Adobe Digital Insights: GenAI Traffic Update

Transcript

Thank you for joining the Adobe Digital Insights Gen AI traffic update. Today, we’re going to unpack the latest findings from ADI’s Q2 2026 AI sourced traffic report. This is drawing on analysis of over one trillion visits across retail, travel, financial services, media and entertainment, and tech software, or as we like to call it, high tech. We’ll explore how AI referred traffic is growing at triple digit rates, why these visitors are consistently more engaged, convert better and bounce less than traditional traffic, and what the shift means for brands as AI assistants increasingly shape discovery, research and purchase decisions. Our speakers today are Vivek Pandya, Director of Adobe Digital Insights, and Marta Frattini, Director of Global Industry Strategy Group. With that, Vivek, I’m going to hand things over to you.

Thanks so much, Marla, and great to be walking through our data updates for quarterly AI trends. For those of you who don’t know Adobe Digital Insights, Adobe Digital Insights provides over a trillion visits. It looks across over a trillion visits and provides data insights for broader thought leadership to our customers. We’re able to kind of showcase what’s happening in the broader digital economy and what’s happening with AI trends. So we’re very much known for our Black Friday online shopping data trends, but we’re also sharing a lot of data around AI traffic and conversion and things like that. So let’s dive right in. When we look at our quarterly report, we’ve updated data up to March, so we’ve gotten some really key takeaways that we’ll be kind of tapping into this session. So what we’re seeing in terms of visit share growth, conversion, we’ve built out a new capability around citation and AI readability. So we’ve got a lot to cover, so let’s just dive right in. Right up out the gate, we do still continue to see accelerated growth for AI visit share. That’s up 393% year on year in 2026. So for retail, it’s continuing to surge. We’re still seeing continued momentum across major sectors like travel, media and entertainment, tech and software. As consumers become more and more comfortable using generative AI platforms for different use cases, we’ve seen about 54 consumers say that they’ve turned to AI more, about 60% have said they’ve used AI. So there’s a big opportunity there given how much different users are using AI for different needs and purposes, and how that’s changed over the past two years in a pretty significant way. Now when we look at retail, as I mentioned, that’s up 393% year on year in terms of visit share, and it’s up over a thousand percent relative to the levels a couple of years ago in October 2024. So we have consumers now, about 40% of them have said that they’ve used AI for various use cases within their shopping journeys, and that’s really materializing in the traffic data we’re seeing and how much we’re landing onto these retail sites from generative AI platforms. The other key driver here and the important takeaway I’d say from a lot of our insights is we’re now finding that this AI source traffic is converting 42% better than non-AI source traffic. I mean about a year ago it was actually improving from a conversion standpoint, but it was still underperforming non-AI source traffic. To have it now overperforming non-AI source traffic at about 42%, it’s really showing that consumers are very comfortable with the answers that they’re getting from these generative AI platforms, the research activities that they’re doing on the platforms themselves. Then once they make it over to the site, they’re exploring around, they’re feeling good about what they’re seeing, and they’re converting very effectively. So there’s a big upshot and there’s high stakes in attracting more of this traffic and more of these users who are sort of using generative AI platforms across their consumer journeys. So big implications for brands and retailers out there. And when we look across other metrics, we still see it holding up pretty well. So this is as recent as March 2026, we see 12% better engagement. And that’s really indicative of the type of metrics we want to see out of these users. These are things that brands and retailers spend a lot of time optimizing. So the fact that this type of traffic is spending 12% more time and engaged more on these platforms is absolutely important. Another key validating metric here is AI source traffic is boasting a 32% better bounce rate than non-AI source traffic. Again, bounce rates are things that brands for decades have been trying to work to improve, try to hold and have that stickiness across the web experiences. So the fact that this AI traffic is just so engaged and exploring pages and bouncing off less is a really good marker for these brands around this traffic and how they can think about continuing to scale and drive more of it in their direction.

