How to Win AI Search GEO Strategies That Actually Work
Aditya Jain · April 27, 2026
ai optimizationai in marketing
**Welcome to AI Insiders**
(0:05) Everyone, welcome to another edition of AI insiders from AI marketers guild and marketecture. I'm your host David Berkowitz, here with a guest for a subject I'm pretty passionate about, pun intended. We've got Aditya Jain from Passionfruit. It's one of these AI optimization dashboards and recommendation engines that I've wound up using, and I've showed off in some of my AI training to make me look smart to other clients and attendees. They've got some terrific visuals to understand how your ranking in AI engines. I'm here to learn and trying to keep up because I feel as soon as someone comes out with a recommendation for what to do, someone says, "no, don't do that." I think it's a very confusing, murky space, but hopefully, you want to make some sense of all this today.
**Breaking Down the Noise**
(1:04) Appreciate that, David. The layup in the end was perfect. The goal here is to break some of the noise in the market and show what we're seeing. This will be as educational as I can make it. While I go through it, I know everybody might have different backgrounds. If you have any questions or want me to dive deeper into anything I share, just make me pause, and I'd love to dig deeper and make anything more personalized to anybody's personal problem statement with any of the points we discussed today. To kick things off, maybe I'll share my screen and dive right in.
**About Aditya Jain and Passionfruit**
(1:41) For context, an artifact I made on Claude, which I think is lovely, highly recommend. To begin with a quick intro, guys. My name's Adi. I was a growth operator for the past decade and founded in my undergrad and masters at Stanford. Passionfruit is a lovechild and our second company with my co-founder and I. We run an AI marketing operating system for SEO and AIO. We marry what we call marketing engineers in-house with our own AI tech stack and work with about 500 plus brands globally. Last year, we drove a billion dollars plus in revenue for our clients and worked directly with many providers across Google, OpenAI, Anthropic, and Perplexity.
**Hot Takes and Blueprints**
(2:28) The goal for today's talk is to share some of my hot takes on the industry and what people are pitching. As much as it is about my thesis, I want to share some blueprints that I think are really working for us internally that I think people can take home and implement within your orgs. To dive into my comment here, what we think in today's world is that playbooks as a concept are dead.
**Playbooks Are Dead**
(3:12) What I mean by that is every agency you go to or every marketer you speak with is selling the fact that they've cracked this new playbook that will make you win. That's what I'm trying to debunk today. There are a couple of reasons why.
**Every Search Changes Results**
(3:35) The first core reason is every search that anybody does on any platform actively changes the next result it dictates. If you see the counter on the left, it's a live feed for how many searches are being done on AI search platforms today. We're already at 115 million searches. Each of those will mildly change the platform, which changes the next answer it gives you. I know people say the answers are different across 30 different searches, but even if you run it on your own instance, every couple of days, you'll see a different answer that each engine will produce.
**Growing User Bases and AI Architecture**
(4:17) Obviously, these platforms are important for us to decipher because the number of users on each of them is growing and scaling very quickly. I saw at the start of this call everybody's talking about Claude, but ChatGPT still reigns supreme in terms of our commercial usage and where we really see a lot of revenue trickle in from. Google itself has made major pushes here, but because a lot of this architecture is AI dominated, we see these change regularly.
**Creating a Marketing Loop for GEO**
(4:48) What I'm going to talk more about is how these platforms are changing and what causes the change. We're talking about how to really create a marketing loop, which in the context where we're talking about today is for GEO. I'd recommend viewing more and more of your platforms in a similar limelight in terms of how you can run those loops on steroids to be able to drive your own data to feed back what changes you make for your next campaigns.
**Platform-Specific Tool Recommendations**
(5:14) For the representative one core belief that I wanted to show you here, we're running these live queries on the back end. What you'll see is for the same query, the top tool that each of the four target platforms recommends is drastically different.
**Why Different Platforms Recommend Different Tools**
(5:37) The core reason behind this is because each platform is catering to a different user base and is pulling from different sources. For example, Perplexity uses Reddit a lot more than Gemini does. Because Reddit users are early adopters to things, share a lot of new insights. So, the platforms they recommend for top tools are drastically different than what a ChatGPT might show you, which focuses now increasingly on Wikipedia.
**Consistency of Findings on a Given Platform**
(6:04) I have to ask already, though, because one of the top things we're grappling with is how consistent are the findings even on a given day within the same platform?
**Topic-Dependent Variability**
(6:21) It depends on how many sample sets you pick. I think one shift is that a lot of platforms use API calls. That's where there's a very high inaccuracy between what they show versus what each of us would see when we use the front end. For prompts that are not being dictated too often, you can do away with 10 to 15 searches, and that won't change as much through the day. But for example, a hot topic, even cloud design, if you see the review for cloud design, that would change drastically because it's a very, very hot topic within that same day. These engines are designed to provide opinions, not just surface results. Depending on the topic, that variability is very, very high, even within the course of a day.
**Three Core Misconceptions**
(7:12) What I wanted to focus on is what are the three core concepts that I think are misconceptions, which are leading us to take incorrect decisions in terms of how we operate.
**Each AI System Has Different Economic Models**
(7:25) The first piece is "optimize for AI search as one channel." The core problem statement here is each AI system has really different economic models that they operate under, which means they sell to four very different audiences.
**Targeting Different Audiences and Data Sets**
(7:46) Because of which, they have very different data sets and very different ways to target each. For example, ChatGPT used to rank Reddit threads very highly when OpenAI was a partner for them. But Google is always focused on driving business to your website because that's where they could generate a lot of the money from. So, they rank brand pages. Perplexity focuses on authoritative content. They focus now more on enterprise sales, so they use what they call citation graphs. That's become core.
**Understanding Channel-Specific Citations**
(8:15) One core concept here is you have to understand which channel matters for you most and then accordingly start functioning on the core citations that those channels focus on for gathering their data. That's your first selection. Depending on that, you should run and pull data from the correct AI search engines.
**Content Playbooks Not Needed**
(8:30) The second piece a lot of people have been talking about is you need a brand new content playbook to win on AI search.
**Measuring Content Success Differently**
(8:49) The thing that's very different is formerly, we've been testing the same type of content we write, just having our feedback loop run differently. What it means was earlier, you would measure the success of your content from how many clicks it drove or how many backlinks it drove from a Google infrastructure. Today, you write the same helpful content for that same target audience. It's just that the systems you look for is this working or not, are citations on channels like Reddit, YouTube, or a forum, which then compounds into what you see on your ChatGPT or Perplexity.
**Focus on Intent, Not Jargon**
(9:33) The core entity and the core physics behind what content you produce is exactly the same. There's a lot of jargon out there about comparison tables. There was jargon about lm.txt, which becomes very popular every 3 months. There's a new wave of new advice that comes in. To be very, very honest, every large player's goal is not to change the way you operate in terms of how the internet works but to find a way to understand the internet the best. As long as you're targeting the right intent and pushing out the right form of content with some basics in place, you're in a good position to scale up your presence and drive results.
**Citations Should Drive Revenue**
(10:13) The last big cop-out has been "citations don't drive revenue." I've heard a couple of people across a bunch of podcasts really push this out where they say, "Track your citations. Forget about revenue. This is part of the funnel that you don't need to measure. Just measure citations, and that's branded presence you're paying for."
**Measuring Pipeline Lift from Citations**
(10:31) Citations directly should be driving a lift in branded searches as well as a lift in your pipeline. There are three ways you should be measuring this pipeline lift. First, ceteris paribus, if you don't see a lift in your branded searches, it means you're targeting the wrong queries that you're getting higher citations from.
**Correlation of Revenue Lift and Engagement**
(10:53) The second piece is from every channel you've seen a citation lift from, you should see a direct correlation of revenue lift from those channels. Revenue could be sign-ups or whatever you're tracking as a proxy for some sort of conversion. The third piece is overall, you should also see organic sessions from other channels or conversion on traffic you're driving from other channels go up, but your engagement rate on average across any single campaign you run should be higher. You should be tracking this, otherwise, you're targeting the wrong query set.
**Tackling Core Lies and Next Steps**
(11:35) These three are core lies that I want to tackle, and what I think the next steps should be. If anybody...
**Perplexity's Current Relevance**
(11:41) I have tons of questions and happy to hear from others too, but how much does Perplexity matter right now? What's your take on that?
**Perplexity's Declining Inbound Traffic**
(11:57) So, I think Perplexity's business has changed drastically. They don't publicly showcase their user count. But over the last 6 months, we've seen inbound traffic across our customers drop drastically from Perplexity. So, at least from the data set that I have and that we use, it's not very, very high. We still see for really technical use cases, especially within research, Perplexity still has a very strong customer base. So, if you're in that industry, I would highly recommend it. If you're in B2C, SaaS, or e-commerce, or slightly mid-tier B2B, I would not really focus on Perplexity as much today.
**Other Key AI Platforms and Grok**
(12:39) So, then, for people researching things with AI, is it mainly ChatGPT, Gemini, Claude? How much does Grok matter right now?
