SEO
Microsoft Unveils Predictive Targeting, AI-Based Advertising Tool
Microsoft announces the launch of Predictive Targeting, an artificial intelligence-powered advertising tool.
The technology relies on machine learning to help advertisers reach new, receptive audiences and drive higher conversion rates.
Finding Hidden Audiences
Predictive Targeting analyzes signals from advertisers’ existing ads and landing pages and Microsoft’s audience data to identify potential new audiences.
The tool automatically targets ads to the audiences most likely to convert without requiring advertisers to build an audience-targeting strategy manually.
Saving Time & Increasing Efficiency
Microsoft claims Predictive Targeting can increase advertisers’ conversion rates by an average of 46 percent while streamlining the ad targeting process.
Advertisers no longer have to spend time researching to determine their target audiences and can rely on Microsoft’s algorithms to find the most promising prospects.
The tool aims to help advertisers maximize their return on investment and gain greater efficiency in their ad campaigns.
Flexibility For Different Needs
Predictive Targeting can be used independently or in combination with advertisers’ existing audience targeting strategies.
When used alone, it provides a comprehensive solution for discovering and reaching relevant audiences.
When layered on top of existing strategies, it helps advertisers expand their reach and find new potential customers outside of their defined target audiences.
This flexibility allows advertisers to tailor the tool to suit their specific needs.
Potential Drawbacks
Before switching to a new targeting solution, it’s essential to consider potential drawbacks.
By relying on Microsoft’s algorithms to determine target audiences, advertisers give up some control over who sees their ads.
The AI may target audiences advertisers did not anticipate or intend to reach.
This could result in wasted ad spend or damage to the brand if the wrong audiences are exposed to the ads.
Advertisers may want to use other targeting and measurement tools in addition to Protective Targeting to avoid complete reliance on Microsoft.
Getting Started
Predictive Targeting will now be the default targeting method for Audience Ads.
Advertisers simply have to activate the tool, and Microsoft’s algorithms will determine the optimal audiences for their ads.
Advertisers can also disable Predictive Targeting and define their audiences as needed.
Microsoft recommends that advertisers use compelling ad copy aligned with their target customers, automated bidding, expanded reach across ad groups, and campaign performance monitoring.
The company predicts Predictive Targeting will help propel advertisers’ Audience Ads campaigns forward and usher in the future of targeted advertising.
Featured image: Screenshot from about.ads.microsoft.com, June 2023.
Source: Microsoft
SEO
Holistic Marketing Strategies That Drive Revenue [SaaS Case Study]
Brands are seeing success driving quality pipeline and revenue growth. It’s all about building an intentional customer journey, aligning sales + marketing, plus measuring ROI.
Check out this executive panel on-demand, as we show you how we do it.
With Ryann Hogan, senior demand generation manager at CallRail, and our very own Heather Campbell and Jessica Cromwell, we chatted about driving demand, lead gen, revenue, and proper attribution.
This B2B leadership forum provided insights you can use in your strategy tomorrow, like:
- The importance of the customer journey, and the keys to matching content to your ideal personas.
- How to align marketing and sales efforts to guide leads through an effective journey to conversion.
- Methods to measure ROI and determine if your strategies are delivering results.
While the case study is SaaS, these strategies are for any brand.
Watch on-demand and be part of the conversation.
Join Us For Our Next Webinar!
Navigating SERP Complexity: How to Leverage Search Intent for SEO
Join us live as we break down all of these complexities and reveal how to identify valuable opportunities in your space. We’ll show you how to tap into the searcher’s motivation behind each query (and how Google responds to it in kind).
SEO
What Marketers Need to Learn From Hunter S. Thompson
We’ve passed the high-water mark of content marketing—at least, content marketing in its current form.
After thirteen years in content marketing, I think it’s fair to say that most of the content on company blogs was created by people with zero firsthand experience of their subject matter. We have built a profession of armchair commentators, a class of marketers who exist almost entirely in a world of theory and abstraction.
I count myself among their number. I have hundreds of bylines about subfloor moisture management, information security, SaaS pricing models, agency resource management. I am an expert in none of these topics.
This has been the happy reality of content marketing for over a decade, a natural consequence of the incentives created by early Google Search. Historically, being a great content marketer required precisely no subject matter expertise. It was enough to read widely and write quickly.
Mountains of organic traffic have been built on the backs of armchair commentators like myself. Time spent doing deep, detailed research was, generally speaking, wasted, because 80% of the returns came from simply shuffling other people’s ideas around and slapping a few keyword-targeted H2s in the right places.
But this doesn’t work today.
