0:00 Welcome in, everybody. We're just gonna give it a couple more minutes as everybody is filing in. While we're waiting, why don't you go ahead and put in the chat where you're coming in from? 0:09 Maybe let us know what you've been searching lately using AI, and we'll just give everybody a couple more minutes to get in. It's good to see everybody. 0:21 Yep. Welcome in. Alright, everybody. Alright. I think we're gonna get started here. Welcome. Thank you so much for joining us for this session. 0:30 This is actually one of my favorite topics. And, Dave, I think you'll agree. We are gonna talk about winning the AI shelf with UGC. We're gonna be pulling from our brand leader's playbook for product discoverability. 0:42 We're gonna put a link to that in the chat. But we are gonna be focusing on one specific area today that I think is just getting, you know, a ton of attention right now, especially with research, as we are showing what is really, driving lift in recommendations for, for AI search for brands. 1:03 And so, we'll get started here, and, we'll start with some of our introductions. So I'm Joe Callahan. I am the VP for brand strategy and experience here at Bazaar Voice. I am joined by my friend, Dave Nieweh. 1:15 Over to you, Dave. Hey, everybody. I'm Dave Nieweh as advertised. I am the vice president of product marketing here at Bazaar Voice, which includes several functions actually besides product marketing. 1:27 It includes solutions marketing, insights, analytics, value management, and pricing and packaging. So very happy to be here, and thanks for welcoming me, Joe. 1:35 Oh, well, it's a pleasure to have you. You know, we we go way back, and I think the, having having so much time spent together on the brand side of the business, both in media and brand marketing, this is this is such a fascinating subject. 1:50 And as we're looking at just the shift in in search behavior and the the shift in commerce and shopping behavior, there's starting to be lines that are that are blurring across media, across commerce, across brand marketing, and discoverability. 2:04 And so, this is just such a fascinating subject and such a pleasure to be talking about it with you today. Feel like it's mutual. 2:13 Alright. We're gonna get started. I I wanna level set with everyone first. I don't think this is new news for anyone on this call. But as you know, you know, the AI search is now the new storefront. 2:26 And so we're seeing a massive, massive shift in in that search behavior. And a lot of shopping is starting with within the within the AI applications. 2:37 And so shouldn't be a surprise to anyone here, but we do like to level set with that. Another piece of really interesting research that was coming out and something that we're seeing a massive trend in is that ratings and reviews and other UGC are playing such a critical role in how brands are surfaced and discovered within within AI search. 3:00 So, yeah, there was a recent study published in Harvard Business Review, and that was really finding this new credibility anchor. 3:09 There have been a number of studies, but this one was really, really specific around showing that star ratings were one of the only tactics that consistently moved AI agent selection of brand. 3:21 They set out to test how traditional marketing tactics were gonna influence AI shopping, and the result was that most of traditional marketing tactics failed. 3:31 They failed to move the needle, but in some cases, seem to decrease a brand's chance of being selected. But out of all of these tactics tested, there was only one aside from a low price point that consistently won across all of these models and product categories, and that was authentic ratings and reviews. 3:49 So we know that shopping behavior has changed. We know that what is playing a role in shopping behavior and driving and driving that credibility source for for AI applications is authentic ratings and reviews. 4:02 And so now, we'll get into talking a little bit more about how we can start to, work through that content to drive the results for brands through, through landing messaging, in in ratings and reviews. 4:15 And I think the good news here too so the you know, we have the shift in AI shopping behavior. The other shift that we're starting to notice is not only where consumers are starting their search, but it's how they're searching. 4:28 And so this is a really meaningful shift. And you think about it, when a consumer went to a traditional search bar, they were really typing in three to four keywords, and that was an approximation of what they were looking for. 4:41 You know, typing things like lightweight jacket or charcuterie board, one of my favorites. And that really restricted product data to match that behavior. 4:50 And so we're focusing on, like, five or six basic things, color, size, ingredients, because that's what was driving conversion. But if you look at how people are shopping when they're using AI applications, they're not just typing keywords. 5:04 They're actually writing this sort of detailed buying brief. And they're telling AI things like the occasion, the headcount, the dietary restrictions, the aesthetic. 5:12 They're telling stories, and they're looking for real answers in in in in-depth. And so if your product catalog is containing bare bones specifications, if your review content is thin and not really highlighting contextual relevance of your product or brand attributes, you know, the AI applications are not gonna have enough data to match that buyer's highly specific, context. 5:36 And so when we look at winning in this new era of commerce, bringing storytelling front and center is really where we need to be. 5:44 And we need to enrich our data so that when AI agents go hunting for the solution, our products tell the complete story that helps the AI applications match that shopper's true intent. 5:57 So this is why this is one of my favorite topics right now, because I think if you've been in this industry for a really long time, you've seen storytelling sort of dip down in in relevance. 