**Grok for Twitter Presence**
(12:56) So, I think Grok, what we use primarily is for promoting ourselves more on Twitter. The model is integrated, it's important to have Grok understand your presence and your brand because it's frequented there, and that's where you get a lot of citation data. So, I would focus on these four. But as I said, you should be focusing on any channel you're seeing traffic trickling from first before you go on a spray and pray approach trying to understand all four and optimize for all four, because each of them operate very differently and will tell you different stories.
**Perplexity for Large Enterprises**
(13:47) It's just great to get the latest because even now it's like I was wondering how long does Perplexity stay as far as like one of the top things even modern because I hear much less about it as far as influence goes right now.
**GEO as a Dynamic Mechanism**
(14:05) If you're selling to large enterprises, Perplexity is important because they have very good B2B sales, and a lot of vendor selection is now moved to AI search engines, so Perplexity still holds strong value there. One important thing I want to showcase with this is actually on pace of iteration. This is what happens when you're trying to think of GEO as slightly a static mechanism. This is actually a client of ours data that I'm showcasing here over a 90-day period where we were tracking ChatGPT and Perplexity. We were seeing some inbound here, so we focused on a couple of activities, and we got the client to drive up visibility. They picked at about 15% overall visibility across a particular query set.
**Rapid Model Updates and Lost Visibility**
(14:48) Within 6 weeks ahead of that, they were back down to close to zero. The reason behind this is the model updated the source set where a lot of the blogs we generated and new service level pages we generated no longer stood the test of time because Reddit became a dominant source for the insight that the answer that ChatGPT was generating. Because of which, very quickly we lost all the visibility we tried to work for in the first 6 weeks.
**Why Playbooks Go Out of Importance Quickly**
(15:13) This is what I wanted to highlight in terms of what I'm going to talk about the system in terms of how quickly these models change, which is why playbooks go out of importance very quickly. Reddit was important and YouTube, now you're seeing a pick up of Wikipedia. That's why I dive into the next piece where I don't think playbooks are important. I think building a loop is very important, which has four steps: measure, attribute, produce, and detect decay for anything you have outputted.
**The Four-Step Loop**
(15:58) Four very simple steps. First, with measure, you need to be able to track on a weekly cadence. If you track on a monthly cadence, you are already late. What's your share of voice across any search engine you deem fit, be it ChatGPT, Perplexity, Google AI overviews. Understand the queries that you want to track, what's relevant for you that could be driving revenue, what's not. Understand your movement over the course of a week. Which queries are stable, which queries are not.
**Understanding Citations and Content Freshness**
(16:40) Understand what citations are driving change here and how fresh is the content on each of these query clusters you focus on. For example, if I'm looking for the best CRM software, generally they're using Reddit threads or G2 reviews that have been there for over 6 months. But if I'm looking at the best shoe to wear for the New York Marathon, it's slightly more new, so you look at newer Reddit threads that are slightly more recent. That's where you really see how important it is for you to keep engaging on that thread versus an older thread.
**Attributing Revenue to Channels**
(17:09) The second piece is to really understand attribution. For each of these channels, each of these citations, what are the core KPIs you will track to measure revenue for yourself within that? Either you directly measure from GA4 any AI referral traffic that's coming in to make that your core KPI that you really want to drive to make a business case for it.
**Measuring Branded Search Lift and Engaged Sessions**
(17:41) The second thing we've seen a lot of brands do is measure branded search lift. You can track on a Google search how many more branded searches am I driving every month, and how much of that is translating into revenue for me. The third thing a lot of people have started doing was measuring the average engaged sessions and how much of a lift is that over time. So that you can see if overall my higher presence is really driving better traffic to my website and making them stay on my website for slightly longer.
**Justifying ROI**
(18:05) There are a couple of methods here, but it's really helpful to continue making a business case for the channel that most people are struggling with justifying ROI from, aside from just overall brand presence.
**Producing Content Based on Citations**
(18:23) Third is using these two data sets, what you should start producing. If Reddit is important, or some PR campaigns are important, or some blogs are important, understand what's being cited to answer the questions in each of these engines and accordingly start generating those pieces here.
**Focus on Entities, Not Keywords**
(18:44) The most important thing here is don't generate those pieces based on keywords. I saw this prompt; this is what I'm going to generate. Focus on entities. For example, I'm going to focus on this ICP or this pain point first and create a wheelhouse of assets that I want to generate over the next four weeks or eight weeks. That's how planning should work.
**Avoiding Scattered Content Efforts**
(19:05) Generally, what we see is people will try to generate separate assets for 30 different queries that are targeting different personas. You don't make meaningful progress on any, because of which you don't really see any movement, and then you start from scratch every new week. It's better to understand one entity first and really build production there.
**Detecting and Addressing Content Decay**
(19:22) The last thing I'd focus on is every week you will see movement where some assets of yours were cited last week but have lost citation or have gone down. That's where you want to understand where you are seeing decay for any assets you've already pushed out. Those are your low-hanging fruits where you can easily refresh those assets or re-engage on those posts, bring them up, and automatically refresh them.
**Four-Step Process Results**
(19:51) These are the four-step processes that I'd highly recommend. There are complexities within this that I'm happy to dive into and happy to take questions somebody has on this. But, by using these four simple steps, we've run this across different types of industries. These are the type of results you can get to see in a very, very simple system.
**Achieving Significant Citation Lift**
(20:19) Where people have gone from zero citations to driving 38% of the citations across their category in under 6 months across search engines. We've had companies focus on Reddit as a category and build up their presence. People have used Quora. People have focused on updating their schemas, building a YouTube corpus, and scaled up.
**Building a Real Engine**
(20:44) Once you realize what works for you based on what the AI search engine tells you, it's really easy to build a real engine where you spend 2 to 6 hours a week and scale that up even without hiring somebody externally for that use case.
**Key Elements for the System**
(20:58) The four elements I'd highlight as important are: choose a data ingestion layer. This is like your typical crawlers or tools like profound. David, you had them on the platform on one of these webinars before. They're good at these things. They help you ingest the data, connect with your GA GSC, also provide you data on citations.
**Attribution and Execution Tools**
(21:27) The second piece on the attribution piece, I think, is important. Use traditional tooling like GA4 or even use tooling like HubSpot and Salesforce if they've built out for you to build that attribution funnel for yourselves and understand the core levers you need to move on. On the execution piece, I think it's either using content writers in-house or using the data you have, hire agencies that focus either on Reddit or on YouTube to find the core levers you want to move and hire experts that are core for each of those systems. I think that would be great.
**The Role of AI in Content Generation**
(22:07) As we get into AI to the content generation piece and using that, one of the things I've heard mixed opinions on is the role of using AI to create a lot of that content. I've heard mixed opinions on how much human in the loop, ranging from zero to lots, is recommended for that. Do you have some informed opinions on this one?
**Industry-Dependent AI/Human Mix**
(22:33) 100%. I generally think there's not a single correct answer. It depends on your industry. For example, if you're writing best listicles for different softwares or different products, it's important to have an opinion. If you're in a category like insurance, it's very important to have an author for your content.
**Aggregating Content at Scale**
(22:54) If you're also in a CRM space or a very generic space where there's a lot of content out there, being able to aggregate that content at scale and be that one source that's citing 10 other pieces underneath it is very helpful for any AI search engine because you are becoming that one source because they are all optimizing token counts on their end. If they can crawl one source to get eight sources, they will always choose you over going through eight sub-sources.
**Recognizing AI vs. Human Content**
(23:37) For every different type of piece, even if you look at the citations that are commonly being cited for any answer you look for, you'll be able to see that variability where you can see something is AI generated versus something is not. That tells you what can work, what can't work. That's why it's important to have human in the loop as a larger strategy where you have that mix, but everybody is doing a mixed strategy here. If you just focus on humans generating every piece, you're most likely to be left behind unless you have the ability to spend on volume.
**Surprising AI Content Results**
(24:06) I've seen some surprising results even with some tests where someone a while back had asked me for content for some SEO requirements. I'm like, let me see how good Gemini can be at putting this together. Their opinion was like, we can't use AI for this at all. I submitted some of this Gemini-created content, and they started ranking right away in the AI overviews. This was a tiny consultancy. So, my own assumptions have often been tested and proven wrong here. But it's what I love hearing from someone like yourself who's so in the weeds.
**Cloud-Generated Visuals and Data**
(24:58) 100%. Maybe I'll, before I go into the next piece, talk about a couple of things. Lisa, this whole thing was generated on Cloud Code. The point of AI slop is you can generate a lot of these things that look pretty as long as the data is correct. This stuff digests. For me to generate this, it's taken me over 2 days to come up with something like this. The point of generating a lot using bases, as long as your data source is correct, it serves the purpose. As I said in the call out, this was generated on Cloud.
**GA4 in Shopify Environment**
(25:40) The second question, Jennifer, you had was, is GA4 within a Shopify environment helpful? We work with a lot of D2C brands. The core importance here is if your tracking on GA4 is set up correctly for Shopify, only then will it work. Otherwise, you might see variability where your Shopify data is 30 to 60% off from your GA4 data. That's where you have a lot of issues, especially when cookies are not enabled. For attribution, I would first suggest getting a GTM set up correctly before you use only GA4 for attribution.
