189 - Behavior Patterns That Predict Conversion - with Keith Zubchevich

1 00:00:32,729 --> 00:00:34,649 SPEAKER_01: Welcome to Product Led Growth Leaders. 2 00:00:34,809 --> 00:00:37,929 We've got a great guest for the show today, Keith Zubcevic. 3 00:00:38,169 --> 00:00:41,129 He has spent decades building and transforming technology 4 00:00:41,129 --> 00:00:44,809 companies with executive roles at Cisco, Riverbed Technology. 5 00:00:44,969 --> 00:00:50,409 He's had about six companies of his own and has been at Conviva 6 00:00:50,729 --> 00:00:54,569 for 15 years, rising from strategy to the top seat. 7 00:00:54,729 --> 00:00:59,769 He is today president and CEO of Conviva, globally leading 8 00:00:59,769 --> 00:01:05,049 operational analytics that are experience-centric, helping the 9 00:01:05,049 --> 00:01:08,889 world's largest digital businesses understand what their 10 00:01:08,889 --> 00:01:11,129 users are actually experiencing in real time. 11 00:01:11,289 --> 00:01:12,810 Keith, welcome to the show. 12 00:01:13,129 --> 00:01:13,449 SPEAKER_00: Thank you. 13 00:01:13,530 --> 00:01:14,569 I appreciate your having me. 14 00:01:14,650 --> 00:01:16,090 This is uh this is gonna be a lot of fun. 15 00:01:16,250 --> 00:01:19,449 By the way, I've been part of very uh uh startups, many 16 00:01:19,449 --> 00:01:19,769 startups. 17 00:01:19,849 --> 00:01:21,449 SPEAKER_01: This is uh many startups, okay. 18 00:01:22,169 --> 00:01:25,289 SPEAKER_00: So not my own, but but joined my my dad used to 19 00:01:25,289 --> 00:01:27,849 tell me either be valedictorian or be best friends with one. 20 00:01:27,930 --> 00:01:29,049 So I chose the latter. 21 00:01:29,289 --> 00:01:32,250 So I I've I've been fortunate to find a lot of really smart 22 00:01:32,250 --> 00:01:35,129 people and be part of companies that were starting out and 23 00:01:35,129 --> 00:01:36,489 turned into massive successes. 24 00:01:36,649 --> 00:01:37,769 So it's been a great experience. 25 00:01:38,409 --> 00:01:40,810 SPEAKER_01: Yeah, I think you're like me a lot of the times, uh 26 00:01:41,049 --> 00:01:44,729 in the past 10-15 years, joined a lot of startups, and you get 27 00:01:44,729 --> 00:01:47,369 to be a part of a great team, small team. 28 00:01:47,530 --> 00:01:49,609 Everyone's wearing a lot of different hats. 29 00:01:49,929 --> 00:01:52,569 SPEAKER_00: Yes, yes, yeah, uh free revenue, right? 30 00:01:52,649 --> 00:01:54,890 They're trying to figure out how to build a business and watching 31 00:01:54,969 --> 00:01:55,369 it scale. 32 00:01:55,450 --> 00:01:58,969 So the good and fortunate I've I've never uh been a part of uh 33 00:01:58,969 --> 00:01:59,849 of a failure. 34 00:01:59,929 --> 00:02:02,729 So every company that I was fortunate enough to join had a 35 00:02:02,729 --> 00:02:04,569 great ending, great success, great exit. 36 00:02:04,649 --> 00:02:08,409 Um, and so my my hope is to continue that streak. 37 00:02:09,050 --> 00:02:09,449 SPEAKER_01: Right. 38 00:02:09,689 --> 00:02:12,490 Well, let's talk about the problem that you're solving 39 00:02:12,490 --> 00:02:12,810 today. 40 00:02:12,969 --> 00:02:16,889 Let's let's steep ourselves and the audience into thinking about 41 00:02:16,889 --> 00:02:20,649 the problem that people run into, that the world uh runs 42 00:02:20,649 --> 00:02:23,209 into, that we'll be talking about the solution for today. 43 00:02:24,649 --> 00:02:27,050 SPEAKER_00: The main thing is, and in digital business, I think 44 00:02:27,050 --> 00:02:29,849 it's something that we bring to the forefront that is really not 45 00:02:29,849 --> 00:02:32,170 very visible to digital businesses today, and that's 46 00:02:32,170 --> 00:02:33,449 consumer behaviors, right? 47 00:02:33,610 --> 00:02:36,409 Everybody looks at how people buy in digital products today, 48 00:02:36,490 --> 00:02:40,250 and it's really sequential funny funnels or predictable journeys 49 00:02:40,250 --> 00:02:42,810 that you try and set and then see how many people followed 50 00:02:42,810 --> 00:02:43,449 that journey. 51 00:02:43,689 --> 00:02:47,049 If you think about how personal people's decisions are to buy 52 00:02:47,049 --> 00:02:49,370 anything, you know, whether that's a single transaction or 53 00:02:49,370 --> 00:02:53,609 very complex high high-end purchases, all of us have a very 54 00:02:53,609 --> 00:02:56,250 specific pattern that we go through to buy. 55 00:02:56,409 --> 00:02:58,090 And it's very, it's very personal. 56 00:02:58,169 --> 00:02:59,129 It's it's unique to us. 57 00:02:59,289 --> 00:03:01,849 You and I might buy the exact same product in two very 58 00:03:01,849 --> 00:03:02,569 different ways. 59 00:03:02,729 --> 00:03:06,009 So a digital business building a single product or a single way 60 00:03:06,009 --> 00:03:09,689 to see the world is missing either one of us or even both of 61 00:03:09,689 --> 00:03:09,930 us. 62 00:03:10,090 --> 00:03:13,609 And so Conviva's mission and our job is to really understand what 63 00:03:13,609 --> 00:03:16,650 is the consumer experience and behaviors, not funnels, 64 00:03:16,810 --> 00:03:17,210 predictive. 65 00:03:17,530 --> 00:03:20,810 It is really about collecting every session and organizing 66 00:03:20,810 --> 00:03:23,530 that into a behavior pattern and assigning things like segment or 67 00:03:23,530 --> 00:03:26,169 archetypes so you can group people into some very similar 68 00:03:26,169 --> 00:03:27,210 behavior patterns. 69 00:03:27,370 --> 00:03:30,090 But that really surfaces an entirely new way to see your 70 00:03:30,090 --> 00:03:33,849 consumers, not funnels because you lose actually a lot, but 71 00:03:33,849 --> 00:03:35,370 really understanding how people want to buy. 72 00:03:35,449 --> 00:03:38,729 And if you can meet them where they are and you you you treat 73 00:03:38,729 --> 00:03:42,009 them and you you cater to how they want to buy, they'll buy. 74 00:03:42,090 --> 00:03:45,129 That is a very similar concept in brick and mortar business 75 00:03:45,129 --> 00:03:47,689 today that doesn't get replicated in digital businesses 76 00:03:47,689 --> 00:03:47,930 enough. 