Eric Soufert (2:46)

So Bloom Ridge is an established company, right. So when did that transition start? Like if you had to and just got not, not you know, specific to, you know, the marketing use case, but, but as you've Seen it like you just kind of the agentic adaptation. To my mind, there's like kind of two distinct waves and maybe correct me if I'm wrong or if you just saw a different timeline, there's like the kind of very recent adoption with, with things like openclaw and, you know, people bringing agents into their computer and having them operate across everything. But then there's also been just kind of like this slower, maybe more subtle adaptation of agents, you know, kind of just starting with the introduction of some of these tools from the bigger model providers. But. But then even like just more discrete adaptations before that, going back years and years. Talk to me about how you've seen that timeline evolve.

Christy Augustine (3:31)

Yeah, oh my God. Kind of happened overnight and then happened so quickly in a way, I think for most people. But as you mentioned, AI has been around for a really long time. It's been around since the 1950s. And so from a development standpoint and what we've all wanted to do with software, I think this notion of personalization and AI has been around for a very long time, but there hasn't really been the interface or the connectivity that we're seeing now. And so certainly generative AI was around for a while. Bloom Ridge was playing with things like vector search before people were seeing AI really from ChatGPT and this interface where now as a person I can interact with it. Once that kind of happened and the environment started opening up. Now you're seeing a rate of development that's really quick, but you're also seeing all of these channels that couldn't talk to each other before, now are able to talk to each other and people have even more signal. So it's kind of like we went from, you know, predictive AI with a few signals and you kind of always wondered like, why are you recommending this product to me when it's a terrible idea? This isn't what I wanted to much more accurate predictions because now I can see data from not just email and sms, but I can start to see it from other channels that maybe were closed off before.

Eric Soufert (4:56)

Yeah, I mean, I think that's, that's, that's really helpful context. Maybe just kind of as a, as a broad kind of kickoff question. I usually start the podcast with like a big, big picture, high concept question. What does AI empowered personalization mean in the marketing context? What are we talking about when we talk about AI empowered personalization?

Christy Augustine (5:12)

Yeah, it's such a good question because I feel like 10 years ago we would have said something very different than what we would say today, 10 years ago we would have said, oh, you know, we're sending AI powered personalization means I'm sending an email that hello, Eric, like, okay, cool, like I know your name and it's supposed to be awesome and really cool that I could put your name in the email. And then everyone's getting the same products, the same email subject line, the same everything. But I said, but I said your name. So that was personalized to. Now I think AI empowered personalization and the marketing context recognizes that marketing is a whole host of channels. It's not just that I'm sending you email, but maybe I'm sending you an sms, maybe you're on Instagram, maybe you're, you're looking on Google. But now I'm really, maybe you're on OpenAI and you're looking in the AEO answer engine space. And so now I have a proliferation of channels and I have really complicated customers and so many more signals than I had before. So for me, AI empowered personalization now in marketing means that I can focus on the customer and how they want to behave and how they want to be treated. No matter what channel they're coming from, whether they're coming from email or SMS or they're already on my site, I can start making really smart decisions about what message they need, what products they're looking for, what their intent is, maybe where they are in the journey. And I can trigger based on that, like one of my favorite examples of a customer I'm working with leverages data across the customer behavior across many different ways so they'll know you actually as a customer. If you spend more than two minutes on my website, I should probably pop up an AI agent. I can tell that for you this means you're starting to struggle and you're not sure what you're looking for. Whereas for me it might be 10 minutes. I mean, I love to shop and scroll down. Spending time on a website is not an indicator that I'm not gonna buy. Maybe for someone who's more impatient, you need to know like, oh, at two minutes I need to start helping them versus someone who maybe responds better over text with a discount offer. And knowing what kind of shopper you are or what kind of Persona you are, as well as those signals across all the channels, I think is where AI personalization is just taking it to another level.

Eric Soufert (7:45)

And I guess like as a kind of corollary to that, how much needs to be known about that individual and how much can be inferred from the similarity of their kind of like very early behavioral patterns relative to historical use. Like do you need to know very specific things about that person or is it really just kind of pattern matching across groups of people that have behaved the same?