6:08 And now it's just back in such a big way, which I think is incredibly fun and and and incredibly interesting in how we're landing it these days. 6:18 Before we dive into more of the storytelling, I'm gonna hand it over to Dave. We're gonna talk a little bit about the a little bit more about the AAA framework and accessible accessibility to start. 6:30 If you've been on a Bizarre Voice webinar or seen us present at an event, you will know that our AAA framework is really the key to how how we're talking to our clients about winning in the space. 6:42 And so, you know, it's really around the three a's. Accessibility, this is around ensuring that your content is easily easily findable, readable by AI applications. 6:54 Authenticity is is absolutely key. You hear us talk about authenticity all the time, and that's really, you know, ensuring that you're building trust through authentic, verified content, and a lot of that is UGC. 7:09 And then abundance. And this is really around high volume, depth of content, ensuring that your brand is is available across all touch across all touchpoints and that the depth of the content allows AI applications to really get a good read in context and brand attributes. 7:30 And so I'm gonna pass it over to Dave to talk a little bit about, what is really table stakes here, and that is it really doesn't matter how great your your content is. 7:39 If the AI application cannot find it, cannot read it, doesn't matter. It's invisible. So over to you, Dave, to talk a little bit more about accessibility. Cool. Touche, Joe. Thanks for that. 7:49 So Joe's absolutely right. So you can have the most descriptive, most beautifully written customer stories in the entire world. But if a search engine can't find them, it basically does not exist, just as Joe said. 8:01 So that's why we are addressing the accessibility sort of requirement first. So let's talk a little bit about the infrastructure that helps put an end to AI invisibility and actually helps make your content accessible. 8:15 Ladies and gents, please meet the patent pending Authentic Discovery API. So instead of hoping that a search engine just correctly scrapes your website through thoughts and prayers, this API acts as a direct link, which delivers your cleanly formatted, well structured searcher search ready user generated content right into the hands of AI platforms. 8:37 So think of the Authentic Discovery API as helping you build the right container. So you need to push your authentic customer reviews and q and a's into the exact technical structure that an AI application can read and understand during a shopper search. 8:52 And when your content becomes instantly readable for AI applications, here are some of the visibility gains that we're seeing at early adoption stages. 9:00 And what's really cool here is that, you know, these are the visibility gains that we've seen over just about three weeks with one particular client. But some of these visibility gains include a seven percent lift in how often the brand's products appeared in AI responses after implementing the Authentic Discovery API. 9:17 And we also saw a nearly five percent lift in how much of the AI convo the brand owned relative to the competition here. That's a four you know, four point seven percent share of growth lift. 9:28 And we also saw roughly a six percent lift in how frequently positive reviews surfaced in AI outputs about their products. So we know these these are very early days, but we're very encouraged by the, by the proof we're already seeing, in this, new technology that we're giving to customers. 9:47 That's incredible. Those are some pretty amazing numbers. It is very difficult to lift visibility, and so to see that happen within that period of time is is pretty awesome. 9:57 So thank you for thank you for taking us back through that. I think having the having the right container is, of course, first and foremost of importance, and then really taking a look at the content that fills the container is going to play a massive role here. 10:13 You know, I it's it's funny because when I think about abundance and landing context within content, even if you go back to, like, media conversations, landing brand attributes, branding needs dates, usage occasions, that's really that consideration layer. 10:28 It's really my favorite cons objective. I'm, like, secretly hoping that one day I'll go to a party and there'll be an icebreaker that asks what your favorite comms objective is, I can say consideration. 10:37 I think that there's so much you can do with it, and that's what we're gonna talk about today. And so how do you how do you really authentically link your brand through the eyes of your consumers into that evaluation set by landing everything you you you possibly can in in that context? 10:54 And so, you know, it used to be that we had brands that would have really high media spend or have high baseline awareness, and you think that that would help you surface in in results. 11:08 It's really not the case anymore, especially within AI applications and AI search. AI is really relying on the contextual signals that are found in UGC to confidently surface your products. 11:22 And so it's not as ad spend is not a substitute here. It is really a quality content conversation, and it's a content effectiveness conversation. 11:30 So we've talked about the role that ratings and reviews are playing here, but it's not just it's not enough just to have ratings and reviews. We have to have the right context, the right depth, the right quality of review content to be able to really move the needle on this. 11:45 And so, we're gonna do the deeper dive now on, our third a, which is abundant. And this is really around the more information that AI has about a product, the more confidently it recommends it. 11:55 And so, you know, if we see a product with three reviews, that gives AI very, very little to work with. A product with three three hundred reviews is giving AI a variety of information to understand the product across a range of users, use cases, need states, etcetera. 