77 00:03:48,090 --> 00:03:50,969 So we want to lead that charge of understanding patterns and 78 00:03:50,969 --> 00:03:54,329 how to personalize the digital business to meet every consumer 79 00:03:54,329 --> 00:03:57,689 where they are and how they prefer to purchase to improve 80 00:03:57,689 --> 00:03:58,409 conversion. 81 00:03:58,650 --> 00:03:58,969 SPEAKER_01: Right. 82 00:03:59,129 --> 00:04:02,169 No, so Keith, I'm very excited to have this conversation 83 00:04:02,409 --> 00:04:08,090 because as a UX person who does research and design, and we're 84 00:04:08,090 --> 00:04:11,449 constantly trying to help clients figure out just the best 85 00:04:11,449 --> 00:04:13,049 way to put things together, right? 86 00:04:13,449 --> 00:04:16,970 And there's the sort of human side where you're, you know, 87 00:04:17,129 --> 00:04:19,610 kind of doing the research and you're understanding people. 88 00:04:19,769 --> 00:04:23,610 Then then there's a more sort of automated analytics side where 89 00:04:23,610 --> 00:04:25,610 you are gathering the exact data. 90 00:04:25,769 --> 00:04:28,090 This is how long this person spent on this page. 91 00:04:28,250 --> 00:04:32,730 This is how uh uh percentage of people click on this thing 92 00:04:32,730 --> 00:04:33,370 versus that thing. 93 00:04:33,449 --> 00:04:36,889 And then here's how the funnel goes down over time and the 94 00:04:36,889 --> 00:04:37,769 percentages. 95 00:04:38,009 --> 00:04:40,490 And it's so hard to bridge that gap. 96 00:04:40,569 --> 00:04:42,409 There are it's so jam-ped. 97 00:04:42,649 --> 00:04:45,769 It's easy to take make a uh, you know, sort of this, you know, 98 00:04:45,930 --> 00:04:48,649 big conclusion of like, oh, well, you know, you know, this 99 00:04:48,649 --> 00:04:51,529 button, and if more people click the button, you know, that then 100 00:04:51,529 --> 00:04:53,769 make the button three times the size that we'll get more 101 00:04:53,769 --> 00:04:54,409 clients, right? 102 00:04:54,490 --> 00:04:57,689 It's so easy to make those sort of generalizations. 103 00:04:57,850 --> 00:05:01,850 So so let's walk, let's take our time walking through the the 104 00:05:01,850 --> 00:05:03,129 types of things that we see. 105 00:05:03,450 --> 00:05:03,689 SPEAKER_00: Yeah. 106 00:05:03,769 --> 00:05:06,090 So I think the biggest difference between what you just 107 00:05:06,090 --> 00:05:08,169 described, which is what people are doing, right? 108 00:05:08,250 --> 00:05:10,409 Because that's very easy to collect is you know how many 109 00:05:10,409 --> 00:05:12,490 people did this, how many people clicked that button. 110 00:05:12,649 --> 00:05:16,889 But the real intelligence comes from not only what they did, but 111 00:05:16,889 --> 00:05:17,129 why. 112 00:05:17,210 --> 00:05:19,370 And I'll give you an I'll give you a differentiation between 113 00:05:19,370 --> 00:05:19,769 the two. 114 00:05:20,009 --> 00:05:23,610 To understand somebody went to the review review page is is a 115 00:05:23,610 --> 00:05:24,250 what they did. 116 00:05:24,330 --> 00:05:26,330 Oh, they clicked on this and they read this review. 117 00:05:26,490 --> 00:05:29,210 But if you understand and you back out and you understand that 118 00:05:29,210 --> 00:05:31,769 they went all the way to shopping cart and then went back 119 00:05:31,769 --> 00:05:34,330 to the review page and then got to shopping cart and went back 120 00:05:34,330 --> 00:05:36,889 to review page, now you're seeing what they're doing for 121 00:05:36,889 --> 00:05:39,370 sure, but it exposes a different picture. 122 00:05:39,529 --> 00:05:41,529 We call that a research shopper, right? 123 00:05:41,610 --> 00:05:44,250 It's not just that they went to the review page, it's that you 124 00:05:44,250 --> 00:05:47,049 have to understand the totality of the behavior to understand, 125 00:05:47,129 --> 00:05:49,049 okay, this is how this person wants to buy. 126 00:05:49,210 --> 00:05:51,689 And we will label that automatically by servicing that 127 00:05:51,689 --> 00:05:54,330 pattern and saying this is the pattern of a research shopper. 128 00:05:54,409 --> 00:05:56,409 You don't get that by just saying, well, they clicked the 129 00:05:56,409 --> 00:05:57,129 review page. 130 00:05:57,289 --> 00:05:58,570 Everybody can click a review page. 131 00:05:58,649 --> 00:06:00,250 It doesn't mean we're all research shoppers. 132 00:06:00,330 --> 00:06:03,129 It's the overarching behavior pattern of a loop, how many 133 00:06:03,129 --> 00:06:05,370 times they clicked it, where they went to the review page. 134 00:06:05,450 --> 00:06:07,689 So, for example, they get all the way to shopping cart and 135 00:06:07,689 --> 00:06:10,649 they go back to review, that signals hesitancy, right? 136 00:06:10,809 --> 00:06:14,730 Now all of a sudden, review page has multiple meanings of what of 137 00:06:14,730 --> 00:06:18,250 what they did that is unlocked through understanding why 138 00:06:18,250 --> 00:06:18,889 they're doing that. 139 00:06:19,049 --> 00:06:21,610 And when you understand why someone's doing it, again, you 140 00:06:21,610 --> 00:06:24,090 can cater to them by meeting them where they are and helping 141 00:06:24,090 --> 00:06:27,129 them feel comfortable in very various ways, whether that's a 142 00:06:27,129 --> 00:06:30,009 marketing campaign or if it's an agent or you're building a 143 00:06:30,009 --> 00:06:33,210 product that has the ability to give them easy access to what 144 00:06:33,210 --> 00:06:35,370 they want to find so that they build their confidence up, 145 00:06:35,450 --> 00:06:35,689 right? 146 00:06:35,850 --> 00:06:38,570 Those are the decisions you make off of behaviors, not just 147 00:06:38,570 --> 00:06:39,450 people clicking buttons. 148 00:06:39,769 --> 00:06:41,450 People clicking buttons tells you nothing. 149 00:06:41,689 --> 00:06:42,009 SPEAKER_01: Yeah. 150 00:06:42,250 --> 00:06:42,490 Yeah. 151 00:06:42,649 --> 00:06:42,970 Okay. 152 00:06:43,129 --> 00:06:46,889 So now we're getting to a really, really core uh 153 00:06:46,889 --> 00:06:47,610 challenge. 154 00:06:47,769 --> 00:06:51,289 When you're in the position of you're trying to optimize, make 155 00:06:51,289 --> 00:06:55,129 an experience better, um, so you can help the business, and 156 00:06:55,129 --> 00:06:56,570 you're looking at the data. 157 00:06:56,730 --> 00:07:00,809 So, first of all, there's this sort of problem of detail and 158 00:07:00,809 --> 00:07:02,009 volume with the data. 159 00:07:02,169 --> 00:07:05,610 You get a ton of data, it's a massive amount, especially the 160 00:07:05,610 --> 00:07:06,330 more you're collecting. 