บทถอดความฉบับเต็มของ S8E13 - How AI is Reshaping PLG and User Experiences ft. Satya Ganni, CEO at Userflow & Beamer

uh, so many opportunities, um, you know, that we have before us. 00:04:37.843 --> 00:04:38.134 Yeah. 00:04:39.744 --> 00:04:40.444 Definitely. 00:04:40.553 --> 00:04:42.483 That that's a very interesting aspect. 00:04:42.574 --> 00:04:47.374 Um, so then, you know, given, uh, uh, like our today's topic, right? 00:04:47.374 --> 00:04:56.514 Like how AI is like reshaping, uh, PLG and user experiences again, you know, like PLG has been like, uh, it's, uh, it's not new. 00:04:56.514 --> 00:04:58.824 It's been around for, for many years now. 00:04:58.913 --> 00:05:01.153 Um, but it's definitely like AI is. 00:05:01.624 --> 00:05:06.874 Uh, reinventing like the PLG experience, uh, for a lot of companies. 00:05:06.874 --> 00:05:14.084 And, um, and, and I feel like user flow and bemer at are at the core of, uh, core of it, right? 00:05:14.384 --> 00:05:24.004 So, yeah, like, you know what, what, what do you feel like, you know, what, what's that role Right, uh, of AI in, you know, enhancing these PLG strategies? 00:05:25.254 --> 00:05:26.084 Yeah, yeah. 00:05:26.274 --> 00:05:27.884 No, uh, great question again. 00:05:27.903 --> 00:05:34.204 Um, I think if you look at, uh, product led growth, it's predominantly a funnel driven model, right? 00:05:34.504 --> 00:05:37.113 Um, usually there are five stages of this funnel. 00:05:37.504 --> 00:05:41.584 There is acquisition, there is activation, there is engagement, there is conversion. 00:05:41.944 --> 00:05:44.499 Um, so usually you go, you have these. 00:05:44.608 --> 00:05:51.288 You know, um, both phases, I would say AI is definitely influencing every stage of the PLG funnel. 00:05:51.519 --> 00:05:53.559 Like for example, let's take acquisition, right? 00:05:53.619 --> 00:06:06.473 Um, you know, uh, you, you, you, if you're adopting organic strategies to dive, uh, you know, better we start to sign up conversion or a better, Uh, time to sign up or or a reduction in CAC. 00:06:06.483 --> 00:06:11.863 So you can use AI to generate the content, publish the content and drive visitors back, right? 00:06:12.244 --> 00:06:16.793 A second thing is even the personalization of the website or landing pages. 00:06:16.793 --> 00:06:27.278 So for example, if you sell to two, three different user personas, and um, when you use the content to drive them to a website or landing pages, you can actually personalize the landing page. 00:06:27.798 --> 00:06:31.678 landing pages or the websites that suit to their needs, um, right? 00:06:31.678 --> 00:06:33.189 So that's acquisition. 00:06:33.379 --> 00:06:45.338 But even when you look at activation, um, as I said, if you have a large volume of users coming in, how do you cohort them appropriately to say, Hey, for each cohort, I want to accelerate the time to value. 00:06:45.439 --> 00:06:47.228 I want to improve the activation rate. 00:06:47.509 --> 00:06:48.408 Um, similarly. 00:06:48.408 --> 00:06:52.418 So I think, I think at every step of the PLG funnel, you can use. 00:06:52.538 --> 00:06:58.348 Um, a led tools or techniques to be able to personalize and drive better conversion. 00:06:58.369 --> 00:07:00.649 So that's, that's how I would think about it, right? 00:07:00.939 --> 00:07:03.379 Um, PLD is all funnel driven. 00:07:03.608 --> 00:07:13.848 And if you can, uh, think about a led strategies at each stage of the funnel, um, I think that, that, that's what would produce a value. 00:07:15.629 --> 00:07:16.288 Nice, nice. 00:07:16.288 --> 00:07:27.439 That's, that's, uh, you know, I think that would bring a lot of, um, you know, seamlessness as to, to the entire user flow, right? 00:07:27.509 --> 00:07:36.988 Um, where, you know, what they are, they see an ad somewhere, you know, um, you know, which is again personalized and they come to a landing page, which is personalized, they sign up. 00:07:37.389 --> 00:07:38.399 It's again personalized. 