**Client Split and AI Ranking Measurement**
(26:11) We work with B2B and D2C clients across the board. Our split is about 70% in e-commerce and 30% in B2B. Lisa, the way we measure AI ranking is, depending on the different use cases we have for clients, we have our own tool that we use for which we've partnered directly with a lot of the LLM providers. But then some clients have their own tooling that they use. So, HubSpot has their own AO tool that we use and plug into, and then we use that data to verify citation data.
**Claude's Commercial Intent and Revenue**
(26:50) Paul, the reason why Claude is something I haven't spoken about here primarily is Claude is not an entity that's being used for commercial intent, since they don't want to push people out of the Claude ecosystem when they provide suggestions. So, we haven't seen major updates in revenue attribution coming from Claude yet, which is why most of the commercial use case we focused on ChatGPT, Perplexity, and Google.
**Core Use Cases for Client Websites**
(27:18) For client websites, there are four core use cases that I would highly focus on. First, whenever you see a citation that comes up, you can see what types of pages are being cited. So, either you create a new page that you don't have that's commonly being cited.
**Updating Existing Pages and Missing Sections**
(27:42) Second is if you have a similar page, update it based on the most common trend you see on pages. For example, in B2B websites, what we commonly see is people don't have strong FAQ sections, people don't have testimonials, G2 integrations onto their plug-in pages. They don't have competitor comparison sections, which are very, very common. So, that's the second approach where you update your existing pages to tag on those pages better.
**Critical Guide Pages and Brand Control**
(28:10) Third piece is within websites, people are scared to write content on best use cases for my X product or compare their products directly with other products by writing best-of guides. Writing those guide pages, writing those best-of articles are really, really critical for building out that presence on your own website so you control the narrative about your brand.
**Backend Architecture and Schema Implementation**
(28:31) Lastly, there's a lot of back-end architecture around schema implementation as well as allowing the correct bots onto your website. That's the core hygiene that I'd highly, highly recommend. For example, generally most websites allow the traditional Google bot and the standard crawlers, but they don't allow a lot of the ChatGPT bots as well as Claude bots. That's the first use case that everybody should update. The second piece is implementing the correct schemas across the website and the architecture so that any crawler that does land on your website can easily script through and go through the complete website.
**Ads on GPT and Investment**
(29:10) Jennifer, can you give me an opinion and any experience with ads on GPT and how this impacts unpaid? Yes. Ads on GPT requires a minimum investment of 100K. Focused on two plans: the free plan and the $8 plan. The companies that we've seen run good pilots on ChatGPT are the users that are targeting where their end customer is at the bottom of that quartile in terms of how much money they're willing to spend on AI search.
**HubSpot's Success with Intent-Based Ads**
(29:51) A company that's done really well on ChatGPT ads currently has been HubSpot. The way ChatGPT ads is working is they don't target the queries, they target intent. Across a very large intent forum, they're showing HubSpot ads, and that's doing really, really well for HubSpot.
**Impact of Paid Ads on Unpaid Queries**
(30:12) Unfortunately, I don't have data on how that's impacting unpaid ChatGPT queries and result behavior just because the channel itself is growing so large, and currently, we don't have enough data of which queries are surfacing ads or not, and that pilot is not that large yet. So, unfortunately, I won't be able to answer that question in terms of how it's impacting unpaid, but I am seeing very promising results on the paid forum right now.
**Connecting Sites to Google Search Console**
(30:36) I'm just going to answer Marshall's comment. There's some pretty built-in analytics. Marshall, great. I think you've answered another question on top, so lovely. One of those things also for those, because there's a lot at the intersection here of all this GA optimization and vibe coding, which we've been talking about a bit. One of those useful things that comes up is GA4 for sure. You have connecting your sites to that. Asking your platform or search, does it have any connectors to Google Search Console, for instance?
**Proactive Monitoring and Best Practices**
(31:23) To what extent can it proactively monitor and address some of the things that invariably come up: duplicative content, and all these structural things that tend to happen whenever you're adding a lot to your site? It's being able to use some of these more traditional best practices. In the past, I barely was ever on Google Search Console because I wasn't usually the one managing as much of this directly. Now because of AI, I'm managing much more directly, so I have to learn the stuff that some people learned 10 or 20 years ago.
**Connecting Data Across Platforms**
(31:55) 100%. What we're seeing across the board is one tool is no longer sufficing any attribution or team in terms of what their needs are. A lot of people have been connecting their data across different platforms. They're even downloading that and putting it on a singular platform to understand the correlation across different channels to better inform decisions.
**Ecosystem Understanding**
(32:31) A lot of people are now using GSC, GA4, as well as the data they get from an LLM visibility platform to better understand how the full ecosystem is working together and what are the best next steps they should be taking across this.
**Platform-Specific Attribution Setups**
(32:50) I would suggest I don't think GA4 or Shopify are the single best use cases for everybody else. Depending on your hosting platform, be it Webflow or Shopify, or Framer, or if you have a lovable website, the platform that you should be using is unique, a system you've set up and where your end attribution happens. For Shopify, obviously, it's happening on your website, but some people have end attribution on HubSpot. That's what changes a lot of the setups that individually you need to evaluate and recommend, we'd recommend individually to focus on.
**Easy Way to Push Out a Sprint**
(33:23) The one easy way I'd recommend pushing out this sprint is you can pick 10 initial queries. These should be buyer intent, commercial-focused queries where either you've seen search trickling from these, or this is something that you would imagine yourself searching for if you're looking for the product that you're selling.
**Manual Query Audit and Action**
(33:40) I would either manually run these queries across ChatGPT, Perplexity AIO, and Gemini. See the results you get. I would log those manually to see which ones are even relevant for us today. If you see completely different competitors are coming up, that's never going to be an important channel for you. Only if you see that relevance, I would focus on that channel and then track what URLs are coming up, what was the answer. Use that data to decide what actions I want to take tomorrow. On Wednesday, take a couple of actions from the top cited pages in the answers. Be it a new page you need to create, a new content piece you need to write, whatever it may be, understand that and push that out.
**Weekly Audit and Tangible Results**
(34:32) On Thursday, rerun your audit and see if anything changed. I would recommend doing that again the following week. Every week, if you spend an hour or two doing this, it becomes a simple process to start using and adopting GEO and understanding that better to see what's working for you, what's not. Once you do this for a couple of weeks, you'll be able to start seeing tangible results because these engines sometimes shock you in terms of how quickly they can change and show you being cited. That's where I think we've seen companies build up capabilities and scale their own presence and attribution from each of these channels.
**Use Cases for Improving Lift**
(35:14) My question for you, Aditya, thank you so much for breaking it down for me. This is not my wheelhouse, but I'm curious to hear more about some of the use cases you're seeing their clients, whether they're B2B or B2C. How are they using this to help improve lift that you helped hit it out of the park before?
**E-commerce and B2C SaaS Strategies**
(35:34) In e-commerce, what's working for folks is creating new category pages that target niche users and pushing up content supporting those niche pain points or ICPs that they target. Then spinning up YouTube channels that use their social media content because YouTube is a highly cited source. For B2C SaaS companies, engaging with users on Quora, Reddit, and doing targeted PR activities on commonly cited sources has been extremely helpful.
**Direct Comparison Pages and Product Updates**
(36:13) Then them creating direct comparison pages for each core competitor that a ChatGPT is citing has been extremely useful. Writing product updates for core. Generally, we have service pages that show for any user what their product does, but writing larger product updates that really go into the in-depth version of the solutions has been extremely helpful.
**Research and Product Research**
(36:40) It sounds like it's for the research and also for product research.
**B2B Content and Documentation Strategies**
(36:44) Yes. For large B2B companies, they're building heavy published content which they used to hide behind paywalls. Now direct for any crawler to come up, which is based on proprietary data highly reviewed by their experts internally. Then go out there and co-publishing pages with top experts in their industries, pushing that out, and really focusing on G2 reviews and their outdated website architectures where they're opening up a lot of their documentation out there. All of these crawlers can come and be able to see it.
**Embracing Conversational Engagement**
(37:26) Sounds like you're teaching your clients to be more open and to engage with their audiences directly from what it sounds like. They used to have more closed systems, but now AI has forced them to be more, let's say, conversational when they're talking to audiences.
**Paywalls Less Helpful**
(37:38) Exactly. Paywalls are no longer as helpful as they used to be.
**Role of Traditional PR in LLMs**
(37:45) What I've seen come up also is the role of traditional PR, more traditional sources. Is this more ammo for bringing more traditional PR and elevating that to impact the LLMs and brand reputation?
**PR Guided by Cited Sources**
(38:09) Traditional PR needs to be better guided with sources that are being cited on each of these channels. Traditional PR focuses on your typical large publishers like Forbes and Times magazine. What we're actually seeing is these channels pick up a lot more unique individuals that could even be running substacks or 100,000 subscriber blogs that are unique to the particular industry. That's what we're seeing play a big role in terms of shaping LLM opinion.