Christy Augustine (8:08)

Oh, that's a great question. Because to date we've really been pattern matching and creating these customer segments and so it's not true. One to one personalization. You're not really identifying me as like a cohort of someone who acts in a certain way. Maybe I'm the cohort that's in your loyal customer base and I'm a high spender or maybe I'm in your cohort that you've never seen before. New shopper. And I like a certain category or I like to be communicated a certain way on email or sms. That's really where we were before. Now I would say AI has opened up this complexity that you can see where not only do I need to know a bit about you and I can know you on a customer level and I can start to do this very specific one to one personalization, but I can also treat you like the complex person you are. I can treat you like a human and not this fortune telling, pixel gathering kind of pattern matching. But I can tell that you, in this visit you're actually buying a gift for someone. So all those signals, maybe, you know, maybe I had a, I used to have a quiz on the site and I'd ask you your size and your color preferences and I'd ask you that you're really only interested in shoes instead of tops. But people are pretty complex. We buy gifts, we purchase on behalf of someone else and then we've got different types of purchases. We've got those impulse purchases because I was on Instagram last night and you know what, they just got me with the feed and I clicked through and I made an impulse purchase. And that's very different than what to me is a high consideration purchase, which is very different for everyone. It's based on their budget, it's based on what they do for someone. Shoes, who's, who's a runner. Their consideration, high consideration for those shoes, they're going to be running in them day and night. For me, tennis shoes get me to work. Not a super high consideration purchase. I don't need to know the wear on the heel and what kind of stride I have. I don't have a stride, but I need to know. And so in that I would say, you know, how much do I need to know? Ideally I need to know about you, but I Need to know what you're doing in the moment, what do you want in the moment. So no amount of quizzing is going to get you into that. It's going to be really on these real time behavioral signals that you're picking up on. And so I think what we're able to do with machine learning now is really interesting to treat people like people and not clicks. And I think it just unlocks a whole new level of types of purchase and conversations you can have with customers.

Eric Soufert (11:05)

So I think like kind of a natural kind of follow up there is like where do you get the most leverage? So there, there are these different customer interaction points that you could apply this to. But my sense is in like you know a lot of my, my experience comes from like the mobile gaming space where like the retention curve is just very, very severe. Right. It's, it's very punishing.

Christy Augustine (11:22)

Yeah.

Eric Soufert (11:23)

And so all the leverage comes from day one hour, one minute up to 10. Right. Like I mean that's it, that's if you can bend the curve a little bit there, like that's where all the leverage is versus like in 30 days. Well that person is going to be retained for forever. Right. But like just the vast majority of people have turned but, but I mean kind of like more of the E Comm sphere right. There are more like win back opportunities and there are more like upsell opportunities and you're thinking more about like different AOV opportunities over time. Like what are the different interaction points where personalization can be applied and like which are the ones that produce the most leverage.

Christy Augustine (12:03)

Yeah. Well I love the way you framed at the beginning because honestly I feel like every industry should treat customers that way really kind of. It's that first, first impression that you're going to give someone. You, that should always be your best moment is that first impress and then definitely want to treat people great along the way. But if you're not starting off on the right foot, you're not in any industry. It's not going to, it's not going to be a great experience. But when I'm talking to customers in E commerce usually the first thing I try to understand is you know, your leverage point. What leverage are you trying to get today? Are you trying to get top line or bottom line leverage? So for example on bottom line leverage cost piece SMS is really expensive to send compared to like email. And so if I am trying to send out more and more SMS and I've identified this customer pool that really does respond to sms, I want to leverage that AI where I know I'm only sending SMS to the people who matter for that and I'm not just sending it out and wasting a lot of money since. Since it's really not a great margin thing for me to be doing. Whereas email is maybe a topline impact or on site is a topline impact where the more personalized my email can be or my landing page can be, the more benefit I'm going to get from a revenue perspective. It's not that expensive for me to be doing, relatively speaking, those people are showing higher intent. So the more I can get them to that purchase intent or that activity that we're looking for for them to take, then the better off I'm going to be. So for me it's. It's kind of like looking at each of those channels and saying how much personalization benefit can drive from a revenue or activity perspective versus what's super costly and can I leverage AI to make that more efficient? And then you've got AEO blowing the whole thing up right now as an emerging channel with traffic increasing but conversion iffy at best. No one's really seeing the dollars come from it. And I think that's kind of throwing an experiment wrench into everyone's plans right now.