161 00:07:06,409 --> 00:07:07,370 People say big data. 162 00:07:07,450 --> 00:07:10,330 Well, the more you're collecting, the more problematic 163 00:07:10,330 --> 00:07:11,769 it is to try to interpret this. 164 00:07:11,929 --> 00:07:17,689 So it sounds like you're saying you're boiling it up to another 165 00:07:17,689 --> 00:07:19,850 more meaningful layer of abstraction. 166 00:07:20,169 --> 00:07:21,610 SPEAKER_00: Yes, that's a great point. 167 00:07:21,769 --> 00:07:22,409 Absolutely. 168 00:07:22,649 --> 00:07:25,850 We we collect every event within the session and we don't just 169 00:07:25,850 --> 00:07:28,169 dump it into a database that requires you to ask. 170 00:07:28,250 --> 00:07:31,049 So the inefficiency of today versus what Canviva is trying to 171 00:07:31,049 --> 00:07:33,850 drive in the market is we have a saying that today you're 172 00:07:33,850 --> 00:07:36,809 overinvested for marginal gain, which means you have a ton of 173 00:07:36,809 --> 00:07:38,570 data, you have a ton of tools, you have a ton of people, you 174 00:07:38,570 --> 00:07:41,370 have a data science, and you're asking a lot of questions, but 175 00:07:41,370 --> 00:07:44,889 you're asking questions that are coming from your own head, and 176 00:07:44,889 --> 00:07:46,570 you have to understand what you need to ask. 177 00:07:46,730 --> 00:07:48,970 Canviva's position is you start from the bottom up. 178 00:07:49,049 --> 00:07:51,929 We collect all of these behavior patterns, we compute them, we 179 00:07:51,929 --> 00:07:54,570 create them, and we put them into an analytics engine that in 180 00:07:54,570 --> 00:07:57,450 real time surfaces trends in behavior patterns. 181 00:07:57,610 --> 00:07:59,610 Because the data should speak for itself. 182 00:07:59,689 --> 00:08:01,049 You shouldn't have to ask questions. 183 00:08:01,129 --> 00:08:03,289 And that's the biggest methodology shift that we have 184 00:08:03,289 --> 00:08:06,409 today versus traditional product methodologies, is we don't 185 00:08:06,409 --> 00:08:08,970 expect you to see a metric change and then you got to go 186 00:08:08,970 --> 00:08:10,330 figure out what happened. 187 00:08:10,570 --> 00:08:14,649 We surface data that actually says this is a behavior pattern, 188 00:08:14,889 --> 00:08:17,689 this happened to interrupt that behavior pattern, by the way. 189 00:08:17,769 --> 00:08:21,210 And if you fix this or you do this, you will move these people 190 00:08:21,210 --> 00:08:21,769 to conversion. 191 00:08:21,850 --> 00:08:23,769 Then we will have a dollar amount, we'll have everything 192 00:08:23,769 --> 00:08:23,929 there. 193 00:08:24,090 --> 00:08:26,970 But that's a bottoms-up method methodology versus your point, 194 00:08:27,049 --> 00:08:29,850 which is all this data, and I just try and come from the top 195 00:08:29,850 --> 00:08:32,330 and ask a bunch of questions and hire a bunch of data scientists 196 00:08:32,330 --> 00:08:34,009 to try and figure out what I don't know. 197 00:08:34,090 --> 00:08:37,529 And it's the oxymoron of I have to ask the question of what I 198 00:08:37,529 --> 00:08:38,090 don't know. 199 00:08:38,250 --> 00:08:40,970 Now, if I already know why I'm asking the question. 200 00:08:41,129 --> 00:08:44,090 So it really is about letting the data speak for itself and 201 00:08:44,090 --> 00:08:45,450 understanding the behavior patterns. 202 00:08:45,529 --> 00:08:48,330 And then once you understand what is happening in the 203 00:08:48,330 --> 00:08:51,610 behavior pattern, I can act on that because it's showing me 204 00:08:51,610 --> 00:08:52,889 what consumers want to do. 205 00:08:53,049 --> 00:08:55,769 Not just showing me what they do, but it's showing me what 206 00:08:55,769 --> 00:08:58,330 they want to do and what potentially got in the way. 207 00:08:58,970 --> 00:09:02,490 SPEAKER_01: So uh is your solution a software solution, a 208 00:09:02,490 --> 00:09:04,330 consulting solution, or something in between? 209 00:09:04,569 --> 00:09:05,129 SPEAKER_00: It's a both. 210 00:09:05,209 --> 00:09:07,610 So we provide the analytics platform, the data collection. 211 00:09:07,689 --> 00:09:10,009 So we collect it, we we sit today in about 8 billion 212 00:09:10,009 --> 00:09:10,409 devices. 213 00:09:10,490 --> 00:09:12,409 So you're right, this is a big data problem. 214 00:09:12,490 --> 00:09:16,089 So my my my founding team, my group of validatorians in this 215 00:09:16,089 --> 00:09:19,370 company uh is uh all academics on big data. 216 00:09:19,449 --> 00:09:21,689 My co-founder, Jan Stoika, actually was the founder of 217 00:09:21,689 --> 00:09:22,250 Databricks. 218 00:09:22,329 --> 00:09:24,009 So that gives you a perspective on Conviva. 219 00:09:24,169 --> 00:09:26,569 We're not a product analytics event simple tech tool. 220 00:09:26,730 --> 00:09:29,129 We're actually a big data company because to your point, 221 00:09:29,289 --> 00:09:32,409 when you collect everybody's session and you compute 222 00:09:32,409 --> 00:09:36,009 everybody's behavior pattern with rich metadata, that's a 223 00:09:36,009 --> 00:09:37,049 massive big data problem. 224 00:09:37,129 --> 00:09:38,490 And we do do that all in real time. 225 00:09:38,569 --> 00:09:41,449 So that's why I said we sit in about 8 billion devices today. 226 00:09:41,610 --> 00:09:44,969 Most of your streaming media apps, we measure all of your 227 00:09:44,969 --> 00:09:47,449 video experience and we're telling those publishers exactly 228 00:09:47,449 --> 00:09:49,449 what you're experiencing and what's in the way of you 229 00:09:49,449 --> 00:09:50,089 watching more. 230 00:09:50,250 --> 00:09:52,569 Whether that's a bad recommendation or if it's a bad 231 00:09:52,569 --> 00:09:55,289 quality issue, or if it's something about the content you 232 00:09:55,289 --> 00:09:58,809 just may not like, all of that data is in the sessions that we 233 00:09:58,809 --> 00:10:01,289 distill up to the publishers to say, if you make this change, 234 00:10:01,370 --> 00:10:02,409 people will watch longer. 