Pełna transkrypcja odcinka 189 - Behavior Patterns That Predict Conversion - with Keith Zubchevich

click the button, you know, that then make the button three times the size that we'll get more clients, right? 00:04:54.490 --> 00:04:57.689 It's so easy to make those sort of generalizations. 00:04:57.850 --> 00:05:03.129 So so let's walk, let's take our time walking through the the types of things that we see. 00:05:03.450 --> 00:05:03.689 Yeah. 00:05:03.769 --> 00:05:08.169 So I think the biggest difference between what you just described, which is what people are doing, right? 00:05:08.250 --> 00:05:12.490 Because that's very easy to collect is you know how many people did this, how many people clicked that button. 00:05:12.649 --> 00:05:17.129 But the real intelligence comes from not only what they did, but why. 00:05:17.210 --> 00:05:19.769 And I'll give you an I'll give you a differentiation between the two. 00:05:20.009 --> 00:05:24.250 To understand somebody went to the review review page is is a what they did. 00:05:24.330 --> 00:05:26.330 Oh, they clicked on this and they read this review. 00:05:26.490 --> 00:05:39.370 But if you understand and you back out and you understand that they went all the way to shopping cart and then went back to the review page and then got to shopping cart and went back to review page, now you're seeing what they're doing for sure, but it exposes a different picture. 00:05:39.529 --> 00:05:41.529 We call that a research shopper, right? 00:05:41.610 --> 00:05:49.049 It's not just that they went to the review page, it's that you have to understand the totality of the behavior to understand, okay, this is how this person wants to buy. 00:05:49.210 --> 00:05:54.330 And we will label that automatically by servicing that pattern and saying this is the pattern of a research shopper. 00:05:54.409 --> 00:05:57.129 You don't get that by just saying, well, they clicked the review page. 00:05:57.289 --> 00:05:58.570 Everybody can click a review page. 00:05:58.649 --> 00:06:00.250 It doesn't mean we're all research shoppers. 00:06:00.330 --> 00:06:05.370 It's the overarching behavior pattern of a loop, how many times they clicked it, where they went to the review page. 00:06:05.450 --> 00:06:10.649 So, for example, they get all the way to shopping cart and they go back to review, that signals hesitancy, right? 00:06:10.809 --> 00:06:18.889 Now all of a sudden, review page has multiple meanings of what of what they did that is unlocked through understanding why they're doing that. 00:06:19.049 --> 00:06:35.689 And when you understand why someone's doing it, again, you can cater to them by meeting them where they are and helping them feel comfortable in very various ways, whether that's a marketing campaign or if it's an agent or you're building a product that has the ability to give them easy access to what they want to find so that they build their confidence up, right? 00:06:35.850 --> 00:06:39.450 Those are the decisions you make off of behaviors, not just people clicking buttons. 00:06:39.769 --> 00:06:41.450 People

clicking buttons tells you nothing. 00:06:41.689 --> 00:06:42.009 Yeah. 00:06:42.250 --> 00:06:42.490 Yeah. 00:06:42.649 --> 00:06:42.970 Okay. 00:06:43.129 --> 00:06:47.610 So now we're getting to a really, really core uh challenge. 00:06:47.769 --> 00:06:56.570 When you're in the position of you're trying to optimize, make an experience better, um, so you can help the business, and you're looking at the data. 00:06:56.730 --> 00:07:02.009 So, first of all, there's this sort of problem of detail and volume with the data. 00:07:02.169 --> 00:07:06.330 You get a ton of data, it's a massive amount, especially the more you're collecting. 00:07:06.409 --> 00:07:07.370 People say big data. 00:07:07.450 --> 00:07:11.769 Well, the more you're collecting, the more problematic it is to try to interpret this. 00:07:11.929 --> 00:07:19.850 So it sounds like you're saying you're boiling it up to another more meaningful layer of abstraction. 00:07:20.169 --> 00:07:21.610 Yes, that's a great point. 00:07:21.769 --> 00:07:22.409 Absolutely. 