Welcome back to Dijorage, I'm Jeff the producer here at Byer Company.
I wrote an article for today's pod titled "Acroniminum, the A to Z of digital marketing
ABCs."
It's a fun and informative for A into the known and not so well known acronyms, including
all the AI-based variations, which are very hard to differentiate.
If you don't know the difference between GEO, AEO and AIO, you are in the right place.
I'm really excited for today's session.
We're going to tackle something that frankly feels like a constant challenge these days.
Just the sheer number of acronyms in digital marketing.
Our mission isn't just listing them.
No, we want to cut through that alphabet soup, you know, and pass the buzzwords, and really
get to the strategic heart of what matters in this super fast paced AI-driven world we're
in.
We're using a great source today, the ultimate list of digital marketing acronyms 2025.
A guide to SEO, GEO, AIO, and more by Jeff Byer.
It just came out September 16, 2025.
And I hooked me right away with this title question, basically asking, "WFT do these acronyms
mean?"
Which is exactly what we're here to figure out for you.
We're going to unpack this new language of digital marketing, covering how things like
search, content, analytics, even personalization are all being reshaped.
You really hit the nail on the head there.
It's not just about learning new letters.
It's about understanding this vocabulary that's evolving so, so quickly.
And it reflects these massive shifts in how businesses are actually connecting with people
online.
These terms aren't just jargon marketers throw around.
They represent the new playing field, strategically speaking.
So if you want to stay genuinely well informed, getting this language isn't just nice to
have.
It's, it's pre-essential for unlocking what actually drives visibility and engagement
now.
Okay.
Let's unpack this then.
This is right, when we talked about getting found online, it was all about SEO.
Search engine optimization.
That was the big one.
Making sure your stuff ranked high in Google's traditional results list.
But this source, it points at some really interesting, frankly, game-changing new ones.
They should just how much search has changed, mostly because of AI, right?
So what happens when the search engine itself is basically an AI?
That's the million dollar question, isn't it?
And it's a huge evolution from traditional SEO.
Take Geo, for example.
It's a lot of engine optimization.
This isn't just optimizing for Google to show like 10 blue links.
It's about optimizing specifically for AI-driven search results.
Thinking about how an AI will pull info together, summarize it, and present it right there
to the user, which connects directly to SGE, the search generative experience.
Our source mentions this is Google's big move into AI search results.
And it totally changes what you see at the top of the page.
Often it's a direct AI-written answer or summary, not just those old links.
OK, so it's less about climbing the list and more about being the answer the AI uses.
Exactly.
And that leads us straight to AEO.
Answer engine optimization.
This is kind of the practical side of GEO and SGE.
It means building your content so it's the clear, trustworthy answer and AI would grab
for a query.
The strategic shift is just massive.
You're not just stuffing keywords anymore.
You're focused on giving comprehensive, easy to understand, trustworthy information that
an AI can process easily and say, yep, this is the answer.
It means your whole content strategy moves from just ranking to directly answering complex
questions.
Sometimes questions the user didn't even type, but the AI figured out they meant.
That's a huge change in thinking.
So the familiar SREP, the search engine results page, got this whole new layer now.
And to succeed there, especially with AI involved, our source really hammers home EET.
Experience, expertise, authoritativeness, trustworthyness.
It's important, but I guess it's even more vital when an AI is judging credibility.
How do you even signal expert to a machine, not just a person?
Yeah, it's all about those signals.
EET is basically your credibility score for the AI.
If the AI can't trust your content, it's not going to use it.
So what is that?
So it's more than just writing well.
It's about clear site structure showing real credentials, being consistent in your
niche, even technical stuff like structured data helps explicitly tell the machines what
your content is about and who wrote it.
And the tech that lets the AI get all this, that's NLP, natural language processing, our
source calls it core to semantic search and AI optimization.
It lets machines understand language like humans do, basically, in context.
Even older ideas like TF IDF, term frequency inverse document frequency measuring term relevance,
and LSI latent semantic indexing, finding related concepts.
There's still kind of humming away in the background influencing how these AI systems
understand content.
They're like the old foundations for this new AI understanding.
So the big takeaway.
With AI shaping search, you've got to prove EET to the machines through everything you
do.
It's holistic.
Yeah, this is where it gets really interesting for me.
If AI changes how we're found, it's definitely changing how we create the content in the first
place.
It's not just what we say, but how it's even made.
