Customerland
Customerland is a podcast about …. Customers. How to get more of them. How to keep them. What makes them tick. We talk to the experts, the technologies and occasionally, actual people – you know, customers – to find out what they’re all about.So if you’re a CX pro, a loyalty marketer, a brand owner, an agency planner … if you’re a CRM & personalization geek, if you’re a customer service / CSAT / NPS nerd – you finally have a home.
Customerland
AI's Double Impact: Converting Data into Customer Insights
Fullstory's Chief Marketing Officer Lindsay Bayuk shares how behavioral data revolutionizes customer understanding by revealing not just what customers do, but why they do it. Companies using AI extensively report twice the impact on user engagement and operational efficiency compared to cautious adopters.
• Behavioral data captures user emotions through signals like "rage clicks" (5 successive clicks in 10 seconds)
• Fullstory helps brands understand the "why" behind customer actions that traditional analytics miss
• Companies extensively adopting AI see 2x impact on user engagement and operational efficiency
• 80% of companies collect data, but only 25% say they use it effectively
• Breaking down data silos and building data literacy creates competitive advantage
• Real-time personalization based on behavioral signals produces measurable ROI
• Pizza Hut achieved 6.5% transaction increase using Fullstory to optimize experiments
• For marketers overwhelmed by technology: pick a few strategic areas rather than changing everything at once
• AI should elevate human capabilities while solving real customer problems
60% are growing their AI usage. Only 13% describe their adoption as extensive. But for those that are leaning in, that are widely adopting AI, they are reporting two times the impact on both user engagement and operational efficiency. So we're acknowledging yes, there's a lot of trial and error in a lot of these tools and practices, but the fruits are there for those that lean in.
Speaker 2:Today, on Customer Land, lindsay Bayock, who is Chief Marketing Officer, at Full Story, and we get to talk about customer data, which is just you know, you say it like that. It already sounds boring, but it's not. It might be the most fascinating topic in this whole realm. So anyway, lindsay, thanks for joining me. I really appreciate it.
Speaker 1:Thanks so much for having me, Mike. It's a pleasure to be here.
Speaker 2:So, in order to kind of set up the rest of this conversation, which is already kind of looking like it's going to be a fascinating one, can you give us a little bit of what is Full Story? What do you do there, what kinds of clients you work with? Any kind of background you think might help set context for this?
Speaker 1:Yeah, sure, happy to. So I'm the Chief Marketing Officer, leading a global marketing team for Full Story. We are the behavioral data platform and our customers are large retailers, travel and hospitality firms, gaming and gambling companies, all looking to really harness the power of behavioral data to create loyal customers for life.
Speaker 2:Sounds easy enough.
Speaker 1:Sure, we like to make it easy.
Speaker 2:Well, why doesn't everybody do that Right? And looking over some of the early materials that your team and I were kind of sending back and forth, I think it was the phrase behavioral data that kind of hooked my attention first. And yes, there are other companies and we talked to them about how they are extracting the data and the insights from that. But it seems to me that at Full Story that's kind of the core business. Do I have that right?
Speaker 1:Yes, yes. So when we talk about behavioral data, we often say we go beyond traditional analytics that retailers might use on their websites or inside of you know, their product experiences those traditional analytics. What our customers tell us is that they give you the what they don't help you understand why your customers or consumers are doing the things they're doing, why they are checking out, why they aren't checking out, and so what we really aim to do is help our customers understand why their consumers are doing the things that they're doing. Consumers are doing the things that they're doing.
Speaker 1:Forbes recently referred to behavioral data as the new gold, because of its potential to really revolutionize digital experiences, most importantly in this AI era. And so you might be wondering what is behavioral data? Are things like pinch or zoom on a mobile app or rage clicks, cursor thrash on a website or in a checkout experience, straight line cursor movements which might indicate an agent is shopping on your website? We can, with our full capture technology, really capture and provide context around all of those more granular and, quite frankly, higher quality behaviors happening in these digital experiences, and then surface those signals for our customers, for them to really better dissect what's happening.
Speaker 2:So we're going to go down a rabbit trail here. I hope you're okay with that. But you know, from those kinds of signals that you're capturing to, to then build that into a story that that speaks to the emotional state at the moment, uh, that those were taking place requires a lot more than just, like you know, data it. It really requires a, a kind of a several different lenses to kind of, um, understand all the different dimensions of the emotionality behind what was going on at that time. So so, um, first, does full story kind of operate in that emotional space like that, and and try and drive those emotional insights? And if so, how do you do that?