For all of its flaws, generative AI is an excellent, truly world-class armchair commentator. If the job-to-be-done is reading a dozen articles and how-to’s and turning them into something semi-original and fairly coherent, AI really is the best tool for the job. Humans cannot out-copycat generative AI.
Put another way, the role of the content marketer as a curator has been rendered obsolete. So where do we go from here?
Hunter S. Thompson popularised the idea of gonzo journalism, “a style of journalism that is written without claims of objectivity, often including the reporter as part of the story using a first-person narrative.”
In other words, Hunter was the story.
When asked to cover the rising phenomenon of the Hell’s Angels, he became a Hell’s Angel. During his coverage of the ‘72 presidential campaign, he openly supported his preferred candidate, George McGovern, and actively disparaged Richard Nixon. His chronicle of the Kentucky Derby focused almost entirely on his own debauchery and chaos-making—a story that has outlasted any factual account of the race itself.
In the same vein, content marketers today need to become their stories.
It’s a content marketing truism that it’s unreasonable to expect writers to become experts. There’s a superficial level of truth to that claim—no content marketer can acquire a decade’s worth of experience in a few days or weeks—but there are great benefits awaiting any company willing to challenge that truism very, very seriously.
As Thompson proved, short, intense periods of firsthand experience can yield incredible insights and stories. So what would happen if you radically reduced your content output and dedicated half of your content team’s time to research and experimentation? If their job was doing things worth writing about, instead of just writing? If skin-in-the-game, no matter how small, was a prerequisite of the role?
We’re already seeing this shift.
Every week, I see more companies hiring marketers who are true, bonafide subject matter experts (I include the Ahrefs content team here—for the majority of our team, “writing” is a skill secondary to a decade of hands-on search and marketing experience). They are expensive, hard to find, and in the era of AI, worth every cent.
I see a growing expectation that marketers will document their experiences and experiments on social media, creating meta-content that often outperforms the “real” content. I see more companies willing to share subjective experiences and stories, and avoid competing solely on the sharing of objective, factual information. I see companies spending money to promote the personal brands of in-house creators, actively encouraging parasocial relationships as their corporate brand accounts lay dormant.
These are ideas that made no sense in the old model of content marketing, but they make much more sense today. This level of effort is fast becoming the only way to gain any kind of moat, creating material that doesn’t already exist on a dozen other company blogs.
In the era of information abundance, our need for information is relatively easy to sate; but we have a near-limitless hunger for entertainment, and personal interaction, and weird, pattern-interrupting experiences.
Gonzo content marketing can deliver.
SEO
I Got 129.7% More Traffic With Related Keywords
A few weeks ago, I optimized one of my blog posts for related keywords. Today, it gets an estimated 2,300 more monthly organic visits:
In this post, I’ll show you how I found and optimized my post for these related keywords.
Related keywords are words and phrases closely linked to your main keyword. There are many ways to find them. You can even just ask ChatGPT.
But here’s the thing: These keywords aren’t useful for optimizing content.
If more traffic is your goal, you need to find keywords that represent subtopics—not just any related ones.
Think of it like this: you improve a recipe by adding the right ingredients, not everything in your fridge!
Below are two methods for finding the right related keywords (including the one I used):
Method 1. Use content optimization tools
Content optimization tools look for keywords on other top-ranking pages but not yours. They usually then recommend adding these keywords to your content a certain number of times.
These tools can be useful if you take their recommendations with a pinch of salt, as some of them can lead you astray.
For example, this tool recommends that I add six mentions of the phrase “favorite features” to our keyword research guide.
Does that seem like an important related keyword to you? It certainly doesn’t to me!
They also usually have a content score that increases as you add the recommended related keywords. This can trick you into believing that something is important when it probably isn’t—especially as content scores have a weak correlation with rankings.
My advice? If you’re going to use these tools, apply common sense and look for recommendations that seem to represent important subtopics.
For example, when I analyze our content audit guide, it suggests adding quite a few keywords related to content quality.
It doesn’t take a genius to work out that this is an extremely important consideration for a content audit—yet our guide mentions nothing about it.
This is a huge oversight and definitely a batch of related keywords worth optimizing for.
Try the beta version of our new AI Content Helper!
Instead of counting terms that you need to include in your content, Content Helper uses AI to identify the core topics for your target keywords and scores your content (as well as your competitors) against those topics as you write it. In effect, it groups related keywords by subtopic, making it easier to optimize for the broader picture.
For example, it looks like my post doesn’t cover Google Business Profile optimization too well. This is something it might be worth going into more detail about.
