Revamping Digital Measurement to Maximize Actionability with Ben Yurchak \\ Marketing Roundtable

Speaker A: Hello, everyone, and welcome back to the Marketing Roundtable, a digital marketing podcast. My name is Brandon Jones and I'm the producer, along with a paid media strategist. And I'm going to kick it over to Brian Cosgrove, the host for today.

Speaker B: Hi, I'm Brian Cosgrove, principal brain do and analytics strategist. And I'm, um, happy to be here today to speak with Ben Yurczyk.

Speaker C: Hi, Ben Yurczyk. I am the founder of NoClick. We're an in the moment user insights company.

Speaker A: Welcome on. It's a pleasure to have you.

Speaker C: Ben M. Thank you. Pleasure to be here.

Speaker B: Awesome. So, Ben, I'm, um, glad we got to make this happen. We've known each other for many years here in the Philadelphia area. You've come to all of our analytics events over these years, and I know that you built your own technology that you've been doing for quite some time now to be able to go and solve some really challenging issues that people aren't able to get out of just looking at clickstream analytics. Can you tell me a little bit about what your company is and what you do?

Speaker C: Sure, yeah. We call ourselves, um, in the moment User Insights because we're trying. We run, um, very high response intercept surveys at websites at key moments when people are willing to give feedback. And every time people hear a survey, it's like, yeah, and I'll take those. There's a lot of concern about that. From how most are done. We do them pretty differently in that we're listening for the right point in time to trigger these and we're also watching what they do so we can ask the most relevant question. So instead of an NPS type question, Net promoter score type question, when someone, shortly after they arrive, we ask them, oh, you left that video pretty quickly. What was missing from that? Or you left the checkout process. What stopped you? What weren't we doing that you needed? Um, and when you start that relevance, you can actually get a good response. And so ours will be 15, 20 response to sometimes 60 or 70 if it's a very high intent event. Um, and so we're hearing from, and a really important point, we're hearing from people in the moment. These people are in the decision journey. They're trying to accomplish something, they're trying to get information, they're trying to do certain actions. And when you hear from them in the moment, typically at the end of their visit, you can really learn a lot about what motivated them, what stopped them, what actually helped them. And It's a level of insight that just web analytics and interactions can't provide. Right. So there's always, always so many, uh, digital analytics, web analytics, so insightful, so informative. But then there's always a ton of unanswered questions. We're trying to answer most of those, um, overall. And so I'll, I'll include some stories throughout this around just some of the things you're able to find that are just big gaps in, in the, in the industry overall.

Speaker B: Yeah, I think this is interesting. I mean so some of our audience here, they haven't done a ton of surveys or some of them maybe did surveys for market research or they might have done Net Promoter Score or. A lot of people here are obviously used to getting surveys after a customer service call or at the end of a chat. Tell me a little bit more about the kinds of surveys. You had already touched on it, but give me some specific examples of where you're inserting a survey that maybe isn't. Is commonplace.

Speaker C: Okay.

Speaker B: And maybe some of the kinds of questions that you might ask that make what you're doing a bit different.

Speaker C: Yeah, no, it's a good, good question overall. So the, there's uh, some things around like. So we have one of our clients has a. In the home improvement space has a build your piece of this house functionality. I won't describe in more detail than that. And so we trigger a survey after they finish that to figure out what that process. Because most of those people use this, they go through all this extensive customization of this certain feature and then they leave the website. It's like, so, so did that help them? Were they frustrated? We had high engagement, which is great. But what did it actually mean? And so we found out is, you know, you had um. And this is a company that doesn't enable much online selling of it. Most of the people actually who had intent to buy wanted to buy online and they didn't know how to actually do that from there. There wasn't surround calls to action on that because that's their, their process is we're going to have a salesperson walk you through this. Um, and so we'll ask questions like did this, you know, did this have what you expected? Um, how did you know, what did you want to do next? And so on. And that's actually the. What the. When someone engages with your site, different types of features, let's say they, you know, B2B site, you download a white paper or something like that, or even you become a leader, you request a demo or you get to ask for a quote or whatever, or you engage with some video. What do you want to do next? Right. So sometimes like people become a lead, I would say over 90% of a lead gen process, you become a lead and it's over. Right. So you get a thank you page. Okay. What we found is we'll often survey people at that point, uh, because you don't want to, you don't want to ask a bunch of questions when like ahead of entering your personal information because you don't want to, you don't want to create friction. We'll ask you often what do you want to do next? What was your intent there? And we'll find out that usually about 2/3 plus want to do something else in the site and they don't know where to go. And so we're able to find out what was important to them and then help to actually not just learn and pass some of that information with consent to the lead gen process. But we'll actually help to drive them to the right content. So sometimes those people want to buy and we help direct them to the purchase process. And we've had even just the act of doing these surveys will sometimes have like a 30% lift in purchase from these people because we're helping direct them. So that's one area that is not like it's shocking to us just how willing those people are who just became a lead or signed up or something like that to share some additional input. Um, yet how few actually do it. Another example would be one of our clients in the travel space. We saw that, you know, they have a very low conversion as typical.

Speaker A: Right.

Speaker C: Because a lot of people are checking things out. But they great at getting people into the booking. Right. So you enter the destination you want to go, you enter dates, number of travelers, all that stuff, you're entering all those things and people go through the booking process and abandon. So, so wah. Right. So we found. So we would ask at exit, which some people do. Right. That's not incredibly uncommon. It's not done really well in most cases. But um, we actually started like shortly after they entered the booking piece. We would just trigger an like actually an embedded survey within it made it feel like part of the experience, like are you intending to book now or some other time? And so if they said some other time, we actually helped to redirect it towards get them the booking information they want and then get their email so you can follow up those people so they will actually come back when they're Ready. And uh, you know, we had like 50% willing to do that, which is shocking, right? Where and what we found was if you said you had intent to book, you converted a decent rate. If you said you weren't planning on booking. Now, almost none converted. So the idea of actually getting their email and be able to follow up them and I mean none converted. I mean they didn't convert now and few ever returned. This is what like a Hilton type site where you already have loyalty and stuff like that. It's a site you use for very occasional processes, right. So there's always a difference in sites that matter. So that's another example. But our uh, most common thing is we're triggering an exit, right? So we're trying to predict, we listen for watch what they're doing, we're trying to predict when they're getting ready to leave. In the industry, the more common thing is actually rated exit. We try to do it about, you know, about 10% earlier because people have much more willingness to answer a few questions at that point. So we're looking for signals that they're about done and we'll ask them a few questions about their experience. These will take like 40, 50 seconds. We get a good four or five questions answered, make uh, this super user friendly and then we. So things like did you find what you wanted right? That's not earth shattering, right? Did you find what you want? How did this help? You know, how did this help you? You know, did this increase your interest in using this company or not? And then a few questions around why. And um, some of them are open ended but most of them close ended because people don't want to type. We're trying to keep this really, really easy and engaging. Um, and then ultimately things like what do you want improved right now we never start with that question because no one will like you don't want to think, right? Once you get people engaged by having some easy relevant questions, then you can ask a few more complicated questions overall. So things like what do you want to improve? Like so for brands that's like I'm considering this or this or this on my site. Well, let's hear from your actual people that stopped and didn't buy from you or if it's an E commerce for people who just engage with your content left, what would have been helpful for them? And you're hearing from sometimes thousands of your visitors right in the moment. That is very weighty information, right? It's very, you know, you can say that this isn't, we can See it, show it's not a skewed sample. These are your actual visitors. And we watch what they did and we can have them tell us what stopped them from going to the key content that you wanted to go to. That, you know, it's not. Some of the things they say they want doesn't move the needles much, but most of them actually has a, has a very direct effect. Wow.

Speaker B: So, so I think about decisioning, I think about um, you know, without this information, people are making guesses or to your point, they just might think, hey, the lead is done. They're not, they don't have any evidence on, on what to do next. And sometimes that devolves into, well, that's just an opinion. There's no sort of gravity to it. You're creating the close, you're closing the loop there where someone says, I think we should do this. It's no longer just a random theory. It is, it's not just based on how confident am I in uh, in, in the person recommending it. It's now saying, no, there's evidence. If we now that we have evidence, if I don't do this, there may be risk. And it feels like it's moving these conversations forward that otherwise might have gotten stuck in theorizing without actual, any information. And so you're providing a distinct data set there. And you mentioned something about trying to predict 10% before they quit.

Speaker C: Mhm.

Speaker B: I always find this part interesting on the web because quitting just means closing the browser, just means clicking away, just means being done somehow. Tell me a little bit more about these exit surveys of how you know that maybe, hey, this is the time to give the survey because there's a good chance they're going to leave.

Speaker C: Yeah, no, it's a great question and it's um, it's super, you know, it's, it's. I'm not trying to obfuscate things. It is really complex around it. Right. So there's certain things that are, you know, the classic exit prediction. And it's not really a prediction, it's someone on desktop mouse is off towards the back button. But of course that's oversimplified because it depends where you are. If you're on the original page, you got, you go towards the back button, you're leaving. In most cases, if you're on page three or four and you go towards the back button, you might be going back to the last page. So you want to listen for that too. Right. So the, the idea.