Speaker 1:Yeah, sure. So my favorite example is rage clicks. Uh, have you heard of a rage click?
Speaker 1:Okay, so our definition of a rage click before. Oh yeah, Totally Okay. So our definition of a rage click is five successive clicks in 10 seconds. And then we have other frustration signals like I said, the cursor thrashing, error clicks, dead clicks, and we sort of define all of those to your point, the more granular data, as frustration signals, and so then we help our customers through our analytics tool really surface those moments and say, hey, look, in this checkout process we're seeing a high spike in frustration signals. That's probably why conversion rates are down, and so it's putting some definition around those and then making them easy to identify and act on in our tools. That helps us bridge the gap between seeing kind of you know, a very standard funnel drop-off and highlighting kind of the emotional reasons that customers are leaving your site.
Speaker 2:Right, what's behind it? I've had, I've had some great conversations, mostly this year, around the ways that retailers online and bricks and mortar are are kind of looking at behavioral data and trying to figure out what to do with it. And, like, this is purely my own kind of sidelines opinion here. There's no data to back this up at all, but my sense is that there's a growing awareness of the need to understand the emotionality of decisions, which you know, they've always been there. But I think, like among marketers, it's like wait, we can actually, we can actually understand that now, where it was kind of a kind of a black boxy kind of a notion before, but there's still not a whole lot of understanding as to how you get there. Like you know, yeah, we, we need to know what the emotional drivers are in this particular case. But you know, I think they're kind of just looking for some way to just like tell me what they're thinking and feeling.
Speaker 1:Yeah, sure, and we see that too, and that's why I think our platform empowers our customers with both that quantitative behavioral data and we pair that then with session replay so you actually see the experience of the users on your website, and that's what really gives our customers more color and really a deeper understanding of that customer experience, which we know is essential. Right, there was a study that said that 80% of consumers are likely to make a repeat purchase if they have a world-class experience on your website. So we know it matters, totally agree, it's been hard to really get at, but that's part of what we're solving for our customers.
Speaker 2:Right, and a little side note I was looking through your site ahead of this conversation and I guess you're funded by you're originally funded by some A-list angels and investors and I think if you didn't understand the power of behavioral data and the insights you derived, just looking at your list of investors would cause you to go. Well, you know, if these guys who are really smart are backing this operation, then maybe we should pay attention, because there really are some big names there.
Speaker 1:Yeah, yeah, there definitely are, and I would say, similarly, we're really proud of some incredible brands that have partnered with us, like Lowe's with us, like Lowe's, chipotle, pizza Hut, patagonia and they're seeing the benefit which is most rewarding for us.
Speaker 2:Yeah, that's huge. At some point I want to get close enough to what Full Story is doing, where I get to see some of that data because I'm just fascinated by it. But we'll get there over time.
Speaker 1:Okay, great, we'd be happy to show that to you.
Speaker 2:One of the things that your team sent over was a copy of this report that you've produced called the Reality Check the 2025 Reality Check, which is full stories, technology leader surveys. So I went through it and, just to go high level, I can pull out, like and cherry pick data points and just pull them out. But I think, even before that, one of the things that uh, um, for whatever this is worth, we see a lot of research, a lot of companies want the research highlighted. We'll absolutely take a look at it. Um, a lot of it is either super granular and we can go there, or it's so high level that there's kind of no real genuine utility there.
Speaker 2:And yet I'm looking at what you sent over for full story and it's like you've you've found the really juicy nuggets and and I'm sure there was some serious digging to get there but you've presented them in such a way that like, look, even someone who's not a data founded marketer can go oh, oh, oh, oh. I kind of get it. And so there are numerous moments, as I was reading your report, like, like you know, oh, that's interesting, I didn't see that angle necessarily, or I haven't seen it kind of positioned that way. So, anyway, props to you and your team for putting this together, because it is fascinating.
Speaker 1:I'm really lucky to have a fabulous team that put a lot of thoughtfulness into that report report.
Speaker 2:I'll make sure to share that with them um, so you've organized according some pretty big themes.