The other key metric that is absolutely incredibly important and valuable for brands is revenue per visit. And we’re finding that the revenue per visit from AI source traffic is 37% higher than non-AI source traffic. So this has huge ripple implications in terms of what overall revenue growth can become, what profitability can become. And it’s important for brands to think about how do they ensure that they’re getting really strong visibility across these generative AI platforms so that they’re appearing instead of a competitor, so that they’re the main answer the LLM is providing back to the user. So it’s absolutely critical. And when we think about the implications there, we go pretty deep and we go detailed into retail product trends. So when we look at March 2026, we saw many of these categories apparel, personal care products, sporting goods, so very strong boosts out of AI traffic. And we see some of these other categories see generally good boost, but they could be higher appliances, electronics. And then the weaker boost categories are things like grocery. We’ve seen pet products have a bit of a weaker boost. So we’ll have to see how this shifts over time. But the fact that in March 2026, we saw such a great variety of different categories benefiting from AI source traffic was really great to see. We see it with luggage, sporting goods. So people think about traveling, sporting goods are active. And as the weather gets better, people are getting out and about more. And things like dresses, we just passed Easter, spring dresses, things like that. All of these are top of mind for the consumer. And they’re leaning on the generative AI platforms to get better ideas on what they can purchase, where the opportunities are on getting the best deals and things like that. The other area we’re able to track is how an event like a Super Bowl that requires people buying maybe merchandise or nachos or different types of grocery items for their Super Bowl parties, things like that, that drives a natural lift to the AI traffic as well. So it becomes very important for these brands to think about not just how they’re performing overall in terms of generative optimization visibility, but how can they tap into these major events and these eventized moments to amplify their presence so that they can drive more upside from this type of traffic. Now, we’ve been producing this report for a couple of years now. And we’ve been always looking to see what additional components can we add to it that can help tell the story of this type of shopping journey, this behavior. And one of the ways we’re doing this is through AI citation readability. So as you might know, Adobe has the LLMO optimizer product. So LLMO allows us to provide brands with a strategy, a product that allows them to ensure that they can get visibility across these platforms. There’s also a plugin that we have that allows different brands to see through Google Chrome, how AI readable their content is. And so we looked across millions of pages across different categories and sectors, and we found that the top drivers of AI VisitShare and the bottom drivers of AI VisitShare had materially different AI readability scores. And that has big implications in terms of the opportunity these brands have to improve their visibility and capture more of this traffic. So we have different deltas between the top performers and the bottom performers across different types of discovery pages. So home pages, search results pages, blog content, buying guides. These are all pages that are machine readable by AI bots. And as a result, we see certain brands that have driven strong visibility drive better scores. And as a result, they stand in the top 20 percentile of brands that are absorbing AI VisitShare. And as we said, because they convert so much better, because they provide a higher revenue per visit, that’s incredibly important. So the opportunities are pretty apparent here for different types of brands that maybe have lower VisitShare from AI platforms to optimize and generate better visibility through our LLMO product in order to ensure that they can capture more of this VisitShare because there’s a big difference. You see the homepage there. There’s a 52% difference in terms of readability scores from the top performers and the bottom performers. So if a brand works to improve their visibility and generative engine optimization tactics, they will see a higher volume of AI VisitShare hitting their pages, hitting their sites, which is a net boon for them. I think the other broader view is that as you get through the deeper pages, some of these category department pages, these collection pages, that in general, even top performers and the bottom performers could do some more work to improve their visibility scores. Basically, the counterfactual is showing for something like a product detail page, about 40% of it is not AI readable. So what can be done to further improve that visibility? That becomes the big question that brands are tackling and they’re leveraging Adobe products to understand that. They’re thinking about how these LLM platforms are exploring across pages and pulling those answers back to the user. Is there too much JavaScript on certain pages that’s making it harder