0:01 Morning, everyone. I'm Doug Straighten. I lead marketing here at Bazaarvoice, and we've been spending a lot of time looking at how people find products today. The overall shopping journey still looks really familiar. 0:12 People research, they compare and decide. But the surface where all that happens has changed. More and more of that discovery now runs through AI in one form or another. 0:20 And when I say discovery, I mean the point at where the shopper first encounters your product and starts to form some sort of confidence in it, whether it happens to be a PDP through a search result or now in an AI summary or shopping agent. 0:34 It's the same job a physical shelf is designed to do, making products visible and comparable. It's just a new modality or service. And what I want to do today is walk through what that shift means for you, where reviews in UGC really start to matter and how we think about staying visible, influencing confident decisions, and ultimately helping shoppers choose your product on this new version of the shelf. 0:56 So let's get to it. We're gonna start with a question that we get almost every week, which is how do we make sure our products still get seen by shoppers and by AI? 1:08 And the short as with anything in business, is discipline and execution, content discipline, and data discipline. Authentic and trusted content, like strong ratings and reviews and the steady flow of UGC, are now part of your core infrastructure along with your brand assets. 1:25 It's as fundamental as your product data or supply chain. It's how AI learns to see you. Why? Because AI is starting to read the same content your shoppers read, and it uses and weights some of that content to decide which brands it can recommend with confidence. 1:41 So your core brand creative product information and your UGC together form your new foundation for discoverability. So let's look at what's changed. 1:49 Roughly seventy percent of Google queries now come back with an AI overview. Organic traffic from those listings is down by as much as sixty percent depending on category. 1:59 And shoppers aren't scrolling through ten options anymore or a few pages of search results. They're seeing two or three that an agent has already filtered, and that's essentially the new shelf. 2:09 Conversational search, minimal scroll, and sometimes zero clicks. So if you're not in that shortlist, you're not on the shelf. But it's not all bad news. 2:18 The people that really use those AI summaries convert about four and a half times higher than organic search shoppers. So the value of showing up is actually increasing even though the space to show up keeps shrinking. 2:31 The game's just essentially shifted. It used to be get to the eye level in the physical world, then it became get to page one in search results. 2:39 Now it's get into the summary. That's where engagement lives, and that's where your AI strategy needs to be built to influence. Now some people hear this type of information and they panic. 2:52 It's very new. It's not at all well understood. It's constantly evolving. This makes us nervous. This is the end of discovery. No, it's not. 3:02 It's just it's just really an evolution of this. And we've moved shelves before from physical aisles to search pages now to AI summaries and shopping agents. We've always found a way as marketeers and as retailers to figure this out. 3:15 And the same principles really apply. If you understands how if you understand how shoppers search, you win the placement. 3:24 LLM driven systems do not invent trust. They pull from inputs they can read and use, structured product facts, signals of authenticity, and real customer proof like ratings, reviews, and other visual UGC. 3:37 The recent announcements of Google's universal protocol matters. It is clear signal as ever that AI is moving from research to transaction, an evolution of not just search and what we think of as a product detail page. 3:51 Bazaarvoice is built for that shift. We keep our clients ratings, reviews, and other UGC structured and syndicated so that they're portable and ultimately machine readable and ready to surface wherever AI led shopping decisions are happening. 4:05 So instead of the sky is falling, just think the shelf has moved. Our job is to make sure our products show up confidently on the new shelf. Now your leaders are demanding that you take action on AI and want to gain an advantage, and everyone thinks they understand this and asks you how you're addressing it. 4:23 But really, only the type of input and output has changed. AI is essentially very simple. For technology, generally, and for shopping, it's always about getting the answer or finding what you want. 4:37 It's just that the modality improves and the friction is lessens. So again, tell them AI is simple. It's just the next evolution of input and output. We need to double down on accurate data, the right types of data, and lots of it. 4:50 So first, with computers themselves, to get an output, it was text in the keyboard. You get some sort of answer on the screen. 4:58 With the web, it was keywords in, rankings out. And now with AI, it is a deep question in and potentially a deep question or deep answer out. But in every case, it's the quality of the data going into the system that affects the output coming out. 5:11 And AI is less forgiving. It makes mistakes. It has biases when fed incorrect or insufficient information. So tell your leaders with confidence. AI is magical, but only one has the access to abundant and accurate and accessible data, the things you've been working on for years, I hope. 5:27 And if you've squirmed just a little bit at that statement, don't worry. We'll talk about content supply chains later on in the day. Now that we understand AI is simple, we can recognize that AI needs to learn and does so from many of the key elements of good digital marketing fundamentals. 5:45 So SEO, strong brand content, great PDP quality, and catalog accuracy remain essential as we move to the new world of GEO and AEO. 5:56 But to be magical, it needs data to learn from lots of data. And for shopping, it likes certain types of data, authentic data, verified data, sentiment data. 6:06 And we now know that verified reviews now sit at the center of AI discovery. We'll talk about that more in a minute. And regulations that were already raising expectations for authenticity prior to AI, you can be fined and we will continue and we should expect more activity in these areas. 6:24 Lots of structured machine readable content ensures you appear consistently and accurately in AI driven summaries, tools, and agents. 6:32 However, brands and retailers must also understand the ramifications of data accessibility to LLMs and the limitations as well. 6:41 You need a strategy that is specific to you, particularly retailers. Do you or do you not want to give your intellectual property, your data, to LLMs? Is it good for you, or is it a threat? 