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Hey, everyone, and welcome back to the Conquer AI Search podcast. It's great to be here with you. Hello.

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It's a pleasure to join you for another crucial conversation today. I'm Avi, and that's Catherine. We're both part of the team here at AI Monitor.

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And today, we're diving into our ninth episode in this 11-part series. That's right. We're exploring the most effective AI optimization techniques to really help you conquer AI searches.

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Think Google AI Overview, Chat GPT, Perplexity AI. Exactly. We want you to thrive in this, well, this new search world.

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And maybe it's helpful to quickly touch on what we've covered so far, just to set the stage. Good idea. Okay.

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So first off, we talked about implementing the LLM's TXT file, sort of like a guide for AI crawlers. Right. Telling them how to use your content.

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Then we looked at Reddit, why participating there strategically matters for AI visibility. A bit counterintuitive for some, but key. Definitely.

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We also covered using authoritative citations, evolving your SEO playbook beyond the traditional stuff. And why PR is suddenly so vital again for AI search. Public relations.

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Yeah. Essential now. And more recently, readability.

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Why that's the secret sauce. Then creating actual quality content that AI recognizes. Not just keyword stuffing, real quality.

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And just last time, we tackled long tail keywords, how to figure out those complex user prompts in AI search to boost your AIO, your artificial intelligence optimization. So that brings us to today, episode nine. And we're tackling something that, well, it keeps coming up as absolutely critical.

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Why getting reviews strategically is essential for AI search visibility. And the keyword there is strategically. Right.

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It's not just about having reviews anymore, is it? It seems like they've become this powerful AI ranking signal, not just for human trust. That's precisely it. The whole landscape is shifting under our feet.

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AI is, I mean, it's fundamentally changing the search experience. You mentioned bulldozing before we started recording. Huh.

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Well, it feels a bit like that sometimes. Look at the Gartner predictions we've seen. They're forecasting a 25% drop in traditional search engine volume by 2026.

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Wow. 25%. That's soon.

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It is. And potentially over 50% by 2028. Users are just migrating.

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They're moving to AI chatbots for instant answers. So people are ditching the 10 blue links for these answer engines like ChatGPT, Google's AI overviews, perplexity. Exactly.

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Which means that traffic, the organic traffic you used to count on coming to your website, well, it's seriously at risk now. If you're not in the AI answer, you're potentially invisible. Precisely.

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And this is where reviews become even more critical. In this AI-driven world, online reviews carry, frankly, more weight than ever. More weight how? They influence consumer decisions, obviously, but now they also heavily influence search engine rankings, AI rankings, if they're moving from being just, you know, conversion tools.

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Helping someone decide to buy once they're on your site. Right. To becoming discovery signals.

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Yeah. Signals that AI uses to understand your business, evaluate it, and decide whether to even show it in the first place. Okay.

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That discovery piece is huge. And you mentioned Google AI overviews specifically. They're showing up a lot, aren't they? Oh, absolutely.

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The data shows they appear in nearly two-thirds of local business search queries. Two-thirds. Yeah.

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So think about that. A user's first impression, their whole perception of your business, maybe even their final decision. That can all happen right there in the AI overview.

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Before they even click a single link to your site. Often, yes. Yeah.

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You're being judged based on what the AI synthesizes about you, largely from reviews. Okay. So how does AI, how does it process these reviews? Not just counting stars, right? Not at all.

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It's much more sophisticated. First, there's sentiment analysis. Okay.

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AI uses natural language processing, NLP, to understand the sentiment. Is the review positive, negative, neutral? What's the underlying feeling? And it's good at this. Surprisingly good.

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NLP tools can nail the sentiment correctly about 85% of the time. Compare that to human analysts, who are around 58%. 85%? Yeah.

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That's a game changer. It means the words matter immensely, not just the rating. Absolutely.

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It elevates the qualitative feedback. Every detail, every nuance in the language can be picked up and factored in. So you need more than just volume.

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You need substance in those reviews. Exactly. And beyond sentiment, reviews are a critical form of user-generated content, UGC.

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Right. Content made by users, not the brand. And Google's own ranking systems prioritize, and they say this explicitly, helpful, reliable, people-first content.

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Reviews are pure gold for that. Because they're authentic. Authentic, diverse, reflecting real-world language, real user intent.

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This UGC helps the AI refine its results, offer better answers, and build trust with users. It's like this huge, constantly updated database of genuine opinions. Okay.