to be machine readable for certain LLM models to capture data? All that is being introduced into the calculus around how these brands can drive better visibility, better traffic. And as a result, there’s a big opportunity here, not just from the bottom performers who are getting very little AI VisitShare to become in the ranks of the top performers who have a lot of AI VisitShare, but overall, the whole space could benefit from better optimization across their different types of pages to ensure they’re capturing more machine readability across their pages. So there’s a big opportunity here. And as we said, we do see differences in the types of pages and the overall visibility scores they’re boasting. So you see, as we showed home pages, blog content pages, they tend to drive higher readability scores. Then we see search engine results pages skewing lower, much lower for the bottom performers there. And then when we get to product detail pages, collection pages, those could also be improved as well. So there’s a lot of ways to think about optimization here and how to ensure that your content drives better visibility and as a result, better impact in terms of outcomes to the site. The other call outs we want to mention are other industries and sectors benefiting from this AI traffic. So something like travel, it’s absolutely emerging as a sector that’s benefiting from strong continued AI VisitShare growth. That was up 233% year on year in the first quarter of 2026. We’re seeing the different ways they’re engaging travel. So they’re using it for research, for budgeting. But what’s different for travel, they’re also using it for packing help. So travel lists of what they need, inspirations and recommendations, ideas of where they would want to visit. These are the different types of ways that AI is informing the travel journey. And so much like retail and some of these other spaces, travel will have to be very dynamic in how it showcases its opportunities for travelers and the different options available to them, providing different content around checklists, around deals, things like that, budgeting, that’ll be incredibly important. Now, when we go to the conversion comparison for travel, it still hasn’t eclipsed non-AI source traffic like retail has, but it’s getting closer. It got pretty close over the holiday season. And now it’s just underperforming the sort of non-AI source traffic is being underperformed by AI traffic in terms of conversion. It’s not that much of a gap. It’s only about 14%. So we could see essentially a repeat of what’s happened with the retail sector where AI conversion is now surging over non-AI source conversion. But we still haven’t seen that quite as much in travel, but it has improved quite a bit over the past couple of years. An area where it is overperforming non-AI source traffic for travel is engagement rates. And it’s also doing quite well in terms of time on site. We’re seeing AI source traffic for travel have sessions that are 61% longer than non-AI source traffic. So again, still highly valuable for the travel space and can continue to get more valuable by the day. Now, when we think about other sectors, a very important one is the FSI space, financial services and insurance. This is a sector that’s very important, very key to the consumers mindset and then absorbing a lot of information to know what decisions they should be making, what they should be thinking about. And what we find is the financial services sites are up about 158% year on year in terms of traffic in Q1 in 2026. And the use case is when we do consumer survey research, we’re finding 24% of consumers say they’re using AI assistance for financial needs and they’re finding it, about a quarter of them say it’s improving their banking experience. So that’s really good to see for the sector. And also when we survey in this particular space, we find different kind of upsides to using AI through financial service decision-making. So we’re finding that they’re using generative AI platforms, their conversations are about banking recommendations, about a third are using it to understand very complex financial topics, and then also understanding the instruments themselves. And then the sort of tertiary considerations, just like you have with travel with shipping lists, you have personalized budgeting guidance, you have investment recommendations. So that’s incredibly important in terms of the calculus for the consumer in thinking about financial service options. And they’re spending more time if they’re an AI visitor on the financial services site. So very important. And then here again, the bounce rate is stronger from that AI source traffic, continue to see that validation there. It’s bouncing 17% less than non-AI source traffic in the financial services space. So good sign around the broader behaviors that are happening cross sector. And the key piece for financial services is about 89% of consumers trust AI to provide financial recommendations without human input. And 46% of them fully follow that advice. Again, this is only within the course of two years. They’re very much trusting. Many are very trusting AI for financial advice. And so it becomes even more important for firms to ensure that their information is