Eric Soufert (14:26)

Yeah, I think with aeo, it's interesting because maybe just for people who aren't familiar with the acronym aeo, I think you're using that synonymously with geo. I've heard it be called, yeah.

Christy Augustine (14:36)

Geo ao. We can't decide as an industry what we want to call it. Right. Generative or answer engine optimization.

Eric Soufert (14:44)

The idea here is that it's publishing content to be discovered by these chatbots to then be picked up and served in an answer. It's very like essentially similar, essentially equivalent to SEO for chatbots.

Christy Augustine (14:57)

Exactly. But for Perplexity and chatgpt. How do I get my content discovered? How do I get it to convert in the conversation or on my website later? How do I get all that directed over to me? And that's just a big open unknown. I mean, a couple weeks ago there was instant checkout on ChatGPT. Now there's not.

Eric Soufert (15:22)

Now there's not.

Christy Augustine (15:23)

Maybe there will be again in the future, we don't know. So it's this big concern I think is people are seeing the SEO drop off and they're assuming that it's kind of cannibalizing for aeo. I think in some cases there's just traffic drop as people spend less right now. But if you assume the one to One, you're seeing this drop in SEO, this increase in aeo, but I don't know how to get discovered in AEO and I don't know how to get it to convert yet. And so there'll be a big place to play there, I think with driving, driving more revenue and traffic once, once people have it figured out.

Eric Soufert (16:03)

Yeah, it's kind of redolent to me of the ASO approach that emerged in the kind of earlier days of the app stores. Right. So there's app search optimization. It was just, just kind of being discoverable from search and using whatever tactics with kind of manipulating your keywords, your app keywords, your app title, even that sort of improved your chances of being at the top of the search rankings. But the problem with that, there's a couple problems with that. One is that it's not systematic. I mean you can kind of test stuff and see how it works, but you're very much at the mercy of what people are searching for. Right. And so, well, if that's cyclical or there was just a short term trend or something that you benefited from, well, that's going to go away. And then the other piece of that is ultimately these platforms want to monetize with ads. And so, you know, if they see an opportunity to do that, it's almost like you're a victim of your own success. If they see that people are actually deriving a lot of value from ASO or SEO or GEO or aeo, they're going to say, well, I should actually just be selling ads and monetizing that value myself. Right. If you're getting a lot of value from being discovered organically, I should claw that back in the form of ads. And that's, you see that with Google search, the entire above the fold is ads. And now they've, you know, it's been replaced mostly with AI overviews and AI mode. You saw that in the app stores themselves. Apple has ads in the search results and they just inserted another second placement. Right. And ChatGPT has ads. So the thing is like AEO, SEO, ASO, all those approaches to drive more organic discovery ultimately get co opted by the platforms themselves because they want to monetize that value.

Christy Augustine (17:43)

Yeah, I totally agree. And that you're right. I mean, I think we definitely have been talking a lot about using SEO as a indicator of where AEO is going to go. I don't know if that's super accurate. I think that on some dimension. Absolutely. There were early days of SEO. Everyone was guessing, how do I get Crawled, how do I flatten my link graph so that I get crawled for more content? How do I get recognized for that content? Let me enrich the content. And certainly one of the biggest topics I hear from everyone right now is my data's not good enough. Is, you know, the blocker. I think to most people trying to optimize advertising any of their channels is this conversation that with AI, I think we've done a very good job educating everyone that AI is only good as the data you give it. And now everyone's turned to, well, my data is not good enough. And as you look at any of the channels, I think the more that the native platforms are retaining the data and hiding that data, the more blind you are to what are people searching for? How do I optimize for that? What data do I need to enrich? And so part of this is really what channels do you control and how do you bring them all together? AI lets you bring a lot of those data signals together. Instead of before where it was kind of all separate, maybe an SMS channel. I had an SMS vendor and I could just see that, but I couldn't really tie it anywhere else. I had separate customers that were SMS customers and separate customers who were email customers. I had separate customers on the app Store and I couldn't bring them together. And so I think as we really start to develop more of this agent to agent profile more MCP options, we're tying more of this data together at the end. I just think will be better for all of us and our experience than we had before.