Speaker B: Interesting.

Speaker C: Yeah. And then there's parts of it where it's. And so this is not a. I always set expectations here. It's not an exact science. We're going to be triggering early in some cases. So the question always, you have to be cautious around. You want a question that can appeal to somebody to start this, um, that isn't like being certain that they're actually done their visit. Right. So how helpful has this been type thing rather than saying, now that you're leaving, what do you think? Right, because it's not, it's not a perfect science. But yeah, so, so signals like you actually lose focus on. For some of you more technical listeners will know this. You lose focus on the website, they go to something else and they come back. Most of those people are actually going to be leaving at that point. Uh, rapidly scroll towards the top of the page a few times and you know, you, you certain mouse signals, they're probably going to leave after that. The idea of rage clicks, we were actually really excited about that years ago. Like, oh, this will be great. We're going to intervene, right? At that point, we'll, we'll figure out why, we'll help redirect them and we find out, ah, it's like 2 or 3% of our visitors just can't do anything right. It's right, right. Yeah. Rage clicks has been one of those, those, those things that you have, uh, high hopes. It sounds awesome. And then you try to execute on. It's nobody. Very few people are doing this. There's much more. I don't care. I'm leading the site rather than I'm going to be angry at it.

Speaker B: I'm also curious, if you start hitting the rage click, are they already emotionally past the point of being able to answer a survey?

Speaker C: Yeah, it's, you know, it's how you ask right at that point. Right. So, and that's, you know, the, the whole voice of customer space has a. You know, it. The potential value it could have is enormous and.

Speaker B: Uh-huh.

Speaker C: Then actual execution and delivery on that is such a disappointment for how people do things. And we don't think we fall in that category. But there's a lot of things we do make work. But the classic things are, okay, I'm gonna get feedback by putting a feedback button on. Okay. You're gonna get like 0.01% of people to respond. They're gonna be the ones who care enough to actually give you the feedback. And if you are looking for technical problems, um, especially if you have a loyal customer base that's A useful thing. Don't ever try to get, say that this is representative of your traffic. When, if you find, so if you find some things that you think might work and they're really easy to fix, sure thing. If you are uh, trying to use that feedback to actually make significant changes to your digital experience or campaigns or whatever it is, you're walking on really, really shaky ground for that.

Speaker A: Right.

Speaker C: And it's going to get shot down. It's like, okay, wait, 01% of people took this. How do we know these are the actual visitors? So that's one example. The other common example, even when people, um, show questions, the most common one by far is Net Promoter Score. So how likely are you to recommend us on a scale of 0 to 10? And you know the whole calculation that's done, you have promoters and detractors and it's a validated methodology for established customers. Right. So they've um, I think it was, I think it's BCG or one of them created that methodology. They checked it out, they found, wow, this is a. And this is whatever 20ish years ago. Um, they found this really predictive at that point in time of people staying a customer or not. Well, so people said, well, what do we ask on our websites? Oh, let's that Net Promoter score, we use that everywhere. Let's do that. So someone's on your site for 30 seconds, you say how likely you recommend us on a scale of 0 to 10, it's like, who are you? I just started looking at this site and it's get out of here, leave me alone. And they'll X out of it or they'll hit. Some of the people hit 5. Right? We see a huge spike in fives on websites for people who are prospects. So generally in a big picture, look, companies are reasonably good at understanding their um, and I say reasonably with a grain of salt here. But their customers, people who've chosen to buy from them, you have a lot more connection to those people, a lot more opportunity for, you know, B2B. You're actually, your salesforce is talking to them. So you have a, you know, there's a direct line. You understand some things. Um, you're, uh, you know, you have E Commerce, you have an opportunity to follow up surveys really easily. A lot of people do post checkout and those are actually, those aren't super hard. Stuff I'm describing is hard. This stuff is not that hard to get a response. Um, but the prospects, people haven't bought from you before. How do you engage and Hear from them and hear their drivers. That's where the big gap is. So I was looking at this like, when you think about like a. And. And we're talking now enterprises overall, not small companies, but they're going to have some market research done on, uh, here's my target audience, here's what they want and think and so on. But those people are just like my by demographics or role. This is what they generally their interests and needs. And here's how they typically go about the buying process and stuff. And how are they getting that information? Well, they're sometimes talking to their customers to find that out. Sometimes they're just talking to people in certain roles overall. But almost none of those people are actually in the journey trying to make a decision. Uh, they're pontificating. And some of that's useful, right? It's like, here's the things I like and dislike, here's what I want and very good to do useful and so on. But when it comes to. When you think about, if you're around your digital experience, okay, I take people in now. They're coming to my site. These people are different than the earlier, uh, journey ones. People aren't in journey.

Speaker B: Right.

Speaker C: They're different than your customers. If they're considering you, it's like, okay, I'm just learning about you. I've looked at other companies in this space. I have a general more sense of what I'm trying to make certain decisions. Here's the thing, like let's say B2B. I'm trying to figure out who should be on my shortlist where I'm trying to make a pitch to my cfo. Um, you're trying to do something there, like healthcare professional site. Right. So healthcare professionals, some of those people already prescribing, they have a new. A patient with a new indication or indication they haven't treated with this before. They're trying to get certain information that they don't describe very well. In other cases, patients. Right. On that side, we do a lot of pharma. Um, you're trying. You just were diagnosed with cancer this morning, right? We get a lot of those people newly diagnosed in the last day or two. It's a completely different mindset and decision process than people who you've surveyed six months after they got diagnosed with cancer who've gone through a very thorough journey to understand the whole process. The anxiety, the uncertainty, all the usability best practices around users. M like the assumption users know their task, put those clear options for them to take around what to go next doesn't work.

Speaker A: Right?

Speaker C: Uh, you think it would work, but it doesn't. Like the mindset is I just found out I need to confirm is what my doctor said. Right. Like I'm hearing these other things. They need help walking through a process, not being self directed. They need you to help direct them. Um, another example outside of that, um, one of our, and we see this pattern a lot, so high consideration products where you have to make a significant decision and research around it. You go to a site it lists. Here are all our offerings for this category. So you see 20 or 30 things. The decision anxiety that users have, that's actually a phrase that is used a fair amount now. ChatGPT introduced me though better way of describing it was uh, I called it like choice paralysis before. But decision uh, anxiety I think fits better. But what we found is when we surveyed people at these pages, like right after they viewed that listing of products and why didn't they continue on to get more information on one of those? The first question we would ask them often is did you know, did you find one of these that you wanted to get that you feel like are fit? And it's always, it's like 60, 70% say unsure.

Speaker B: Right.

Speaker C: We would drill down. Right. So these are the other side about surveys is it shouldn't be a one time fixed thing. It's let's drill down and learn and learn and learn. So what we would often find out is I couldn't, I didn't know how to compare, I didn't know which one was best and so on. And so um, I'll give you one story on this front. Did stuff in the uh, this one space where people were given. They would actually. So, so this is the tire vehicle place space. People would see, they would enter their vehicle information to get tire size, they enter their zip code and they'd see this. You go through our process, you see a long list of tires and we said to this client like, so

Speaker B: we

Speaker C: find that a lot of users are struggling on which one to pick. And like yeah, but we're known for having the best inventory of tires. We can't not show all these. And we're like, oh, your users are really struggling. How about you try a test where you show three or four, um, and do some good merchandising decision where one is the, you know, one of the premium brands, one is the lower price, one is sort of in the, in the middle so people can actually choose. Yeah, we don't like that because it, because we're known for the most tires run as a test. So they ran, to their credit, they ran as a test. Their online conversion more than doubled from that. It's like, okay. But then they said, well, we're trying to get people in the store and we know most people don't get online, so we still think it hurts. I'm like, yeah, the survey metrics are all like, this is super helpful. Like people who saw that page versus the others, they're like, yeah. And we said, okay, how about we do um, a more in depth project where we tie this to your app. You have a database of customers. We'll ask the users on a survey to share their contact info and to follow up with them later and get their consent. And we followed up with them later and we tied it to their database and found out the people who saw the three or four options versus the full list converted in the store at o. At like, I think it was, it was over like 50, 60% higher.

Speaker B: Right?

Speaker C: Show up with dollar values and this is like over 20 million in digital sales.

Speaker B: So like this is incredible.

Speaker C: So you see that it's like. So then it's, it's the people who are sort of like ignoring the methodology, just want a few insights are like, wait, how'd you get this again?

Speaker B: What'd you do?

Speaker C: Like, they dive into it. It's like, okay, now that's a very, very different decision that you make from that. And that's, this isn't like. So we see this in so many spaces. We actually ran a test where um, one of our clients in a communication space had all these different offerings, um, bundled offerings and so on. We said, well, how about if we pre filter this based on which thing you said in the survey that you cared about most? So we ran like basically an arrival survey. Just what services you want. What's your role? Okay, let's get out of your way. It takes 15 seconds. And then we would pre filter for some as a test and leave it as is for control. And in some cases this is like over a 2x lift in purchases by pre filtering it. And what we found was happening is that it depended on which thing we're pre filtering on. Some actually did a little worse, but most did a significantly better because there were some cases where the offering they had, um, that they didn't want was priced so well, but the one they wanted was priced poorly relative to the two. So that like, I can't get this. And other cases where it was vice versa. The one you wanted was price. So you wanted to show them the price badly one because they didn't want that. And the price, really good price one is the one they wanted. It helped them say, oh, I feel good about making this decision. So there's also merchandising decisions around that. Does that, that makes sense at all?