12:14 But abundance is not just about volume. It's about recency, and it's really about quality. And so as AI systems are sensitive to, content depth, we have a product with fifty reviews that are filled with context, specificity, consistency, authenticity cues, even a balanced critique. 12:33 That's much more valuable than, to AI than five hundred generic reviews that say, it's good. So, depth is really crucial for AI to understand that, customer sentiment, and it's found in UGC. 12:48 So, you know, it's interesting. I actually we recently I didn't make it into this presentation because it just came across the wire, but there was a study published in eMarketer that we just got got wind of today that is showing that review depth and recency are now mattering now matter more than the headline number. 13:09 So AI just doesn't isn't just reading the star rating. We know that plays a role, but it it's reading the pattern behind it. So now we have research that's going even deeper here. 13:19 So it's showing that a product, even averaging a four point two star rating, if it has recurring durability complaints, it's it might get flagged away within the results. 13:28 And it's also showing that, thirty eight percent almost thirty eight percent of consumers are explicitly asking AI to summarize reviews for them. 13:37 So that's more than a third of shoppers that are now letting the AI model decide what reviews really say before they've even read one themselves. So this is a massive shift, you know, not only within the the importance of ratings and reviews within, you know, the models, but, it's a star rating conversation. 13:55 And now we have research showing, it is more of a contextual conversation as well. And, you know, the the study that we did have in here, prior to, you know, even having the eMarketer publication was, within Harvard, the Harvard Business Review. 14:12 They had published a study recently where marketing professors from Georgetown University and UVA had tested across a number of AI applications. 14:24 They had tested product recommendations and found a pattern that really reframes how brands should start to think about discovery. And I've seen this in like, anecdotally, I've seen this across some of our some of our brands. 14:39 But now we have research to back that. And it's really that smaller evidence rich brands are surfacing reliably while category leaders and these larger, big bigger brands are not. 14:52 And so what's really interesting again, it's this high, you know, high baseline awareness category leadership does not always mean that you are gonna be surfaced in the results, but it's these smaller evidence rich brands that are landing the right content within the right context that are really surfacing within within the AI applications. 15:10 And so, you know, something that stands out for me within this study is around attributes and narrative. Over half of the brands are framed inconsistently across AI tools as models are assembling the views from scattered data and evidence rather than a brand's self defined narrative. 15:27 So, again, it's just talking about how you need to land land all of your brand attributes and evidence across all of these touch points, and it's really critical. 15:38 So as AI is really mediating this discovery by matching the specific user needs to verifiable product attributes, you have to have that structured information, credible third party evidence, and and and pull all of that together within within user generated content and reviews. 15:56 So I think that this is a really, really fascinating moment in the industry. I think it's a really, really interesting way to start to think about the way we used to land brand attributes by pushing content, media, creative. 16:08 Now we need someone else to tell that story for us, and that's our our consumer set. The three there were three things, that really pulled through within, within this study as the three elements of what they're calling interpretability. 16:25 And the first one is entity clarity, so the brand being clearly identifiable across information sources. The second is attribute structure, and that's really around product features being named, comparable, and measurable. 16:39 And then the third is evidence based. So really thinking about benefit claims that are supported by credible independent sources, and a lot of times that's consumer reviews. 16:49 So no shortage of research here just showing the need for this. Dave, back over to you to talk a little bit about how we start to think about landing this in review content. 16:59 Yeah. Let's talk about the evidence. So as Joe already intimated, winning at this new storefront, the AI shelf, is really about closing the gaps in your evidence. 17:09 And the reason being that AI applications need statistical certainty in order to be able to cite you. For my stats friends, that means p values under point o five. 17:18 But the point is is that if your organic user content fails to answer these five fundamental questions that you see here below, the engine won't risk giving a bad recommendation. 17:29 It's instead going to just cite a competitor. So before a brand can really optimize their products to show up in AI recommendations, they need to understand a few things. 17:38 They need to understand how the AI actually decides what to recommend. Right? When a consumer is asking an AI tool to recommend a product, the AI is not actually guessing. 17:48 It is in the process of evaluating. And more specifically, it's evaluating signals. Right? And the signals that it relies on most heavily are not the ones that brands have traditionally invested in in their content as Joe has already talked about. 18:02 Instead, AI can and will read your product specifications. Right? But what it really needs, what gives it the confidence to surface your product over your competitors is context. 