MARKETING
How to Use STAT to Discover Extra Value in Your Keyword Data
Maximizing the value in your keyword data is crucial if you’re a brand trying to rank on the forever-changing SERPs. With that in mind, Moz Learning and Development Specialist Zoe Pegler walks you through the key features in STAT Search Analytics that will help you glean extra (and important) insights from that data.
Video Transcription
Hi and welcome to another edition of Whiteboard Friday. I’m Zoe. I work in the Learning team here at Moz, and my focus is on developing educational materials and resources to promote understanding of our STAT tool. Today, we’re going to take a look at how you can use STAT to surface additional insights in your keyword data. Looking for value in your keyword data is important if you’re a brand trying to stay visible and on top of the forever-changing SERPs. Looking for those additional insights is paramount to making informed SEO decisions.
What is STAT?
So what is STAT? If you haven’t come across STAT before, it’s a large-scale rank tracker, but it’s also fantastic for many other things, SERP analysis and intent, a competitive landscape tool. Its value is really how you can dig into the data it provides.
Quick wins
So where are the quick wins here? Well, we know that usually improved ranking position means increased or at least some uplift in traffic for that term. Knowing where you sit on the SERPs and what features you’re winning can make or break your content strategy. This is where STAT’s dynamic tags can be hugely useful.
Dynamic tagging
Dynamic tagging allows you to group keywords together based on criteria you set. That group is then automatically populated daily with keywords based on changeable filter criteria. This means you have the ability to create a group of keywords with any criteria that’s important to your business. A great grouping criteria to set here is keywords based on ranking position.
For example, flagging keywords which are sitting just outside the top 3, top 5, or top 10 positions and adding a traffic benchmark means you can easily discover which keywords with traffic potential need just a little bit of extra work to shift them into that better position.
So maybe your strategy includes hunting down a featured snippet. If so, you can use STAT to set up a dynamic tag that monitors keywords that result in featured snippets. If you see keywords that aren’t ranking well within that grouping, they’re not winning those snippets.
So if you’re tracking keywords for a client, this is a great way of seeing where your client owns a snippet and where they don’t. You can try to take the spot from their competitors by finding new opportunities to create optimized content. Using this feature is powerful for getting quick feedback on the intent and type of content that perform best in a keyword set, which ultimately is what you can use to guide your content strategy.
Data views
So we know that organizing your data is key. How do you organize your data so it’s meaningful to you while allowing you to see potential opportunities to quickly report back to a client? In STAT, you can hold all your keyword groups, your tags in a single data view. So keeping selected tags in a data view means you get a single dashboard of metrics for those chosen keyword groups
For example, you could easily put together a data view of tags that reflect the tactical aspirations of your client. If you’re a search marketer, you may choose to set up a data view containing keyword segments that cover stages of the conversion funnel. You really want to split keywords into segments that reflect what your clients want to target. So that could be industry sectors, services, or locations. If product categories are important to your client, you can set up a data view containing keywords tagged as shoes, sportswear, swimwear, or whatever specific attributes you need to track across a product line.
Tags tab
Okay. So maybe you’re looking for a quick health check on how that specific set of keyword groupings are doing. Well, there’s a smart little feature in STAT that allows you to compare all of your tags and how well each of those are performing in terms of visibility online, and it’s called the Tags tab. So this feature is pretty cool because it allows you to see the performance of all those tags in one place. You’ll get the overall picture of how your SEO strategy is progressing and where to focus your attention based on your most important metrics. You can set a specific date range. You can see average rank, top 10 change.
Visibility
Another key piece of information is finding out who you’re up against for those keyword sets you just put together. You can use STAT to view share of voice across that specific set of keyword tags or for that entire market. The share of voice metric used in STAT measures the visibility of a given keyword set on Google. This means you can get invaluable insights, such as where competitors are increasing or decreasing in their visibility.
Now, you may have clients that need formalized insights into the progression of a campaign. What’s the best way of displaying those reports? Well, in STAT, we have built connectors that allow you to pull live data from STAT into Google Data Studio reports. These visualizations make it easier to share insights with clients and for them to see those top level metrics really quickly.
So hopefully these tips and tools mean you can really investigate how much extra value and insights you can squeeze out of your keyword data. Have a great day and thank you for watching this edition of Whiteboard Friday.
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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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