Speaker B: Yeah, no, it does. It's so interesting to me. I mean there's kind of a lot that you just said there that I want to unpack.

Speaker C: So just, sorry, I'm going to do that and I will try not to, but I will anyway.

Speaker B: And that's fine. That's fine. That's, that's, it's so interesting. So the decisioning piece is, you know, when you pride yourself on having the best selection, it's almost like they feel like it's undermining or somehow like threatening uh, what they're doing by, by helping reduce options. Yeah, but to your point, if you're trying to buy tires, you're not trying to buy all the tires. And as much as you might be impressed that they have all the tires, you want to buy the tire for you.

Speaker C: Yep.

Speaker B: And I think about in retail when they're like, customers usually keep this or you know, popular items, you know, things like that, that helps you make those decisions. It helps you work through. You're looking for uh, just a reason to choose one thing over another. And to your point, when you start sharing like a million options, instead of the problem being, hey, do I buy something or do I, you know, go in, go in a direction versus not doing anything, it becomes, but I don't know which direction to go to.

Speaker C: Yeah, yeah.

Speaker B: And so you create the paralysis because you've, you know, it's like if you just made it, hey, are you going to move in a direction or not? Then it's easy. The decision's fairly straightforward. When you start saying, well, do you want to, you know, you can go any of these 18 items and it sounds like your surveys help people cut through them. Like you're helping actively, you're proactively helping with decisioning. And this kind of survey, as you mentioned, is something that you were doing kind of early on. It's, it's, it's an early. Yeah. For that application.

Speaker C: Exactly. That was it. Yeah. And so we can trigger it obviously at almost any time. And the, um, the one of. And it's something that's not intuitive to people. But we'll often ask a few questions at arrival. Those have to be really well designed because you want to be non interruptive, you want to you want to have it feel like it adds to the experience. We've actually run every. The test and control meaning you're eligible. Some people see the survey, some people see nothing. And we track behavior which I haven't talked a whole lot about. But every survey we do ties to behavior which really matters. Um, so, so this. So we can ask a few questions and make sure we're not hurting things. And in most cases it actually has a slight lift in engagement. In some cases it has no. And in rare cases it actually hurts. It's like okay, that's interesting. So but most of the time it's a, it's a positive and sometimes we intentionally do things like uh, so for example with, we helped drive a lot of leads. Um, and it happened by accident.

Speaker A: Right.

Speaker C: It's a classic thing. It's like you like the I, I can't compare this to the invention of penicillin scale. But the, the, the, the anal analogy applies. Right. It's like we're tracking to make sure our surveys don't hurt engagement or conversion. And you. We saw some that had like a third, you know, show the survey that like a 30% lift in conversion versus people who didn't like. What. How is this. There's something wrong. I go back to the team, like are we sure we're doing this right? Are we skewing it? It's like no. It's like then we looked through our questions and we saw, we sometimes we randomize certain questions. We saw when certain questions displayed those people converted higher than when other questions displayed. Like that's interesting. And so some of the time it's. The survey makes you aware of what the site offers. Right. So something about. Did you know, you know something about. What do you think of our online appointments? Oh, I didn't know you had them. I would love to schedule an online appointment. Or did you, you know.

Speaker B: Right.

Speaker C: Asking about our product line. Which things do you want? What's important to you? Um. Oh, you have that right. So that's the thing that creates the biggest lift. Some of the other things are just when you ask good questions that are non intrusive, um, that show you actually care about them rather than that promoter score. Yeah. Again has a place but keep bashing it.

Speaker A: Uh,

Speaker C: there's a connection there. So it helps a little bit. Now that's not going to be like you're going to have a massive impact on conversion that but it's one of those little things that helps add to the uh, to the experience itself. If you can if you do it well.

Speaker B: So it's such a craft to pace the reality of the site, the context, the users and kind of walking in their shoes where they are in the experience and to design kind of the right strategy for this kind of prompting. It's like, you know, just by asking this question, you are indirectly alluding to, hey, we have this feature, you may want to go check it out. You're also potentially getting feedback if they don't care, like you win no matter what. And to your point, that lift, it's like they put so much effort into creating these features or these options within their site, but they have a gap in how they communicate on how to get to it or how they draw attention to it when you're creating sort of a moment in time that, that connects that dot, that, that moves it forward. And sometimes that's all it takes for, like you said, a difference in 30% of conversion.

Speaker C: Yeah, that. And that one's a little bit of an outlier. But it's, it has, it has happened a few times.

Speaker B: Yeah, but the 50% is also pretty wild. I mean you've seen double digit impacts for some of these things just based on um, you know, doing something more than, than what you said, like using an out of the box catch all net promoter score or doing an out of the box sort of catch all feedback form, leaning into it. I don't know if there's enough craftspeople, if there's enough people architecting these kinds of, you know, very specific moments.

Speaker C: Yeah, it's um, hard to find them. There's not a whole, like we generally have to train right for that. And interesting, by the way you use the term architect, we actually used to call ourselves insight architects. I like it, I like, um, never caught on. And, but so I mean, but it's

Speaker B: just like you said, it's, it's so much more than just generic like voice of customer, like you said, could be generic feedback forms. It could be, you know, I could go to ChatGPT and get 10 questions I should ask at the end of my session. And a lot of times people don't go in to the level of depth that you have saying this is the point in time, this is the area that I want to lean into. This is where I'd want to try something. I am curious about even how you engage with companies. Um, you must be doing something different than some traditional survey groups are doing when it comes to this. Tell me kind of even how you get from, hi, I do voice of customer work to These strategies.

Speaker C: Yeah, you know, that's a great question. It's. I've learned very early on we don't say we don't. Well, when I first started we often didn't even say survey.

Speaker B: Right.

Speaker C: Which then people were m. It was. There was a little too black boxy because. But the challenge was everybody hear survey. It's like I never take these even. And funny story, even when I started the company my mother said I don't take surveys online. And when I do, I'm like wait, you just said you don't and then you do. So but, but the perception. Because most of them are bad, right? Most of them are bad experiences. Um, and they're mostly ignored. So what I always described is why I was when I introduced it. I always say high response in the moment. Right. So they. People generally when you start to hear in the moment, there's a differentiation right there. Because we're not trying to like I don't think we'd be much better. We'd be a little better because we know, because we tie to behavior we've learned which questions are actually more predictive of outcomes and which ones aren't. Um, again that's another. I'm going to keep shooting the Net Promoter score one but you know, a lot of people want that. Okay, well measure the outcomes which how predictive of it is actually end purchase when we tie to purchase and things like did you find what you're looking for? Task completion? Um, how are you intending to buy or especially how soon do you plan to buy? So much more predictive as outcomes than promoter score. Um, so again because it's not designed that way. It's designed for retention. Now Net Promoter is very good for retention.

Speaker B: Right.

Speaker C: We've done that with B2B's where we ask in that promoter score. Um, users of that and we looked at their database who stayed around. That's a big. It's a good predictor of it. Um, interestingly, I'm going to just throw a little details and then jump back. But when we've done that, the things that are predictive of retention are what you think of customer support and care. What you think of their pricing when you're already a customer was not very predictive. And so just to walk you through the methodology on this one, we would survey people if they're using say in this case a B2B site or sometimes, sometimes B2C but regular ongoing relationship. And um, then we would capture their attitudes. We have an ID associated again all user opt in all Optional and then tie it, you know, six months later we tie it to their database and see who stayed around as a customer bot who didn't. Now you're starting to get the truth from this because you're seeing which things are really predictive. And you would see things like improvements. Like one, it was like, I think like a third of the people said you got to get better pictures of your products. Those people didn't convert differently. Another one was you have to have improved updates on shipping time. Right. Let me know once it's coming or when there's issues and so on. That was a B2B side, that one. If you said you want to improve that, those people dropped off at by far the worst rate. So like, okay, this one is a huge signal. Like when they say they want this improved, it is a, uh, giant frustration that's driving away, I said to mention the pricing. Price is obviously a big factor in whether you choose a company or not. Uh, but once you've chosen that company, price didn't come into play that much. It was how you were supported over time. So that kind of thing. So you start to see the real drivers and barriers of success and that can be really, really informative for people. That's one we do. We do about 10 or 20% of the time. We're much more about the actual in the moment piece. Uh, overall, I think you were talking about something else and I went on one of my tangents. I want to make sure I'm answering your regular question.