**Rewriting PR Strategy**
(38:50) PR continues to be important. It's a rewrite of the strategy in terms of what PR you focus on and how you attribute success for that PR where it's not about getting it listed on a high visibility, high brand name channel.
**Team Skills and Workflows**
(38:58) We had a question from Sandeep before, SEO optimization was linear with longer cycles. Any comment on what type of team skills and workflows you're seeing now?
**Faster Uplift and Update Cycles**
(39:11) Sandeep, on this timeline, what we are seeing with clients is you can generate uplift within 15 to 30 days at max. That's the timelines I think every marketer should get used to in terms of how they're seeing. Any marketing channel today, because of how quickly these platforms update. Google used to release an update once every year, then once every 6 months, and now they release an update almost every couple of weeks. These AI search channels change a lot quicker. That's the type of core update cycle you should be focusing on.
**Comfort with Change and Volume**
(39:55) From a skill set perspective, first is being comfortable changing the core metrics we evaluate, that's the first shift. But the second is having a larger wheelhouse of capabilities because volume and speed of content and pushing out changes is very, very important.
**Outdated Quarterly Review Cycles**
(40:17) The typical quarterly review cycle where you create a strategy that you execute over 90 days and write maybe two pieces or low volume outputs is no longer feasible because everybody else is trying to operate at a higher throughput. They're iterating their strategies a lot quicker.
**Agility is Key**
(40:39) I don't think you need to know coding, but being comfortable with that level of change and that level of agility is extremely important to actually win.
**Investing in External Platforms**
(40:43) Excellent. I have a question. In response to that, if a business is not able to update their website that frequently, would it be better for them to invest in being active on a platform like Reddit?
**Channel-Specific Strategies**
(40:59) Lisa, depending on the type of citation, either it's Reddit, YouTube, Quora, or G2, whatever that may be, in terms of within the category that's important for you, definitely then work outside of the core website becomes important. That channel may differ depending on what the commonly cited citations are for your industry itself.
**Multimedia Content and LLMs**
(41:27) Thank you. One thing you haven't talked a lot about today is multimedia. People are producing way more video content. How good or bad is that when you're trying to influence LLMs?
**LLM Optimization for Token Usage**
(41:48) The way LLMs work on the core at their back end, what they're optimized for, is to reduce token usage for each search they provide without hampering token quality. What that means is as much text as they can pull and as much pre-data that they already been trained on they can pull to answer the question. The more it will lean on that.
**Shift in Multimedia Indexing**
(42:14) Only for use cases where it doesn't think it'll provide the best outcome for it, will it try to do a web search and then lastly look for net new types of content. A year and a half ago, multimedia content was not something LLMs were really good at indexing and understanding. That since then has shifted drastically. A lot of websites, including YouTube, Instagram, are providing transcription directly on the back end that makes scraping off this content a lot easier.
**Uptick in Multimedia Influence**
(42:37) We've seen a massive uptick. We actually did a study with the YouTube team about how much YouTube as a channel has scaled up and how multimedia is playing a larger and larger role across the board.
**Cross-Pushing Content for Shaping Opinion**
(42:58) What we recommend brands across the board to do is, even though multimedia content is not the biggest driving lever if you're getting started with GA, for example, if you have an issue like Lisa, you can't update your site as much. Or you already have one channel you're pushing out a lot of content to, that could be Instagram, LinkedIn. Cross-pushing that content across other mediums is your lowest hanging fruit to increase your chances of shaping your opinion across any LLM. That dominance of this type of content will increase.
**Google and ChatGPT Leading Multimedia Adoption**
(43:39) We saw this uptick be driven first with a lot of self-help and guides where you see a lot of YouTube content already start flooding the market. Google led the charge, ChatGPT followed suit very quickly. We're going to see that adoption scale up.
**Art, Science, or In Between?**
(43:56) I think this is Lisa's question. Is this an art, a science, or somewhere in between? I think this is definitely right bang in the middle. There's a structural process to it. The art really comes in in terms of what you think is most relevant and what you think your core ICP looks like.
**Decoding LLM Behavior**
(44:25) I do think as LLMs get a lot more structured and there's a lot more data and consistency in their outputs and they become slightly more public in terms of how they operate, it'll become easier for us to decode on a more consistent basis what our uplift and what the ROI from each of these channels can look like over time. For example, Microsoft released their AI search visibility platform last year in November. That really helped people understand and decode a lot of what Bing's behavior looks like. As more and more platforms follow suit, it'll make it more and more of a science than just an art.
**Adding Sources to Notebook LLM**
(44:56) What are the best ways to add sources to Notebook LLM?
**Incognito and VPN for Geo-Targeting**
(45:07) Lovely. What I always recommend is run everything on incognito. Generally, if there's a different geography you're targeting, run that geography as a VPN. The question was what are the best sources to influence Notebook LLM?
**Querying Across Platforms**
(45:31) To add the best apps to add sources to Notebook LLMs. What I do is I just run exactly the same query across all four platforms. I'll skip Claude for now just because I have to log in, but I'll show you representative examples of how it shows up. What are the best sources to run Notebook LLM?
**Analyzing Citation Sources**
(45:52) If you realize this did not default to web search. Generally, whenever it defaults to web search, you'll be able to see the citation sources it's using, its pre-pledged data it already has. This is how it's gathered its resources. For this exact output, all it's done is it's highlighted the different sources it has for each. I'll just wait for this full answer to load. These are the top apps.
**Manual Data Collection**
(46:16) What I do is create a Google Sheet. I should not be working off this. But, I will create a Google Sheet, list out the query I put, put Google Drive as number one, Google Doc as number two, YouTube as number three, or just copy-paste these three. Go on each of the citations that ChatGPT used. If you see two of the citations are both Google. Then it used a platform called Bboom. I hope my full screen is still relevant.
**Engaging with Cited Sources**
(47:01) This is a random source in terms of an article. Arjun published this last year. So, clearly up-to-date data is not as important for. This has zero comments. Extremely recursive source. Used Notion without any clear citation, Obsidian without any clear citation, and cited a couple of automation tools that it got from this Reddit thread. This Reddit thread has only about 22 upvotes and one comment. Post about 24 days ago. I would do this same approach by gathering this data across Google. But, I'll open up the Reddit thread, I'll open up this, and I'll open up this Reddit thread, and put down the different answers I saw for Google, as well as a YouTube video.
**Tailoring Content and Engagement**
(47:47) I'll do that same thing here where in Perplexity you'll see a lot more different documents and pull that same data. This is where a general crawler is slightly helpful. But generally pull for some of the most common ones that I see that are common. As you see the Reddit threads that Google pulled are drastically different than the Reddit thread that ChatGPT pulled.
**Influencing Opinion on Threads and Articles**
(48:04) What I would do is if this was something that I wanted to rank for and have my tool showcase, I would go in, understand what this study is talking about, and actually engage saying what I would recommend and why I would recommend that here. I would look at this blog article and try to create the same article on my own website because I assume I'm selling an automation tool as well.
**Leveraging Google's Structure and Depth**
(48:45) For the Notebook LM help page, now I don't have the authority that a Google has, but I would understand the structure that they've created and see if my own source if I can create an article about Notebook LM with the similar structure and the same depth that Google is citing and cite Google's Notebook help page as well as the Reddit thread, combine them as sources, and create a formal article that I own that's citing these five sources as a combined source for it.
**Reddit Channel Importance**
(49:09) Similarly, this is the same thing for another Google page. This is the same page that we saw here that even Google is citing. This is a different thread. I would go in and engage on this thread. I now realize I can't recreate this thread because it has a lot of reviews, but I know that the r/notebooklm overall channel is extremely important for me. So, I would live and breathe this channel and find five posts I want to comment on this week and five new threads I'd want to start that's relevant to the threads that I've seen here.
**Time to See Results**
(49:38) How long before you can see results if you take this approach?
**Platform Change Speed as a Proxy**
(49:45) Genuinely, it depends on how quickly each of these platforms change. For example, this thread is 6 months old. This thread is just 24 days old. That's how quickly it's cited. So, I would assume on ChatGPT, you can change the opinion on ChatGPT a lot quicker than you'll be able to change the citation frequency on a Google. Directly how recent the thread it cites is actually how quickly it's a proxy for you to start influencing the opinion for that channel. To get 93 upvotes or 250 upvotes is hard, but to get 22 upvotes in one comment is a lot easier.
**Connecting with Aditya Jain**
(50:23) That's how you and the difficulty of it. We're approaching time here. This all terrific, and I appreciate the real-time glimpse. What's the best way for folks here to follow up with you, stay in touch?
**Contact Information**
(50:47) I'll list, drop my email and LinkedIn on chat and happy to, as well as Passionfruit itself. Happy to connect any way possible.
**Brand Reputation and Optimization**
(50:56) Amazing. I've learned a ton. All this info for the best takes on GEO and app optimization right now. We need as much as we can get because this is where brand reputation is and where it's continuing to go. I appreciate you starting right off with busting some myths out there and giving us some of the latest, very well-informed thinking on this. Thanks for coming by. Appreciate everyone with so many great questions today. Look forward to seeing you all next week.