Speaker 2:Um, I wanted to just settle into your ai section let's do it um top findings 60 report significant growth in AI usage, but only 13% describe their adoption as extensive. And that's probably just a function of where we are in the kind of comfort curve with this whole thing, cause I talked to companies who are like we're all in, we're all in, we're kind of encouraging everybody in the in, in our staff and teams, to just go for it. And then there are others who are like yeah, we're going to wait till everybody else makes the mistakes and then we'll jump in there. So you're looking at these statistics, you're seeing how the companies are actually kind of deploying this and how they're working with it. Smarter to jump in now. Smarter to take the like the first Indian always gets the the arrow kind of at the back, kind of a thing. Or you know what are you advising?
Speaker 1:Yeah, I mean we are advising that companies do do take a leadership approach and dive in and dive in. We really believe, as the report describes, that AI won't wait for scaredy cats. We really feel like this is a moment where companies need to lean in. It's a game-changing moment of technological evolution and the results are there. The carrot is there on the other side. We also talked in the report about how, like you mentioned, only 60% are growing their AI usage. Only 13% describe their adoption as extensive. But for those that are leaning in, that are widely adopting AI, that are leaning in, that are widely adopting AI, they are reporting two times the impact on both user engagement and operational efficiency. So we're acknowledging yes, there's a lot of trial and error in a lot of these tools and practices, but the fruits are. You know, the fruits are there for those that lean in.
Speaker 2:Yeah, I completely agree. I'm interested at full story with you and your team. How deeply have you guys embraced the whole AI thing within your teams?
Speaker 1:Yeah, as you would imagine for a data company and a growing startup, we are heavily leaning in, especially in marketing. There are many use cases for us across content creation, across SEO, optimization, project management, you know, kind of overall team productivity, and a lot in our MarTech team where we're adopting new tools, testing AI, sdrs that can really just elevate our game. So we're leaning in, we are testing a lot and seeing what's going to really drive meaningful impact. Not all tools are, but we're very excited about a few that we've adopted more widely.
Speaker 2:Well, we'll offline that, because I'm dying to hear.
Speaker 1:Okay, great, happy to share.
Speaker 2:Thank you. One of the other kind of data, a couple of points that kind of surfaced here. Key barriers to adoption include skill gaps at 16%, integration issues at 13% and data quality concerns at 14%. And I'm interested to know if trust in the tool set was a factor at all. Did that show up in your data?
Speaker 1:Yeah, it did. So when we asked about why brands were a little bit more hesitant to adopt AI, we kind of asked them you know what are some of the barriers. Some were just saying, hey look, we're only going to use AI for low-risk areas. Low-risk areas 17% said trusting AI only with human oversight was sort of how they were approaching it. And then a few lower percentages were just saying, hey look, we're going to scale back our AI usage until the models improve. And so, yeah, I would say trust is definitely in there for those that are playing it safe.
Speaker 2:Yeah, there's all kinds of different dimensions to that one too, and I'm interested actually, we're working on a project to explore that very thing and I think it'd be really interesting to kind of mash up what we're finding with what you're finding, because you're approaching this same idea from your angle and I think your angle is well, obviously really valuable. Thank you, can I move into the data section for a little bit?
Speaker 1:Let's do it.
Speaker 2:Okay, again, I'm cherry picking here. 80% of companies collect data, but only 25 say they use it effectively. To me that's not a huge surprise. People have been kind of you know, crying about that same you know kind of loads of data swimming in data but drowning for insights, kind of a thing, or whatever the appropriate analogy is yes.
Speaker 1:Drowning in data starving for insights that a thing, or whatever the appropriate. Drowning in data starving for insight that's what it was.
Speaker 2:yes yes, somebody here has to know it. Um, so that one's. That was not a huge surprise, um, but apparently you all are tracking kind of, uh, longitudinally how people are improving on that, like like what's really happening. Can you kind of help us unpack that a little bit?
Speaker 1:Yeah, sure, I think some of the more innovative companies that we're working with are just doing a much better job of eliminating what we like to call the data housework kind of the data cleanup, eliminating those data silos across teams, so making more complete and robust data sets available for any team within a large enterprise. And then I would also say back to the earlier point about skill gaps being a barrier to usage. Creating a real culture of data literacy within organizations also helps them more widely adopt the data that they do have available and use it more effectively.