00:07:22.649 --> 00:07:28.169 We we collect every event within the session and we don't just dump it into a database that requires you to ask. 00:07:28.250 --> 00:07:46.570 So the inefficiency of today versus what Canviva is trying to drive in the market is we have a saying that today you're overinvested for marginal gain, which means you have a ton of data, you have a ton of tools, you have a ton of people, you have a data science, and you're asking a lot of questions, but you're asking questions that are coming from your own head, and you have to understand what you need to ask. 00:07:46.730 --> 00:07:48.970 Canviva's position is you start from the bottom up. 00:07:49.049 --> 00:07:57.450 We collect all of these behavior patterns, we compute them, we create them, and we put them into an analytics engine that in real time surfaces trends in behavior patterns. 00:07:57.610 --> 00:07:59.610 Because the data should speak for itself. 00:07:59.689 --> 00:08:01.049 You shouldn't have to ask questions. 00:08:01.129 --> 00:08:10.330 And that's the biggest methodology shift that we have today versus traditional product methodologies, is we don't expect you to see a metric change and then you got to go figure out what happened. 00:08:10.570 --> 00:08:17.689 We surface data that actually says this is a behavior pattern, this happened to interrupt that behavior pattern, by the way. 00:08:17.769 --> 00:08:21.769 And if you fix this or you do this, you will move these people to conversion. 00:08:21.850 --> 00:08:23.929 Then we will have a dollar amount, we'll have everything there. 00:08:24.090 --> 00:08:34.009 But that's a bottoms-up method methodology versus your point, which is all this data, and I just try and come from the top and ask a bunch of questions and hire a bunch of data scientists to try and figure out what I don't know. 00:08:34.090 --> 00:08:38.090 And it's the oxymoron of I have to ask the question of what I don't know.

00:08:38.250 --> 00:08:40.970 Now, if I already know why I'm asking the question. 00:08:41.129 --> 00:08:45.450 So it really is about letting the data speak for itself and understanding the behavior patterns. 00:08:45.529 --> 00:08:52.889 And then once you understand what is happening in the behavior pattern, I can act on that because it's showing me what consumers want to do. 00:08:53.049 --> 00:08:58.330 Not just showing me what they do, but it's showing me what they want to do and what potentially got in the way. 00:08:58.970 --> 00:09:04.330 So uh is your solution a software solution, a consulting solution, or something in between? 00:09:04.569 --> 00:09:05.129 It's a both. 00:09:05.209 --> 00:09:07.610 So we provide the analytics platform, the data collection. 00:09:07.689 --> 00:09:10.409 So we collect it, we we sit today in about 8 billion devices. 00:09:10.490 --> 00:09:12.409 So you're right, this is a big data problem. 00:09:12.490 --> 00:09:19.370 So my my my founding team, my group of validatorians in this company uh is uh all academics on big data. 00:09:19.449 --> 00:09:22.250 My co-founder, Jan Stoika, actually was the founder of Databricks. 00:09:22.329 --> 00:09:24.009 So that gives you a perspective on Conviva. 00:09:24.169 --> 00:09:26.569 We're not a product analytics event simple tech tool. 00:09:26.730 --> 00:09:37.049 We're actually a big data company because to your point, when you collect everybody's session and you compute everybody's behavior pattern with rich metadata, that's a massive big data problem. 00:09:37.129 --> 00:09:38.490 And we do do that all in real time. 00:09:38.569 --> 00:09:41.449 So that's why I said we sit in about 8 billion devices today. 00:09:41.610 --> 00:09:50.089 Most of your streaming media apps, we measure all of your video experience and we're telling those publishers exactly what you're experiencing and what's in the way of you watching more. 00:09:50.250 --> 00:10:02.409 Whether that's a bad recommendation or if it's a bad quality issue, or if it's something about the content you just may not like, all of that data is in the sessions that we distill up to the publishers to say, if you make this change, people will watch longer. 00:10:02.569 --> 00:10:04.569 It's the same with consumer behavior patterns. 00:10:04.730 --> 00:10:12.649 We can definitively tell you that if you do these things, it will unlock that consumer moving forward because we know their intent, we know what they wanted to do. 00:10:12.730 --> 00:10:24.809 And all we're saying is if you can understand what they wanted to do and see what got in the way, remove that and I will get them to conversion versus general metrics and just understanding, okay, people did if I fix that, I'll get more. 00:10:24.969 --> 00:10:28.009 That's a very loose connection to conversion versus us. 00:10:28.089 --> 00:10:38.969 We have a highly, you know, a lot stronger connection to conversion rate because we we actually measure what people want