Method 2. Do a keyword gap analysis (this is the method I used!)
Keyword gaps are when competitors rank for keywords you don’t. If you do this analysis at the page level, it’ll uncover related keywords—some of which will usually represent subtopics.
If possible, I recommend doing this for pages that already rank on the first page for their main target keyword. These pages are doing well already and likely just need a bit of a push to rank high and for more related keywords. You can find these in Site Explorer:
- Enter your domain
- Go to the Organic Keywords report
- Filter for positions 2-10
- Look for the main keywords you’re targeting
Once you have a few contenders, here’s how to do a keyword gap analysis:
a) Find competitors who are beating you
In the Organic Keywords report, hit the SERP dropdown next to the keyword to see the current top-ranking pages. Look for similar pages that are getting more traffic than yours and have fewer referring domains.
For example, our page ranks #10 for “local SEO,” has 909 referring domains, and gets an estimated 813 monthly visits:
All of these competing pages get more traffic with fewer backlinks:
Sidenote.
I’m going to exclude the page from Moz going forward as it’s a blog category page. That’s very different to ours so it’s probably not worth including in our analysis.
b) Send them to the content gap tool
Hit the check boxes next to your competitors, then click “Open In” and choose Content gap.
By default, this will show you keywords where one or more competitors rank in the top 10, but you don’t rank anywhere in the top 100.
I recommend changing this so it shows all keywords competitors rank for, even if you also rank for them. This is because you may still be able to better optimize for related keywords you already rank for.
I also recommend turning the “Main results only” filter on to exclude rankings in sitelinks and other SERP features:
c) Look for related keywords worth optimizing for
This is where common sense comes into play. Your task is to scan the list for related keywords that could represent important subtopics.
For example, keywords like these aren’t particularly useful because they’re just different ways of searching for the main topic of local SEO:
But a related keyword like “what is local SEO” is useful because it represents a subtopic searchers are looking for:
If this process feels too much like trying to find a needle in a haystack, try exporting the full list of keywords, pasting them into Keywords Explorer, and going to the “Cluster by terms” report. As the name suggests, this groups keywords into clusters by common terms:
This is useful because it can highlight common themes among related keywords and helps you to spot broader gaps.
For example, when I was looking for related keywords for our SEO pricing guide (more on this later!), I saw 17 related keywords containing the term “month”:
Upon checking the keywords, I noticed that they’re all ways of searching for how much SEO costs per month:
This is an easy batch of related keywords to optimize for. All I need to do is answer that question in the post.
If you’re still struggling to spot good related keywords, look for ones sending competing pages way more traffic than you. This usually happens because competitors’ pages are better optimized for those terms.
You can spot these in the content gap report by comparing the traffic columns.
For example, every competing page is getting more traffic than us for the keyword “how much does SEO cost”—and Forbes is getting over 300 more visits!
Now you have a bunch of related keywords, what should you do with them?
This is a nuanced process, so I’m going to show you exactly how I did it for our local SEO guide. Its estimated organic traffic grew by 135% after my optimizations for related keywords:
Sidenote.
Google kindly rolled out a Core update the day after I did these optimizations, so there’s always a chance the traffic increase is unrelated. That said, traffic to our blog as a whole stayed pretty consistent after the update, while this post’s traffic grew massively. I’m pretty sure the related keyword optimization is what caused this.
Here are the related keywords I optimized it for and how:
Related keyword 1: “What is local SEO”
Every competing page was getting significantly more traffic than us for this keyword (and ranking significantly higher). One page was even getting an estimated 457 more visits than ours per month:
People were also searching for this in a bunch of different ways:
My theory on why we weren’t performing well for this? Although we did have a definition on the page, it wasn’t great. It was also buried under a H3 with a lot of fluff to read before you get to it.
I tried to solve this by getting rid of the fluff, improving the definition (with a little help from ChatGPT), and moving it under a H2.
Result? The page jumped multiple positions for the keyword “what is local SEO” and a few other similar related keywords:
Related keyword 2: Local SEO strategy
Once again, all competing pages were getting more traffic than ours from this keyword.
I feel like the issue here may be that there’s no mention of “strategy” in our post, whereas competitors mention it multiple times.
To solve this, I added a short section about local SEO strategy.
I also asked ChatGPT to add “strategy” to the definition of local SEO. (I’m probably clutching at straws with this one, but it reads nicely with the addition, so… why not?)
Result? The page jumped seven positions from the bottom of page two to page one for the related keyword:
Related keyword 3: “How to do local SEO”
Most of the competing pages were getting more traffic than us for this keyword—albeit not a lot.