Speaker 2:Do you see, maybe a better way to frame this question is yes, um, let me think about this because there's there are about three questions I'm trying to pack into something sensible here. When you talk about companies that are interested in kind of accelerating their own data culture by promoting data literacy, how are they going about that? Because you know, sometimes that's that really is a culture and those things aren't easy to shift.
Speaker 1:Yeah, sure. So I think there's a variety of ways that they're approaching this. This is something we've talked about quite a bit on social, et cetera, with a really incredible data leader. His name's Jordan Morrow. He talks a lot about data literacy and he's got kind of his approaches to it talking about leading from the front, talking about empowering teams with ways to learn about data and even the fundamentals, talking about why, and then making the tools available to everyone. And that's one of the things we see at Full Story is when more than one team is able to access our platform. So let's say, the engineering team, product team, data team and support engineering team, product team, data team and support and everyone is really empowered to use that data and tap into improving the customer experience. The business just reaps so many more rewards by enabling that tooling for everyone or for a wider proportion of the company.
Speaker 2:So that's worth unpacking because I talk to a lot of technology providers. You know hardware, software, data, insights, analytics and everything in between, and it's really interesting to me if you can take kind of a macro view of the way these companies are pursuing their own growth. It's there are a handful of them that I think are getting it right by starting high level. You know, leading at the front I think might be what you're saying you know you get. You get an ambassador, you get a cheerleader, you get somebody in the C-suite who sees the kind of bottom line potential of it. And you know you get the sale, you get the benefits the company derives, all of its insights and actionability. That needs to.
Speaker 2:But there are still a lot of companies out there who are, who are going well, we have this great product and a lot of them really do.
Speaker 2:Going well, we have this great product and a lot of them really do. But haven't quite figured out that the way to sell this, the way to really kind of sell it in and get it you know so it's, so it's producing the benefits is to attach an ROI to the thing right up front. And, um, you know, I don't know if, if a full story has that issue, because it seems like what you produce has fairly direct bottom line benefits that you can. You know, attributing those bottom line effects is probably fairly easy to do, but there are a lot of companies out there that are kind of just like, well, wait a minute, I can't quite get there from now. So if you were talking to anybody, say, in your partner network who was trying to figure that out and saying here, look, this is the full story way, this is how we're successful in kind of selling ourselves in and, you know, getting embedded into the culture and the utility there, what would you tell them?
Speaker 1:Yeah, sure, I'd probably highlight some of the success that our customers have had.
Speaker 1:One story that comes to mind is with Pizza Hut, which you know we love working with the Pizza Hut team and that team, like I mentioned, our product is used there across their UX design team, their experimentation team and their CRO team and they'll use it for their experiments and A-B testing. And so it's really that cross-team usage, that curiosity and focus around how do we drive a better customer experience and how do we optimize our site, paired with you know what our product provides, what our platform provides. And there to your point about it being pretty concrete, roi, they were running an A-B test and didn't realize that the test was not appearing in all transactions. It was a test in the checkout flow and FullStory was able to identify that for them that it wasn't appearing, that it wasn't a failed experiment but rather an issue in the deployment of that experiment, but rather an issue in the deployment of that experiment and, as a result, we were able to help them fix the test, deploy it and drive a 6.5% increase in transactions with the variant.
Speaker 2:And so those are real dollars, especially given the fact that Pizza Hut is running this across many geographies at a global scale. Wow, okay, well, you just gave me the opening I was looking for to start talking about some of the UX findings.
Speaker 2:Sure, sure, let's go there next. That beautifully crafted segue Couldn't have done better myself. Again cherry picking, let's see, again cherry picking, let's see. Businesses that improve their journeys reported a 15% increase in retention rates. Seen data like that one, to me, is really worth unpacking, because what we can do with personalization now is light years beyond what we could even do, maybe even two years ago. So how does Full Story approach that? You know, what is it that Full Story is kind of bringing them that allows that kind of personalization is?
Speaker 1:kind of bringing them that allows that kind of personalization? Yeah, sure, so we're very excited about what you know, the potential of pairing behavioral data with AI and what it will really enable, and one of the ways that we're doing that is, uh, empowering, um, empowering our customers with the ability to personalize their websites on a one-to-one basis based on those behaviors. So some of the use cases that we're working on with our customers are being able to, let's say, someone's working through a checkout flow, again shopping, and they, you know there's some component of the site that's not working and they execute a rage click right. They're frustrated With our real-time APIs. We could enable them to trigger a discount code or a coupon or something that is displaying only for those displaying a certain behavior, but not for any sort of broader swath or segment on their site, and we think there's real power in that. More true, one-to-one, in-the-moment personalization that, to your point, just hasn't been really possible in the past.
