AI. You need the best tools and strategies that are available. You can’t just stick the ‘AI’ label on it and expect them all to perform the same way.
Here’s the thing. Most advertising platforms have their own AI strategies and features integrated and ready for you to use. But when you look at AI tools to help you out, they’re all external. Why? Because internal tools aren’t good enough.
It’s a bold statement to make. So, we’re here to back it up, using Google Ads as an example. Here are some of the most popular AI-powered tools they offer and an unbiased assessment of how good they are.
Not-so-smart bidding
Okay, we had to start this article up with Google’s Ads smart bidding. There’s a good reason for this, as it’s one of the most hotly debated topics amongst PPC-ers. Right, let’s jump in.
Smart bidding is Google’s automated bidding strategy. You’re supposed to set your goal, including maximizing conversions or ROAS (Return On Ad Spend) and Google will automatically set and adjust your budgets to achieve this. These changes are powered by an AI that can analyze 70 million signals in 100 milliseconds. Wow.
The reality is a little far from this.
We’re not denying that for some companies, smart bidding has been paramount to increasing ROAS. But at the minute, it’s notoriously underdeveloped. The functionality just isn’t what it should be and it’s a complete no go for those who haven’t got the past data to work off.
They’ve added features like campaign level conversions and even got rid of accelerated ad delivery in an attempt to push advertisers towards it. But it’s not enough right now.
DSA (Dynamic Search Ads) are adverts that are automatically created for you. Well, within certain parameters of course.
Instead of using keywords, you submit your website or product feed to Google. When someone searches for a relevant term, an advert is generated with text pulled from the website.
But automatic creation means you lose creative control. You’ll end up with adverts that lack personality and soul, like this example:
That’s not the only downside. If your website isn’t up to date, you risk serving broken adverts that do not work. Which means, your quality score will plummet potentially damaging other campaigns you might have running.
DSAs are useful for large businesses with huge catalogs. But for everyone else? It might be more hassle than it’s worth.
Time-consuming Responsive Search Ads
RSAs (Responsive Search Ads) are where you input up to 15 adaptive headlines and descriptions to test which one works best.
Unlike DSA, you’ll retain creative control over the exact text you use. The AI just ‘learns’ which variations perform best and then puts these out to up your clicks and conversions.
The downside? It takes a lot of time to set it up.
Let’s say you’ve got 5 campaigns, all with 10 ad groups inside of it. If you run RSAs for these, that’s 750 adaptative headlines you need to create. That’s what, a week of your time to set up what is essentially AI-powered ad testing?
Automated rules falling short of the throne
Automated rules are the AI leaders of the PPC marketing world.
They work like this: when a certain event happens, like your CPC (cost-per-click) dropping below a certain amount, your keyword, ad or campaign will be automatically changed in response.
This saves so much manual time and surveillance doing it yourself. There are a few hoops you have to jump through to get these working on Google. And, annoyingly enough there isn’t a single screen that they’re all displayed in and managed from.
Secondly, the time they run limits the benefits you get. For example, if you set them to run at 9am and your action was triggered at 2am the night before, your campaigns will be running for 7 hours under the wrong bid or status.
Lastly, email is the only notification that Google support, which isn’t useful for all businesses.
The premise is good. The actual execution? Not so.
PPC scripts are coded scripts that are supposed to automate part of your management. One of the best examples is the Zero Impressions Alarm, which alerts you if your impressions fall to 0.
Most PPC scripts require good Javascript knowledge to build and customize them to your exact needs.
Basically, people noticed the need for AI and the missing gap in the platforms. So, they created their own solutions for it, far before Google did. They’re ahead of the game and Google is behind.
The least Google could do was recognize this and integrate them directly into the platform. But that isn’t the case. PPC scripts remain distinctly separate. This is made obvious as soon as a Google update is pushed live and most scripts suddenly fail to work. Yet again, you’re back to the code.
Get the results you need from external tools
The internal AI tools that Google and other PPC platforms offer have their benefits. But they’re just not good enough… yet.
They feel like the great ideas from someone who was 5 years late to the party. Unfortunately, in the time it will take for them to catch up – external scripts and tools will be ahead of the crowd.
That’s because they were built to solve problems that people were already facing. They saw it from PPC managers perspective and knew how to use the AI to make life easier.
If you want to leverage AI, don’t stick to internal tools. Discover what other types of PPC software are out there and see how they compare. It might just make your life a whole lot easier.
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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