18:12 And the context being the the kind that tells it not just what a product is but who it's really for. Alright. 18:23 Let's take a look at what what this looks like in practice. Having spent a lot of time in the in the media and creative world as we have, Dave, we talk a lot about the anatomy of a good piece of content or the anatomy of an ad or the anatomy of, you know, what what is what are the things that you need to land in a piece of content to be effective? 18:44 We can do the same thing for a contextual review. And in fact, the more I think about it, the more I start to consider these part of campaign assets. 18:54 I mean, realistically, we are trying to get our consumers to help land our brand attributes, need states, usage occasions. All of the information that we try to land as brands, we're trying to get someone else to repeat that back to us in an authentic way and give their authentic opinion. 19:11 And you know, when when we look at what a a really good contextual deep review might look like, it might look something like this. 19:22 And the reality is and, you know, you guys have probably heard this before. You know, AI can't experience a product in the way that a human can. 19:33 So it can't create it's not trying on a pair of pants. It's not drinking a soda. It's not vacuuming a carpet at your house with two toddlers and a German Shepherd, as this review would indicate. 19:43 And these are really experiences only humans can have. And the only place that we can really capture those human experiences at scale in a form that AI can read and learn from is within user generated content and specifically within reviews. 20:00 So when we have this clear, concise, relevant content, it can be easier for the AI models to source when answering a specific query. 20:09 And so when it's looking for those specific details, it's gonna be looking for a few things. Here are some examples. In this example, we have first and foremost, we have context. 20:19 So who who used it? It's a family with small kids and a very furry dog. Where did they use it? They used it on carpeted floors, and they also talked a little bit about how it compared to a previous vacuum experiences. 20:31 So you have a nice comparison as well. The second area that this review is hitting on is really around specificity. 20:41 They've included details about the model, the setup, the value for the money. Those are all really important and critical elements as well. 20:50 This review also includes consistency. So if you look at things like adjust its power based on level of dirt, that is most likely something the brand is also talking about as a product feature, as an attribute. 21:05 And so having that consistency across touch points, not only within brand messaging, but also within the consumer review, massively helpful. 21:14 And then the the the last piece, and this is this is a really interesting one. You know, we talk about authenticity and trust. We we, you know, talk about verified review content. 21:25 It's also about a balanced critique. So in this case, you know, the the consumer is leaving, a transparent review, about what doesn't work for them, but it adds that emotional resonance. 21:38 And and that's really key here. And so when you look at these four elements within within this asset, this is doing a really amazing job of telling the brand story through attributes, use cases, needs, dates, all of the above, in an authentic way, and it's helping land the story so that when AI search gets those really detailed queries, it can go and match very, very quickly. 22:03 Dave, I'm gonna turn it back to you to talk a little bit more about how this how this might come to life within a couple of categories. Cool. Let's do it. So before we do that, actually, let's just take a moment and admire, how we used to optimize for search. 22:17 It wasn't that long ago that we used to chase keywords like bath shampoo or just top moisturizer, but AI search has completely flipped the script here. 22:25 And instead, consumers are not really searching in these sort of broad categories anymore. They're searching in very specific need states. 22:34 So a modern beauty consumer doesn't just type best shampoo anymore. They probably are typing a very hyper specific prompt like, I need a sulfate free shampoo for color treated hair that won't weigh down fine curls in humid weather. 22:46 That's not actually about me, but that is something I would type. Or if you're east of the Mississippi today, in the presence of Canadian wildfires. So this really creates a massive discovery hurdle. 22:56 So if your product reviews only say generic things like smells great or loved it, AI search engines won't recognize that your product actually solves that exact combinations of problems. 23:06 So just like Joe said, specificity is really important here. So we need to intentionally prompt our reviewers to talk about the specific hair types, the weather conditions, and styling challenges. 23:18 And that granular needs based text is really the hard working content that attaches brand to the exact problems that consumers are trying to solve. 23:26 It's not just like a volume and star rating play. To Joe's point, it's really more of a contextual play. And with that in mind, you need review assets. 23:35 You need consumers you need consumers to be able to play back those attributes to you and really being able to put yourself in the consumer's mind, making yourself very relevant across these need states. 23:47 So, you know, this is really all about this idea that, like, every contextual need that's going to matter to your consumer really is all about ensuring that you're surfaced within that recommended set. 23:57 And these are kind of the hardworking needs based content that really attaches the brand to the solution. Just thinking about another example here, the same shift is happening across categories like food and beverage as well, which is driven very heavily by usage occasions and also in part by dietary nuances. 