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Eric Soufert (20:23)

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Eric Soufert (20:29)

So when you're thinking about the kind of, you know, AI empowered personalization regime, how much of the success that you see with that, with, with the personalization efforts is determined by the availability of that data and then how much then does that exercise become a function of surfacing more data of actually building the mechanisms to surface more data. So like, like a product development or, you know, a product management actually discipline of saying, look, when the person hits the landing page, we need to be able to find out these things about them. We've got to create ways to find. So like our product management approach should be to try to build mechanisms that they can interface with that actually give us those signals, that data to then personalize against. How much of this is driven by product, I guess, is the question.

Christy Augustine (21:15)

Yeah, great question. I think in this agentic world, 50% of it is going to be driven by product and 50 is going to be impacted by the customers, which is kind of cool. The customer has never been in the driver's seat before. And what I mean by that is, you know, we talk, we've talked a lot about needing great data before and the great data was great. Put a pixel on your website, track all the user clicks. Let me guess if that click is a good or bad click, right? A bounce is a bad click. I don't know. Maybe I'm exploring. Maybe I liked that product, but just not today. I'm filing it away. I don't know. But we're reading the tea leaves. And now that we have agents and we can have conversations with our customers, the customer gets to tell us what they want even more explicitly. But also we're getting all kind of signals that we couldn't accommodate before because we didn't have the algorithms that let us handle this unstructured data in as efficient or constructive of a way as we could before. We didn't know what to do with the conversations. We couldn't really structure and read the patterns like we can now. So now customers can tell us so much more, right? I couldn't. Other than maybe putting a search query and two or three words into a search box. I couldn't have a conversation back and forth and tell it, no, that's not what I meant. Please give me better results than you just gave me so I can actually feed in and give you feedback and tell you, no, I really was interested in this pair of shoes, but you didn't tell me how much they weigh. And I really need to know how much they weigh before I'm going to buy them because I'm going on a hike. I don't want five pound boots. Now I can tell you that that's a data signal I maybe didn't know I needed before. It's a data attribute that maybe I didn't know I needed before because I never gave it to the customer. So I feel like part of this is being very thoughtful with your product on what data signals you get, how do you acquire them, how do you open it up for more feedback. And now more of it gets to go into the customer's hands and tell us what they really want. What were they interested in?

Eric Soufert (23:37)

It sounds like what you're saying is there are these mechanisms that, you know, that we can give to the customers to sort of let them express themselves. I'm thinking more like in terms of the customer journey. Right. You kind of brought up the example before of like someone is on the landing page and just a prompt to expose a. A chatbot to them. That's just a way to collect signal at that point. Right. I mean, they're giving me basically describing what they want. And then in that way I can. That's. That's something that I can use. I can tether to. To. To personalize the experience downstream for them.

Christy Augustine (24:05)

Exactly. Maybe before you just see them clicking all over your website, looking at different skirts or dresses or whatever. And now if I can surface up that conversational agent or I can have ask AI ask me anything available all the time, then they can tell me, well, I'm looking for something to wear to Wimbledon that's very different. And now I know kind of not only types of products, but I know the style and what I should be recommending to you. Where before I was just kind of guessing. Looks like you're looking at a lot of white skirts. Do you want more white skirts? Right. What do I need to. Now I can tell you as a customer. Yeah. Like who's got on their website a specific category. I haven't seen it. That was like going to the Taylor Swift concert. Need an outfit. Maybe I need like a sparkly blazer for my husband. That's not a category. But now I can have a conversation and be more explicit and have the. Have the site help me find what I'm looking for.

Eric Soufert (25:09)

Yeah. And I mean not, you know, not to mention this. A Wimbledon attendee is probably like a dream scenario for an econ retailer.

Christy Augustine (25:15)

It is. They're probably working with a personal shopper and they're not so, you know, high

Eric Soufert (25:21)

propensity to spend there.

Christy Augustine (25:23)

We can all aspire.

Eric Soufert (25:24)

Right. You know, that's not just a potential customer just hit the landing page proposition. That's kind of throughout the lifecycle. That can be the case, right?