and what they want to do, and then we give that intelligence back to the cons uh to the e-commerce company. 00:10:39.289 --> 00:10:42.169 Let's give the audience an idea of what it's like to work with Conviva. 00:10:42.329 --> 00:10:53.289 Are they are they primarily operating within a software that you've diagnosed them and set up and they're primarily in a dashboard, or are they mostly consulting with you with software tools coming along with it? 00:10:53.529 --> 00:10:53.689 Yeah. 00:10:53.769 --> 00:10:56.409 So we do the integration on with the with the business. 00:10:56.490 --> 00:11:00.889 So we'll integrate into the product website or apps, and then we immediately start auto-collecting. 00:11:00.969 --> 00:11:04.490 So there is no pre-configuration, there's no labeling, there's no tagging. 00:11:04.649 --> 00:11:09.289 We have an auto-collect capability because we do all the compute on the platform. 00:11:09.370 --> 00:11:11.049 That's where we become a big data company. 00:11:11.129 --> 00:11:14.409 So we don't have to protect by limiting the number of events. 00:11:14.490 --> 00:11:20.169 Our mission is to collect everything because we have the DNA and we have the horsepower to compute everything. 00:11:20.329 --> 00:11:25.370 So we open it up, we collect everything from the client side, and we start to build and compute these patterns. 00:11:25.449 --> 00:11:30.649 And we start to then provide the analytics that gives the customer the ability to, it surfaces automatically. 00:11:30.730 --> 00:11:34.730 Like I said, it's not a question of you have to ask and you have to go figure out what happened. 00:11:34.889 --> 00:11:44.329 Our dashboards, once we do the integration and we help the customer set up, literally starts identifying high value patterns that if you make changes, will improve conversion. 00:11:44.409 --> 00:11:51.129 And we also show, by the way, I have a simple, you know, I grew up in Arkansas, so I have a simple business philosophy of do more of what works, less of what doesn't. 00:11:51.289 --> 00:11:52.730 We tend to lose that in business. 00:11:52.889 --> 00:11:55.289 You know, we have we overcomplicate it to keep it simple. 00:11:55.370 --> 00:11:57.129 Just do more of what works, less of what doesn't. 00:11:57.289 --> 00:12:03.449 So we show high value segments, we show high value patterns and what got in the way of those high value, high conversion rate patterns. 00:12:03.529 --> 00:12:07.209 So again, it's very highly probabilistic that if you make that change, they'll convert. 00:12:07.449 --> 00:12:11.370 But we also show here's bouncers, here's things that you should ignore. 00:12:11.529 --> 00:12:17.929 Now, in our world, in the product world, in some cases, bouncers will trigger a product decision because, oh wow, they left, right? 00:12:18.569 --> 00:12:19.449 We need to fix that. 00:12:19.610 --> 00:12:29.289 But it but if it was a low value customer that was never going to buy, you're investing in solving

a problem that is never gonna convert versus solving problems that highly probable will convert. 00:12:29.449 --> 00:12:30.889 That's the difference in our methodology. 00:12:30.969 --> 00:12:32.169 So we surface both. 00:12:32.329 --> 00:12:39.289 We show the bouncers, we show the things you shouldn't spend time on, we show the things that you could spend time on, and if you did, it would probably improve conversion. 00:12:39.370 --> 00:12:43.610 And then we have a section of behavior patterns that if you do this, you will absolutely convert. 00:12:43.689 --> 00:12:48.889 These are your core customers, these are the high intent, highly converting, and something got in the way. 00:12:49.049 --> 00:12:56.089 That that that is the that that's the methodology that gives you know the e-commerce teams the ability to react and prioritize. 00:12:56.409 --> 00:13:09.449 Ladies and gentlemen, we are speaking with uh Keith Zubchevich of Conviva, uh Conviva, I'm sorry, conviva.ai, um C-O-N-V-I-V-A.ai. 00:13:10.250 --> 00:13:15.209 Um and so uh how do you grapple with the problem of correctness, Keith? 00:13:15.289 --> 00:13:32.649 This is something that I'm personally very curious about because AI tooling, you know, we build our software just like we did 20 years ago, but now we have this awesome opportunity to streamline certain aspects of what the software does, you know, categorization and some other things behind the scenes, right? 00:13:32.969 --> 00:13:35.850 And, you know, we want it to be as correct as possible. 