(0:05) Everyone, welcome to another edition of AI insiders from AI marketers guild and marketecture. I'm your host David Berkowitz, here with a guest for a subject I'm pretty passionate about, pun intended. We've got Aditya Jain from Passionfruit. It's one of these AI optimization dashboards and recommendation engines that I've wound up using, and I've showed off in some of my AI training to make me look smart to other clients and attendees. They've got some terrific visuals to understand how your ranking in AI engines. I'm here to learn and trying to keep up because I feel as soon as someone comes out with a recommendation for what to do, someone says, "no, don't do that." I think it's a very confusing, murky space, but hopefully, you want to make some sense of all this today.
**Breaking Down the Noise**
(1:04) Appreciate that, David. The layup in the end was perfect. The goal here is to break some of the noise in the market and show what we're seeing. This will be as educational as I can make it. While I go through it, I know everybody might have different backgrounds. If you have any questions or want me to dive deeper into anything I share, just make me pause, and I'd love to dig deeper and make anything more personalized to anybody's personal problem statement with any of the points we discussed today. To kick things off, maybe I'll share my screen and dive right in.
**About Aditya Jain and Passionfruit**
(1:41) For context, an artifact I made on Claude, which I think is lovely, highly recommend. To begin with a quick intro, guys. My name's Adi. I was a growth operator for the past decade and founded in my undergrad and masters at Stanford. Passionfruit is a lovechild and our second company with my co-founder and I. We run an AI marketing operating system for SEO and AIO. We marry what we call marketing engineers in-house with our own AI tech stack and work with about 500 plus brands globally. Last year, we drove a billion dollars plus in revenue for our clients and worked directly with many providers across Google, OpenAI, Anthropic, and Perplexity.
**Hot Takes and Blueprints**
(2:28) The goal for today's talk is to share some of my hot takes on the industry and what people are pitching. As much as it is about my thesis, I want to share some blueprints that I think are really working for us internally that I think people can take home and implement within your orgs. To dive into my comment here, what we think in today's world is that playbooks as a concept are dead.
**Playbooks Are Dead**
(3:12) What I mean by that is every agency you go to or every marketer you speak with is selling the fact that they've cracked this new playbook that will make you win. That's what I'm trying to debunk today. There are a couple of reasons why.
**Every Search Changes Results**
(3:35) The first core reason is every search that anybody does on any platform actively changes the next result it dictates. If you see the counter on the left, it's a live feed for how many searches are being done on AI search platforms today. We're already at 115 million searches. Each of those will mildly change the platform, which changes the next answer it gives you. I know people say the answers are different across 30 different searches, but even if you run it on your own instance, every couple of days, you'll see a different answer that each engine will produce.
**Growing User Bases and AI Architecture**
(4:17) Obviously, these platforms are important for us to decipher because the number of users on each of them is growing and scaling very quickly. I saw at the start of this call everybody's talking about Claude, but ChatGPT still reigns supreme in terms of our commercial usage and where we really see a lot of revenue trickle in from. Google itself has made major pushes here, but because a lot of this architecture is AI dominated, we see these change regularly.
**Creating a Marketing Loop for GEO**
(4:48) What I'm going to talk more about is how these platforms are changing and what causes the change. We're talking about how to really create a marketing loop, which in the context where we're talking about today is for GEO. I'd recommend viewing more and more of your platforms in a similar limelight in terms of how you can run those loops on steroids to be able to drive your own data to feed back what changes you make for your next campaigns.
**Platform-Specific Tool Recommendations**
(5:14) For the representative one core belief that I wanted to show you here, we're running these live queries on the back end. What you'll see is for the same query, the top tool that each of the four target platforms recommends is drastically different.
**Why Different Platforms Recommend Different Tools**
(5:37) The core reason behind this is because each platform is catering to a different user base and is pulling from different sources. For example, Perplexity uses Reddit a lot more than Gemini does. Because Reddit users are early adopters to things, share a lot of new insights. So, the platforms they recommend for top tools are drastically different than what a ChatGPT might show you, which focuses now increasingly on Wikipedia.
**Consistency of Findings on a Given Platform**
(6:04) I have to ask already, though, because one of the top things we're grappling with is how consistent are the findings even on a given day within the same platform?
**Topic-Dependent Variability**
(6:21) It depends on how many sample sets you pick. I think one shift is that a lot of platforms use API calls. That's where there's a very high inaccuracy between what they show versus what each of us would see when we use the front end. For prompts that are not being dictated too often, you can do away with 10 to 15 searches, and that won't change as much through the day. But for example, a hot topic, even cloud design, if you see the review for cloud design, that would change drastically because it's a very, very hot topic within that same day. These engines are designed to provide opinions, not just surface results. Depending on the topic, that variability is very, very high, even within the course of a day.
**Three Core Misconceptions**
(7:12) What I wanted to focus on is what are the three core concepts that I think are misconceptions, which are leading us to take incorrect decisions in terms of how we operate.
**Each AI System Has Different Economic Models**
(7:25) The first piece is "optimize for AI search as one channel." The core problem statement here is each AI system has really different economic models that they operate under, which means they sell to four very different audiences.
**Targeting Different Audiences and Data Sets**
(7:46) Because of which, they have very different data sets and very different ways to target each. For example, ChatGPT used to rank Reddit threads very highly when OpenAI was a partner for them. But Google is always focused on driving business to your website because that's where they could generate a lot of the money from. So, they rank brand pages. Perplexity focuses on authoritative content. They focus now more on enterprise sales, so they use what they call citation graphs. That's become core.
**Understanding Channel-Specific Citations**
(8:15) One core concept here is you have to understand which channel matters for you most and then accordingly start functioning on the core citations that those channels focus on for gathering their data. That's your first selection. Depending on that, you should run and pull data from the correct AI search engines.
**Content Playbooks Not Needed**
(8:30) The second piece a lot of people have been talking about is you need a brand new content playbook to win on AI search.
**Measuring Content Success Differently**
(8:49) The thing that's very different is formerly, we've been testing the same type of content we write, just having our feedback loop run differently. What it means was earlier, you would measure the success of your content from how many clicks it drove or how many backlinks it drove from a Google infrastructure. Today, you write the same helpful content for that same target audience. It's just that the systems you look for is this working or not, are citations on channels like Reddit, YouTube, or a forum, which then compounds into what you see on your ChatGPT or Perplexity.
**Focus on Intent, Not Jargon**
(9:33) The core entity and the core physics behind what content you produce is exactly the same. There's a lot of jargon out there about comparison tables. There was jargon about lm.txt, which becomes very popular every 3 months. There's a new wave of new advice that comes in. To be very, very honest, every large player's goal is not to change the way you operate in terms of how the internet works but to find a way to understand the internet the best. As long as you're targeting the right intent and pushing out the right form of content with some basics in place, you're in a good position to scale up your presence and drive results.
**Citations Should Drive Revenue**
(10:13) The last big cop-out has been "citations don't drive revenue." I've heard a couple of people across a bunch of podcasts really push this out where they say, "Track your citations. Forget about revenue. This is part of the funnel that you don't need to measure. Just measure citations, and that's branded presence you're paying for."
**Measuring Pipeline Lift from Citations**
(10:31) Citations directly should be driving a lift in branded searches as well as a lift in your pipeline. There are three ways you should be measuring this pipeline lift. First, ceteris paribus, if you don't see a lift in your branded searches, it means you're targeting the wrong queries that you're getting higher citations from.
**Correlation of Revenue Lift and Engagement**
(10:53) The second piece is from every channel you've seen a citation lift from, you should see a direct correlation of revenue lift from those channels. Revenue could be sign-ups or whatever you're tracking as a proxy for some sort of conversion. The third piece is overall, you should also see organic sessions from other channels or conversion on traffic you're driving from other channels go up, but your engagement rate on average across any single campaign you run should be higher. You should be tracking this, otherwise, you're targeting the wrong query set.
**Tackling Core Lies and Next Steps**
(11:35) These three are core lies that I want to tackle, and what I think the next steps should be. If anybody...
**Perplexity's Current Relevance**
(11:41) I have tons of questions and happy to hear from others too, but how much does Perplexity matter right now? What's your take on that?
**Perplexity's Declining Inbound Traffic**
(11:57) So, I think Perplexity's business has changed drastically. They don't publicly showcase their user count. But over the last 6 months, we've seen inbound traffic across our customers drop drastically from Perplexity. So, at least from the data set that I have and that we use, it's not very, very high. We still see for really technical use cases, especially within research, Perplexity still has a very strong customer base. So, if you're in that industry, I would highly recommend it. If you're in B2C, SaaS, or e-commerce, or slightly mid-tier B2B, I would not really focus on Perplexity as much today.
**Other Key AI Platforms and Grok**
(12:39) So, then, for people researching things with AI, is it mainly ChatGPT, Gemini, Claude? How much does Grok matter right now?
