MARKETING
How to Do Better, Lazier Keyword Research
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.
This post is an expansion on something I discussed in my talk at MozCon this year: my view that a lot of time spent on keyword research is essentially wasted.
Don’t get me wrong — keyword research is, of course, important. SEOs and businesses use keyword research to decide which parts of their business to prioritize, to forecast the results of their activities, to appraise possible opportunities for expansion, and of course to write title tags, brief copywriters, or engage in other tactical activity. The point is, if you paid a non-SEO consultant — perhaps a management consultant — for this level of strategic insight, you’d pay a fortune, and you’d listen very carefully.
And yet, in SEO businesses, keyword research is the task most likely to be delegated to the most junior member of the team. It’s considered grunt work. It’s boring, tedious, repetitive, and easy — so we think. I know this, because I have made this (mistaken) assumption many times as a senior SEO, and was on the receiving end of that “grunt work” early in my career.
There are three main ways I think we’re turning what should be an involved piece of strategic thinking into tedium. I’ll cover them below, along with what to focus on instead.
Quantity vs. quality
If you hit up your favorite search engine and look for some guides on how to conduct keyword research, you’ll find that a common theme is to start by amassing the most exhaustive list of potential keywords possible. If you run out of rows in Excel, or cells in Google Sheets, that is seemingly a badge of honor.
Perhaps you’ll use tools like keyword multipliers, Google Search Console, and GA Site Search to add as many obscure variants of your target keywords as you can find.
This is a fool’s errand, though.
The very blog you’re reading right now gets 48% of its daily traffic from keywords that drive only a single click. And it’s not like we’re getting the same selection of low traffic keywords every day, either. Google themselves have said repeatedly that 15% of the keywords they see every day are totally new to them.
In this context, how can we hope to truly capture every possible keyword someone might use to reach our site? It seems entirely pointless.
Why not save ourselves an absolute shit ton of time, and greatly simplify our analysis, by just capturing the few main keywords for each unique intent we wish to target?
It’s easy to produce an enormous list of keywords that contains perhaps three or four intents, but it’s a grand waste of time, as you’ll be producing some small fraction of a vast unknowable sea of keywords, and you’re going to optimize for the main ones anyway. Not to mention, it makes the rest of your analysis a total pain, and extremely difficult to consume afterwards.
Instead, try to capture 90% of the intents for your potential new page, product, or site, rather than 90% of the potential keywords. It’s far more realistic, and you can spend the time you save making strategic choices rather than swearing at Excel. On which note…
Removing automation
Another common piece of advice is to manually use the Google SERPs as a keyword research tool. This is fine in principle, and it’s advice I’ve given, particularly to editorial teams researching individual pieces of content, as it helps to make the research feel more grounded in what they’re actually trying to affect (Google SERPs).
However, for at-scale keyword research conducted by an SEO professional, this is an overly manual and redundant step. Why?
Because you’re probably already doing this, possibly twice, in other parts of your process. If you use a popular SEO suite — preferably Moz Pro, of course, but it’s not just us — this data is very likely already baked into any suggestions you’ve downloaded. Save yourself the manual data collection (or worse yet, the unreliable and finickety SERP scraping on your own personal computer) and just collect this valuable information once.
Similarly, if you’re mainly looking for keywords you ought to rank for rather than the wide open ocean of opportunity, you’ll get 90%+ of that by seeing who your competitors are, and what they rank for that you don’t.
It really doesn’t have to be some massive ordeal. Again, this is about spending more time on the important bit, and less time on the grunt work.
The wrong metrics
“The important bit”, though, is probably prioritization, which means it’s probably about metrics.
Typically, the primary metric involved in keyword research is search volume, and that’s probably unavoidable (although, not all search volumes are created equal — watch out for a Whiteboard Friday on this in the Autumn), but even the most accurate search volumes can miss the full story.
The core issue here is that click-through rates for keywords vary massively. The below range is for a random sample from MozCast:
The chart shows that only around a third of the keywords in this random set had a CTR close to 100% for all organic results combined. It also shows the high variance in total CTRs across the keywords in this group.
This is not untypical, and well-discussed in the SEO space at this point. Many SERPs have organic results that start essentially below the fold. What it means for keyword research is that volume is not that great a metric. It’s an important component — you need both volume and CTR to work out how many clicks might be available — but on its own, it’s a little suspect.
Again, this doesn’t have to be a massive ordeal, though, many tools, including Moz Pro, will give you CTR estimates for your keywords. So in the same place you get your volumes, you can get a metric that will stop you prioritizing the wrong things, or in other words, stop you further wasting your time.
TL;DR: stop wasting your time
There’s a huge amount of skill, nuance, and experience that comes into keyword research that I’ve not covered here. But my hope is that we can get into the habit of focusing on those bits, and not just screaming into the void spreadsheet.
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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