Speaker B: Yeah, no, I think you are. I mean, ultimately what I'm getting at is that there is a lot of decisioning you're doing on your end. Figure out how to find high value moments to go and do this kind of work. There's a lot of effort on your end figuring out how to phrase things and making decisions. Like you said, is it a binary yes, no. Or is it something that requires free form piece and even laddering up to that? It's like you're fine tuning kind of what's going to work in calibrating and micro calibrating. And a lot of that seems like it comes out of just experience, looking at the data, seeing the impacts downstream and connecting those dots. But a lot of this isn't immediately intuitive. You know, as you're describing it to me, even very smart analysts would not necessarily have the kind of calibration that it sounds like you're getting just out of doing this over and over again across, you know, sort of various, uh, various strategies. I'm um, interested in your perspective. You know, what should people interested in voice of customer be doing differently when they're approaching. When they're approaching these kinds of sites and these kinds of opportunities, what should they be doing differently than they're doing today? Like, what do you.

Speaker C: Yeah, yeah, it's, it's so the, um. So it depends on what they're, you know, something we're doing today. So. So a lot of people, it's. You're doing not. You're. You're doing surveys of your existing customers, which you should do. Right. And so the gap there is sometimes the question about future intent, right. That's often, um, a mixture and why do you choose this company and how can you improve? Sometimes those are asked, sometimes those aren't. Um, a lot of the whys are key. But what I'd say the bigger gap with them is more do they tie that at the individual level to that customer stay on or not. Right. That is, that's one of the holy grails. Right? Because now, as I mentioned with that, that frust, you know, the people who said, you don't contact me early enough, alert me if there's going to be a delay in delivery, that, you know, notifications, um, that's when you find out who's really dropping off and who isn't. I mean, you can personalize things too. You can actually ask certain questions, but you got to do it gently, um, in a way that you can actually personalize some of your communications to it. Uh, so that's a gap on that front. But generally, I think, like, the tying to actual outcomes is a big gap. But most, most companies are going to survey or talk to their customers in some way. So they're pretty good there, um, on their digital experiences themselves. Uh, that's the biggest gap, you know, so the most common is nothing. The next most common is a feedback button. But you got to think about who's the thing that actually I'm trying to answer. Right. You always want to design for measure, you know, to measure against your actual goals. Um, and a feedback button cut it. The challenge there is that most of them who try to do something more proactive, like what we do is they're not asking. So, so there's. I describe this often a chain like each link has to not break to do it. Right. Right. You got to hit them at the right time. You hit one, the right.

Speaker B: First.

Speaker C: First question has to be prominent enough, but not too, not too intrusive. And then you have to actually tie it to, to. To your Interactions to get the most actionable insight. And if you don't do any, if you don't do one part well you're not going to get much value out of it. Um so I think the biggest thing that I think people who are more beginning can do is trying something on exit and the question like did you find what you needed? Um what were you looking for? And uh, which best describes you right Overall. So those are questions that aren't too hard to create. Um uh, they need to be really short and simple. Right? You don't want a long list of I mean we have clients that are like yeah, I want the 15 potential different tasks that people be coming for. Like yeah, no, you're gonna have everybody lead that survey, boil it down to five or six and make them short, make it very easy to take in that point and, and you'll get, you'll get some very interesting feedback that you can use. So, so that's a thing can, that people can do the other side. When you're really trying to drive into the drive to understand the drivers and barriers of purchase or, or actions you really need some expert, right? It's just I'm um, and I'm not being doing that for self serving. It's just it's, it takes years to get to that point of understand. So for example why didn't you buy right. One of the options will high. Okay never, never ever ever ever settle for that. Because that can mean so many different things and we always like you say price try. Okay so let's, let's do a few things. So one is let's drill down into what do you actually mean it's more than I can spend, it's more than others where I don't see the value of this product. Right. Very different things. If it's more than I can spend it's like okay, driving the wrong traffic. If it's um, something I don't see the value or I think it's more than others. Is it more than others directly? Like the actual financial price was too high or did you not get the value prop right. So often we see the biggest thing is they didn't understand why this product is better. They didn't get the value proposition you know then and it can vary a lot um by organization but that's like we'll have them, we'll actually drill down. It's like so if you say price is too high we think how do you think this product compares to others on these three factors? And you start to see oh, that's the thing. Okay. So they're not even aware that you're you off like one of our clients. It was like they offered a money back guarantee, uh, overall and that was a way of making users confident. Um, almost none were aware that they did that. So. Okay, make that prominent in the checkout process. Money back guarantee for 60 days after purchase. Okay. Guess what, conversion goes up when you do that. Um, because we already have it up there, but it's not prominent and you don't have it when they're actually at that decision point, they're ignoring that message. Like that gap between what people were aware of and what they did. So the um, couple other things on that front around what are they missing? What I was talking about before, like that everybody should do, um, but you have to do pretty well after if you have a lead gen process. At the end of that lead gen you've captured their name and email trigger an optional survey at that point. What do you want to do next? Why did you do this? Um, what do you expect? And then to some extent like we've had clients who are good at playing um, in the margins of that. Like get a quick quote, right. Where users think like we find out when we survey them. Yeah. 70% thought they're going to get instant quote when they put that in and instead they're going to call or they're going to get emails and then a call. So it's oh, our next step of uh, we're going to actually we're going to call you. It'll come from this area code. It'll be in the next, this point in time. Here's some things that you could ask that uh, could help for that. And now it's like, okay, I know what to expect next. I'm much more likely to answer the call. We've actually measured that. I mean sometimes 10, 20 percentage point lift in actual engagement with that because you're now setting expectations about what happens. So yeah, post lead, like you can do lead quality, you can do all that kind of stuff around, you know something people want to be contacted right now. Uh, you know, uh, you can do some great things with that and you're not. There's no friction in the lead process because it's after they became a lead that can be added to that.

Speaker B: Right. So you've already got the option and it's not going to get worse by asking them these questions. But to your point, sometimes it sounds like just by asking the question, it's a different way to prompt Them to kind of fill in a gap in their understanding.

Speaker C: Yeah.

Speaker B: Other cases it's just, it's filling in a gap of your understanding about like, like, I, I just, I, uh, keep seeing this pattern of like almost a dual use or a dual value happening that you're asking questions that are, that are serving. Like they're actually helping increase conversion directly, but they're also helping you get insight at the same time. And to me, I guess that's kind of the art. That's the calibration. That's where, like you said, a professional who's done this and seen these patterns can implement it knows exactly like you said, those lines of questioning that you just fired off, they sound so natural from, you know, from your position. But I feel like a lot of people that again, I've just been doing feedback forms. The people especially, they're asking like, there's 15 question pieces. It's coming from a good place of these are all the things we want answers to. But it's not coming from a place of this is what they're likely to answer. And that isn't going to do any harm and might do the most good.

Speaker C: Yeah, exactly right.

Speaker B: And that to me is like the next level of all of this that takes us past just sort of a research exercise and turns it into active optimization.

Speaker C: Yeah, yeah, yeah. And it's funny you say it. Cause some of the times we have a few questions where we actually say, when we're having clients review it for approval, it's say, you may not care about the results of this question. That's not the purpose. The purpose is to get people started this, this process. Right. So it's all around, what are they doing in that moment and their expectations, how can we help them? And then it's, it starts off with, oh, this isn't to get information from me. Um, that is. But it's, this might actually help me. Right. We actually find. And there's always a right balance because we don't want to be misleading with our surveys overall. And you know, in a lot of cases, we'll, we'll use our surveys based on what we found. Like, we'll look at what page are they on right now or what path do they take to get there and be like, yeah, you're in the wrong spot from what you said. Right. A better, you know, you know, our users who are like you, the people who said they have needs like yours, find that page X is more is very helpful. Do you want us to take you there to that, to, to that now when we ask them that it's like 70, 80% say yes.

Speaker B: Like it's right.

Speaker C: I was like, I was shocked it was that high. It's like okay, I guess they want help, right? So, so that's part of the reason they're. They, they. There's an intuitive sense. Like we've asked, we, we used to, we don't this much anymore but ask people. So why, why'd you help us out? Thank you. At the end of it it's like. And there was a decent portion that said because I, I wanted to be, I wanted to be directed some where to go. It's like ah, uh, o. So it's always like we're listening even to our, even to the people we're surveying for our own purposes. So there's some stuff around that. But you ask about applications. So after leads, the other side is what I described is like anytime someone is interacting with a site and entering information so that you can give them, um, show them the right products or offering or localized pricing or whatever it is, that is the perfect time to add in a couple questions to the process to help to narrow down those choices. So the tire example, right? Uh, you entered your vehicle, all this stuff. It's like which types of tires are you looking for? You're looking for. I'm looking for you know, good ones but, but really looking for, for you know, cheaper as I can, but still, still, still decent tread life. Um, or I'm looking for a really well known brand. I'm looking really to care about safety. I want a good value, but I'm really care. So now all of a sudden you can show, you can do some merchandising decisions, right? And because if that's integrated, it's not going to cause people to drop off at all because it's relevant to what they did. They're already. The harder part is entering your vehicle information. And now you can be smarter with what you show now what you ask and how you ask it. There's an art, right? We always even just help them test. Like we'll actually say we'll start, we'll do these directly, we'll embed these. Um, and then you can either choose to use us ongoing for that or you can just. We'll teach you what was best, right? And you can use that from there. So the idea of travel booking where you're entering all this stuff, okay. Again, ask about their intent, right? Are they planning on booking this now or asking about some of their other preferences so you can show Your personalizing. Then when you say most popular, you say most popular for people who cared most about, um, you know, the most family friendly resort.