00:13:36.009 --> 00:13:53.449 Now I see some software tools nowadays that jump too fast and too far with the AI, where it's just so assumption ridden and it's this overly confident but good looking sort of stuff versus um some that are more modest with it. 00:13:53.689 --> 00:14:09.449 I'm just curious about your approach to you know how you think about the data integrity, where taking all of these little bits of information at the bottom about usage data, and then we're boiling it up um to things that are much more meaningful and usable. 00:14:09.689 --> 00:14:11.529 So, how do you think about that and approach it? 00:14:11.769 --> 00:14:14.730 Yeah, no, that's a great point because we're really leaning into agente. 00:14:15.209 --> 00:14:23.209 So our belief is when you look at the number of behavior patterns we can generate for any digital business, and we're talking thousands, because you think about it, we're all personal in our decision making. 00:14:23.289 --> 00:14:27.209 So there's a there's literally probably millions of decision patterns that people will go through. 00:14:27.370 --> 00:14:34.009 But we we distill those into archetypes or segments automatically, by the way, not predicted segments that you try and jam square pegs into round holes. 00:14:34.169 --> 00:14:39.769 We literally take all the patterns and we surface them based on the pattern and the and the segment and the dimension into groups. 00:14:39.850 --> 00:14:41.529 So now I can act on larger numbers. 00:14:41.610 --> 00:14:45.929 So I really can look at hey, if I do this, this is the number of people that had this issue. 00:14:46.009 --> 00:14:47.370 If I do that, I will convert this number. 00:14:47.449 --> 00:14:50.329 So there's a direct correlation between data and decision. 00:14:50.569 --> 00:14:53.850 But the reality for us is that we're a technology platform first. 00:14:54.009 --> 00:15:00.089 And so this is a great question with agentic because our view is agentic should operate on good data to begin with. 00:15:00.329 --> 00:15:17.129 And so when we talk about thousands of patterns, see the view for us in agentic is that the agent should be pulling from all of these patterns, not creating them, not trying to organize them, because that's where the data accuracy becomes a problem, is AI is not a necessarily an accurate compute system. 00:15:17.209 --> 00:15:18.889 It's a it's an aggregation system. 00:15:19.049 --> 00:15:21.769 So the sorry for French, but shit in, shit out. 00:15:21.850 --> 00:15:24.250 If you have bad data, your agent's not gonna make it any better. 00:15:24.409 --> 00:15:28.969 It's gonna actually take the data that that it that it has access to and make a decision. 00:15:29.049 --> 00:15:30.409 And it could be totally wrong. 00:15:30.569 --> 00:15:38.009 So we start with compute the data, we put the data into our platform, and then agents or agentic actually access those patterns. 00:15:38.250 --> 00:15:44.009 Most companies, and this is what people need to understand, is you see all of these tech companies move to agentic. 00:15:44.329 --> 00:15:51.370 If if the data wasn't accurate and it wasn't, it wasn't specific, it wasn't directly correlated to something before, agents not going to solve that. 00:15:51.529 --> 00:16:08.089 And so everybody sort of hides, you know, I speak to a lot of investors, and they call it AI wash because all of these sort of modest technology companies, and I call them modest because they have very low tech, they have very low number of events, they're sort of general in what they're reporting, that then say, oh, agents are the answer. 00:16:08.250 --> 00:16:13.209 Now you'll be able to find that all the things you want to know through deploying an agent front end on this. 00:16:13.370 --> 00:16:17.129 But it's just front-ending the old decisions that you could have made before. 00:16:17.209 --> 00:16:24.009 It's not creating new things, it's not like it can organize, structure, and clean the data that then surfaces it to you in a better way. 00:16:24.250 --> 00:16:28.649 Whatever was collected, computed, and stored before is the limiting factor of an agent. 00:16:28.730 --> 00:16:33.289 And so the question that every consumer, every digital business needs to ask is what's your core IP? 00:16:33.370 --> 00:16:34.329 What does the data look like? 00:16:34.490 --> 00:16:37.929 Without an agent, could I have gotten that answer without an