being out there, being picked up, and is correct so that they can have that continuity between what the user hears about financial guidance from the generative AI platform and what they’re seeing on the sites themselves. The other sector that we want to capture here that’s pretty popular is media and entertainment. This is continuing to grow, sustain momentum, about 84% year on year growth. And we’re finding that they’re using generative AI platforms for ideation. Again, a different mix of things. What TV shows or movies they should be watching, trying to get updates on news and current events and how that’s linking over. And then also additional social media and influencer content and guidance around that, as well as books and podcast recommendations. So ideation and discovery is a theme across all these sectors. And it really holds through for the media and entertainment space. And again, here we see a comparable uptick in overall AI source performance for user engagement. It’s performing at about 14.49% better than non-AI source traffic. And 84% of consumers say they’re using AI for media and entertainment forces. And many say they’re very likely to purchase based on recommendations there. So definitely important to get involved in that part of the journey. They’re staying longer across the sites, which is absolutely important in the media and entertainment space when it’s about content and continuing to engage with that content and spend time with it. So again, a big opportunity to keep people on your media and entertainment sites if they’re coming for a longer period of time. And the bounce rate, again, more validation here, much better bounce rate. It’s 17% in terms of how much it’s improving over non-AI source traffic. So it’s a long way of saying when we see these signals validated time and time and again, it really helps us confirm the upside and opportunity with this type of traffic. The last industry we’ll profile here is tech and software. Tech and software is at the forefront in terms of sectors benefiting from AI discovery, AI level decision-making. And as a result, we find that 47% of consumers report using AI to understand, troubleshoot, or make decisions, which becomes definitely a part of the journey for the tech and software space. So that’s growing 63% year on year continued momentum, but it also holds the largest visit share across industries. So it’s growing on a higher base. And we see that helps crystallize what we’ve been seeing across the other industries and their growth rates. So median entertainment, tech and software, a bit slower in terms of growth relative to retail, FSI, and travel, but they have higher visit share. So they’re growing a larger base volume. The other key part here is for tech and software, again, the bounce rate is better from AI sources. And we find that they’re spending much longer, far more longer than other industries on tech and software sites, especially as they’re troubleshooting, getting more information. So they’re spending 40% more time as an AI visitor on the websites for tech and software spaces versus non-AI sourced traffic. So big upside there if you’re looking to have people spend more time across your software sites and understand your software platforms. And also they’re exploring more pages. So 23% more page level exploration. So it’s incredibly crucial to think about given the tech space and the platforms and the software and the need for the user to feel comfortable with what they’re using, they need to have some of these tech software options put into their consideration set by the LLM. And then we do find when that happens, significantly more time than their non-AI source counterparts.

And then lastly, we’ll just get into the sort of AI demographics here, because who are these visitors who are coming through these AI consumer journeys and spending more time trusting the results. And we find from a geographic standpoint, we do see Virginia, Washington, New York, California, as you’d expect, Massachusetts really driving adoption. They’re using AI systems more regularly. They’re turning to it from more use cases and they’re trusting the results far more than the average. So these are definitely some of the states you’d want to target from an opportunity standpoint. We also do see the urban rural divide in terms of users across these different spaces and how they’re essentially maybe a little bit more comfortable using generative AI platforms for different types of actions. And so we do see urban and suburban parts of the country taking to this a little stronger than the rural areas, but that can change over time. The other differentiator, we do see a bit of a contrast in the delta from people who are familiar with AI assistance and believe they can use AI to accomplish more. And we do see that be a bit higher with people who have at least a degree. But what we see the smaller differentiating part on is the trust. So once they actually use it, how much are they willing to trust it? And so there’s a much smaller differentiator in terms of that particular cut of the data. And then in terms of broader optimism and familiarity with AI and thinking it will make life easier, that’s where we see the respondents across the board show a lot of enthusiasm. Many