Speaker B: Right.

Speaker C: I don't care. What, I mean, I care, but I don't care as much about what's popular with everybody. I care about what's popular with people like me. Right?

Speaker B: Yes.

Speaker C: My interests. That's more persuasive overall.

Speaker A: This podcast is powered by Brando, a digital marketing agency that not only provides a, uh, diverse mix of digital services, but offers guidance, experience and true partnership to bring your business to the next level. We offer expertise in analytics, paid media, web development, SEO and design, just to name a few. We've worked with businesses on every level, from local operations to Fortune 500 companies across a variety of industries. We have expertise to help strategize and execute your digital marketing needs. No matter what step in the process you're at, we're there for you. From wireframes to paid campaigns, brand new experts in everything digital. Please reach out today to learn more about how we can help your business grow.

Speaker B: I think about overlap with a few different areas. So you know, there's, there's personalization that people can do and that they're trying to do. There are these kinds of AI assistant type bots that they're trying to build in that are never quite work as well as you, you hope. But maybe in customer service areas they could help a little bit there. There's different things there, but they're not exactly, uh, they don't fit in as neatly in the experience as what you're doing, which is kind of more directly in the interface and it's more int, it's more selected. It's not the customer just kind of going off and trying out the help chat tool in the corner. I'm, uh, I guess. How do you see the voice of customer estate changing moving forward?

Speaker C: Yeah, it's, I really haven't thought and I thought some about the general voice of customer in the big picture, like just, just understanding customers from a website perspective. We actually spent a fair amount of time in the main AI tools to see, okay, what do they actually predict and say right. So where are they going? Where aren't they? And so when we asked it general things for things that are often asked. So for E commerce we actually asked it what percent of people, um, do you think, um, who visit an E commerce site and tend to buy it said, you know, 10 to 15%. Like that's actually pretty close to what we see across now. It varies by individual clients like, but, but. And I don't know which ones we actually do, but it's like, okay, that's a pretty good prediction. Then we said, took them some healthcare sites. We said, go to the site. What do you think the audience is here?

Speaker B: Right?

Speaker C: Based on comment, it's wildly off. Like one of them was. It said, we said, you know, which portion do you think are pharmacists? Nurses, uh, lists and PCPs. Right? This, it had like no pharmacists. Like 1% pharmacist. It was like 30% pharmacist is the actual. Right. So it's not even close. We asked them, how successful do you think users are at this, this site? Sometimes it's like everybody should be successful. Sometimes it's like. And that's most times skews very positive. Your actual users are saying completely different at this point. AI is only good with what it's been trained on. Right. So it's. Right, it is pretty good. And so this type of insight around who's actually coming, what do they need, what are they thinking? It's not very good for most of it. E commerce example, like the intent to purchase, that's a pretty common question. So it's been trained on that. Um, but how it varies by individual site. It's really bad at being able to predict that. Now it might be able to say, hey, your calls to action aren't, aren't prominent enough. You know, all the general things that would be good for someone to learn and test out. But when it comes to what's in the head right around that decision anxiety and persuasion around wanting to buy from this, like, this is the right offering for me. I can trust these guys. It's not even close right now. It might get there, but it's not even close. Um, now and then because of chatbots, like on that front, I like your skepticism there. I think that those things for customer service, I think they'll be great over time.

Speaker B: Right?

Speaker C: I high, high expectations for a really good, um, experience in customer service over time. For most, it's not there yet. And that's going to be a barrier to adoption or a lot of the chatbots that arrive when you go to a website that you're not even logging into. So it's like, you know, we've done a lot of research around, um, those sites and like, why don't you use this? Because I hate them. It's like, that's the most useless. I hate these things. So the challenge there is the industry, the quality of those is going to have to be way far ahead, uh, for a while before you get adoption. Because what we see is when people have bad experiences, that sticks with them for years. Like, so I'll give you a story here. We had one client where they registered users over time. And so we asked, we did a survey of users and uh, asked about their experience with this company. And I forget why, but we did it by when they. I think we're trying to get something else, but this has happened. We looked at it by month and year of registration. We saw this really horrible spike like two and a half to three years prior around all this negative sentiment. Like what's. What is this about? And so we actually showed them this. Like we don't understand this at all. Like what, like this is two to three years ago. Like, oh, uh, we had a really slow site with a lot of technical difficulties for a period of time around then. So the people who had just joined, that was your first experience and they were just like. And they, they're like, they're still talking about three years later.

Speaker A: Right.

Speaker C: So yeah, all those problems have been fixed and guess what we asked them what improved. It's like fix the technical issues. It's like, like those have been fixed like two and a half years later. So the point is you like adopt like this has. What is that? Was it Ogilvy? You said the best way to kill a bad product is with great advertising or something like that. Right. Try to get adoption. And so vice versa, you've gotten. People have really pushed this and it's a bad experience. People are being burned for a while until eventually, like your use of customer support, that turns out to be really, really good. And then a few of those people are going to start trying to sites, then there'll be more and more and then you'll have some that work pretty well. But ultimately what we find is, you know, most of the chatbots are still type in your thing. The smarter ones make it look more like a survey. They give you a couple options and then type in. People are much more likely to click. Like if we ever start with open end question, like, forget like our response. Our average response is somewhere around like 15 to 20% for a. Not like someone who didn't just do a significant thing like a lead one, it's much harder. But um, if we start open end, we're getting about at best 1% to take it.

Speaker B: Wow.

Speaker C: So, yeah, so the tactics, I still

Speaker B: think, I think, I still think that you're 15 to 20% is amazing.

Speaker C: Yeah. If, if Someone said that to me, like, before I started the company, like, yeah, no way. There's no chance.

Speaker B: No way. Yeah, yeah, there's no chance. One of the big parts that you are doing differently than maybe a lot of pure survey tools is the amount of session activity before and after and sort of the fidelity of what you capture there. You know, uh, we. People almost split these things up. They're doing analysis over here, and then they're doing their survey stuff over there. These data sets aren't fully perfectly integrated. Um, tell me a little bit about kind of you've been doing. And you've been doing this for a while. Tell me a little bit about how critical that is to doing this.

Speaker C: Well, it's. It's the foundation, right. And it's actually. So I started the company primarily to get insights from behavior and survey attitudes together. Um, over time, we just got really, really good at getting response.

Speaker A: Right.

Speaker C: We didn't start out there. We were, we were. We were pretty bad. We were pretty bad in the beginning. We tested out. Competitive approach is we. We accidentally did things like, you know, for example, you know, yes and no. Thanks for participating. One time we put no by accident, and it got much better response. That's weird. Oh, people don't want to be rude. Okay, so you start to slowly learn the psychology of these things. Uh, sorry, I lost my train of thought here because I get excited about these things. Oh, survey and behavior. Yes. We have some, um, we've had some clients in the past, they're like, yeah, is it cheaper to just do the survey? Yeah, can we just do that instead of tying it to behavior? Because every single survey we do, we track their behavior with it and integrate that and tell you how your users. Where users are strumming where they are. And the beauty of that is you're not just hearing, hey, half your visitors can't find what they need. You're finding, where do they go to try to find it?

Speaker A: Right.

Speaker C: What are the gaps between. Okay, so let's say on the healthcare professional side, common problem is, like, you're looking for a mechanism of action. How does this product work?

Speaker B: Right.

Speaker C: We can actually watch where they go. We see, like, usually it's like 20, 30% ever reach that page. That's a much more insightful gap than I couldn't find what I wanted. Right? It was. You went. You watched them where they go, which they clicked on. It's like, oh, they're trying to find it here. Why don't we move the mechanism of action from this support section to the product information section. How about that? And same thing like I came to buy online and then you see a bunch of those people never even start the checkout process. It's like. And some of the people don't even reach the product they want. It's like, oh, there's a problem here. Like you, you, you gotta convince them upfront. So yeah, so we actually, so I was going back, circling back to that story. We've had some clients that just want survey only. And we do that. It's so frustrating when we're trying to come up with recommendations, right. We can say things like, well yeah, your users are really struggling to find, uh, you know, let's just to compare products. Which ones? I don't know. Or their users are really frustrated by high pricing. Some users are really frustrated high pricing. Well, which ones? Which products? I don't know. Like, so when you tie it to behavior, you can start to see, oh, people went to product line A, they're really pissed. Product line B and C, you're good, right? You start to. And people who said the information wasn't detailed enough or it missing, missing details. Oh, product line A, it was good. Product line B, really missing a lot of details. Now you start to see where the problem's occurring. So the actionability is just vastly better. Right. And one of my favorite is, and this applies for almost every site, um, entry pages, right? So in the last, whatever, how many years you have a lot more traffic entering deep in the site than at your homepage. Your homepage is generally one that's really designed to meet a variety of needs. The gap between what people expected and what the landing page had can be dramatic. Most of the time there, you know, I'd say the average is less than 50% alignment between what those users wanted and what that offers. So we've had communication companies where they're landing at um, one page specializing home based organizations and two thirds of the people are looking for business service. Huge gap on healthcare professionals. So it's just gonna be done. So many of them, uh, you know, you have people entering at a, you know, a efficacy page, you know, product effectiveness. And usually it's like 15, 20% are looking for effectiveness, the rest of them are looking for something else. And healthcare professionals, they're not spending much time, right? If you don't look like you have what I want, quickly, I'm gone. And so huge gap there. And even just by staging the bi. By the stage in the journey, where are you when you're trying to do like A patient site, there's often huge gaps between. Like you might be getting a bunch of people who are undiagnosed, but getting tested for a condition but you thought they were all diagnosed. Or you might have a lot of people who just got prescribed this product, and you're trying to tell people to go to the doctor to see if they have this condition or if this product right for them. You're not designing it for the people. So when you know what they actually wanted by where they entered, that's among the more actionable things because it's like, oh, I need to add some prominent links or, um, some blurbs for these types of visitors so I can help get them to the content they want it. And you tie it to behavior so we can see at each of the top landing pages, here's your user's profile and here's the top things they want. It's, here's the top three to five things, and then we can even watch who's able to get them anyway and who's just bailing.