Speaker 2:Yeah, I mean I'm nodding Can't see this because it's an audio medium, but yes, I'm nodding vigorously with that one. Yeah, I mean, think back to what personalization meant, say just two years ago it's just you know or five years ago, when personalization meant, you know, in the email it used your first name.
Speaker 1:Right, right. We've come a long way.
Speaker 2:We've come a long way, and so much further to go too.
Speaker 1:Part of what we found in the study is that everyone knows personalization is meaningful and important to their customers. Again, whoever delivers the best customer experience wins, but it has been challenging to truly realize the potential of personalization. I think that's how I would sort of summarize the findings. And so, yeah, we're excited to help our customers make a lot more progress there.
Speaker 2:So I'm thinking of a handful of the marketing organizations that I know well enough to make this kind of comment Really smart people, really dedicated to the craft, really staying on top of the technology, working it hard. I think I've still noted, though, the technology moves so much faster than marketers are able to keep up, even the really good ones, the people who are just really in it Because capabilities, because new data sources, because new kinds of insights, new dimensions that can affect the way we've thought about things in the past, all of which kind of brings me to this place of like, what's a marketer to do right now? And it's a big question. You don't even have to have an answer, I'm just kind of putting it out there, but if you do, fantastic. I just happen to think that there are a lot of marketers who are really excited about all this potential and the things like companies like Full Story are doing, but are kind of like going okay, where do I start? You know there is game-changing potential here. What do I do first?
Speaker 1:Yeah, sure, I mean. I guess my response to that is, you know, kind of like you would tackle any big, complex problem is don't try to boil the ocean. Problem is don't try to boil the ocean, don't try to test out AI tools or new data sources for every step of your funnel or for every persona type or what have you. Pick a couple places where you want to place some big bets and go deeper on those things. I think to your point.
Speaker 1:I've heard a lot of marketers trying to sort of change out everything all at once and becoming a little overwhelmed. And let's be honest, the human ability to evolve and change is absolutely a factor. Sometimes it doesn't matter if the tool sets are there if the human, the team, the culture is not ready and really primed and enabled for that change. So I would say pick a couple big bets and really bring your teams along for that journey. Be willing to test and iterate, be willing to fail for that journey. Be willing to test and iterate, be willing to fail and yeah, I think those just learnings and eventually successes will come back tenfold.
Speaker 2:You heard it right here, lindsay Bayock. Okay, last question, but if you had the worldwide microphone for a few minutes, what would you, based on what you see through your work at Full Story, the data that comes through there, the gaps and opportunities that probably nobody else gets to see just by virtue of what you guys do there, what would be the one thing you would tell brands, retailers, marketers, whoever, about what they need to be paying attention to in the next six months to a year?
Speaker 1:That's a fabulous question and also a big one.
Speaker 2:Um, you could, I guess I would answer we're living in really exciting times.
Speaker 1:We're living in times where there's a lot of opportunity for winners and losers, for those that embrace AI, for those that lean in, for those that are willing to throw out the old playbooks and embrace change and look, I think there's still kind of two core principles to come back to. One is don't lose sight of how you're solving your customers' problems, because I think, at the end of the day, that's what will drive enduring businesses. And our belief is that AI is really there to elevate humans, whether it's customers or your team members humans, whether it's customers or your team members, and so we really think AI empowering humans is the right perspective to have with how teams are harnessing all of these tools. So I would say lean in, be willing to throw out the playbook, but let's not forget kind of core principles.
Speaker 2:I love it. I love it. Well, um, you know, I've been taking notes as we've been going along here, and I have I have at least two more conversations worth of stuff we we should probably be talking about, but, um, we'll wait until next time. For now, though, um, lindsay, thanks a million for this. I think I think uh, one. Next time you put out a report, please include me in it, because unpacking it with you is really insightful, and I'll try hard to get on your schedule next time.
Speaker 1:Perfect. Well, it's been an absolute pleasure, Mike. Thank you so much for the opportunity.