Does this mean like content creation as we know it is broken or just evolving?
What's the biggest thing we need to get ready for?
Oh, it's definitely evolving, not broken and the biggest shift.
Using the power of humans plus AI, working together, our source brings up AIO, AI optimized
content.
That term captures it perfectly.
It usually doesn't mean 100% robot-written stuff.
It means AI assists in boost human creativity.
That human element is still critical for quality, for nuance, especially for tricky subjects.
The tech doing the writing itself.
That's NLG, natural language generation, machines producing human-like text.
The engines behind tools like chat GPT, they're LLMs, large language models.
And GPT itself stands for generative, pre-trained transformer.
These are the huge AI models trained on tons of text data.
They can predict and generate really coherent stuff, but they're not perfect.
Right, I've heard they can sometimes hallucinate or just make things up.
So how do we make sure the AI generated content is actually, well, true?
Yeah, that's a major challenge.
And that's where something like our rag comes in retrieval augmented generation.
It's super important.
It's an AI technique where the system first retrieves facts from trusted external sources
before it generates the answer.
Our rag makes AI less likely to just invent stuff and more likely to give you verifiable
info like an AI tool using a rag might check your company's own knowledge base first
to make sure the output is accurate and sounds like you.
It's key for quality, trust worthy content.
But even with all this tech, you can't just set it and forget it, can you?
Exactly.
Speaking of quality and making sure things work, the source reminds us about a B T, which
just means always be testing.
It stresses that need for constant A B testing or even more complex tests, even with AI helping
create the content.
You might get three great headlines from an AI, but you still got a test which one people
actually click on.
It's that feedback loop is an AI creates humans tweak testing proves what works.
Always be testing is crucial because even the smartest AI needs that real world check.
Is this output actually resonating with your audience?
Is it hitting your goals?
I've seen clients get amazing sounding AI subject lines that just bombed because they skipped
the A B T steps.
So LLMs, R, A, I, O, they push the limits, but A B T keeps it grounded in actual performance.
Okay, so we're building these smart AI powered content strategies testing them constantly.
But the bottom line question is always, how do we know if it's working and maybe more importantly,
how do we prove it's worth the investment?
That leads us to the next batch of acronyms, right?
Precisely.
The ultimate is absolutely key and AI is changing how we do it too.
You probably know KPIs, keep performance indicators, those specific measurable things showing if
you're hitting targets and OKRs, objective and key results which give you the framework
for setting those goals and tracking them.
These are standard, but AI changes how we use them.
The ultimate measure naturally is ROI, return on investment.
What's really changing now is how AI helps us predict future ROI based on current KPIs.
Moving from just reporting what happened to guiding future actions.
And then for customer value and efficiency, there's CLV, customer lifetime value, how much
it customers worth over time, and CAC, customer acquisition, cost what it costs you to get that
customer.
AI lets us predict CLV much more accurately, find our best customers, and really fine tune
CACs that we're spending money efficiently, getting profitable customers, not just clicks.
Wow, so it's like AI is giving our dashboards a bit of a crystal ball.
Our source mentions GAI, generative AI being used in marketing tech and analytics.
I can see how that would be super powerful for spotting trends or insights you'd otherwise
miss.
This must tie into BI, business intelligence too, AI making those platforms smarter.
Absolutely, the synergy there is huge.
GAI and machine learning are transforming BI platforms.
It's not just tracking KPIs and ROI anymore, it's predicting future CLV, spotting churn
risks before they happen, optimizing CAC with incredible detail.
Imagine AI digging through mountains of data, finding subtle patterns humans wouldn't catch,
and then generating reports in plain English that explain complex trends and even suggest
what to do next.
It's shifting us from reacting to data to proactively making smarter decisions that boost
the bottom line.
It's about building systems that learn and adapt, constantly improving how we measure and
understand performance, even predicting market shifts.
Okay, so we've got AI-driven search, smarter content, predictive analytics.
How do we actually do all this stuff?
The tech hold it all together, the source talks about the tech stacks behind successful
digital marketing.
What are the key pieces?
Yeah, it's definitely an ecosystem.
The source points at several key platforms that need to work together.
First, the CDP customer data platform.
That's vital for pulling all your customer data from different places into one single
view, your customer truth source, basically.
Then the CMS content management system, the backbone for your website content.
Pretty standard.
For organizing images, videos, all that stuff, you need damn digital asset management.