Speaker B: Right.

Speaker C: Overall, yeah, yeah.

Speaker B: Major, major, um, prioritization value there.

Speaker C: Yeah. And that's one of the easier fixes out there. Uh, is this that gap or, or even just like navigation labels? So, so we've, you know, so many things where the client uses their own terms and users misunderstand and like, again, go back to healthcare professional or healthcare side. Clinical trial results is a very common thing in, uh, saying. And on the patient side, it's like we find out people go to the clinical trial results. It's like half the people were thinking they could enroll in a clinical trial. It's like, no. Right. This is the actual product effect. It's like the labeling can be so off.

Speaker B: All right, so there's a lot of ground here that you're covering. And it sounds like there's even a lot more that, uh, some people should check out. I, I want to pivot a little bit to getting to know a little bit about how you got into this. But before I do that, just at this point I'd like to ask you if you could just tell the audience a little bit about, um, kind of where you're publishing some of your findings and kind of where they can learn more about the services that you're doing.

Speaker C: Yeah, yeah, the, um, we publish a little bit of the findings. We actually don't do a good job at this, uh, looking at. We actually don't have a whole process going on where we're looking back a lot of our stories overall. And we publish, um, things that are, we always have to, we like to share the details. So we have to blind them and we have to make sure they're not specific to an individual client. So, so the, for instance, the tire example I gave that was from a few years ago. Uh, I used to not say tires because that was an insight that was not as known. And now most of the companies are doing something like that. Like, okay, that's, that's fair to share at this point. Um, just the decision anxiety they have. They, now they don't always know why, but, but, but that's a different story. So yeah, we, we, we, we are actually working to collect these and disseminate them. Like some of the key insights like the myths and the gaps. Um, so for example, things like, okay, so behaviors and engagement on a website, what does it actually mean? Right. And so the one will go into some detail. Um, I actually had an article years ago on this is engaged but not happy. Right. So we found there's a curve. Right. The people who spend less than 30 seconds on a site, yeah. Generally they're going to be pretty dissatisfied. But we saw that people who spent like looked at more than five pages and six page seven. They're often much more dissatisfied too.

Speaker B: Right.

Speaker C: It's a different level of frustration. One was disappointed. Like you don't have. I want them out. In other words, I really want this and I can't get it. I'm getting mad. Right. It's not rage clicks yet. But, uh, they're disappointed in expectations. So people are looking at and say, oh, wow, we got these like 6, 7, 8. This is great.

Speaker B: It's like.

Speaker C: No, no, no, no, no. Uh, no, no, no, no, it's not. Um, so that's one we'll talk about a little bit more. And it varies by type of site. It also varies by task. Um, some tasks that I've like. So that's the classic, what do you call it? We're trying to label this the bounce blind spot. Right. You've seen this all the time. You bounce it high bounce rate at a, at a certain landing page. Right. And we know the bounces have gotten worse over time, so why is that?

Speaker B: Right.

Speaker C: Um, it's often one of three main things. Right. So sometimes the page didn't have what they expected. Okay, that's a problem. Sometimes it's the wrong audience.

Speaker B: Right.

Speaker C: And which page might not have expected, but you have the wrong audience and others. Yeah, this is a great page. I got my one. I had a quick question. I'm out. Right. So the one thing everybody can do, and I'm sure you do this a lot, is the first thing you got to really truly track time on that first page. Not for people who engage further, which the default thing, but actually put in scroll tracking and that type of stuff and we found is if you're spending 40, 50 seconds on that page and then leaving, it's a mixed bag. Right. Sometimes that's a good thing, sometimes it's not. Depending on the actual page, um, if you're spending, you know, 20 seconds or less, you're almost never getting the answer you wanted. Right. So, so there's, that's, that's a good way of. When you tie to paid media, right. It's not about necessarily overall engagement. It's about um, like at least remove the people who spend less than 20 seconds because you're just, it's not, it's not working. Um, but then the side of. Okay, so is it my page, my landing page or is it my traffic? Right? Is it uh, am I driving people who have a completely different intent or different value? And so we'll often, because we're tying survey data to um, clickstream, we're able to actually match it to the actual campaign. So you can start to see which campaigns are driving better intent people, which campaigns are driving people who are going to buy anyway, which campaigns are driving people who have certain needs that you might not meet, but they're super high value. Change your site to help meet those needs. So it's really demystifying that um,

Speaker B: that makes it.

Speaker C: I might have gone down a different rabbit hole again. I think you get that story.

Speaker B: No, I do, I do. Um, and so yeah, hopefully they can find some of that information from you in the future.

Speaker C: Yeah, we're putting that again. We're going to be sharing more and more of that over time and you know, even just like so presenting at the year to a little shout out for you guys. Appreciate all the stuff you do in the community because it's aw awesome to help bring people together and, and um. But I'm presenting, I think the end of march on um, actionability, how to get things to be very actionable. Um, which will be. It'll be skewed towards some of the stuff we get just because that's the examples we have. But it's even just like an example. The key thing would be the more you can quantify, try to put a dollar value to something even if it's not perfect. You'll almost never be perfect, but have a Rationale and defense around that people are much more likely to act when you actually can put a dollar value not, hey, we have these visitors and these are engaged. And we think this will be, you know, the site will be a fair amount better if we do X. It's like we think we're going to drive somewhere between 2 and 6 million dollars in additional sales because of these reasons if we do this. Oh, okay. Well, uh, yeah, let's, let's do that.

Speaker B: Prove it. That's awesome. You're not.

Speaker C: Prove it. But. But show me why you believe that. You gotta defend it. But yeah, so that's. And they said what, what. I think the other question was what got me into this?

Speaker B: That's what I was going to get to, you know, a lot of people. Again, you've built all of this refinement over time. You've built all this calibration, you've looked at all of the behavior. You've tried lots of different strategies for deploying these kinds of things and you've gotten some pretty epic results over time. But there are a lot of people in our audience that might want to get into this kind of work.

Speaker C: Yeah.

Speaker B: And it's helpful for them to kind of hear how someone even gets into this. Now we know that you started your own company. We know that's a big part of it is, is the entrepreneurship. But I think it would be great if you could just tell a bit of your story of kind of, you know, what inspired you to kind of get into this direction. Okay.

Speaker C: Yeah.

Speaker B: Yeah.

Speaker C: And I'll try to make sure you follow up with me on the how this can be tips. Specifically helpful for people because there's a whole site on how people can start doing something like this on their sites or that they control. So mine, I actually started as an engineer designing building systems like so for large campuses, laboratories, data centers. Uh, it turns out like. So we did a lot of the electrical, um, and H VAC systems for data centers to make sure. And a lot of redundancy because if that data center goes down, we got big problems. Yeah. And so that was, you know, so that was. This is in the late 1990s. Um, okay, so data centers were a big thing. And now it's like that's, you know, you think about Elon talking about putting these out in space, which tough to even comprehend that. But it's because of the electrical load and heat load on these things. They're just tremendous amounts of that. But, uh, so I designed those systems. I would design, um, I did A couple other interesting projects. Underground. Um, facility built for a nuclear war in South Georgia. That was fun. So if I messed up and it didn't work, I would never know it'll be dead. Uh, and then the one, uh. That is always a good icebreaker. And they say, tell us something about you and believe. I'm like, I spent 90 different. I say 90 days in a federal penitentiary. Like, huh. I don't believe that it was 90. It was. Sorry, sorry. 30 days. It was 30 different days. Um, but actually designed the underground steam system in an Atlanta federal penitentiary. So. So pretty wild stuff. And that's what drove me away. Not because it was good stories. I mean, you're, you're with these. A side note, this is funny one. You know, I'm the first. Like, this is my final third day. I'm like 23 or 24 years old, and I'm, um. You go between these huge cell blocks and they, they, they're, they're converging. And I'm with this little security guard. He's like this. I mean, he's like, probably like barely five foot. The prisoners go in and there's like 50 prisoners. And then they lock the gates around it because they're about to transfer. I'm like, what do I do if these guys get mad? That was interesting by itself.