235 00:10:02,569 --> 00:10:04,569 It's the same with consumer behavior patterns. 236 00:10:04,730 --> 00:10:07,689 We can definitively tell you that if you do these things, it 237 00:10:07,689 --> 00:10:10,649 will unlock that consumer moving forward because we know their 238 00:10:10,649 --> 00:10:12,649 intent, we know what they wanted to do. 239 00:10:12,730 --> 00:10:15,209 And all we're saying is if you can understand what they wanted 240 00:10:15,209 --> 00:10:18,569 to do and see what got in the way, remove that and I will get 241 00:10:18,569 --> 00:10:21,850 them to conversion versus general metrics and just 242 00:10:21,850 --> 00:10:24,809 understanding, okay, people did if I fix that, I'll get more. 243 00:10:24,969 --> 00:10:28,009 That's a very loose connection to conversion versus us. 244 00:10:28,089 --> 00:10:32,009 We have a highly, you know, a lot stronger connection to 245 00:10:32,009 --> 00:10:34,329 conversion rate because we we actually measure what people 246 00:10:34,329 --> 00:10:36,169 want and what they want to do, and then we give that 247 00:10:36,169 --> 00:10:38,969 intelligence back to the cons uh to the e-commerce company. 248 00:10:39,289 --> 00:10:41,049 SPEAKER_01: Let's give the audience an idea of what it's 249 00:10:41,049 --> 00:10:42,169 like to work with Conviva. 250 00:10:42,329 --> 00:10:45,209 Are they are they primarily operating within a software that 251 00:10:45,209 --> 00:10:48,169 you've diagnosed them and set up and they're primarily in a 252 00:10:48,169 --> 00:10:51,049 dashboard, or are they mostly consulting with you with 253 00:10:51,049 --> 00:10:53,289 software tools coming along with it? 254 00:10:53,529 --> 00:10:53,689 SPEAKER_00: Yeah. 255 00:10:53,769 --> 00:10:56,409 So we do the integration on with the with the business. 256 00:10:56,490 --> 00:10:59,209 So we'll integrate into the product website or apps, and 257 00:10:59,209 --> 00:11:00,889 then we immediately start auto-collecting. 258 00:11:00,969 --> 00:11:02,809 So there is no pre-configuration, there's no 259 00:11:02,809 --> 00:11:04,490 labeling, there's no tagging. 260 00:11:04,649 --> 00:11:07,449 We have an auto-collect capability because we do all the 261 00:11:08,089 --> 00:11:09,289 compute on the platform. 262 00:11:09,370 --> 00:11:11,049 That's where we become a big data company. 263 00:11:11,129 --> 00:11:14,409 So we don't have to protect by limiting the number of events. 264 00:11:14,490 --> 00:11:17,370 Our mission is to collect everything because we have the 265 00:11:17,529 --> 00:11:20,169 DNA and we have the horsepower to compute everything. 266 00:11:20,329 --> 00:11:23,129 So we open it up, we collect everything from the client side, 267 00:11:23,209 --> 00:11:25,370 and we start to build and compute these patterns. 268 00:11:25,449 --> 00:11:28,250 And we start to then provide the analytics that gives the 269 00:11:28,250 --> 00:11:30,649 customer the ability to, it surfaces automatically. 270 00:11:30,730 --> 00:11:33,449 Like I said, it's not a question of you have to ask and you have 271 00:11:33,449 --> 00:11:34,730 to go figure out what happened. 272 00:11:34,889 --> 00:11:37,289 Our dashboards, once we do the integration and we help the 273 00:11:37,289 --> 00:11:41,449 customer set up, literally starts identifying high value 274 00:11:41,449 --> 00:11:43,850 patterns that if you make changes, will improve 275 00:11:43,850 --> 00:11:44,329 conversion. 276 00:11:44,409 --> 00:11:47,289 And we also show, by the way, I have a simple, you know, I grew 277 00:11:47,289 --> 00:11:49,610 up in Arkansas, so I have a simple business philosophy of do 278 00:11:49,610 --> 00:11:51,129 more of what works, less of what doesn't. 279 00:11:51,289 --> 00:11:52,730 We tend to lose that in business. 280 00:11:52,889 --> 00:11:55,049 You know, we have we overcomplicate it to keep it 281 00:11:55,049 --> 00:11:55,289 simple. 282 00:11:55,370 --> 00:11:57,129 Just do more of what works, less of what doesn't. 283 00:11:57,289 --> 00:12:00,649 So we show high value segments, we show high value patterns and 284 00:12:00,649 --> 00:12:03,209 what got in the way of those high value, high conversion rate 285 00:12:03,209 --> 00:12:03,449 patterns. 286 00:12:03,529 --> 00:12:05,929 So again, it's very highly probabilistic that if you make 287 00:12:05,929 --> 00:12:07,209 that change, they'll convert. 288 00:12:07,449 --> 00:12:10,809 But we also show here's bouncers, here's things that you 289 00:12:10,809 --> 00:12:11,370 should ignore. 290 00:12:11,529 --> 00:12:14,569 Now, in our world, in the product world, in some cases, 291 00:12:14,730 --> 00:12:17,449 bouncers will trigger a product decision because, oh wow, they 292 00:12:17,449 --> 00:12:17,929 left, right? 293 00:12:18,569 --> 00:12:19,449 We need to fix that. 294 00:12:19,610 --> 00:12:22,169 But it but if it was a low value customer that was never going to 295 00:12:22,169 --> 00:12:24,969 buy, you're investing in solving a problem that is never gonna 296 00:12:24,969 --> 00:12:28,809 convert versus solving problems that highly probable will 297 00:12:28,809 --> 00:12:29,289 convert. 298 00:12:29,449 --> 00:12:30,889 That's the difference in our methodology. 299 00:12:30,969 --> 00:12:32,169 So we surface both. 300 00:12:32,329 --> 00:12:34,969 We show the bouncers, we show the things you shouldn't spend 301 00:12:34,969 --> 00:12:37,689 time on, we show the things that you could spend time on, and if 302 00:12:37,689 --> 00:12:39,289 you did, it would probably improve conversion. 303 00:12:39,370 --> 00:12:42,089 And then we have a section of behavior patterns that if you do 304 00:12:42,089 --> 00:12:43,610 this, you will absolutely convert. 305 00:12:43,689 --> 00:12:46,169 These are your core customers, these are the high intent, 306 00:12:46,409 --> 00:12:48,889 highly converting, and something got in the way. 307 00:12:49,049 --> 00:12:51,610 That that that is the that that's the methodology that 308 00:12:51,610 --> 00:12:55,129 gives you know the e-commerce teams the ability to react and 309 00:12:55,129 --> 00:12:56,089 prioritize. 