A step up from that is the DXP digital experience platform that's broader designed to manage
and deliver personalized experiences across lots of channels, not just the website.
Then managing customer interactions, the CRM customer relationship management system,
everyone knows that one.
And for automating marketing tasks like emails or leadscoring, use a nemapit marketing
automation platform.
The secret sauce often running inside many of these is ML, machine learning.
That's the intelligence engine for personalization, segmentation, automation.
And sometimes you just hear AISCO is kind of a catch-all for using AI and SEO across these
tools.
What really strikes me here is how these aren't just isolated tools anymore.
It sounds like they're becoming parts of an interconnected system, always learning,
like they're designed to work together with ML linking them to make the customer journey
feel smooth, almost intuitive.
And then all talking to each other must be the real challenge though.
You absolutely nailed it.
The goal is integrated experiences.
And ML is like the nervous system connecting it all.
It lets each part learn from data and get better recommending content on a DXP, segmenting
audiences in a CDP, personalizing emails from MAP.
And yes, the biggest hurdle is usually integration, getting these systems to share data smoothly
in real time.
So many companies still struggle with data silos, you know, CRM doesn't talk to MAP,
website data is separate.
Breaking those down is key to building that smart cohesive ecosystem where AI can really
shine and deliver those personalized experiences.
Okay, final piece.
We've got the tech, the AI-powered content, the measurement.
How do we make sure our actual messages are outreach hits the right person at the right
time?
How do we refine that connection?
It feels like it comes down to precision targeting and testing.
Exactly.
Precision is the name of the game here, optimization tactics are essential.
A/B testing is the classic method, right? Compare version A to version B of a page, email,
whatever, see what works better.
Small improvements add up.
Then there's MDT, multivariate testing.
That lets you test multiple changes on multiple elements all at once.
More complex, but gives deeper insights.
Like testing three headlines, two images and two buttons simultaneously.
For outreach, especially email, your ESP email service provider is key.
The source stresses AI-optimized personalization here, making emails super relevant, maybe
even changing content dynamically based on what the user does.
In paid ads, there's RTB real-time bidding.
That's AI-driving programmatic ads, bidding for ads slots in milliseconds, matching ads
with people instantly.
And of course, PPC, PiperClick.
That familiar ad model is now turbocharged by AI, optimizing bids and targeting way beyond
what humans could manage manually.
This part really brings back that always-be-testing idea we talked about, doesn't it?
It's like this constant cycle of refinement.
Using these tools, these techniques to make every single interaction as effective as possible.
It's learning, adapting, tweaking with AI, giving us these incredible tools to do it faster
and smarter.
It absolutely does.
That always-be-testing mindset is baked right into all these tactics.
From ABN/MBT, finding tiny user preferences, to AI personalization and ESPs, to the instant
adjustments of RTB and PPC.
It's all about that cycle, hypothesized, test, analyze, improve, repeat.
AI just speeds up that cycle incredibly.
It lets marketers optimize campaigns at a stale and speed we couldn't even dream of a few
years back.
Think about AI and RTB adjusting millions of bids every second based on predicted engagement.
That's optimizing every single penny spent.
It's how marketers stay effective, adapting constantly to how people behave online and how
the digital space keeps changing.
Wow.
OK.
So, we have really dug deep into what might have felt like just Alphabet soup before.
Hopefully you now have a much better handle on the strategic language of modern digital marketing.
We went beyond just defining terms like GEO and STE to see how they reshape search with
AI.
We looked at how AIO and RT impact content creation, how AI boosts analytics for measuring
success.
We mapped out the Martech Toolkit with things like DXPs and saw how AB testing and RTB
intelligently optimize outreach.
This dive into Jeff Byer's ultimate list really gives you I think a powerful shortcut
to not just navigate but really get this complex rapidly changing field.
You should feel more fluent now in the strategic shifts behind the acronyms.
And that understanding is just so vital because this whole area is constantly moving.
The real question isn't just what these acronyms mean today.
It's what comes next.
Think about it.
With AI and digital platforms evolving so fast, what new acronyms might pop up by the
end of next year.
What new ways of thinking about optimization will emerge.
How will they challenge how we connect to communicate online right now?
Being curious constantly exploring that's not just good advice.
It's really the key to staying truly informed on this amazing digital frontier.
Reach out to us at jbuyer.com for comments and questions.
Follow us at buyer company on social media.
And if you'd be so kind, please rate and review us in your podcast app.
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