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So reviews are feeding the AI, shaping how it sees and presents businesses. Given that, how do we actually use this? How do we manage reviews strategically? Well, let's look at a practical example, the ReviewRecap.io case study that came up in the materials. Right.

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The platform that aggregates and blends reviews. Correct. They aggregate reviews from multiple sources, but also blend them with authentic, human-written reviews on their own site.

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They actually came to us at AI Monitor for some consulting. Oh, interesting. What do they need help with? They specifically wanted to know how to structure and distribute their content to get maximum exposure in those AI-generated responses.

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And how did that work out? The results were pretty striking. Their content became 138% more likely to be cited by AI responses on platforms like ChatGPT, Google AI Overviews, and Perplexity. 138%.

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Wow. Yeah. It really hammers home the point.

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If your content is optimized for AI, especially combining structured summaries with real user reviews, it's significantly more likely to get surfaced. It's not just having reviews, it's making them digestible for the AI. Precisely.

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Digestible and credible. Which leads right into the need for a multi-platform review strategy, doesn't it? AI isn't just looking at Google reviews. Not at all.

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That's a crucial takeaway. AI overviews aggregate info from all over the web. In fact, over 60% of the citations in AI overviews, they come from non-Google sources.

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60%? Like where? Reddit, TripAdvisor, Yelp, local blogs, industry-specific directories. You name it. So focusing only on your Google business profile, while important, is, well, it's not enough.

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It leaves you invisible in a huge chunk of potential AI answers. You need that broad digital reputation, that consistency across relevant platforms. Okay, so what about specific platforms, like Yelp? It often dominates local search, right? It does.

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Especially for those broader plural queries, like plumbers in Denver. But there are ways to compete. How so? Businesses can target highly specific, long-tail keywords where Yelp might not be as strong.

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Think affordable private anxiety therapist in Chicago available on weekends. Very specific. Right, much more niche.

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Or you can really double down on optimizing your Google business profile to win in that local three-pack that still appears. And some users actively avoid Yelp because you need the app. Ah, good point.

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That creates openings elsewhere. It does. And for anyone listening in the SaaS or tech world, there's one platform that stands out dramatically.

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Which one's that? G2. The data is incredibly clear here. Yeah.

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Reviews on G2 are 43% more likely to be quoted by AI summaries than any other platform in that B2B tech space. 43% more likely. That's massive.

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It's a huge signal. If you're in SaaS or tech, you absolutely must focus on G2. Not just getting reviews, but getting detailed ones.

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Both the quantitative scores and that rich, qualitative feedback. Because G2's format is easy for AI to parse and trust. Exactly.

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Structured, verified reviews. AI loves that. Okay, so platform matters.

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What about the type of reviews? You see distinctions between first-party and third-party. Yes, definitely. First-party reviews are the ones you collect and host yourself on your own website.

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You can use schema markup on these to help AI understand them. Like adding structured data tags. Correct.

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Then you have third-party reviews on places like Google, Yelp, Facebook, TripAdvisor, Reddit, G2, etc. These are often seen as more objective by users. And AI seems to cite them frequently.

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Very frequently, yes. Because of that perceived independence. The bottom line is, you really need both.

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A mix for comprehensive coverage and trust. Exactly. You need that consistent story wherever someone or some AI looks.

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Makes sense. Now what about the characteristics within the reviews themselves? What does AI value? Okay, several key things here. First, recency.

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This is huge. How recent? Well, think about users. More than two-thirds prioritize recent reviews.

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So businesses need a consistent, steady flow. Not just a big push once a year. Right.

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A flood followed by silence isn't ideal. AI, like humans, needs current signals that you're trustworthy now. An old five-star review doesn't mean nearly as much as a steady stream of recent four-star ones.

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Okay, steady velocity. What about star ratings? Is it still all about getting five stars? It matters. But maybe not how you think.

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Yes, 73% of consumers won't even consider businesses below four stars. That's kind of the table state. So four is the minimum target.

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Pretty much. But here's the interesting part. 69% of people are actually okay with brands that don't have a perfect 5.0, as long as the reviews are recent and feel authentic.

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So chasing perfection might be less important than being consistently good and current. Precisely. Focus on solid four-star-plus reviews with real detail.

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A few honest, maybe even slightly critical but well-addressed points could actually build more trust than a suspiciously perfect record. Authenticity trumps perfection. Got it.

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What else? Content depth. Encourage customers to share details, specifics. Why depth? Because AI enables much more granular searches.