310 00:12:56,409 --> 00:12:58,329 SPEAKER_01: Ladies and gentlemen, we are speaking with 311 00:12:58,569 --> 00:13:03,370 uh Keith Zubchevich of Conviva, uh Conviva, I'm sorry, 312 00:13:03,529 --> 00:13:09,449 conviva.ai, um C-O-N-V-I-V-A.ai. 313 00:13:10,250 --> 00:13:14,809 Um and so uh how do you grapple with the problem of correctness, 314 00:13:14,889 --> 00:13:15,209 Keith? 315 00:13:15,289 --> 00:13:18,009 This is something that I'm personally very curious about 316 00:13:18,250 --> 00:13:22,009 because AI tooling, you know, we build our software just like we 317 00:13:22,009 --> 00:13:26,089 did 20 years ago, but now we have this awesome opportunity to 318 00:13:26,250 --> 00:13:29,129 streamline certain aspects of what the software does, you 319 00:13:29,129 --> 00:13:32,250 know, categorization and some other things behind the scenes, 320 00:13:32,409 --> 00:13:32,649 right? 321 00:13:32,969 --> 00:13:35,850 And, you know, we want it to be as correct as possible. 322 00:13:36,009 --> 00:13:41,610 Now I see some software tools nowadays that jump too fast and 323 00:13:41,610 --> 00:13:46,730 too far with the AI, where it's just so assumption ridden and 324 00:13:46,730 --> 00:13:51,049 it's this overly confident but good looking sort of stuff 325 00:13:51,289 --> 00:13:53,449 versus um some that are more modest with it. 326 00:13:53,689 --> 00:13:57,769 I'm just curious about your approach to you know how you 327 00:13:57,769 --> 00:14:01,610 think about the data integrity, where taking all of these little 328 00:14:01,610 --> 00:14:04,809 bits of information at the bottom about usage data, and 329 00:14:04,809 --> 00:14:08,250 then we're boiling it up um to things that are much more 330 00:14:08,250 --> 00:14:09,449 meaningful and usable. 331 00:14:09,689 --> 00:14:11,529 So, how do you think about that and approach it? 332 00:14:11,769 --> 00:14:13,769 SPEAKER_00: Yeah, no, that's a great point because we're really 333 00:14:13,929 --> 00:14:14,730 leaning into agente. 334 00:14:15,209 --> 00:14:18,009 So our belief is when you look at the number of behavior 335 00:14:18,009 --> 00:14:20,329 patterns we can generate for any digital business, and we're 336 00:14:20,329 --> 00:14:21,929 talking thousands, because you think about it, we're all 337 00:14:21,929 --> 00:14:23,209 personal in our decision making. 338 00:14:23,289 --> 00:14:25,769 So there's a there's literally probably millions of decision 339 00:14:25,769 --> 00:14:27,209 patterns that people will go through. 340 00:14:27,370 --> 00:14:29,610 But we we distill those into archetypes or segments 341 00:14:29,769 --> 00:14:32,329 automatically, by the way, not predicted segments that you try 342 00:14:32,329 --> 00:14:34,009 and jam square pegs into round holes. 343 00:14:34,169 --> 00:14:36,169 We literally take all the patterns and we surface them 344 00:14:36,169 --> 00:14:38,649 based on the pattern and the and the segment and the dimension 345 00:14:38,809 --> 00:14:39,769 into groups. 346 00:14:39,850 --> 00:14:41,529 So now I can act on larger numbers. 347 00:14:41,610 --> 00:14:45,049 So I really can look at hey, if I do this, this is the number of 348 00:14:45,049 --> 00:14:45,929 people that had this issue. 349 00:14:46,009 --> 00:14:47,370 If I do that, I will convert this number. 350 00:14:47,449 --> 00:14:50,329 So there's a direct correlation between data and decision. 351 00:14:50,569 --> 00:14:53,610 But the reality for us is that we're a technology platform 352 00:14:53,610 --> 00:14:53,850 first. 353 00:14:54,009 --> 00:14:56,809 And so this is a great question with agentic because our view is 354 00:14:56,889 --> 00:15:00,089 agentic should operate on good data to begin with. 355 00:15:00,329 --> 00:15:02,969 And so when we talk about thousands of patterns, see the 356 00:15:02,969 --> 00:15:06,009 view for us in agentic is that the agent should be pulling from 357 00:15:06,009 --> 00:15:09,850 all of these patterns, not creating them, not trying to 358 00:15:09,850 --> 00:15:12,730 organize them, because that's where the data accuracy becomes 359 00:15:12,730 --> 00:15:16,889 a problem, is AI is not a necessarily an accurate compute 360 00:15:16,889 --> 00:15:17,129 system. 361 00:15:17,209 --> 00:15:18,889 It's a it's an aggregation system. 362 00:15:19,049 --> 00:15:21,769 So the sorry for French, but shit in, shit out. 363 00:15:21,850 --> 00:15:23,929 If you have bad data, your agent's not gonna make it any 364 00:15:23,929 --> 00:15:24,250 better. 365 00:15:24,409 --> 00:15:27,610 It's gonna actually take the data that that it that it has 366 00:15:27,610 --> 00:15:28,969 access to and make a decision. 367 00:15:29,049 --> 00:15:30,409 And it could be totally wrong. 368 00:15:30,569 --> 00:15:34,009 So we start with compute the data, we put the data into our 369 00:15:34,009 --> 00:15:37,449 platform, and then agents or agentic actually access those 370 00:15:37,449 --> 00:15:38,009 patterns. 371 00:15:38,250 --> 00:15:41,049 Most companies, and this is what people need to understand, is 372 00:15:41,049 --> 00:15:44,009 you see all of these tech companies move to agentic. 373 00:15:44,329 --> 00:15:46,889 If if the data wasn't accurate and it wasn't, it wasn't 374 00:15:46,889 --> 00:15:49,689 specific, it wasn't directly correlated to something before, 375 00:15:49,929 --> 00:15:51,370 agents not going to solve that. 376 00:15:51,529 --> 00:15:54,329 And so everybody sort of hides, you know, I speak to a lot of 377 00:15:54,329 --> 00:15:58,009 investors, and they call it AI wash because all of these sort 378 00:15:58,009 --> 00:16:01,129 of modest technology companies, and I call them modest because 379 00:16:01,129 --> 00:16:03,449 they have very low tech, they have very low number of events, 380 00:16:03,529 --> 00:16:06,329 they're sort of general in what they're reporting, that then 381 00:16:06,329 --> 00:16:08,089 say, oh, agents are the answer. 382 00:16:08,250 --> 00:16:11,049 Now you'll be able to find that all the things you want to know 383 00:16:11,049 --> 00:16:13,209 through deploying an agent front end on this. 384 00:16:13,370 --> 00:16:16,409 But it's just front-ending the old decisions that you could 385 00:16:16,409 --> 00:16:17,129 have made before. 