Speaker B: But.

Speaker C: But actually I joked around a lot with these guys. I made sure I didn't say something stupid. But they're. They're pretty, pretty funny. Maximum security. So these were not. These are not your friendly guys. Um, so the story there was the whole space was like, I did projects where it's like, I know I didn't design the best system. I know there's some issues with it. People are like, oh, this is great. And then the prison one, I actually had some really good engineering design ideas that changed how, like saved the client a ton of money on the Steam system. The contractor messed it up because it was non conventional because he didn't really listen to the plans very much. And it's like he's going to do it his way. And, um, I had to be in a room when I'm like 24 years old and there's like me as the engineer and 20 contractors telling the client why I was wrong. I was like, but here's why it's right. It's like clear as can be. It saved you all this money. And I look bad. I'm like, okay, this is kind of like innovation in that space is not rewarded. So that really hit me and that kind of drove me away from that. I went back and got an mba. Um, actually, before I go to that, the one thing that really stuck with me in that whole space though was, um, you would do really extensive plans. So building, you're designing a building. You're have a very sophisticated design with all the materials spec'd out everything else. And when I look at the level of plans we had versus the way measurement plans are done, just a completely different world measurement, uh, plans, you know, we're constantly pushing our clients, like, have an actual plan. What are your goals? What do you want? And we'll help you get that. But it's often done as an afterthought. And, and that's one of the things, like if you have a really good plan and you design to capture the measures that you're going to need, you're much, much more likely to be successful. So that was one of my things that sort of took from there. But anyway, I went back to the mba. Then I landed a job running um, a strategy team, um, for agency. And uh, we were working for Fortune 500 companies. And the third day of my job they would say, tell me, okay, what are your recommendations for this site? Like, I don't know, like I don't have any information. So, um, yeah, I remember saying, well, what, what, what, what are the visitors doing? Like, oh, we don't know. Like, what do you mean you don't know? We don't track that. I'm like, you guys don't have any data? Well, let me ask. So go down these things and we started to get data on. It's like, well, they're using this and not this. Like, okay, well that's when you start to see, okay, well this isn't across these sites. No one's using these features, so why are we wasting time developing them? But on this site, um, they're really using this feature in the site. This other site, they're not. So is it due to the navigation? Is it due to the different audience? What is it caused by? And then ultimately it's like for some of them that aren't E Commerce, like, what does it mean? What value does it have? And so that ultimately led to the idea of like, let's actually hear from people in the moment and find out. Ask those questions that web analytics couldn't answer. And really eye opening, right? So many ways across. Like for, for, for most spaces, I'd say the one area where it's just, it's useful. But we don't really focus on much. It's just media and entertainment. Right. There's the things that are going to drive that. Like you're coming back to that site a lot. Yeah, we'll learn some stuff and improve but it's not gonna be as transformational as a high consideration site.

Speaker A: Right.

Speaker C: Or your B2B Pharma or any of those. We're gonna find a ton of stuff that like our average conversion lift for using us for a year is over 40%. Right. That's after they've done a lot of the good things already in place because we're finding the things that they have missed because you're not hearing from users in the moment, not finding those problems. Right. So you've already fixed the calls to action, you've already fixed these basic things. Um, you have a good, you know, you remove friction where you can, but you don't remove the persuasion friction. Right. Because you don't know it exists.

Speaker B: So you know, when I think on this story, I mean the engineering background resonates a lot with me. I was in engineering before I got into any of this M marketing or website or usability piece. And uh, I was in space systems engineering kind of functional availability and models, uh, and resource um, allocation models and things of just physical things.

Speaker C: Oh nice.

Speaker B: And, and to your point, when I stepped into marketing it felt like a lot of that rigor that we're used to in, in in engineering just wasn't normal, it wasn't mainstream. It's actually a superpower. I think for a lot of people that are in engineering if they are able to transpose some of that kind of thinking over. Mhm. To your point, how you approached the detail is you're approaching it like hey, if I build a building and it's, and we haven't done our measurements or our tolerances or our materials decisions correctly, it could fall over. You know, people are disastrous things. It's a different story. Steam could explode. Right. Like there are all these things that could happen. And uh, and, and so you know, it forces you into a sort of, of rigor where you're like I want all of my details, I want all of my information lined up and I want to make good clear decisioning. And also the fact that you know, I, I wonder like with the building design piece there's obviously you can design an amazing uh, thing but you have to by default think about the people, think about the maintenance, think about all the other pieces that go along with it. What's interesting on the web and with what we do is making Some changes to UX is usually relatively a low effort task. It's not hard to change a link. It's not hard to um, you know, to make some UX adjustments. Is, does seem to be hard, is to your point, to bring that rigor to your information collection and the thoughtfulness and sort of the detail that's required to be able to know that this is actually the link that's worth changing. Uh, that could make all the difference in the world. So I just, I think that's super interesting and I would like you to, you know, tell the audience some tips, especially if they're thinking about, you know, moving in this direction.

Speaker C: Yeah.

Speaker B: Uh, sometimes we do find people that are in other forms of engineering and they're like, could I even make it over into this marketing world? There are other people in marketing that, you know, I just, I think it would be helpful to hear from you who's kind of been doing this for quite some time now, pretty much almost as long as the web's been around. Um, and I'm saying that as a positive thing. Um, I think it would be good if you, you know, if you could just share some tips for people that, you know, from a career pathing standpoint that might be interested in getting into this.

Speaker C: Yeah. Um, and, and just really before I go into some detail that. So the idea of it's, it's very interesting with um, especially if you're coming from an engineering perspective or you're, or more aerospace perspective. Marketing is a different animal. It's changing over time, but it still has its heritage.

Speaker B: Yes.

Speaker C: Um, and what the challenge we still face, and it's not specific to marketing, but when you don't have clear data to make decisions. Right. Um, the highest paid person in the room, the hippo concept is often gonna get their way or the most persuasive person is gonna get their way or maybe the bully. Right. When you do things that um, uncover what the people who you're trying to influence really need and think and you can make that information very trustworthy and make it very clear. It's really hard to argue against that. Right. You have to. And, and here's where it comes down. Like when you have, like when you have a story that align that, that you can make some quick changes like UX change and stuff, you don't need to defend it a whole lot, especially because you can often test it. Now pharma is a different animal because you have to defend it because it's a huge pain to change anything. But for most Sites like especially E Commerce. Yeah. Just have that right. When we make recommendations around UX tweaks, they usually don't ask much about the methodology. Beyond that's like okay, yeah, you think that great. Your stuff is right before. So we'll do that. Make recommendations and find some insights that require operational changes or are ones that so basically like one of ours was, I can go into more detail if you want later. Um, one of ours was, oh, you have a lot of people coming for same day appointments. It's costing you 5 million in lost sales because you don't offer same day appointments. You should do that. They cared about the methodology. How do you know that's true? Walk them through the details of it. You have to prove it. Why that matters. Um, so generally in marketing, if you're coming from a more technical basis, uh, more rigorous basis, you want to get in a place where you are going to be armed with the information and data to make those types help inform that type of decision. And then your challenge is how do you simplify it it and you do know BR shirt. It's like, oh, it's so hard. It is so hard to rigorously to get rigorous day. You know all these things and then you're trying to boil until just a. This versus this. Right, right. It's, it's ah, it's and I, you know, I'm the one. I, I, I, I, I always like my team. I think, okay, we got to make this simpler. We got to make this simpler. And I'm sure it drives them crazy. But then I say that m like yeah, guess what? When I do these presentations in the past where I'd create these things, I look back two weeks later, I'm like, oh, why didn't I make this simpler? So, so you generally, you need, you need time away, you need outside voice. But that's the other like it's, people don't act when they're overwhelmed with details. Right. You have to boil down and then you have to support it. Like you don't want, you don't want to lose the details, but you have to boil things down into the bigger picture. That was my, my, my, my biggest takeaway from my MBA was wow, I gotta learn how to simplify what I say. Cause it's just not, it's not working. Um, and on that front like, and if you say here we should change this because we think it's costing us 4 million in lost sales. That's a simple story. That's a persuasive story. Again, you have to back it up. But rather than saying, well, we have to change this because only 5% of users used it and they were 13% more engaged and they came from these URLs and did this, it's like, uh, what do I do with that?

Speaker B: Right, Exactly.

Speaker C: Doesn't work.

Speaker B: You're expecting them to kind of do all the mental math. Yeah, so, so the main tips are, simplification is a huge asset here. And getting the right data to uncover, um, details is kind of the, the. Also the other unsung hero here. Yeah. The people that can make that happen.

