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