**Grok for Twitter Presence**
(12:56) So, I think Grok, what we use primarily is for promoting ourselves more on Twitter. The model is integrated, it's important to have Grok understand your presence and your brand because it's frequented there, and that's where you get a lot of citation data. So, I would focus on these four. But as I said, you should be focusing on any channel you're seeing traffic trickling from first before you go on a spray and pray approach trying to understand all four and optimize for all four, because each of them operate very differently and will tell you different stories.
**Perplexity for Large Enterprises**
(13:47) It's just great to get the latest because even now it's like I was wondering how long does Perplexity stay as far as like one of the top things even modern because I hear much less about it as far as influence goes right now.
**GEO as a Dynamic Mechanism**
(14:05) If you're selling to large enterprises, Perplexity is important because they have very good B2B sales, and a lot of vendor selection is now moved to AI search engines, so Perplexity still holds strong value there. One important thing I want to showcase with this is actually on pace of iteration. This is what happens when you're trying to think of GEO as slightly a static mechanism. This is actually a client of ours data that I'm showcasing here over a 90-day period where we were tracking ChatGPT and Perplexity. We were seeing some inbound here, so we focused on a couple of activities, and we got the client to drive up visibility. They picked at about 15% overall visibility across a particular query set.
**Rapid Model Updates and Lost Visibility**
(14:48) Within 6 weeks ahead of that, they were back down to close to zero. The reason behind this is the model updated the source set where a lot of the blogs we generated and new service level pages we generated no longer stood the test of time because Reddit became a dominant source for the insight that the answer that ChatGPT was generating. Because of which, very quickly we lost all the visibility we tried to work for in the first 6 weeks.
**Why Playbooks Go Out of Importance Quickly**
(15:13) This is what I wanted to highlight in terms of what I'm going to talk about the system in terms of how quickly these models change, which is why playbooks go out of importance very quickly. Reddit was important and YouTube, now you're seeing a pick up of Wikipedia. That's why I dive into the next piece where I don't think playbooks are important. I think building a loop is very important, which has four steps: measure, attribute, produce, and detect decay for anything you have outputted.
**The Four-Step Loop**
(15:58) Four very simple steps. First, with measure, you need to be able to track on a weekly cadence. If you track on a monthly cadence, you are already late. What's your share of voice across any search engine you deem fit, be it ChatGPT, Perplexity, Google AI overviews. Understand the queries that you want to track, what's relevant for you that could be driving revenue, what's not. Understand your movement over the course of a week. Which queries are stable, which queries are not.
**Understanding Citations and Content Freshness**
(16:40) Understand what citations are driving change here and how fresh is the content on each of these query clusters you focus on. For example, if I'm looking for the best CRM software, generally they're using Reddit threads or G2 reviews that have been there for over 6 months. But if I'm looking at the best shoe to wear for the New York Marathon, it's slightly more new, so you look at newer Reddit threads that are slightly more recent. That's where you really see how important it is for you to keep engaging on that thread versus an older thread.
**Attributing Revenue to Channels**
(17:09) The second piece is to really understand attribution. For each of these channels, each of these citations, what are the core KPIs you will track to measure revenue for yourself within that? Either you directly measure from GA4 any AI referral traffic that's coming in to make that your core KPI that you really want to drive to make a business case for it.
**Measuring Branded Search Lift and Engaged Sessions**
(17:41) The second thing we've seen a lot of brands do is measure branded search lift. You can track on a Google search how many more branded searches am I driving every month, and how much of that is translating into revenue for me. The third thing a lot of people have started doing was measuring the average engaged sessions and how much of a lift is that over time. So that you can see if overall my higher presence is really driving better traffic to my website and making them stay on my website for slightly longer.
**Justifying ROI**
(18:05) There are a couple of methods here, but it's really helpful to continue making a business case for the channel that most people are struggling with justifying ROI from, aside from just overall brand presence.
**Producing Content Based on Citations**
(18:23) Third is using these two data sets, what you should start producing. If Reddit is important, or some PR campaigns are important, or some blogs are important, understand what's being cited to answer the questions in each of these engines and accordingly start generating those pieces here.
**Focus on Entities, Not Keywords**
(18:44) The most important thing here is don't generate those pieces based on keywords. I saw this prompt; this is what I'm going to generate. Focus on entities. For example, I'm going to focus on this ICP or this pain point first and create a wheelhouse of assets that I want to generate over the next four weeks or eight weeks. That's how planning should work.
**Avoiding Scattered Content Efforts**
(19:05) Generally, what we see is people will try to generate separate assets for 30 different queries that are targeting different personas. You don't make meaningful progress on any, because of which you don't really see any movement, and then you start from scratch every new week. It's better to understand one entity first and really build production there.
**Detecting and Addressing Content Decay**
(19:22) The last thing I'd focus on is every week you will see movement where some assets of yours were cited last week but have lost citation or have gone down. That's where you want to understand where you are seeing decay for any assets you've already pushed out. Those are your low-hanging fruits where you can easily refresh those assets or re-engage on those posts, bring them up, and automatically refresh them.
**Four-Step Process Results**
(19:51) These are the four-step processes that I'd highly recommend. There are complexities within this that I'm happy to dive into and happy to take questions somebody has on this. But, by using these four simple steps, we've run this across different types of industries. These are the type of results you can get to see in a very, very simple system.
**Achieving Significant Citation Lift**
(20:19) Where people have gone from zero citations to driving 38% of the citations across their category in under 6 months across search engines. We've had companies focus on Reddit as a category and build up their presence. People have used Quora. People have focused on updating their schemas, building a YouTube corpus, and scaled up.
**Building a Real Engine**
(20:44) Once you realize what works for you based on what the AI search engine tells you, it's really easy to build a real engine where you spend 2 to 6 hours a week and scale that up even without hiring somebody externally for that use case.
**Key Elements for the System**
(20:58) The four elements I'd highlight as important are: choose a data ingestion layer. This is like your typical crawlers or tools like profound. David, you had them on the platform on one of these webinars before. They're good at these things. They help you ingest the data, connect with your GA GSC, also provide you data on citations.
**Attribution and Execution Tools**
(21:27) The second piece on the attribution piece, I think, is important. Use traditional tooling like GA4 or even use tooling like HubSpot and Salesforce if they've built out for you to build that attribution funnel for yourselves and understand the core levers you need to move on. On the execution piece, I think it's either using content writers in-house or using the data you have, hire agencies that focus either on Reddit or on YouTube to find the core levers you want to move and hire experts that are core for each of those systems. I think that would be great.
**The Role of AI in Content Generation**
(22:07) As we get into AI to the content generation piece and using that, one of the things I've heard mixed opinions on is the role of using AI to create a lot of that content. I've heard mixed opinions on how much human in the loop, ranging from zero to lots, is recommended for that. Do you have some informed opinions on this one?
**Industry-Dependent AI/Human Mix**
(22:33) 100%. I generally think there's not a single correct answer. It depends on your industry. For example, if you're writing best listicles for different softwares or different products, it's important to have an opinion. If you're in a category like insurance, it's very important to have an author for your content.
**Aggregating Content at Scale**
(22:54) If you're also in a CRM space or a very generic space where there's a lot of content out there, being able to aggregate that content at scale and be that one source that's citing 10 other pieces underneath it is very helpful for any AI search engine because you are becoming that one source because they are all optimizing token counts on their end. If they can crawl one source to get eight sources, they will always choose you over going through eight sub-sources.
**Recognizing AI vs. Human Content**
(23:37) For every different type of piece, even if you look at the citations that are commonly being cited for any answer you look for, you'll be able to see that variability where you can see something is AI generated versus something is not. That tells you what can work, what can't work. That's why it's important to have human in the loop as a larger strategy where you have that mix, but everybody is doing a mixed strategy here. If you just focus on humans generating every piece, you're most likely to be left behind unless you have the ability to spend on volume.
**Surprising AI Content Results**
(24:06) I've seen some surprising results even with some tests where someone a while back had asked me for content for some SEO requirements. I'm like, let me see how good Gemini can be at putting this together. Their opinion was like, we can't use AI for this at all. I submitted some of this Gemini-created content, and they started ranking right away in the AI overviews. This was a tiny consultancy. So, my own assumptions have often been tested and proven wrong here. But it's what I love hearing from someone like yourself who's so in the weeds.
**Cloud-Generated Visuals and Data**
(24:58) 100%. Maybe I'll, before I go into the next piece, talk about a couple of things. Lisa, this whole thing was generated on Cloud Code. The point of AI slop is you can generate a lot of these things that look pretty as long as the data is correct. This stuff digests. For me to generate this, it's taken me over 2 days to come up with something like this. The point of generating a lot using bases, as long as your data source is correct, it serves the purpose. As I said in the call out, this was generated on Cloud.
**GA4 in Shopify Environment**
(25:40) The second question, Jennifer, you had was, is GA4 within a Shopify environment helpful? We work with a lot of D2C brands. The core importance here is if your tracking on GA4 is set up correctly for Shopify, only then will it work. Otherwise, you might see variability where your Shopify data is 30 to 60% off from your GA4 data. That's where you have a lot of issues, especially when cookies are not enabled. For attribution, I would first suggest getting a GTM set up correctly before you use only GA4 for attribution.