However, I also noticed Google shows this keyword in the “things to know” section when you search for local SEO—so it seems pretty important.
I’d also imagine that anyone searching for local SEO wants to know how to do it.
Unfortunately, although our guide does show you how to do local SEO, it’s kind of buried in a bunch of uninspiring chapters. There’s no obvious “how to do it” subheading for readers (or Google) to skim, so you have to read between the lines to figure out the “how.”
In an attempt to solve this, I restructured the content into steps and put it under a new H2 titled “How to do local SEO”:
Result? Position #7 → #4
No. Nothing in SEO is guaranteed, and this is no different.
In fact, I optimized our SEO pricing guide for related keywords on the same day, and—although traffic did improve—it only improved by around 23%:
Sidenote.
You might have noticed the results were a bit delayed here. I think this is because the keywords the post ranks for aren’t so popular, so they’re not updated as often in Ahrefs.
For full transparency, here’s every related keyword I optimized the post for and the results:
Related keyword 1: “How much does SEO cost”
Each competing page got more traffic than ours from this keyword, with one getting an estimated 317 more monthly visits:
When I clustered the keywords by terms in Keywords Explorer, I also saw ~70 keywords containing the word “much” (this was around 19% of all keywords in the Content Gap report!):
These were all different ways of searching for how much SEO costs:
The issue here appears to be that although we do answer the question on the page, it’s quite buried. There’s no obvious subheading with the answer below it, making it hard for searchers (and possibly Google) to skim and find what they’re looking for:
To solve this, I added a H2 titled “How much does SEO cost?” and added a direct answer below.
Result? No change in rankings for the related keyword itself, but the page did win a few snippets for longer-tail variations thanks to the copy I added:
Related keyword 2: “SEO cost per month”
Nearly all competing pages were getting more traffic than us for this keyword, with one getting an estimated 72 monthly visits more than more us.
The term clustering report in Keywords Explorer also showed that people are searching for the monthly cost of SEO in different ways:
This is not the case for hourly or retainer pricing; there are virtually no searches for this.
I think we’re not ranking for this because we haven’t prioritized this information on the page. The first subheading is all about hourly pricing, which nobody cares about. Monthly pricing data is buried below that.
To fix this, I moved the data on monthly pricing further up the page and wrote a more descriptive subheading (“Monthly retainer pricing” →“Monthly retainer pricing: How much does SEO cost per month?”).
I also changed the key takeaways in the intro to focus more on monthly pricing, as this is clearly what people care about. Plus, I simplified it and made it more prominent so searchers can find the information they’re actually looking for faster.
Result? The page won the featured snippet for this related keyword and a few other variations:
Related keyword 3: “Local SEO pricing”
I found this one in the term clustering report in Keywords Explorer, as 16 keywords contained the term “local.”
Upon further inspection, I realized these were all different ways of searching for the cost of local SEO services.
I think the problem here is although our post has some data on local SEO pricing, it doesn’t have the snappy figure searchers are likely looking for. Plus, even the information we did have was buried deep on the page.
So… I actually pulled new statistics from the data we collected for the post, then put them under a new H3 titled “How much does local SEO cost?”
Result? Small but notable improvements for this keyword and a few other variations:
Related keyword 4: “How much does SEO cost for a small business”
I saw that one competing page was getting an estimated 105 more monthly organic visits than us from this term.
When clustering by terms in Keywords Explorer, I also saw a cluster of nine keywords containing the word “small.” These were all different ways of searching for small business SEO pricing:
Once again, the issue here is clear: the information people are looking for isn’t on the page. There’s not even a mention of small businesses.
This is good as it means the solution is simple: add an answer to the page. I did this and put it under a new H3 titled “How much does SEO cost for small businesses?”
Result? #15 → #5 for this related keyword, and notable improvements for a few other variations:
Related keyword 5: “SEO pricing models”
This related keyword probably isn’t that important, but I spotted it looking through the Content gap report and thought it’d be pretty easy to optimize for.
All I did was create a new H2 titled “SEO pricing models: a deeper breakdown of costs.” I then briefly explained the three common pricing models under this and re-jigged and nested the rest of the content from the page under there.
Result? #5 → #1:
Final thoughts
Related keyword optimization isn’t about shoehorning a bunch of keyword variations into your content. Google is smart enough to know that things like “SEO” and “search engine optimization” mean the same thing.
Instead, look for keywords that represent subtopics and make sure you’re covering them well. This might involve adding a new section or reformatting an existing section for more clarity.
This is easy to do. It took me around 2-3 hours per page.
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