Christy Augustine (25:34)

Absolutely. I mean, maybe I was looking at Wimbledon outfits before, but now maybe we're getting closer to the holidays and I'm looking for gifting items and I just really don't know what to get my nephew. We saw a lot of that actually this holiday where engagement would start to spike with conversational because people, people would come to different websites and they're not searching for themselves anymore and they're searching for someone they really care about. But maybe they don't know, right? Little kids, preferences change all the time. So they're like, homie, what should I be buying for my seven year old nephew? He likes video games. Maybe as a shopper I don't know much about video games and now I can be more thoughtful about what I'm giving so much more. Those journeys and those purchase cycles totally change. Just because I nailed you in the first five minutes and knew what you were looking for doesn't mean you're coming back looking for the same stuff.

Eric Soufert (26:28)

Have you seen cases where customers reacted poorly to personalization?

Christy Augustine (26:32)

You know, I think that's a really interesting one because I mean I think we all see it right now where we think it's, we think it's personalization, we think it's awful, right? This morning, just this morning I got an email from a E commerce company saying, you know, still interested in this and it wasn't anything I'd ever looked at before. And then there were like a whole bunch of products underneath of it and I was like, I'm, I'm not a six foot man. Like what are these products? I, I've never bought anything bright red in my life. Why are all kind of wrong at the same time? I mean, where's the harm? Not really going to be bothering me. But I was like, this is weird. I think that most people are so shy about personalization that we haven't really gone to the explicit version of personalization. We haven't gone really deep. In fact, the balance I see right now is people are so cautious to be overly personalized. They think a lot of money is being left on the table because you're reading the signals and saying I'm just going to make a really small change for a really small amount of people. You end up in this AI is meant for us to scale, do personalization at scale and I'm going to hold it back to a very small amount of scale and see if it's okay, see if everyone reacts okay to it. I think the consumer behavior is changing. You can see people having more conversations, being okay with their data conversations being used to, to inform further, further conversations. So I, I'm curious where we're going to Go here. And if we're going to become more and more explicit. And that's when I think personalization has a chance of going wrong, is when you start to get really explicit and you try to be very definitive about an answer and you get it wrong. Which is why I think, you know, AI is so good when there's no one right answer. So I can kind of get it right. So we'll see. I think people have been pretty cautious to date on personalization. So even when it's wrong, not that bad.

Eric Soufert (28:48)

Yeah, I mean, I think, I mean, my position on this is that first of all, like, there's a lot of cases like the one you just cited where, you know, you get the email and it's for something that you never engaged with or interacted with, and you just dismiss it and don't think about it again. But then you might get the email for something that you did browse or whatever, and that feels like personalization. But again, it could just be totally random. I mean, because it's just the things that are irrelevant you just ignore. Right. And that's where I think a lot of the confusion arises around people thinking their phone is listening to them. I mean, that truly is just ignoring the 99 or whatever, the 97% of ads that you see that are totally irrelevant and then anchoring to. Because you recently talked about taking a cruise on Disney, anchoring to the Disney cruise ads. Like, oh, they must be listening because I was talking about that today. Well, that only resonates because A, you were talking about it today, so it's top of mind. And B, because it's. It's not noteworthy for all the ads you saw today and there were thousands that just were totally irrelevant. Right. And so it's. You're just ignoring the totality here. You know, the other thing is I, I do think on net, I mean, people, they, they truly do appreciate a personalized experience that's more relevant to them. And, and it's really just more about the context being appropriate or not. I mean, personalize some E Com product that I would like to buy a retail product on Instagram and people love it. But if you chose the wrong content, if you chose an unfortunate context to do that, then yeah, people would bristle at it.

Christy Augustine (30:14)

You're totally right. I mean, context is super important. If this were my bank, they better get it right. If this were healthcare and I'm going into my doctor, don't give me the wrong. Don't give me the wrong medication or the wrong diagnosis. Right. You have to be absolutely precise in this area. The harm of personalization or I got it wrong. The risk of getting it wrong is less so it's going to be harder for me to have an adverse outcome or offend someone as possible. But I think we try to be really thoughtful and not as explicit in those areas. But there are industries where it 100% matters. And you can't just have no one right answer or I kind of got it right. You have to be perfect.