Speaker C: Yeah, absolutely. And so, so some people who are getting into that, like, if you. So you have to be there to actually get some of the stuff. The other side is if you're trying to sort of dabble in it, reach out to some nonprofits and say, you know, learn, Learn Google Analytics. As nightmarish as that is right now, unfortunately, um, we've had to build our own visualization just because it's just, it's so brutal. But if you can even work with some nonprofits, get even, just get uh, some of the, some of the, um, like a full story or mouse flow where they actually show individual users interacting with the site. Watch what they do, and then tell them some of the results. You're going to be able to share some interaction, happening interactions of actual users in a way that they haven't seen before. So you'll start to understand that now. You'll be able to develop a story around. You'll be able to understand, okay, do I, am I, do I actually like this stuff? Right? Am I actually good at this? And then can I pitch myself? And so why I said, like, if you're not in the space, offer yourself for free for doing some work for, uh, Brian's company or, um, or for a nonprofit just like there. We actually do some pro bono work for some nonprofits. Um, they love it.

Speaker B: Right.

Speaker C: And so that's a good way of just sort of getting some experience if you don't have it. Because it's hard, like. Right. So it'd be hard for you to hire somebody with no experience in this space. It's a lot easier when someone actually can show you stuff and you can see how they think.

Speaker B: Yeah, there is a talent gap. And I think that's a big part of what you're saying, is that there is an experience gap. Really. Um, not a talent gap, but an experience gap.

Speaker C: So if you're interested in trying to understand the actual people who are visiting, that's an essential thing. Um, even in organizations, people that inside Baseball concept, they know their own product too well. So showing other people, um, the site who might be in the target audience and just getting feedback, that's a good. A few initial steps. It's not even very hard. Say, what do you think? Like imagine you're doing this task. Now the challenge there is it's not the actual people who are visiting, so but it's a good sort of first step. Other things, you can use a basic tool like a hot jar or something like that and start running some short surveys, start experimenting with that measure response rate, measure what they're saying and then test different questions. We learned most of our stuff from just tests, just constant tests. Like even my. One of my favorites is okay, we're doing this, let's do the opposite. Let's see what happens. Right. Uh, test something completely different. And you know, I would say AI is interesting on that front because it actually has been helpful for coming up with other ideas that we wouldn't think of. Right. So, so word it this way. What do you think? It's actually really bad at coming up with the exact questions we should ever use. Um, right now. But from ideation, phenomenal. I mean that's where you know, on the, the. So experimenting, trying, just running a lot of tests, that's. That's by far the best way to learn something on this front. On um, and then the AI front, like the tips around that, it's really tough to tell where that's going to go. Right. We know it's going to replace or it's going to replace tasks that we do. It's going to replace certain jobs over time. It's tough to tell exactly to what. How fast that will happen and what roles will, in detail will sort of start to disappear. The one thing that. But you have to think about what's not going to disappear.

Speaker B: Right?

Speaker C: Uh, that simplification concept, telling a meaningful story to the company that's not going to disappear. That is the most fundamental thing anybody who wants to be in analysis and even marketing should be able to do really well is tell that really simple story but based on real actual data. Second one from an analyst perspective is validation. This probably doesn't get talked enough, but AI is used more and more for things trying to find insights. Um, and we use it to try to find insights. Um, and it's mixed. Um, it's much more better for mediation at this point, but it's actually not dissimilar from when people like I've had people on my team and people, uh, worked with who they pull some data and they make some recommendation based on some data. Uh, are you sure that's right? So it's always came from our system. It says that it's like, yeah, check into that. Right. Just because it was spit out by a computer doesn't mean it's accurate. And so being able to check what AI recommendations are like, ask it for its rationale and then, and then actually check into the data. The validation piece is going to be an absolutely essential thing. Um, for just to make sure. That's actually, that's actually right. So you're sort of doing some auditing, which isn't super sexy, but it's, but it's a necessary thing. Um, on the other side, I mean, just, I think using, learning how to use AI for expanding the model and analysis. Right. So finding patterns you might have missed, but also saying, you know, when you make it secure in certain things and not training sets, um, say, okay, so what other trends are going on that might have affected this? Look broadly and try to find that it's trained pretty well to do that type of stuff. And so that's going to find things you would never have had time to look at. Um, but it can be really good, like, really good. Now is it going to know your individual thing exactly? Like, we'll see how quickly it's able to figure that out and produce useful insights. But will it tie to other sources? Absolutely. And on the survey front, we found it phenomenally good at summarizing and qualifying and quantifying open end text. Right. So that one, it's AI's phenomena. So we still do some on our own, but it's like we're just asking AI questions at that point. Um, so there are certain things that just. It does really well now. It's going to do some other things well later it will be able to probably tell some stories with a lot of training and, and so there's value in you training in that. But at the end of the day, it's like you got, you got to be guiding that. You got to be the one driving that story itself. That's, that's, you know, my take overall. And, and some of that's gonna be wrong. We'll find out how it, how it all goes. I don't know. Brian, have you any, any tips from your perspective with, with AI coming up? Well, coming full bore.

Speaker B: Uh, yeah. I mean, there's quite a bit.

Speaker C: It.

Speaker B: The short answer is I, I feel like AI is always great at, ah, giving you 80% of what you're asking for. 80%. Right. 80% of the time. And it's really amazing at that. And it does create a lot of challenges to your point of like false positives or sort of, you know, slightly adjacent conclusions. I think that what you're saying makes a ton of sense. I know that part of what we're dealing with right now is, you know, there are challenges with websites and bots that are um, really agents that are trying to access some of our designs, our interfaces that were meant for humans. And we have to think about how to deal with that in our data and how to separate it out and how to segment it. And there are, you know, some things that we're working on right now. I guess I feel like you're talking about how to use agents and it's certainly a big part of it, how we can use agents to better process our data sets and how to draw more insights. And I think it's getting pretty good at that to be honest. 80%. Right. 80% of the time is actually still very helpful. Um, directionally it cuts a lot out and like you said, when you follow up with the validation step, it's extremely helpful. The um, I guess the, the other side of kind of what's happening here is figuring out when we're going to have to create applications and sites to hand, you know, for the agents and how do we start improving usability for, for them, uh, to make sure that they're able to do tasks that hopefully send a positive message back to our, you know, their, their user. They're, they're, they're human and uh, that will do business with us. And so I just feel like there's a lot in the space that, to explore. There's a lot of opportunity in the space for us to jump into. And um, you know, I'm looking forward for these tools to get better over time. I think sometimes various things, other forms of machine learning are, are better than LLMs, depending on the use case I think for text, LLMs obviously are, are at the top. Um, but yeah, I mean it's definitely a space that we see where the trends are going and you know, where we prioritize moving forward should be, um, making sure we make, you know, there are micro uses for these tools that we want to make sure that we're, you know, we're inserting them into those work workflows. It's not the answer for everything and it's not going to replace us, but it is a very Useful set of tools that we can use to do our jobs better or faster.

Speaker C: Yeah, absolutely. So, yeah, the more we can use that tool and learn it and how it applies better. But that's the, the other. Yeah. Aside, like, I can't underemphasize, like when you said the validation piece too, for, especially for people with less experience, that validation is so essential. Right. It's gonna. Because with measurement, um, integrity, accuracy is like once you, once people lose trust in you because you made some mistakes that go like, it's hard to gain it back.

Speaker B: It's like the whole program is now under. Right. Under microscope.

Speaker A: Yeah.

Speaker C: It's, it's human nature. It's, it's what you remember when that now if you say things like, hey, we found a bunch of recommendations and these are easy tweaks and tests we can try out and we think we're not gonna, some of these are gonna be wrong. They're not gonna work if you set that expectation right. Because they're easy tweaks.

Speaker A: Yeah.

Speaker C: You don't need to validate some of that stuff. So you're 80% right. Like, if they're easy changes that you can test, man, if you're changing operations at a company or half million dollar thing on that, validate that and make sure it's really, really true.

Speaker B: Well, and these tools can help you do that. They can help you find some of the validation data. They can help you figure out what your selects are. And that is a big step up from a day when you had to come up with the ideas yourselves. You had to go and pull the data not knowing if there's anything valuable in it. And you had to kind of do all of that thinking in advance. Point. This should speed up the learning curve for people to be able to do this at a much better level. So looking forward to that. So this is great. So, uh, thank you. Thank you for coming on the show.

Speaker C: Thank you. Great questions. Really, really enjoyable discussion. I appreciate your, your patience with my pontificating. I hope the audience does, doesn't mind the, uh, the rabbit holes that I sometimes go down.

Speaker B: I think it's great. I think they're here for it.

Speaker A: Yeah. I mean, being able to pick the brain of an expert such as yourself is just like super intriguing. Especially, you know, there's like so much psychology that goes into proper analytics and being able to see how you approach the inner workings of just refinement is super valuable. So thank you so much for coming on. Um, this has been, this has been great.

Speaker C: Awesome. Thank you again. I really just saying appreciate how much you guys do for the community and helping to support and advocate analytics. It's awesome. You guys are just great. So thank you.

Speaker A: So thank you everybody for spending time with us. Uh, you can learn more about Brain do and our marketing services on our website at www.brain.de. uh, if you'd like to see more for us as well, we're also on social media such as Instagram, uh, and TikTok, along with Facebook and LinkedIn. Typically our handles are Brain Do. So if you learned anything helpful and would like to dive more into a conversation with us, please leave us a comment. And we would love to answer any questions as well. Otherwise, we'll see you next time at the Roundtable.

Speaker B: The Roundtable.