agent giving it to me? 00:16:38.089 --> 00:16:45.449 If the answer is no, and I look at your old dashboards and I look at your old capabilities and it wasn't there before, the agent's not going to invent it. 00:16:45.610 --> 00:16:49.129 It's not the end-all be-all for fixing bad technology and bad data. 00:16:49.289 --> 00:17:00.329 So that's why we consider ourselves a big data company that enables agents to be smarter on consumer behavior patterns, not be the end-all be-all of consumer behavior patterns, and it's it does everything for you. 00:17:00.490 --> 00:17:02.169 That's a that's a flawed premise. 00:17:02.250 --> 00:17:07.690 And it's unfortunately being propagated by tech companies who were trying to hide bad technology. 00:17:08.009 --> 00:17:08.809 Oh, absolutely. 00:17:08.970 --> 00:17:22.250 And so, as a big data company that's clearly doing something new and important based on this premise of, you know, humans have our nuanced behaviors, and you just can't over-summarize just based on overly simplistic things. 00:17:22.410 --> 00:17:23.450 It's not going to work. 00:17:23.690 --> 00:17:29.289 In creating this kind of a product, I think it's you know, probably just very timely. 00:17:29.529 --> 00:17:37.850 Um, how big do you see the role of UX for you guys as the creators of the product and user experience? 00:17:38.570 --> 00:17:39.130 Critical. 00:17:39.210 --> 00:17:40.250 I I think there's two things. 00:17:40.330 --> 00:17:42.009 One is personalization of the UX. 00:17:42.090 --> 00:17:49.529 I think, you know, again, we just talked about if everything is in a monolithic level and everybody else's and every of your behavior patterns are personal, then you have a you have a conflict. 00:17:49.690 --> 00:17:56.570 But I think you know, people are looking to agentic webs, to agentic applications that are personalizing the website, personalizing the UI to a person. 00:17:56.810 --> 00:18:00.009 Provide those behavior patterns to that agent and personalize the UI. 00:18:00.090 --> 00:18:01.290 That's a great outcome, by the way. 00:18:01.370 --> 00:18:02.170 That's a great outcome. 00:18:02.410 --> 00:18:05.370 The second wave is deploying agents in UI, right? 00:18:05.450 --> 00:18:12.810 So you have some level of of click-through data, you have some level of of collection, and then it flips to an agent to say, hey, I collected all this. 00:18:12.970 --> 00:18:14.250 I see that you did all these things. 00:18:14.330 --> 00:18:17.690 Now let me help you take this to purchase or let me help you add on to this. 00:18:17.769 --> 00:18:19.610 Let me answer some questions if you have questions. 00:18:19.850 --> 00:18:21.930 So in some cases, it would be a hybrid. 00:18:22.009 --> 00:18:25.529 And then long term, the UI becomes an agentic interface, right? 00:18:25.610 --> 00:18:32.810 And so this it's sort of a migratory approach to getting to personalization and high levels of consumer experience. 00:18:33.130 --> 00:18:46.970 Yeah, and part of the reason I ask that is a lot of folks don't realize how much of just the experience the UX is embedded in everything, even the LLMs that we use every day, the fact that you can talk to it like a human and you don't have to learn any kind of syntax. 00:18:47.130 --> 00:18:56.970 And you know, let's say you're working on a document or a deck and it pops up on the side, and now you can read it and talk to your LLM at the same time, that's all a user experience. 00:18:57.050 --> 00:18:59.050 And that gap is closing rapidly. 00:18:59.370 --> 00:19:03.130 And context is key there because we we we talk about context in two forms. 00:19:03.290 --> 00:19:17.850 In the example of you have click-through data that then flips to an agent, for example, we see in our customers, if you don't have the collection of the web and application behavior data that you then give to the agent as the person flips to the agent, the agent starts off with, Hi, how are you? 00:19:17.930 --> 00:19:18.570 What do you want? 00:19:18.730 --> 00:19:27.769 That that's a waste of time because the consumer just went through a ton of things that you should, they think you should already know, but the agent is a separate thing that then starts them cold. 00:19:28.009 --> 00:19:31.930 We take the context data from the previous before the conversation starts. 00:19:32.090 --> 00:19:33.050 We provide that to the agent. 00:19:33.130 --> 00:19:42.650 So when you hit the agent, the agent says, Ah, hi Thomas, I saw that you were looking for this, I saw that you went to the review page, I saw that you got to the purchase point, I saw you saw shipping and then stop. 00:19:42.810 --> 00:19:44.090 Is it a shipping cost issue? 