MARKETING
New Competitive Research Suite: Actionable Data to Drive Real Results
I once compared keyword research to an avalanche – it’s loud, exciting, and you’re likely to end up buried alive. Over the years, as I’ve tested new product ideas (even with enterprise SEOs), I’ve found that people don’t really want all the data. They want the right data.
I’m thrilled to announce Moz’s Competitive Research Suite, built from the ground up to drive targeted data and actionable insights about your competitors, your competitive keyword gap, and your content gap. Instead of telling you, though, let us show you.
Your keyword gap, reinvented
I recently bought some sunglasses from Goodr. Let’s pretend I’m analyzing their competitive SEO landscape, and I’ve picked three targeted competitors in the online sunglasses market that specialize in active customers and sports sunglasses. I’d first enter the sites in the mini-wizard:
You can choose your market and either Domain or Subdomain for the target site and each competitor. After some summary statistics about the sites, you’ll see the “Keywords to Improve” section, which looks something like this:
Scroll horizontally to see our all-new Traffic Lift metric, Keyword Volume, Keyword Difficulty, your current ranking, and the ranking of each of your chosen competitors.
More signal = actionable results
If you did a traditional keyword gap analysis, you might look at each competitor individually and manually dig through the intersections. Let’s say we put SportEyes.com into our own Keyword Explorer. The first few results look something like this:
This is perfectly useful data about one competitor’s rankings, except for one problem — Goodr doesn’t sell swim goggles or ski goggles. Even intersecting a couple of competitors could easily produce irrelevant results, competitors’ branded terms, or keywords where your site already outranks competitors and has very little to gain. Put simply, there’s a lot of noise.
Keywords to Improve is a new way of thinking about the competitive keyword gap. We focus on keywords where your site ranks in the top 20 (you can easily expand this in the filters), but is outranked by one or more competitors. We also attempt — by analyzing on-SERP signals — to filter out branded and brand-like terms.
We cut through the noise, boost your SEO signal, and surface actionable results.
Lift your traffic, lift your ROI
We SEOs love big keyword volume numbers, but here’s the hard truth — even if we could perfectly accurately estimate volume, it’s a bit of a fantasy. If you create a competitive keyword research spreadsheet with 10,000 keywords with an average volume of 1,000, are you going to guarantee your boss those 10 Million visitors? Of course not.
What if you have no capacity to rank for that keyword? What if sites like yours (including your competitors) have a realistic ranking cap? SEO isn’t a process of going from no ranking to #1 on every keyword imaginable, and even #1 doesn’t get all the clicks.
All of this is why we’re introducing Traffic Lift. The Traffic Lift column looks at what we think you could gain by moving from your current ranking to your competitors’ best current ranking. In part, it’s tough love. Living in a fantasy isn’t good for business. More importantly, it’s a way to prioritize. See the sample results below:
Unlike swimming goggles, a product Goodr doesn’t even sell, cycling and running sunglasses are product categories that are very relevant and where they’re outranked by similar competitors. There is ample room for improvement here and real ROI. Traffic Lift finds the wins.
Your competitors’ top content
A little more tough love — keywords aren’t action. Keywords are potential. A mountain of keywords is more likely to bury you than benefit you. We can help you find the best keywords, but ultimately, we want to understand how those keywords are shaped into content.
Our first-generation (and there’s much more to come) Top Competing Content report shows you how your keyword gap is being served by your competitors. Let’s look at the Goodr data:
The “Top Ranking Keywords” are just a sampling, but here we can see, for example, how one competitor’s page is capturing multiple keywords related to “cycling sunglasses”. Now, you can start to see how those keywords function as a concept and you’ve got specific competitor pages to target.
This is the next step of competitive keyword research — going beyond a pile of individual, unrelated, and even irrelevant keywords to a plan of action that includes targeted, high-lift keywords, targeted content, and a top-level view of the competitive landscape.
If you want broader data or a different viewpoint, there’s a full range of filters and sorts to let you adjust our default settings. Of course, you can also export both Keywords to Improve and Top Content Competitors to carve through the pile as you please.
True competitors, truer results
In September of 2021, we launched True Competitor, and I promised that it was a first step in Moz’s new approach to competitive analysis. True Competitor is now more than a stand-alone tool — it’s a starting point to understanding your keyword and content gaps:
From True Competitor, you can now easily select up to three competitors and run your Keyword Gap analysis. As you can see, this is how I kicked off my Goodr example, even though I had almost no knowledge of their competitive landscape. Imagine what you could do with your actual knowledge of your site, your customers, and even your prospects.
Even for Goodr, this journey I took is just one possible journey. I chose to focus on sports sunglasses, but there are dozens of niches that they could explore, even as a relatively small site. Competitive analysis isn’t one and done — it’s a process of surfacing opportunities, acting on those opportunities, and re-evaluating as your competitors evolve.
The Competitive Analysis Suite is now available to all Moz Pro customers, and we’d love to hear your feedback via the ‘Make a Suggestion’ button in the app.
Sign up for a free trial to access the Competitive Research Suite!
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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