24:14 So, you know, a consumer looking for a snack is not just typing healthy snacks into an AI search bar. They're probably asking, what's a high protein, gluten free, nut free snack that I can pack in a school lunch that won't melt in a backpack? 24:26 Right? Or what is a low sodium plant based dip for a Friday night cocktail party? And this is where context really becomes your highest performing media asset. 24:36 Because if your review profile doesn't map out these diverse needs states, like the when, the where, the why, and how people consume your product, then the AI engine won't really service you on the recommended set. 24:48 So, again, in this example here, generic reviews like taste delicious are not sufficient anymore. You really need hardworking content where users explicitly playback these real world scenarios, and not just users, authentic users. 25:00 So we need reviews that really mention, you know, school lunches or movie nights or post workout recovery. Because all of that descriptive organic context is ultimately the connective tissue that ensures that products that your products are the ones that are recommended when a consumer asks AI what to buy. 25:20 Amazing. I think, you know, when, when when we see this, it makes a ton of sense as as marketers, as as creative professionals, as media professionals. 25:30 The question that I think crosses everyone's mind when when they they see and understand what needs to happen within review content and UGC is how do you how do you do that? 25:41 Because it's easy to think about how you would brief a creative agency to create an asset that lands these attributes, that lands use cases, need states, you know, all of all of these occasions. 25:52 But it is more difficult to do so within content that you don't have control over as a brand. 26:03 The reality is you may not be able to control what people say about your brand, but you can shape how they say it in an authentic way. And so as we think about everything that we've pulled together here today and the reason why contextual detail matters in review content and how. 26:25 We have a few ways that we can that we talk to our clients about how we start to approach this. We know that UGC is is really critical here. 26:36 So there are a number of ways that we can start to land this. We know that we can brief creators on the questions their content should answer, not just features to highlight. 26:46 That's one that's one area. But if we're looking at written review content, even social elements, specifically around written ratings and reviews, running a sampling program to collect to source reviews is a really nice way to boost volume and also context and depth. 27:09 So if you have a well designed sampling program, you can put your product in the hands of real consumers. You can generate the contextual, experiential UGC that AI depends on, both written and visual. 27:22 And it's going to drive both volume and recency simultaneously. And it tends to generate higher quality reviews than organic collection alone. 27:31 So you'll get that depth. You'll get more of the context that we've seen AI needs. And one of the other ways we can we can do this is with review coach and content coach. 27:42 You can encourage topics and deeper context within this feature. And so authentically prompting consumers by helping them write review content, saying, hey, lean into maybe talk about the texture. 27:58 Talk about the flavor. Talk about how you used it. Maintaining the authenticity of the content, but but really driving that depth and and hitting on those contextually relevant elements that that AI search that AI search needs. 28:12 And these systems are going to ensure you have your brand storytelling that translates into visibility, landing your unique brand differentiators at scale, and doing it cohesively across the digital space. 28:25 So these are just some of the areas we like to talk to our clients about how we land this in an authentic and scalable way. Alright. 28:33 So we talked all about a lot today, and I'm so glad we got to dive deeper in on on the AAA framework, abundance specifically, the depth and the context and the evidence needed within authentic UGC. 28:48 And so a couple of follow ups for you all here. And if you have any questions, put them in the chat. The team will get back to you as soon as possible on anything that we didn't answer here today. 29:02 But the three things I look at here in order to move forward, one is knowing your gaps is really step one. 29:10 So in order to close the gaps, do an audit of the space. Generally, where are you seeing content that lacks contextual depth? 29:20 Just overall, where can you start to make an impact? And then deploy a sampling program or another sourcing method that will help collect reviews for products or categories with weak contextual coverage. 29:37 And then, again, use the guided reviewer prompts. Guide reviewers in an authentic way to prompt them to share specific descriptive details about real life situations, about, again, flavor, texture, specific needs, sizing, whatever it may be that is important for your product, your category to land, to be surfaced within results. 30:02 Where are your competitors winning, where you could play an authentic role? Start to look at those things and help your consumers write reviews that speak to those things. 30:12 And then we have again, we'll put a link in the chat. We do have the brand leaders playbook for discoverability. 30:20 It's going to hit on everything we talked about here today, plus more around the AAA framework, more around the new consumer behavior, and what you need to do to win and make sure your brand and products are surfaced within AI Search. 30:33 So that said, really appreciate you guys joining us today. Any questions, get let us know, and we just really appreciate everybody's time. 30:43 See you soon. Thanks, guys.