00:19:44.330 --> 00:19:47.610 Now it's an agent that's providing benefit to the consumer. 00:19:47.769 --> 00:19:56.810 We see in our customers that if you don't provide that context, it's three minutes of wasted resetting of context, which means you're wasting consumers three. 00:19:56.890 --> 00:19:57.450 I mean, three minutes. 00:19:57.529 --> 00:20:00.810 If you and I sat quiet for three minutes, that's a long time. 00:20:01.450 --> 00:20:10.009 And every business that starts an agent with high and doesn't know what happened before the conversation started is already in the experience hole. 00:20:10.250 --> 00:20:12.970 Because now I'm frustrated because you don't know what I just did. 00:20:13.050 --> 00:20:17.610 I just spent this time, and I'm gonna spend three minutes re-explaining myself. 00:20:18.090 --> 00:20:18.890 Guess what happens? 00:20:19.050 --> 00:20:27.210 I'm either leaving then and we see a high abandonment rate right then, or I'm gonna do this because I need to get it done, but I'm never coming back because you don't have a good experience. 00:20:27.370 --> 00:20:35.370 So it wasn't that the agent wasn't accurate, wasn't that it didn't ask a question, it's that it didn't have the context to make my experience optimized. 00:20:35.450 -->

00:20:40.570 It didn't know enough for me to understand that you hear me, you see me, and you're here to help me. 00:20:40.730 --> 00:20:43.130 You're not just automating a process. 00:20:43.769 --> 00:20:44.490 Yeah, yeah. 00:20:44.650 --> 00:20:54.490 And so, and returning to something you said earlier about being a big data company, I want to make sure that I kind of have the um the segmentation right on y'all. 00:20:54.650 --> 00:21:07.210 It you're not trying to be in the same space as sort of like a full story or pendo or the product data, or or or might that be kind of roadmap, but today you're solving a more surgically precise problem. 00:21:07.529 --> 00:21:09.450 Yeah, we're picking up where they leave off, right? 00:21:09.769 --> 00:21:21.210 All of you know, you look at you look at all the traditional product analytics companies, they they all do sort of funnel creation, you know, predefined uh journeys, and then and then they try and collect, oh, you had some friction here, we think it probably affected this. 00:21:21.290 --> 00:21:26.810 So there's some high level of of impact within the product and heat maps and session replay. 00:21:26.890 --> 00:21:29.130 There's a ton of things that you can do with those tools. 00:21:29.290 --> 00:21:35.130 We sort of pick up where they left off in understanding how people want to buy, which is a larger, more richer data set. 00:21:35.370 --> 00:21:37.050 Now you could run them in tandem. 00:21:37.130 --> 00:21:53.930 So some some of our customers replace those product analytics tools because they sort of realize, well, shit, that's a very low, you know, sort of low, like I said in the start, it's it's high investment, low marginal gains, um, you know, versus man, let's invest in consumer behavior patterns because it exposes what we call precise decisions, right? 00:21:54.009 --> 00:21:59.610 I'm making decisions that I can actually move people through their behavior pattern, but it also sets the stage for agentic. 00:21:59.850 --> 00:22:13.450 So the difference between us and all product analytics is we have a we have a significant improvement in conversion because we have a direct connection to how people want to buy and you could solve how they want to buy and personalize their experience, but it is the foundation for agentic into the future. 00:22:13.610 --> 00:22:16.170 Funnels and agents will never cross, right? 00:22:16.250 --> 00:22:20.890 Because again, an agent is a conversation, it's not a prescriptive sequence of things. 00:22:21.050 --> 00:22:21.210 Right. 00:22:21.610 --> 00:22:24.730 I could ask a question, you could ask a question, we go two totally different ways. 00:22:24.970 --> 00:22:32.170 The agent has to be agile enough to respond to what we want and be able to move us through how we want to buy versus you know, sequence. 00:22:32.730 --> 00:22:36.009 The agent's like, well, I'm gonna do this and then this and then this and then this. 00:22:36.170 --> 00:22:36.490 Right. 00:22:36.650 --> 00:22:38.090 That doesn't work in agentics. 00:22:38.170 --> 00:22:41.690 So the patterns are are beneficial today and set the foundation for the future. 