users think AI will make their lives easier and we’ve seen the familiarity quotient go up. We still have a lot of room for many users across the country to think about how they can use it more regularly and what sorts of activities they want to operationalize with AI. We do have some cutting edge, bleeding edge, sort of I would say tech enthusiasts who are doing things like creating agents. They’re really many different ways, but then you do have large cohorts of the population still looking at ways to think about how they can operationalize AI in their day-to-day life. And then as you’d expect in terms of the generational breakout, we do see Gen Z and millennials driving the trends for AI adoption and really shooting forward in terms of AI adoption use. But we do see Gen X and baby boomers still growing in terms of AI adoption. And we do find that they’re supported broadly. And we’re seeing some of the older age cohorts actually take to AI at a much faster pace than they did things like social media or the broader digitization of e-commerce. So I think that speaks to many audiences out there of different generational levels still being confident and not wanting to be left behind by the AI phenomenon. So they’re willing to experiment and willing to test things out. And then the other key part here that we also hone in on is trust and having AI be able to accomplish more things with agentic AI. Again, the perception is from Gen Z and millennials that these AI technologies will drive them much forward. They’ll drive them forward at a much faster pace. But we do see in terms of making life easier, a smaller delta there between the younger and older cohorts. And then lastly, confidence. That’s where we see many of the numbers start to kind of getting closer to one another. You find that once they’ve broken the seal, some of these older age cohorts, they start to feel more comfortable with the results. They start to trust them. And as a result, we expect them to be able to experiment and test more use cases and use it more than they used to. So that’s the third breakout over there, which is across the age spectrum, many generations saying they’re willing to use it more than they used to. They’re finding themselves using it more than they used to. And that’s very much because of the experiences they have and how well many of these models have produced strong experiences that many of these age cohorts have really responded well to and are willing to try different use cases out again. And then the last kind of breakout we have is income-based. And this the area where we do see a bit of a contrast, not massive shifts between lower income and higher income groups, but we do see a little more acceleration in both utilization and confidence to complete tasks in higher income groups compared to lower income groups. But again, over time, we expect this to balance out more as it becomes more of the norm for many different types of activities. And with all that, I’m going to hand it over to Marta to kind of get into the implications. We did talk a little bit about readability and it’s very exciting that we’re able to tap into that. So I’m going to let Marta take it from there. Thank you, Vivek. And thank you for all the insights. Always extremely interesting. As Marla mentioned before, I’m part of Adobe Digital Strategy Group. So I work with a lot of our customers on digital transformation and personalization at scale. So I want to talk a little bit about we heard a lot from Vivek on the numbers and the evolution of consumer behavior. What does this mean for the companies and brands we work with? So I wanted to like, re-ground us in some of the key stats that Vivek shared. That front door of how consumers engage with brands is really changing. He talked about a 393% increase year-over-year in AI-driven traffic. And we see this traffic being valuable, right? Higher conversion, higher revenue per visit, more engagement, lower bounce rate. The way people are using it, Vivek spent a bit of time there as well, right? At the very top, we see research. I think what’s interesting is that his team actually ran this same survey probably about a year ago and that research phase was about 70%. So people were really using AI back then as initial exploration. But today, it’s getting much closer to the bottom of the funnel, the intent, the actual valuable audiences that we then see reflected in on-site behavior. So research, product recommendations, finding deals like who has the best Friday, Black Friday, offers and opportunities, ideating for gifts and shopping lists are some of the big use cases in the retail space. If we go to the next slide, what this means is also that in the LLM world, LLMs are deciding what gets seen and what gets trusted. So it’s not just about how you show up on your own website. It’s about how do you show up in the ecosystem. How do you influence the conversation on social media, on Reddit, in gift guides, in shopping lists? How is your brand presented? How much is your brand talked about? So it is as much about how you architect and design your own website as much as it is about what is your brand presence outside in the world and how can you actually effectively monitor that, maximize that and optimize that.