Eric Soufert (31:05)

Right. And that's nuance that needs to be recognized. I mean, they're just, they're like you said, I mean, there's just, there's, there's circumstances where there's no room for regret.

Christy Augustine (31:12)

Yeah.

Eric Soufert (31:13)

And, yeah, that just, that requires a lot of thoughtfulness and care. And then there's. There's circumstances where you can be experimental and actually there's just very little downside to getting something wrong. And there, I mean, I think in those circumstances or in those contexts, people are just. They're very forgiving of a misfire. Right. Actually, I don't want a baseball cap. I don't wear baseball caps. You shouldn't have recommended that to me. But at the end of the day, that didn't really derail my life to see that. Right?

Christy Augustine (31:39)

Yeah, exactly. You can recommend baseball hats to me all day. That's typically what I live in.

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Christy Augustine (32:31)

how

Eric Soufert (32:31)

have you seen clients implement conversational shopping? Are there specific verticals for which that works best? Just having that chatbot and actually talking through the purchase?

Christy Augustine (32:41)

Yes. This is like one of my favorite topics because I think for the first time in like 26 years, e commerce is getting a complete revamp. And there's so much talk about having conversational. And now is all of E commerce going to go to conversational? And I think that aside from verticals, I mean, there are certain kind of attributes to a vertical that makes it Great for conversational shopping. I mean, if you've got a very large assortment, if you're very particular, maybe it's B2B and you really need guided selling because customers aren't exactly sure the they know the make model in their car, but they don't know the exact part that goes with that. You have a higher chance that the conversation is going to be something you want on your website. But really, I see it coming down to the purchase interaction and the customer journey. And so first, like, not every purchase requires a conversation. If I went into the grocery store to go get milk and the people at the front stopped me and said, can I help you with something today? You know, we have something on sale. I'm going to be like, oh my gosh, I'm out of milk. And I, this was a 30 second purchase, right? It's that Instagram impulse purchase. Don't, don't interrupt my flow. Because if you do, I'm likely just not to make this purchase. But there are a lot of situations where it becomes high consideration or I want to, I want to learn more. I want to have a conversation. And websites are really limited real estate. No one's going to scroll. Like, even if you think, I've got infinite scroll on a category page or I've got a product page and you can scroll down, those Amazon product pages are I don't know how many real pages long, right? They go on forever. No one's scrolling. You really do have limited real estate. But a conversation opens it up to infinite real estate. Now I can ask you anything. I can find out about reviews without reading the reviews. I can find out about this. The weight of those shoes, even though you don't actually have that on the site anywhere. I can find out the fit of the product. Is this shirt, is it tight or is it loose fit shirt? Is it cropped? Maybe? I can't tell from the photo. So I can kind of open up all of those conversations and that data and a really limited amount of real estate. So I think from a vertical perspective, I think a lot less about, oh, this is travel or this is an apparel company. I don't know if this is applicable because every user's definition of high consideration is so different. What's high consideration to me and my budget and what I love, or I have sensitive skin, so I'm way more interested in the details on cosmetics than the next person. It really changes the conversation and means that I think every vertical has the potential for. I need to engage with some subset of my Users in a different way

Eric Soufert (35:54)

here we're talking about like on the on site, right. So they're on the retailer site, you know, and this is like the opportunity to sort of engage with, you know, a chat bot that kind of functions as like a personal assistant. You could think we're talking in the retail kind of E commerce. Let's talk about like, let's just narrow it down to like the clothing. Right. So I'm on an E comm retailer's website and there's this opportunity to engage with the chat bot and use that like a personal assistant. Like, if I'm thinking about it from the retailer's standpoint, there's obviously probably improving conversion because more sort of readily route them to the thing that they're, you know, likely to want to buy. It's. There's not much discovery needed. They don't need to click around and they might not. They're less likely to get frustrated and just leave because I'll just leave them right there. But there's probably other stuff too. It's, I mean, you know, you think about like, maybe it's finding more accessories, like other things that they also are interested in as a result of like kind of getting this information. It's not just a matter of more efficiently leading to them to that thing and getting them to the destination, but probably enriching their shopping experience, giving them exposure, other things maybe they didn't even know they were interested in.