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People won't just ask, best car dealer near me. They might ask, which car dealer near me has the best reputation for fast oil changes without pushy upselling? Ah, okay. So descriptive reviews with relevant keywords help you match those super specific queries? Exactly.

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The more detail, the more potential matches for those complex AI prompts. And visuals. Do pictures and reviews matter? Yes.

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User-generated visuals are often overlooked but increasingly important. Encourage customers to post photos, maybe even short videos. How does that help with AI? High quality images, especially if they have clear file names and alt text, boost discoverability through visual search, which AI is integrating more and more.

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Video. Optimize video reviews for platforms like YouTube. Remember, YouTube is the second biggest search engine.

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Visuals add a whole other layer of rich data for AI. Okay. This is incredibly helpful.

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We understand the landscape, why reviews matter more, what AI looks for. So let's get really practical. What should people listening be doing right now? What are the actionable strategies? Right.

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Let's shift to the how-to. First and foremost, you absolutely have to monitor and respond to reviews. All of them.

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Both positive and negative? Both. Actively monitoring and responding is critical. Google actually considers owner responses an SEO signal.

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Really? Responding helps your ranking. It can, yes. Prompt professional replies show you're engaged, you care about customer service.

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And think about the user perception. 92% of consumers say responding is just basic customer service. And doesn't it give you a second chance sometimes? Absolutely.

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73% of people say they'll give a business a second chance if they see a thoughtful reply to a complaint. It's not just being polite. It's smart business and good AIO.

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Okay. Monitor and respond. What about those negative reviews? They sting, but can you use them? You can actually leverage negative reviews.

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It sounds odd, but bear with me. Negative reviews, while not ideal, show user engagement. When you respond professionally, you demonstrate that you're listening, you're committed to improvement.

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That active participation can actually be viewed positively by search algorithms. So engaging shows you're active and trying. Exactly.

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And publicly resolving a complaint. That enhances the user experience for everyone reading, which is a key ranking factor. It shows you're real, not hiding.

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It turns a negative into a potential positive signal. Okay. What else? Utilize review content for keywords and ideas.

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Reviews are just a goldmine of customer language. How so? They tell you customer preferences, their pain points, and crucially, they're packed with the long-tail keywords your customers actually use. The exact phrases people search for.

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Right. So take that language. Use it to inspire new content.

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Blog posts, FAQs, videos. Address the questions and concerns you see in reviews using the words your customers use. Like turning a complaint about parking into a guide on where to park.

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Perfect example. It's responsive, helpful, and uses relevant keywords naturally. Smart.

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What about the technical side? Schema. Yes. Absolutely essential.

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Implement review schema markup. This is structured data that helps search engines and AI systems understand your review content. It's how the stars show up in search results.

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That's one benefit. Yes. Yeah.

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Which increases click-through rates. But schema, especially things like FAQ schema built from review questions, aligns perfectly with how AI processes information. It makes your content easier for AI to categorize and surface, boosting visibility in things like people also ask and featured snippets.

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You're basically spoon-feeding the AI the structured data it needs. Pretty much. Make it easy for the AI to understand you're relevant and authoritative based on what your customers say.

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One more. A simple one, but effective. Display positive reviews prominently on your own website.

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Like on product pages or a testimonials page. Exactly. Put them where potential customers will see them.

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It builds immediate trust, credibility, it's powerful social proof reinforcing your value to humans, not just the AI. So pulling this all together, it really feels like reputation management, especially through reviews, isn't just a nice-to-have anymore, it's infrastructure. That's a great way to put it.

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It's foundational infrastructure for your online presence in this AI era. It underpins everything. It really does.

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And maybe the final thought to leave listeners with is this. In this rapidly evolving AI-driven search landscape, your customers' voices are genuinely more powerful than ever before. Amplified by AI.

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Amplified by AI. So the critical question for you is, how will you ensure these authentic, real-world insights are not just heard, but strategically amplified by AI to truly conquer your online visibility? That is the question. Fantastic insights today.

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That wraps up today's episode of Conquer AI Search. Thanks so much for tuning in. If you enjoyed the conversation, make sure to follow or subscribe wherever you're listening, whether that's Spotify, Apple Podcasts, Audible, Amazon Music, or right here on YouTube.

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And hey, if you got value out of this episode, perhaps consider leaving a rating or review yourself. It really does help us reach more listeners like you. See you next Saturday with our 10th AI Optimization or Generative Engine Optimization Technique.