386 00:16:17,209 --> 00:16:20,089 It's not creating new things, it's not like it can organize, 387 00:16:20,250 --> 00:16:23,209 structure, and clean the data that then surfaces it to you in 388 00:16:23,209 --> 00:16:24,009 a better way. 389 00:16:24,250 --> 00:16:27,209 Whatever was collected, computed, and stored before is 390 00:16:27,209 --> 00:16:28,649 the limiting factor of an agent. 391 00:16:28,730 --> 00:16:31,289 And so the question that every consumer, every digital business 392 00:16:31,289 --> 00:16:33,289 needs to ask is what's your core IP? 393 00:16:33,370 --> 00:16:34,329 What does the data look like? 394 00:16:34,490 --> 00:16:37,129 Without an agent, could I have gotten that answer without an 395 00:16:37,129 --> 00:16:37,929 agent giving it to me? 396 00:16:38,089 --> 00:16:40,889 If the answer is no, and I look at your old dashboards and I 397 00:16:40,889 --> 00:16:44,169 look at your old capabilities and it wasn't there before, the 398 00:16:44,169 --> 00:16:45,449 agent's not going to invent it. 399 00:16:45,610 --> 00:16:48,889 It's not the end-all be-all for fixing bad technology and bad 400 00:16:48,889 --> 00:16:49,129 data. 401 00:16:49,289 --> 00:16:51,610 So that's why we consider ourselves a big data company 402 00:16:51,610 --> 00:16:54,409 that enables agents to be smarter on consumer behavior 403 00:16:54,409 --> 00:16:58,329 patterns, not be the end-all be-all of consumer behavior 404 00:16:58,329 --> 00:17:00,329 patterns, and it's it does everything for you. 405 00:17:00,490 --> 00:17:02,169 That's a that's a flawed premise. 406 00:17:02,250 --> 00:17:05,210 And it's unfortunately being propagated by tech companies who 407 00:17:05,370 --> 00:17:07,690 were trying to hide bad technology. 408 00:17:08,009 --> 00:17:08,809 SPEAKER_01: Oh, absolutely. 409 00:17:08,970 --> 00:17:12,490 And so, as a big data company that's clearly doing something 410 00:17:12,490 --> 00:17:16,250 new and important based on this premise of, you know, humans 411 00:17:16,250 --> 00:17:20,009 have our nuanced behaviors, and you just can't over-summarize 412 00:17:20,089 --> 00:17:22,250 just based on overly simplistic things. 413 00:17:22,410 --> 00:17:23,450 It's not going to work. 414 00:17:23,690 --> 00:17:27,690 In creating this kind of a product, I think it's you know, 415 00:17:27,769 --> 00:17:29,289 probably just very timely. 416 00:17:29,529 --> 00:17:35,210 Um, how big do you see the role of UX for you guys as the 417 00:17:35,210 --> 00:17:37,850 creators of the product and user experience? 418 00:17:38,570 --> 00:17:39,130 SPEAKER_00: Critical. 419 00:17:39,210 --> 00:17:40,250 I I think there's two things. 420 00:17:40,330 --> 00:17:42,009 One is personalization of the UX. 421 00:17:42,090 --> 00:17:43,850 I think, you know, again, we just talked about if everything 422 00:17:43,850 --> 00:17:46,330 is in a monolithic level and everybody else's and every of 423 00:17:46,330 --> 00:17:48,570 your behavior patterns are personal, then you have a you 424 00:17:48,570 --> 00:17:49,529 have a conflict. 425 00:17:49,690 --> 00:17:52,170 But I think you know, people are looking to agentic webs, to 426 00:17:52,170 --> 00:17:54,490 agentic applications that are personalizing the website, 427 00:17:54,650 --> 00:17:56,570 personalizing the UI to a person. 428 00:17:56,810 --> 00:17:59,529 Provide those behavior patterns to that agent and personalize 429 00:17:59,529 --> 00:18:00,009 the UI. 430 00:18:00,090 --> 00:18:01,290 That's a great outcome, by the way. 431 00:18:01,370 --> 00:18:02,170 That's a great outcome. 432 00:18:02,410 --> 00:18:05,370 The second wave is deploying agents in UI, right? 433 00:18:05,450 --> 00:18:08,650 So you have some level of of click-through data, you have 434 00:18:08,650 --> 00:18:11,529 some level of of collection, and then it flips to an agent to 435 00:18:11,529 --> 00:18:12,810 say, hey, I collected all this. 436 00:18:12,970 --> 00:18:14,250 I see that you did all these things. 437 00:18:14,330 --> 00:18:17,290 Now let me help you take this to purchase or let me help you add 438 00:18:17,290 --> 00:18:17,690 on to this. 439 00:18:17,769 --> 00:18:19,610 Let me answer some questions if you have questions. 440 00:18:19,850 --> 00:18:21,930 So in some cases, it would be a hybrid. 441 00:18:22,009 --> 00:18:25,290 And then long term, the UI becomes an agentic interface, 442 00:18:25,370 --> 00:18:25,529 right? 443 00:18:25,610 --> 00:18:29,370 And so this it's sort of a migratory approach to getting to 444 00:18:29,370 --> 00:18:32,810 personalization and high levels of consumer experience. 445 00:18:33,130 --> 00:18:35,130 SPEAKER_01: Yeah, and part of the reason I ask that is a lot 446 00:18:35,130 --> 00:18:39,210 of folks don't realize how much of just the experience the UX is 447 00:18:39,210 --> 00:18:42,570 embedded in everything, even the LLMs that we use every day, the 448 00:18:42,570 --> 00:18:45,450 fact that you can talk to it like a human and you don't have 449 00:18:45,450 --> 00:18:46,970 to learn any kind of syntax. 450 00:18:47,130 --> 00:18:50,410 And you know, let's say you're working on a document or a deck 451 00:18:50,490 --> 00:18:53,690 and it pops up on the side, and now you can read it and talk to 452 00:18:53,690 --> 00:18:56,970 your LLM at the same time, that's all a user experience. 453 00:18:57,050 --> 00:18:59,050 And that gap is closing rapidly. 454 00:18:59,370 --> 00:19:01,690 SPEAKER_00: And context is key there because we we we talk 455 00:19:01,690 --> 00:19:03,130 about context in two forms. 456 00:19:03,290 --> 00:19:05,930 In the example of you have click-through data that then 457 00:19:05,930 --> 00:19:09,529 flips to an agent, for example, we see in our customers, if you 458 00:19:09,529 --> 00:19:12,330 don't have the collection of the web and application behavior 459 00:19:12,330 --> 00:19:15,370 data that you then give to the agent as the person flips to the 460 00:19:15,370 --> 00:19:17,850 agent, the agent starts off with, Hi, how are you? 461 00:19:17,930 --> 00:19:18,570 What do you want? 462 00:19:18,730 --> 00:19:21,290 That that's a waste of time because the consumer just went 463 00:19:21,290 --> 00:19:24,090 through a ton of things that you should, they think you should 464 00:19:24,090 --> 00:19:26,970 already know, but the agent is a separate thing that then starts 465 00:19:26,970 --> 00:19:27,769 them cold. 466 00:19:28,009 --> 00:19:31,050 We take the context data from the previous before the 467 00:19:31,050 --> 00:19:31,930 conversation starts. 