Christy Augustine (36:56)

Yeah, I think, you know, that the two surprises I've seen the most when people have put conversational shopping on their website is number one is returns. And so your purchase confidence as a consumer goes up much higher when you can ask and engage more questions. And so before, when I'd buy those shoes and then I'd get on and I'd find out, man, okay, turns out they run large and they weigh a lot. And if I went back to the website and I really digested all the information, turns out they told me that it runs large at the bottom of the page in a review that I didn't read. But maybe they didn't tell me that it weighs five pounds. And so there's this returns aspect where now I'm taking people on the product page and I'm increasing their confidence to either buy or not buy in some cases. That conversion rate not going up can also be a healthy thing in conversations, is that I can actually help users at that moment and understand kind of which product they're looking to get. The second, I would say, is this notion of Voice of customer. There's really no place on your website that you can converse with a customer. So finding out that your customers really cared about the fit of the shirt and what size is the model wearing in the photo or is this product easy to assemble? Well, I had a bunch of assembly information, but I didn't realize like I needed that particular. Being able to have all these conversations and see that your customers, they're cluing you in. And what we see over and over again is these trends where you look and you say, wow, there are like really five things customers really wanted to know and they can't find it on your website. Now they're getting, they're getting that actual voice instead of maybe two words in the search bar. Yes, I knew everyone wanted black dresses. I didn't realize that what they wanted to know was the hem and the length of that dress and that that was a really, really big deal to 80% of my customers.

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Eric Soufert (39:38)

for the first six months. I want to switch gears to the advertising use case. So how can advertisers utilize AI based personalization tools outside of what's available on the actual ad platforms themselves? What do they have available to them to improve their advertising outcomes?

Christy Augustine (39:57)

The hardest part about these large platforms really is how do I, how do I obviously get the best return, but how do I deal with the fact that on any of these platforms they may or may not share data with me? It's siloed data. And so as much as I can get data from these different platforms and channels, at least what I can do is be leveraging the AI to create better audience strategies. Whether that's unique to particular platforms and recognize that certain platforms have different audiences, but creating that better audience strategy per platform, per personalization, personalizing the actual destination from those platforms, making sure that my messaging and targeting and creative are actually personalized and correct for that audience or that person, and making sure that I've got a really strong feedback loop on that. So now I can do this more in real time behavior or real time testing and really get quickly to which audience strategy, which content strategy is actually winning on which channels and optimize much quicker. Where I think before we would spend maybe a month of data and analysis and then say okay, turns out the A B test that we just run on personalization, B1 now we can know within a day and be optimizing. That's a lot of money that you're leaving on the table if you're running these A B tests or control tests for a long period of time.

Eric Soufert (41:36)

Is there a risk that you know any, any approach like that where you're analyzing the data set on your own and then, and then using that to adjust the, the platform levers, the, the platform settings. Are there any, any risk at that that those sort of are incongruent or like. Because a lot of the, the platform is. Well, we see now there's this bifurcation of the very large platforms that, that all run these totally automated systems. People call them black boxes. Right. So you've got, you know, Facebook and Google were kind of like early leaders there even you've got Reddit, Pinterest, TikTok, they've all got essentially like end to end automation. And you know, I think with most platforms following suit, is there any risk that what an advertiser is doing on their own then kind of works against or undermines what the, what the platform's doing in this kind of black box environment?

Christy Augustine (42:19)

Yeah, I think there are, there are two big risks there. You bring up a great point because your native platform does have configurations, optimization, different data, very mismatch, audience logic, maybe audience definition, different goals that they're trying to accomplish and optimize for. All of these algorithms are giant math formulas with different coefficients or different goal setting that they have built in. And so I think this is the world we live in today, which is why customers feel like it's such a discongruent experience based on whether they're on your site and went to your brand in particular or whether they're on one of these native platforms. I think you've got a lot of that off platform personalization not necessarily being complimented by the native tools. And then I think you've got the other things with these native platforms is you've got user generated content. The user generated content is opinion based, not always factual at the moment it's what someone thought the answer was, but it wasn't no ill intent, they just got it wrong or it was their experience. And that experience isn't relevant for me. It's relevant for someone else. And so you do get these conflicting signals. I think right now people generally understand that. But you're right, if you're building these strategies, you need to be very thoughtful about how much weight you give a signal that you're not confident in or if you use it at all. And what do you want to use from those platforms?