00:22:42.009 --> 00:22:50.009 So you guys are going in to try to really do a good job at that piece first, and then whoever knows, you know, where you evolve from there, you know. 00:22:50.090 --> 00:23:00.810 And I really love products that really try to, you know, think of themselves as part of an ecosystem where you are one specific, you know, plant or animal in that ecosystem that does a really good job at that. 00:23:00.890 --> 00:23:04.090 And then you can just sort of link with the larger structure. 00:23:04.250 --> 00:23:06.810 Um that's our hedgehog concept is simple. 00:23:06.890 --> 00:23:13.450 It's it's understanding consumer behavior patterns to personalize a consumer's experience to improve conversion, right? 00:23:13.610 --> 00:23:13.930 Yes. 00:23:14.170 --> 00:23:16.570 That's that is a that's a very specific methodology. 00:23:16.650 --> 00:23:21.850 We're very good at building consumer scale behavior patterns and reporting on those and trending on those in real time. 00:23:21.930 --> 00:23:28.009 And then again, providing that to an agent so that you can improve personalization to a what we call hyper-personalization, almost a personal. 00:23:28.890 --> 00:23:30.009 How do people get started? 00:23:30.090 --> 00:23:33.529 This uh conviva.ai, C O N V I V A. 00:23:33.930 --> 00:23:36.330 Do they start by talking to you guys or downloading something? 00:23:36.570 --> 00:23:38.890 No, they they they come to the website, they go to the website. 00:23:39.050 --> 00:23:40.330 You know, we we do free POCs. 00:23:40.410 --> 00:23:43.050 So if people want to because that's where data comes to life. 00:23:43.290 --> 00:24:00.330 So I'll say the difference between us and all product analytics companies, the the traditional product analytics is you know, once you see a funnel and product analytics, and then you see consume the behavior patterns, you and you literally see all of how your consumers want to buy, it's an it's an unbelievable experience. 00:24:00.490 --> 00:24:05.529 So we do a free POC to actually show digital businesses here's your consumer behavior patterns. 00:24:05.610 --> 00:24:12.490 And by the way, the average behavior pattern of a larger e-commerce company is over 50 steps. 00:24:13.050 --> 00:24:14.170 Put that in perspective. 00:24:14.410 --> 00:24:20.410 People are building six and seven-step funnels, but the average consumer behavior pattern is over 50. 00:24:20.570 --> 00:24:25.529 We see some consumer behavior patterns in our large e-com companies of 150 steps, right? 00:24:25.769 --> 00:24:29.690 This behavior patterns are not simple sequential steps. 00:24:29.850 --> 00:24:30.730 We want it to be, right? 00:24:30.810 --> 00:24:34.650 Because that's how we organize our thoughts, and that's how product organizes their thoughts. 00:24:34.890 --> 00:24:42.090 But if you really think how we buy, we'll have you know multiple steps we go through to buy anything, and that all gets lost in a funnel. 00:24:42.170 --> 00:24:49.529 So when we talk about bringing the data to life, really exposing how people want to buy your product is an incredibly enlightening step. 00:24:49.610 --> 00:24:54.009 And we love it because the light bulb comes on, and then all of a sudden they're like, wow, if I did this, I'd move that. 00:24:54.090 --> 00:24:57.529 If I made a marketing decision here, I'd I'd convert much, much faster. 00:24:57.690 --> 00:25:03.450 So there's a ton of things that unlock and become immediately visible when you really see how people want to buy your product. 00:25:03.610 --> 00:25:06.810 Not what they buy, but how they buy. 00:25:07.769 --> 00:25:10.650 Wow, super, super interesting stuff and exciting things. 00:25:10.730 --> 00:25:15.290 And you guys are illuminating the nuances and details of that world. 00:25:15.450 --> 00:25:16.490 Um, very exciting. 00:25:16.650 --> 00:25:17.930 Everyone, please check it out. 00:25:18.009 --> 00:25:19.610 Uh conviva.ai. 00:25:20.330 --> 00:25:21.769 Um, anything else, Keith? 00:25:22.009 --> 00:25:23.210 No, I appreciate your time. 00:25:23.290 --> 00:25:24.170 I I love this topic. 00:25:24.650 --> 00:25:28.570 We're on the forefront of an amazing revolution with AgenTic, and we're just getting started. 00:25:28.650 --> 00:25:30.250 So it's a fun time to be in product. 00:25:30.330 --> 00:25:31.690 It's a fun time to be in tech. 00:25:31.850 --> 00:25:32.650 Oh, heck yeah. 00:25:32.730 --> 00:25:33.529 Everybody check it out. 00:25:33.690 --> 00:25:34.970 Keith, thanks for being with us today. 00:25:35.610 --> 00:25:36.250 Thanks for having me.