It feels that for a lot of the companies we talk to, it feels like a black box, but it’s not. These are real results aggregated from customers who are doing Adobe LLM optimizer pilots with us. So when you do actually make your content AI readable, your citation rates jump. At an aggregated level, what we are seeing with our customers is a 20% lift in visibility. How many times is your brand actually cited and mentioned in those LLM results? The other aspect is a 50% increase in citations and citations are clickable links that direct traffic directly to the retailer website. And last but not least, the readability itself of the product pages. Product pages tend to actually not be very readable unless you’re intentionally designing them for LLMs and systems. So we are seeing significant increases there that really help with that visibility and citation stats. There are also interesting soft learnings and I think this is where really brands need to evolve how they think about the world. It’s not just SEO, it’s SEO and GEO. The first pillar is we are seeing a lot of the conversation evolving from traffic to trust. In a traditional SEO world, brands were monitoring how many times they show up in Google results, on page one, in the first three. Today, brands don’t need to only monitor how much are you showing up in LLM but also how are you showing up. Is the LLM, chargee, p keep, perplexity, etc. Is the LLM getting your brand right? Is it saying about your brand what you want it to say? Is it representing it in a positive or negative tone? A lot of monitoring that then translates into again, what do you need to do from a brand awareness and a brand influence perspective to actually change that conversation and that perception. The second aspect is moving from keywords to prompts. People on LLM are not searching for a black bag. They’re searching for a handbag that goes from morning to night, costs less than $200 and has a modern look and feel. As an example, you really need to know your customer base, you really need to understand how they’re searching, you need to monitor those queries and you also need to decide what do you stand for as a brand and what do you want to rank for. The third pillar is from pages to systems. We talked about that a little bit with the product page. You are designing websites and product pages that are not only serving the human eye and the beautiful storytelling and product characteristics that you want a human shopper to see. You need to think about your pages and making sure that you’re surfacing the entire system of information in a structured way that leaves behind your product catalog. That includes obviously all of the product attributes, all of the tagging, updated pricing and offer, updated availability, everything that LLM platforms will need to actually return clear, accurate results as close to real-time as possible.

Winning in this world means that you’re building your website, your experiences and your brand expression for two audiences. The human interactions that we are all used to but you now also have these adjointing interactions you need to take into consideration. It’s a double layer, it makes sense to have a lot of people in the world that are interested and this is not just a human issue. You don’t just solve that with more skis or more people which of course you’re going to have to build but you have to solve that with the right technology as well that enables you to actually monitor, track and optimize for this new world. Part of that is what you do with offsite sources which we spend a lot of time on. There’s a lot that you can do in the world of generative engine optimization to really optimize how you shop for that discovery in traffic in AI driven search channels. I said it before it’s not an order, it’s an end. So how does your optimization work both for geo as well as for SEO which continues to be a big portion of the traffic. But then you also have to think about how do you optimize the on-site experience as that AI driven traffic lands on your website because we saw this is a very high intent, high value traffic. So you need to make sure that you’re connecting the dots between what brought those visitors in and the experience that they receive. Landing pages need to be relevant, page performance needs to be high, need to be accessible, readable, the content needs to be relevant and personalized. You need to think about the conversion as well and one thing we are seeing emerge more and more is AI powered assistance on websites. As people start using LLM more and more as a platform for search, what we are seeing from a consumer perspective is also they are developing a natural tendency to search and interact with product in natural language. So the patience for that little search box is kind of running thin and we’re seeing more and more brands surface contextually aware personalized AI powered assistance.

So that was a quick overview of what we are seeing in terms of what’s happening in the industry and in the market in response to all the amazing stats that Vivek shared. If you want to learn more, you will receive the links to all of these resources. We have actually a Chrome plugin that helps you check what is the actual readability of your own website. That’s free, you can do that today. You can learn more about our LLM optimizer solution on the Adobe website and then please feel free to connect with Vivek, myself or your own Adobe team to continue the conversation. Thanks everyone. Thanks so much, Marta. Thanks everyone.