**Client Split and AI Ranking Measurement**
(26:11) We work with B2B and D2C clients across the board. Our split is about 70% in e-commerce and 30% in B2B. Lisa, the way we measure AI ranking is, depending on the different use cases we have for clients, we have our own tool that we use for which we've partnered directly with a lot of the LLM providers. But then some clients have their own tooling that they use. So, HubSpot has their own AO tool that we use and plug into, and then we use that data to verify citation data.
**Claude's Commercial Intent and Revenue**
(26:50) Paul, the reason why Claude is something I haven't spoken about here primarily is Claude is not an entity that's being used for commercial intent, since they don't want to push people out of the Claude ecosystem when they provide suggestions. So, we haven't seen major updates in revenue attribution coming from Claude yet, which is why most of the commercial use case we focused on ChatGPT, Perplexity, and Google.
**Core Use Cases for Client Websites**
(27:18) For client websites, there are four core use cases that I would highly focus on. First, whenever you see a citation that comes up, you can see what types of pages are being cited. So, either you create a new page that you don't have that's commonly being cited.
**Updating Existing Pages and Missing Sections**
(27:42) Second is if you have a similar page, update it based on the most common trend you see on pages. For example, in B2B websites, what we commonly see is people don't have strong FAQ sections, people don't have testimonials, G2 integrations onto their plug-in pages. They don't have competitor comparison sections, which are very, very common. So, that's the second approach where you update your existing pages to tag on those pages better.
**Critical Guide Pages and Brand Control**
(28:10) Third piece is within websites, people are scared to write content on best use cases for my X product or compare their products directly with other products by writing best-of guides. Writing those guide pages, writing those best-of articles are really, really critical for building out that presence on your own website so you control the narrative about your brand.
**Backend Architecture and Schema Implementation**
(28:31) Lastly, there's a lot of back-end architecture around schema implementation as well as allowing the correct bots onto your website. That's the core hygiene that I'd highly, highly recommend. For example, generally most websites allow the traditional Google bot and the standard crawlers, but they don't allow a lot of the ChatGPT bots as well as Claude bots. That's the first use case that everybody should update. The second piece is implementing the correct schemas across the website and the architecture so that any crawler that does land on your website can easily script through and go through the complete website.
**Ads on GPT and Investment**
(29:10) Jennifer, can you give me an opinion and any experience with ads on GPT and how this impacts unpaid? Yes. Ads on GPT requires a minimum investment of 100K. Focused on two plans: the free plan and the $8 plan. The companies that we've seen run good pilots on ChatGPT are the users that are targeting where their end customer is at the bottom of that quartile in terms of how much money they're willing to spend on AI search.
**HubSpot's Success with Intent-Based Ads**
(29:51) A company that's done really well on ChatGPT ads currently has been HubSpot. The way ChatGPT ads is working is they don't target the queries, they target intent. Across a very large intent forum, they're showing HubSpot ads, and that's doing really, really well for HubSpot.
**Impact of Paid Ads on Unpaid Queries**
(30:12) Unfortunately, I don't have data on how that's impacting unpaid ChatGPT queries and result behavior just because the channel itself is growing so large, and currently, we don't have enough data of which queries are surfacing ads or not, and that pilot is not that large yet. So, unfortunately, I won't be able to answer that question in terms of how it's impacting unpaid, but I am seeing very promising results on the paid forum right now.
**Connecting Sites to Google Search Console**
(30:36) I'm just going to answer Marshall's comment. There's some pretty built-in analytics. Marshall, great. I think you've answered another question on top, so lovely. One of those things also for those, because there's a lot at the intersection here of all this GA optimization and vibe coding, which we've been talking about a bit. One of those useful things that comes up is GA4 for sure. You have connecting your sites to that. Asking your platform or search, does it have any connectors to Google Search Console, for instance?
**Proactive Monitoring and Best Practices**
(31:23) To what extent can it proactively monitor and address some of the things that invariably come up: duplicative content, and all these structural things that tend to happen whenever you're adding a lot to your site? It's being able to use some of these more traditional best practices. In the past, I barely was ever on Google Search Console because I wasn't usually the one managing as much of this directly. Now because of AI, I'm managing much more directly, so I have to learn the stuff that some people learned 10 or 20 years ago.
**Connecting Data Across Platforms**
(31:55) 100%. What we're seeing across the board is one tool is no longer sufficing any attribution or team in terms of what their needs are. A lot of people have been connecting their data across different platforms. They're even downloading that and putting it on a singular platform to understand the correlation across different channels to better inform decisions.
**Ecosystem Understanding**
(32:31) A lot of people are now using GSC, GA4, as well as the data they get from an LLM visibility platform to better understand how the full ecosystem is working together and what are the best next steps they should be taking across this.
**Platform-Specific Attribution Setups**
(32:50) I would suggest I don't think GA4 or Shopify are the single best use cases for everybody else. Depending on your hosting platform, be it Webflow or Shopify, or Framer, or if you have a lovable website, the platform that you should be using is unique, a system you've set up and where your end attribution happens. For Shopify, obviously, it's happening on your website, but some people have end attribution on HubSpot. That's what changes a lot of the setups that individually you need to evaluate and recommend, we'd recommend individually to focus on.
**Easy Way to Push Out a Sprint**
(33:23) The one easy way I'd recommend pushing out this sprint is you can pick 10 initial queries. These should be buyer intent, commercial-focused queries where either you've seen search trickling from these, or this is something that you would imagine yourself searching for if you're looking for the product that you're selling.
**Manual Query Audit and Action**
(33:40) I would either manually run these queries across ChatGPT, Perplexity AIO, and Gemini. See the results you get. I would log those manually to see which ones are even relevant for us today. If you see completely different competitors are coming up, that's never going to be an important channel for you. Only if you see that relevance, I would focus on that channel and then track what URLs are coming up, what was the answer. Use that data to decide what actions I want to take tomorrow. On Wednesday, take a couple of actions from the top cited pages in the answers. Be it a new page you need to create, a new content piece you need to write, whatever it may be, understand that and push that out.
**Weekly Audit and Tangible Results**
(34:32) On Thursday, rerun your audit and see if anything changed. I would recommend doing that again the following week. Every week, if you spend an hour or two doing this, it becomes a simple process to start using and adopting GEO and understanding that better to see what's working for you, what's not. Once you do this for a couple of weeks, you'll be able to start seeing tangible results because these engines sometimes shock you in terms of how quickly they can change and show you being cited. That's where I think we've seen companies build up capabilities and scale their own presence and attribution from each of these channels.
**Use Cases for Improving Lift**
(35:14) My question for you, Aditya, thank you so much for breaking it down for me. This is not my wheelhouse, but I'm curious to hear more about some of the use cases you're seeing their clients, whether they're B2B or B2C. How are they using this to help improve lift that you helped hit it out of the park before?
**E-commerce and B2C SaaS Strategies**
(35:34) In e-commerce, what's working for folks is creating new category pages that target niche users and pushing up content supporting those niche pain points or ICPs that they target. Then spinning up YouTube channels that use their social media content because YouTube is a highly cited source. For B2C SaaS companies, engaging with users on Quora, Reddit, and doing targeted PR activities on commonly cited sources has been extremely helpful.
**Direct Comparison Pages and Product Updates**
(36:13) Then them creating direct comparison pages for each core competitor that a ChatGPT is citing has been extremely useful. Writing product updates for core. Generally, we have service pages that show for any user what their product does, but writing larger product updates that really go into the in-depth version of the solutions has been extremely helpful.
**Research and Product Research**
(36:40) It sounds like it's for the research and also for product research.
**B2B Content and Documentation Strategies**
(36:44) Yes. For large B2B companies, they're building heavy published content which they used to hide behind paywalls. Now direct for any crawler to come up, which is based on proprietary data highly reviewed by their experts internally. Then go out there and co-publishing pages with top experts in their industries, pushing that out, and really focusing on G2 reviews and their outdated website architectures where they're opening up a lot of their documentation out there. All of these crawlers can come and be able to see it.
**Embracing Conversational Engagement**
(37:26) Sounds like you're teaching your clients to be more open and to engage with their audiences directly from what it sounds like. They used to have more closed systems, but now AI has forced them to be more, let's say, conversational when they're talking to audiences.
**Paywalls Less Helpful**
(37:38) Exactly. Paywalls are no longer as helpful as they used to be.
**Role of Traditional PR in LLMs**
(37:45) What I've seen come up also is the role of traditional PR, more traditional sources. Is this more ammo for bringing more traditional PR and elevating that to impact the LLMs and brand reputation?
**PR Guided by Cited Sources**
(38:09) Traditional PR needs to be better guided with sources that are being cited on each of these channels. Traditional PR focuses on your typical large publishers like Forbes and Times magazine. What we're actually seeing is these channels pick up a lot more unique individuals that could even be running substacks or 100,000 subscriber blogs that are unique to the particular industry. That's what we're seeing play a big role in terms of shaping LLM opinion.