6:51 We can advise on these things, but only you can make that call. So AI is simple. AI wants to learn, but it needs to be trusted and reviews are the new currency. 7:02 When shoppers and consumers meet AI today, they typically see it in two ways. First are the summaries, the answer boxes at the top of search results that condense information into a single recommendation. 7:13 Second are agents, AI shopping assistants that guide or even make purchase decisions on the shopper's behalf. That would include chat GPT or perplexity as they will shift modalities, include shopping functionality if queried appropriately. 7:27 It could also be an agent within a retailer. But all of these need to be trusted. And to decide what to surface, they rely on strong trust signals, reviews, other social proof like images and video. 7:39 We know this because we are speaking with them. We also know that this is this because a Yale Columbia study on AI shopping agents proved how important ratings and reviews are. 7:49 More on that in the next slide. Now the influence creates, you know, a distorted incentive. 8:00 When a fractional rating increase is worth more to an algorithm than say a massive price drop, it creates a high temptation for system manipulation. 8:09 And this makes fake reviews or deceptive listings a significant market risk. The only way to protect your brand from these algorithmic swings is through the sheer force of verified authentic data. 8:19 And this is why scaling and volume is essential and what we've known about human shoppers for twenty years. People trust other people and so does AI. 8:28 We will continue to work with those providers to ensure that they're getting the the best, most authentic inputs possible. Now let's shift back to this Yale Columbia study, that I mentioned just a minute ago. 8:42 They looked at how AI agents, Claude, GPT four dot one, Gemini decide which products to recommend and what kind of pricing they'll stand behind. 8:50 When review volume went up, aggregated review volume, basically when there was twice as much feedback, the models were more likely to recommend the product even when the price was higher. 9:01 In some cases, thirty plus percent higher. Similar things happen with improvements to average ratings, and that's pretty remarkable. It means the systems are interpreting these aggregate signals as data points that are related to trust. 9:15 And that's we before we talk about the actual sentiment that UGC sometimes refers to. So the more credible structured UGC ultimately that we can feed into these systems, the more confidence these systems will have in your products and brands. 9:28 And that confidence will show up everywhere from how you're surfaced to how you're recommended. And clearly, Curie Masters says, reviews are the new currency of the digital shelf. 9:39 Much more on this as we go through the rest of the day. Now the shift we're talking about here means that your content now has a second consumer. 9:48 It's no longer just the shopper making a decision. It's the AI systems interpreting information on their behalf as well. The quality of what they learn from you as a brand or as a retailer determines whether you're gonna be surface recommended or ignored and the amount of trust a consumer is going to put in your product. 10:06 Your brand storytelling and creative still matter, but the foundation of discoverability today is user generated content. That's what gives AI a factual trustworthy signal. 10:16 And it's how the models will ultimately learn who you are, what you stand for, and whether it should treat your products as credible. Inside Bazaarvoice, we look at this as through what we call triple a, making data accessible, keeping it authentic, and ensuring abundance. 10:31 It's the operational checklist that underpins everything we do. Accessible essentially means your data and your content are structured, connected, and ultimately machine readable across every surface. 10:42 This will continue to become more important as time goes on. And this doesn't matter whether it's a retailer site, Google AI overviews, the broader large language model or LLM ecosystem. 10:53 If your information is fragmented, inconsistent, or unreadable, those systems simply can't see or won't see enough of you. And so we're gonna want to ensure that we address that. 11:03 Now authentic is verified human experience. That's a differentiator. In an AI first world, authenticity becomes a data quality issue. 11:13 And we remove every detected instance of fraud from billions of reviews because if the input isn't credible, the output can't be trusted by people or by machines. 11:23 Abundant is scale with relevance. These systems learn through repetition and recency. They need a constant flow of credible content to build confidence in your brand. 11:32 A dozen reviews won't move the dial. Depth and freshness across your prod key products will. And triple a is just the checklist to make sure that you your brand is read readable and intelligible by AI so that it can read you accurately, represent you fairly, and continue to recommend you. 11:49 It's the practical groundwork for visibility and trust in this new environment. Now finally, once you understand the framework, the next question is always where do I start? 11:59 Because you can't fix everything at once. This is just a portfolio man management question. Not all products carry equal weight. So where you focus in terms of visibility and sales impact, is critically important. 12:12 Start with the twenty percent of SKUs, of course, that drive eighty percent of the revenue, and that's where improving data quality, review volume, and recency will deliver the fastest return. 12:21 Core products are your workhorses. Keep their data clean, review coverage strong, and content current. Innovation of new items, make sure they get early authentic review volume so AI learns them quickly and doesn't default to older items. 12:34 And then in terms of renovation, restage, or rebrands, ensure that you're connecting them carefully to past data so the models don't lose context ultimately or treat them as new so they don't revert to a cold start in discoverability. 12:46 This allows you to prioritize and phase in full optimization over time in a way that appropriately drives your business while you protect your brand's discoverability and pricing power across the portfolio. 12:58 Consider this framework your anchor for today. We have designed the next three hours to take these high level concepts and drill down into execution. And by the time we wrap up, you have the specific tactics you need to lead in this AI driven world. 13:11 Thank you.