Okay, I am looking in the chat pod and so far I’m not seeing any questions from the group. We may not get any today, but just to close things out, I actually had a question for Vivek and Marta if you don’t mind indulging me. So I wondered as the gen AI trends are emerging and becoming quickly, quickly more prevalent, I’ve noticed in the customers that I work with, there is a lot of curiosity. There are a lot of questions, but there isn’t a lot of strong, strategic both vision and execution when it comes to how they are adopting to the LLM traffic, for example. Do you have any examples of brands who are doing it really well, folks who are on the cutting edge of what we think good looks like and what it is that they are doing? So I think there’s two aspects to this conversation. There’s a here and now, which is all that Vivek shared about AI traffic and generality of engine optimization and people using these platforms to look for the product. That’s a reality and we work with several retailers that are tackling that and I think so we did a session at NRF with a fashion brand that it was tapestry and one of the things that we’re talking about is this traffic might still be a relatively small percentage, but when you look at it in terms of like quarter over quarter growth, you know that this traffic is actually going to be a really big, probably in the easily in the 10% penetration range by a year, right? And at that point, it’s at the same level as many of like the major traffic and marketing traffic sources you care a lot about. And so I think the number one is really that awareness of the importance of it and really started to look inside again, like not just what do you rank for, but what do you want to rank for? What do you want to stand for as a brand? I think that’s the here and now. And then what happens from an agentic commerce perspective? I think that’s a bit more of like the tomorrow, right? And I think there I feel some of it is here. I feel like the future is here. It’s just not like evenly distributed in a sense. So there I always look at some of the leading brands like Walmart and Target and Sephora and how are they thinking about both sides of the equation, right? One side is how do I enable my own AI assistance and capabilities on my website like Walmart does with Sparky and a lot of these like intent driven commerce. On the other end, which is what Sephora and Target are doing is how do I make my own apps and data and experiences available into platforms like Chajpity and OpenAI to actually bring some of that brand controlled experience to that endpoint? And I think that is a very interesting evolution that we are seeing in the market, right? Chajpity itself has kind of deprecated their instant checkout in favor of these like brand-owned experiences. And I think what’s telling us is that the brands that are like leading and testing are seeing that, yes, the traffic is there, but then what you as a brand know about the customer, about the experience, about your product catalog, that matters just as much. And you have to infuse that like branded layer into that LLM experience. Yeah, that absolutely makes sense. But, what do you think? Those are some great examples, and Marta, broke down. I think there’s a couple of different things at play. I think one is understanding that these LLMs are able to contextualize a lot in their response back to the user. And the user wants that peace of mind and that confirmation that whatever the LLM produces is exactly what they wanted and will deliver really well. So, it puts a pressure on the LLM to kind of triangulate information, get the validation right, and make sure that your reviews for your products from multiple sources are cooperating one another. So, then something like digital PR and being able to get on top 10 ranked lists and things like that, that becomes kind of important because it’s giving value to those third-party authorities. And it’s also tapping into how recent that feedback has come in. So, putting your best foot forward as a brand, doing outreach and engagement is a big part of it. I also think back from what we were talking about in terms of readability scores, and I’m so glad Marta shouted out the LLM Visibility Checker. It’s a great plugin, and we are seeing there’s a big difference between the brands that are far more AI readable with their content and the ones that aren’t. And there’s a lot of opportunity and upside whether you fall in the top performer, you definitely have some room to improve. If you’re in the bottom performer, there’s so many things you can be doing. And so, how do you make sure that your tagging is right? How do you make sure that you’re getting the right sort of prompt type of scenarios, absorbing what those could be, especially looking at our LLM solution, LLMO, really helps you understand what types of content is really engaging in getting that visibility. So, I would say we’re kind of in a place where there’s a lot of different tactics and things that brands could be thinking about. But back to what Marta said about prioritizing what’s most important, building around that, ensuring that you’re doing it on a multi-pronged strategy, which is onsite and experience optimization, but then also broader engagement and showing up in multiple sources and really owning the conversation around what you offer, that can put the LLM on a much more sure footing about you as a brand and a product, and much more willing to serve it up to users.

Yeah, really, really good insights, very helpful for brands across the entire spectrum of maturity there. Okay, so I think that brings us to the end of our Q&A portion. So, I will go ahead and close this out. For those of you who are live or who will be watching this recorded, this will be available as a recording in the future for you to reference again. But thank you so much to Marta and Vivek for sharing these insights with us, and we look forward to visiting this again in months. Thank you. Thank you. Bye everyone.