**Rewriting PR Strategy**
(38:50) PR continues to be important. It's a rewrite of the strategy in terms of what PR you focus on and how you attribute success for that PR where it's not about getting it listed on a high visibility, high brand name channel.
**Team Skills and Workflows**
(38:58) We had a question from Sandeep before, SEO optimization was linear with longer cycles. Any comment on what type of team skills and workflows you're seeing now?
**Faster Uplift and Update Cycles**
(39:11) Sandeep, on this timeline, what we are seeing with clients is you can generate uplift within 15 to 30 days at max. That's the timelines I think every marketer should get used to in terms of how they're seeing. Any marketing channel today, because of how quickly these platforms update. Google used to release an update once every year, then once every 6 months, and now they release an update almost every couple of weeks. These AI search channels change a lot quicker. That's the type of core update cycle you should be focusing on.
**Comfort with Change and Volume**
(39:55) From a skill set perspective, first is being comfortable changing the core metrics we evaluate, that's the first shift. But the second is having a larger wheelhouse of capabilities because volume and speed of content and pushing out changes is very, very important.
**Outdated Quarterly Review Cycles**
(40:17) The typical quarterly review cycle where you create a strategy that you execute over 90 days and write maybe two pieces or low volume outputs is no longer feasible because everybody else is trying to operate at a higher throughput. They're iterating their strategies a lot quicker.
**Agility is Key**
(40:39) I don't think you need to know coding, but being comfortable with that level of change and that level of agility is extremely important to actually win.
**Investing in External Platforms**
(40:43) Excellent. I have a question. In response to that, if a business is not able to update their website that frequently, would it be better for them to invest in being active on a platform like Reddit?
**Channel-Specific Strategies**
(40:59) Lisa, depending on the type of citation, either it's Reddit, YouTube, Quora, or G2, whatever that may be, in terms of within the category that's important for you, definitely then work outside of the core website becomes important. That channel may differ depending on what the commonly cited citations are for your industry itself.
**Multimedia Content and LLMs**
(41:27) Thank you. One thing you haven't talked a lot about today is multimedia. People are producing way more video content. How good or bad is that when you're trying to influence LLMs?
**LLM Optimization for Token Usage**
(41:48) The way LLMs work on the core at their back end, what they're optimized for, is to reduce token usage for each search they provide without hampering token quality. What that means is as much text as they can pull and as much pre-data that they already been trained on they can pull to answer the question. The more it will lean on that.
**Shift in Multimedia Indexing**
(42:14) Only for use cases where it doesn't think it'll provide the best outcome for it, will it try to do a web search and then lastly look for net new types of content. A year and a half ago, multimedia content was not something LLMs were really good at indexing and understanding. That since then has shifted drastically. A lot of websites, including YouTube, Instagram, are providing transcription directly on the back end that makes scraping off this content a lot easier.
**Uptick in Multimedia Influence**
(42:37) We've seen a massive uptick. We actually did a study with the YouTube team about how much YouTube as a channel has scaled up and how multimedia is playing a larger and larger role across the board.
**Cross-Pushing Content for Shaping Opinion**
(42:58) What we recommend brands across the board to do is, even though multimedia content is not the biggest driving lever if you're getting started with GA, for example, if you have an issue like Lisa, you can't update your site as much. Or you already have one channel you're pushing out a lot of content to, that could be Instagram, LinkedIn. Cross-pushing that content across other mediums is your lowest hanging fruit to increase your chances of shaping your opinion across any LLM. That dominance of this type of content will increase.
**Google and ChatGPT Leading Multimedia Adoption**
(43:39) We saw this uptick be driven first with a lot of self-help and guides where you see a lot of YouTube content already start flooding the market. Google led the charge, ChatGPT followed suit very quickly. We're going to see that adoption scale up.
**Art, Science, or In Between?**
(43:56) I think this is Lisa's question. Is this an art, a science, or somewhere in between? I think this is definitely right bang in the middle. There's a structural process to it. The art really comes in in terms of what you think is most relevant and what you think your core ICP looks like.
**Decoding LLM Behavior**
(44:25) I do think as LLMs get a lot more structured and there's a lot more data and consistency in their outputs and they become slightly more public in terms of how they operate, it'll become easier for us to decode on a more consistent basis what our uplift and what the ROI from each of these channels can look like over time. For example, Microsoft released their AI search visibility platform last year in November. That really helped people understand and decode a lot of what Bing's behavior looks like. As more and more platforms follow suit, it'll make it more and more of a science than just an art.
**Adding Sources to Notebook LLM**
(44:56) What are the best ways to add sources to Notebook LLM?
**Incognito and VPN for Geo-Targeting**
(45:07) Lovely. What I always recommend is run everything on incognito. Generally, if there's a different geography you're targeting, run that geography as a VPN. The question was what are the best sources to influence Notebook LLM?
**Querying Across Platforms**
(45:31) To add the best apps to add sources to Notebook LLMs. What I do is I just run exactly the same query across all four platforms. I'll skip Claude for now just because I have to log in, but I'll show you representative examples of how it shows up. What are the best sources to run Notebook LLM?
**Analyzing Citation Sources**
(45:52) If you realize this did not default to web search. Generally, whenever it defaults to web search, you'll be able to see the citation sources it's using, its pre-pledged data it already has. This is how it's gathered its resources. For this exact output, all it's done is it's highlighted the different sources it has for each. I'll just wait for this full answer to load. These are the top apps.
**Manual Data Collection**
(46:16) What I do is create a Google Sheet. I should not be working off this. But, I will create a Google Sheet, list out the query I put, put Google Drive as number one, Google Doc as number two, YouTube as number three, or just copy-paste these three. Go on each of the citations that ChatGPT used. If you see two of the citations are both Google. Then it used a platform called Bboom. I hope my full screen is still relevant.
**Engaging with Cited Sources**
(47:01) This is a random source in terms of an article. Arjun published this last year. So, clearly up-to-date data is not as important for. This has zero comments. Extremely recursive source. Used Notion without any clear citation, Obsidian without any clear citation, and cited a couple of automation tools that it got from this Reddit thread. This Reddit thread has only about 22 upvotes and one comment. Post about 24 days ago. I would do this same approach by gathering this data across Google. But, I'll open up the Reddit thread, I'll open up this, and I'll open up this Reddit thread, and put down the different answers I saw for Google, as well as a YouTube video.
**Tailoring Content and Engagement**
(47:47) I'll do that same thing here where in Perplexity you'll see a lot more different documents and pull that same data. This is where a general crawler is slightly helpful. But generally pull for some of the most common ones that I see that are common. As you see the Reddit threads that Google pulled are drastically different than the Reddit thread that ChatGPT pulled.
**Influencing Opinion on Threads and Articles**
(48:04) What I would do is if this was something that I wanted to rank for and have my tool showcase, I would go in, understand what this study is talking about, and actually engage saying what I would recommend and why I would recommend that here. I would look at this blog article and try to create the same article on my own website because I assume I'm selling an automation tool as well.
**Leveraging Google's Structure and Depth**
(48:45) For the Notebook LM help page, now I don't have the authority that a Google has, but I would understand the structure that they've created and see if my own source if I can create an article about Notebook LM with the similar structure and the same depth that Google is citing and cite Google's Notebook help page as well as the Reddit thread, combine them as sources, and create a formal article that I own that's citing these five sources as a combined source for it.
**Reddit Channel Importance**
(49:09) Similarly, this is the same thing for another Google page. This is the same page that we saw here that even Google is citing. This is a different thread. I would go in and engage on this thread. I now realize I can't recreate this thread because it has a lot of reviews, but I know that the r/notebooklm overall channel is extremely important for me. So, I would live and breathe this channel and find five posts I want to comment on this week and five new threads I'd want to start that's relevant to the threads that I've seen here.
**Time to See Results**
(49:38) How long before you can see results if you take this approach?
**Platform Change Speed as a Proxy**
(49:45) Genuinely, it depends on how quickly each of these platforms change. For example, this thread is 6 months old. This thread is just 24 days old. That's how quickly it's cited. So, I would assume on ChatGPT, you can change the opinion on ChatGPT a lot quicker than you'll be able to change the citation frequency on a Google. Directly how recent the thread it cites is actually how quickly it's a proxy for you to start influencing the opinion for that channel. To get 93 upvotes or 250 upvotes is hard, but to get 22 upvotes in one comment is a lot easier.
**Connecting with Aditya Jain**
(50:23) That's how you and the difficulty of it. We're approaching time here. This all terrific, and I appreciate the real-time glimpse. What's the best way for folks here to follow up with you, stay in touch?
**Contact Information**
(50:47) I'll list, drop my email and LinkedIn on chat and happy to, as well as Passionfruit itself. Happy to connect any way possible.
**Brand Reputation and Optimization**
(50:56) Amazing. I've learned a ton. All this info for the best takes on GEO and app optimization right now. We need as much as we can get because this is where brand reputation is and where it's continuing to go. I appreciate you starting right off with busting some myths out there and giving us some of the latest, very well-informed thinking on this. Thanks for coming by. Appreciate everyone with so many great questions today. Look forward to seeing you all next week.