468 00:19:32,090 --> 00:19:33,050 We provide that to the agent. 469 00:19:33,130 --> 00:19:36,890 So when you hit the agent, the agent says, Ah, hi Thomas, I saw 470 00:19:36,890 --> 00:19:38,650 that you were looking for this, I saw that you went to the 471 00:19:38,650 --> 00:19:41,610 review page, I saw that you got to the purchase point, I saw you 472 00:19:41,610 --> 00:19:42,650 saw shipping and then stop. 473 00:19:42,810 --> 00:19:44,090 Is it a shipping cost issue? 474 00:19:44,330 --> 00:19:47,050 Now it's an agent that's providing benefit to the 475 00:19:47,050 --> 00:19:47,610 consumer. 476 00:19:47,769 --> 00:19:50,730 We see in our customers that if you don't provide that context, 477 00:19:50,810 --> 00:19:54,890 it's three minutes of wasted resetting of context, which 478 00:19:54,890 --> 00:19:56,810 means you're wasting consumers three. 479 00:19:56,890 --> 00:19:57,450 I mean, three minutes. 480 00:19:57,529 --> 00:20:00,810 If you and I sat quiet for three minutes, that's a long time. 481 00:20:01,450 --> 00:20:05,850 And every business that starts an agent with high and doesn't 482 00:20:05,850 --> 00:20:08,730 know what happened before the conversation started is already 483 00:20:08,730 --> 00:20:10,009 in the experience hole. 484 00:20:10,250 --> 00:20:12,650 Because now I'm frustrated because you don't know what I 485 00:20:12,650 --> 00:20:12,970 just did. 486 00:20:13,050 --> 00:20:15,930 I just spent this time, and I'm gonna spend three minutes 487 00:20:16,330 --> 00:20:17,610 re-explaining myself. 488 00:20:18,090 --> 00:20:18,890 Guess what happens? 489 00:20:19,050 --> 00:20:21,610 I'm either leaving then and we see a high abandonment rate 490 00:20:21,690 --> 00:20:24,410 right then, or I'm gonna do this because I need to get it done, 491 00:20:24,490 --> 00:20:26,650 but I'm never coming back because you don't have a good 492 00:20:26,650 --> 00:20:27,210 experience. 493 00:20:27,370 --> 00:20:30,570 So it wasn't that the agent wasn't accurate, wasn't that it 494 00:20:30,570 --> 00:20:33,529 didn't ask a question, it's that it didn't have the context to 495 00:20:33,529 --> 00:20:35,370 make my experience optimized. 496 00:20:35,450 --> 00:20:38,890 It didn't know enough for me to understand that you hear me, you 497 00:20:38,890 --> 00:20:40,570 see me, and you're here to help me. 498 00:20:40,730 --> 00:20:43,130 You're not just automating a process. 499 00:20:43,769 --> 00:20:44,490 SPEAKER_01: Yeah, yeah. 500 00:20:44,650 --> 00:20:48,410 And so, and returning to something you said earlier about 501 00:20:48,410 --> 00:20:51,450 being a big data company, I want to make sure that I kind of have 502 00:20:51,450 --> 00:20:54,490 the um the segmentation right on y'all. 503 00:20:54,650 --> 00:20:58,890 It you're not trying to be in the same space as sort of like a 504 00:20:58,890 --> 00:21:02,890 full story or pendo or the product data, or or or might 505 00:21:02,890 --> 00:21:05,769 that be kind of roadmap, but today you're solving a more 506 00:21:05,769 --> 00:21:07,210 surgically precise problem. 507 00:21:07,529 --> 00:21:09,450 SPEAKER_00: Yeah, we're picking up where they leave off, right? 508 00:21:09,769 --> 00:21:11,850 All of you know, you look at you look at all the traditional 509 00:21:11,850 --> 00:21:14,970 product analytics companies, they they all do sort of funnel 510 00:21:14,970 --> 00:21:17,690 creation, you know, predefined uh journeys, and then and then 511 00:21:17,690 --> 00:21:20,250 they try and collect, oh, you had some friction here, we think 512 00:21:20,250 --> 00:21:21,210 it probably affected this. 513 00:21:21,290 --> 00:21:24,810 So there's some high level of of impact within the product and 514 00:21:24,890 --> 00:21:26,810 heat maps and session replay. 515 00:21:26,890 --> 00:21:29,130 There's a ton of things that you can do with those tools. 516 00:21:29,290 --> 00:21:31,930 We sort of pick up where they left off in understanding how 517 00:21:31,930 --> 00:21:35,130 people want to buy, which is a larger, more richer data set. 518 00:21:35,370 --> 00:21:37,050 Now you could run them in tandem. 519 00:21:37,130 --> 00:21:39,690 So some some of our customers replace those product analytics 520 00:21:39,690 --> 00:21:42,090 tools because they sort of realize, well, shit, that's a 521 00:21:42,090 --> 00:21:45,370 very low, you know, sort of low, like I said in the start, it's 522 00:21:45,450 --> 00:21:48,570 it's high investment, low marginal gains, um, you know, 523 00:21:48,730 --> 00:21:51,210 versus man, let's invest in consumer behavior patterns 524 00:21:51,290 --> 00:21:53,930 because it exposes what we call precise decisions, right? 525 00:21:54,009 --> 00:21:56,330 I'm making decisions that I can actually move people through 526 00:21:56,330 --> 00:21:59,610 their behavior pattern, but it also sets the stage for agentic. 527 00:21:59,850 --> 00:22:03,529 So the difference between us and all product analytics is we have 528 00:22:03,529 --> 00:22:06,250 a we have a significant improvement in conversion 529 00:22:06,250 --> 00:22:08,170 because we have a direct connection to how people want to 530 00:22:08,170 --> 00:22:10,570 buy and you could solve how they want to buy and personalize 531 00:22:10,570 --> 00:22:13,210 their experience, but it is the foundation for agentic into the 532 00:22:13,210 --> 00:22:13,450 future. 533 00:22:13,610 --> 00:22:16,170 Funnels and agents will never cross, right? 534 00:22:16,250 --> 00:22:18,970 Because again, an agent is a conversation, it's not a 535 00:22:18,970 --> 00:22:20,890 prescriptive sequence of things. 536 00:22:21,050 --> 00:22:21,210 Right. 537 00:22:21,610 --> 00:22:23,850 I could ask a question, you could ask a question, we go two 538 00:22:23,850 --> 00:22:24,730 totally different ways. 539 00:22:24,970 --> 00:22:29,210 The agent has to be agile enough to respond to what we want and 540 00:22:29,210 --> 00:22:31,690 be able to move us through how we want to buy versus you know, 541 00:22:31,769 --> 00:22:32,170 sequence. 