Eric Soufert (44:00)

What are some cutting edge applications of AI based personalization? So you know, what is, what is bloomreach doing? What are your clients asking you for? What are your clients having the most success with? Like, what's coming down the pike? What's kind of like the most exciting kind of frontier here in AI based personalization?

Christy Augustine (44:15)

I think we're starting to see some of them. They're just not as prevalent. Like I'm super excited about the conversational shopping agents you see it with Rufus. I love the Magic apron at Home Depot because it's just not my, I'm not an expert there. So it's very helpful. But I think what next, really, I'm very excited about the, the agent to agent experience. The fact that we can really start scaling. I think in, in marketing in particular right now you're kind of limited by how many campaigns you can come up with and run. But the fact that we're moving to agentic campaigns, the fact that I can do not only more campaigns than I could do before at micro segments instead of broad segments, but I can also feed in signals that I couldn't feed in before and the AI will optimize for those outcomes just means that I think our customers are getting a better experience with those campaigns. But I'm going to have way better outcomes than I could have with the fact that I don't think anyone ever feels like their marketing budget is enough. Right. We never sit here and say, you know, I have enough people and enough time and now there's going to be more throughput on that there. So I think, you know, really the kind of the agentic automated marketing, the campaigns that we're going to be able to come up with, it's one of the things I'm, I'm most excited about.

Eric Soufert (45:42)

Have you seen anything that surprised you? Like things that your clients are doing or just applications of this that had, you know, dramatically better results than you would have expected? Just, just something that, that happened that kind of took you by surprise?

Christy Augustine (45:53)

You know, I guess I thought, you know, I've got one customer I work with, does a lot of the Funko Pop products, they do a lot of gaming products and really started experimenting with these agentic campaigns. And I thought that, I thought they'd get a little bit of a pop from it. You know, I hoped, right, that we would get a little bit of pop. Right. But the thing that I didn't factor in the human element of it is when you get into certain of these categories, like gaming or like pop culture, your fan base is so important and so bought in that getting that right drives so much more of an impact than if you kind of got it right or if you threw them into the population of general. I mean, I guess that, you know, there, there are, and there are certain universes that don't play well together. I mean, if you like dc, but then you. I don't even know the superheroes, so I don't even want to attempt to this analogy, but I guess they're not all the same. And you don't want to get that wrong with your customer base. And so they really leveraged. I got it right and the pop was a lot bigger than I thought it would be.

Eric Soufert (47:06)

That's actually a really interesting thought. It's kind of like, you know, AI. A lot of the AI tools allow you to, to go beyond the Pareto rule, right? I mean, you know, you can actually say, well, no, I've got. I mean, it's relatively costless for me to actually take this to 100% versus before. I was like, look, the trade off is you kind of run into a, a cliff at the 80, 20 threshold that it's not really worth going the extra mile. But when you've got kind of a fan base of certain categories that's fanatical, going to, you know, taking that to the extreme actually does sort of really unlock all the value. And it's almost like an inverse Pareto rule, right, for some of these groups.

Christy Augustine (47:45)

And it's the long tail, right. There's always been this holy grail of like, oh, I wish I could optimize the long tail. But as you mentioned, like that extra 10%, it's going to be monumental effort. So forget about the long tail. I'm just going to 80, 20 it and focus on the top 20, get 80% of the value. But now I can go after the long tail. And even though each one of those is maybe pennies in aggregate, it's a

Eric Soufert (48:12)

huge impact and unlocks all the value for those fanatical fans, but who are probably have a higher willingness to pay. And that creates a pretty compelling opportunity. That's, that's. I never really thought about that. That's a really interesting idea. Christine, this was great. Appreciate you coming on the podcast. How can people learn more about bloomreach? How can people engage with your company?

Christy Augustine (48:34)

Yeah, absolutely. I mean, we're@bloomreach.com so, you know, follow us on LinkedIn, come visit our website. Be happy to chat with you.

Eric Soufert (48:43)

Cheers. Well, thank you so much for your time today.

Christy Augustine (48:44)

Thanks for having.

Eric Soufert (48:51)

It.