542 00:22:32,730 --> 00:22:35,050 The agent's like, well, I'm gonna do this and then this and 543 00:22:35,050 --> 00:22:36,009 then this and then this. 544 00:22:36,170 --> 00:22:36,490 Right. 545 00:22:36,650 --> 00:22:38,090 That doesn't work in agentics. 546 00:22:38,170 --> 00:22:40,810 So the patterns are are beneficial today and set the 547 00:22:40,810 --> 00:22:41,690 foundation for the future. 548 00:22:42,009 --> 00:22:44,570 SPEAKER_01: So you guys are going in to try to really do a 549 00:22:44,570 --> 00:22:48,009 good job at that piece first, and then whoever knows, you 550 00:22:48,009 --> 00:22:50,009 know, where you evolve from there, you know. 551 00:22:50,090 --> 00:22:52,970 And I really love products that really try to, you know, think 552 00:22:52,970 --> 00:22:56,490 of themselves as part of an ecosystem where you are one 553 00:22:56,490 --> 00:22:59,769 specific, you know, plant or animal in that ecosystem that 554 00:22:59,769 --> 00:23:00,810 does a really good job at that. 555 00:23:00,890 --> 00:23:04,090 And then you can just sort of link with the larger structure. 556 00:23:04,250 --> 00:23:06,810 SPEAKER_00: Um that's our hedgehog concept is simple. 557 00:23:06,890 --> 00:23:10,410 It's it's understanding consumer behavior patterns to personalize 558 00:23:10,410 --> 00:23:13,450 a consumer's experience to improve conversion, right? 559 00:23:13,610 --> 00:23:13,930 Yes. 560 00:23:14,170 --> 00:23:16,570 That's that is a that's a very specific methodology. 561 00:23:16,650 --> 00:23:19,450 We're very good at building consumer scale behavior patterns 562 00:23:19,450 --> 00:23:21,850 and reporting on those and trending on those in real time. 563 00:23:21,930 --> 00:23:24,410 And then again, providing that to an agent so that you can 564 00:23:24,410 --> 00:23:26,170 improve personalization to a what we call 565 00:23:26,170 --> 00:23:28,009 hyper-personalization, almost a personal. 566 00:23:28,890 --> 00:23:30,009 SPEAKER_01: How do people get started? 567 00:23:30,090 --> 00:23:33,529 This uh conviva.ai, C O N V I V A. 568 00:23:33,930 --> 00:23:36,330 Do they start by talking to you guys or downloading something? 569 00:23:36,570 --> 00:23:38,570 SPEAKER_00: No, they they they come to the website, they go to 570 00:23:38,570 --> 00:23:38,890 the website. 571 00:23:39,050 --> 00:23:40,330 You know, we we do free POCs. 572 00:23:40,410 --> 00:23:43,050 So if people want to because that's where data comes to life. 573 00:23:43,290 --> 00:23:45,529 So I'll say the difference between us and all product 574 00:23:45,529 --> 00:23:49,050 analytics companies, the the traditional product analytics is 575 00:23:49,130 --> 00:23:52,330 you know, once you see a funnel and product analytics, and then 576 00:23:52,330 --> 00:23:55,610 you see consume the behavior patterns, you and you literally 577 00:23:55,610 --> 00:23:59,450 see all of how your consumers want to buy, it's an it's an 578 00:23:59,450 --> 00:24:00,330 unbelievable experience. 579 00:24:00,490 --> 00:24:04,330 So we do a free POC to actually show digital businesses here's 580 00:24:04,330 --> 00:24:05,529 your consumer behavior patterns. 581 00:24:05,610 --> 00:24:10,090 And by the way, the average behavior pattern of a larger 582 00:24:10,090 --> 00:24:12,490 e-commerce company is over 50 steps. 583 00:24:13,050 --> 00:24:14,170 Put that in perspective. 584 00:24:14,410 --> 00:24:17,370 People are building six and seven-step funnels, but the 585 00:24:17,370 --> 00:24:20,410 average consumer behavior pattern is over 50. 586 00:24:20,570 --> 00:24:22,970 We see some consumer behavior patterns in our large e-com 587 00:24:23,130 --> 00:24:25,529 companies of 150 steps, right? 588 00:24:25,769 --> 00:24:29,690 This behavior patterns are not simple sequential steps. 589 00:24:29,850 --> 00:24:30,730 We want it to be, right? 590 00:24:30,810 --> 00:24:32,650 Because that's how we organize our thoughts, and that's how 591 00:24:32,810 --> 00:24:34,650 product organizes their thoughts. 592 00:24:34,890 --> 00:24:38,170 But if you really think how we buy, we'll have you know 593 00:24:38,410 --> 00:24:41,370 multiple steps we go through to buy anything, and that all gets 594 00:24:41,370 --> 00:24:42,090 lost in a funnel. 595 00:24:42,170 --> 00:24:44,890 So when we talk about bringing the data to life, really 596 00:24:44,890 --> 00:24:48,570 exposing how people want to buy your product is an incredibly 597 00:24:48,570 --> 00:24:49,529 enlightening step. 598 00:24:49,610 --> 00:24:52,170 And we love it because the light bulb comes on, and then all of a 599 00:24:52,170 --> 00:24:54,009 sudden they're like, wow, if I did this, I'd move that. 600 00:24:54,090 --> 00:24:56,970 If I made a marketing decision here, I'd I'd convert much, much 601 00:24:56,970 --> 00:24:57,529 faster. 602 00:24:57,690 --> 00:25:00,810 So there's a ton of things that unlock and become immediately 603 00:25:00,810 --> 00:25:03,450 visible when you really see how people want to buy your product. 604 00:25:03,610 --> 00:25:06,810 Not what they buy, but how they buy. 605 00:25:07,769 --> 00:25:10,410 SPEAKER_01: Wow, super, super interesting stuff and exciting 606 00:25:10,410 --> 00:25:10,650 things. 607 00:25:10,730 --> 00:25:14,890 And you guys are illuminating the nuances and details of that 608 00:25:14,890 --> 00:25:15,290 world. 609 00:25:15,450 --> 00:25:16,490 Um, very exciting. 610 00:25:16,650 --> 00:25:17,930 Everyone, please check it out. 611 00:25:18,009 --> 00:25:19,610 Uh conviva.ai. 612 00:25:20,330 --> 00:25:21,769 Um, anything else, Keith? 613 00:25:22,009 --> 00:25:23,210 SPEAKER_00: No, I appreciate your time. 614 00:25:23,290 --> 00:25:24,170 I I love this topic. 615 00:25:24,650 --> 00:25:27,370 We're on the forefront of an amazing revolution with AgenTic, 616 00:25:27,450 --> 00:25:28,570 and we're just getting started. 617 00:25:28,650 --> 00:25:30,250 So it's a fun time to be in product. 618 00:25:30,330 --> 00:25:31,690 It's a fun time to be in tech. 619 00:25:31,850 --> 00:25:32,650 SPEAKER_01: Oh, heck yeah. 620 00:25:32,730 --> 00:25:33,529 Everybody check it out. 621 00:25:33,690 --> 00:25:34,970 Keith, thanks for being with us today. 622 00:25:35,610 --> 00:25:36,250 Thanks for having me.