00:07:38.399 --> 00:07:47.678 And, you know, uh, then they go through the activation and onboarding, as you mentioned, which is like, again, very personalized to them or their role or their industry, like different levers that you can pull. 00:07:47.759 --> 00:07:48.009 Right. 00:07:48.009 --> 00:07:55.389 So, uh, but I'm sure like, you know, some, some, sometimes, uh, this personalization is, is driven by data, right. 00:07:55.418 --> 00:08:09.329 Um, and data, yes, you know, that you get from, let's say, you know, intent platforms or, you know, something like an Apollo or zoom for, you know, maybe they have collect, you have collected, you know, It's a crunch base or, you know, multiple, you know, sources, right. 00:08:09.329 --> 00:08:13.129 And you never know, like, you know, like how accurate is the data, right. 00:08:13.129 --> 00:08:14.249 Because you're getting it. 00:08:14.509 --> 00:08:25.228 So, and, and, and if you solely like very much rely on, uh, you know, this, this level of personalization, which you, uh, are collecting or you're, we are organizing it. 00:08:25.324 --> 00:08:30.733 Um, from data that of collecting from multiple sources, the personalization can go for a toss as well. 00:08:30.774 --> 00:08:30.983 Right. 00:08:30.983 --> 00:08:43.754 So it's sometimes like, obviously it can be great if you blend it correctly, but because of like data not being right, might be like one problem, but there can be like several other problems, uh, where it can go wrong as well. 00:08:44.043 --> 00:08:44.323 Right. 00:08:44.484 --> 00:08:51.533 So where do you think in your experience where you've seen like PLG really win and in, in, in some cases where it, you know, it does fail. 00:08:53.813 --> 00:09:03.984 Yeah, I think, um, I, I, you know, my personal belief is that, uh, still there are so many misnomers about what, uh, PLG is and how it works and why it works, right? 00:09:04.374 --> 00:09:10.734 Um, I think the number one precursor to PLG, uh, is you absolutely have to be in a massive market. 00:09:11.333 --> 00:09:12.413 that's number one, right?

00:09:12.443 --> 00:09:13.818 So if you, if you. 00:09:14.234 --> 00:09:19.583 If you're selling in a very, very niche market, um, then then PLJ is not the best fit for you. 00:09:19.594 --> 00:09:24.124 So obviously, you know, you want to be in a wider, um, and a large market. 00:09:24.163 --> 00:09:33.724 I think the second, uh, uh, precursor, uh, is that you want to have some elements of, uh, uh, virality, um, in your app or collaboration, right? 00:09:33.734 --> 00:09:39.844 So for example, Uh, products like Slack or, or Zoom is a perfect example, which is what we are on, right? 00:09:39.844 --> 00:09:42.394 So I can't do Zoom talking to myself, right? 00:09:42.394 --> 00:09:44.153 I need a person on the other side of it. 00:09:44.183 --> 00:09:48.653 So that means that I have to invite you and you have to be part of the conversation. 00:09:48.653 --> 00:10:00.193 So I think certain products have these natural ingredients, whether you call Miro, whether you call Slack or Zoom, and you know, a lot, a lot, you know, wide majority of SaaS products don't have this virality, right? 00:10:00.394 --> 00:10:08.163 So first of all, I think we have to, uh, figure out, uh, the misnomers of, uh, PNG, um, and, and, uh, do it. 00:10:08.394 --> 00:10:13.553 Second thing is, I think the inter, the con, uh, interfaces are changing very rapidly. 00:10:13.604 --> 00:10:30.469 I think we'll see more and more of, um, I think right now we have this, uh, classic, uh, application structure model where you have a front end that's driven by, uh, you know, components and, and, uh, you guys, I think, um, it will change to more conversational interfaces. 00:10:30.469 --> 00:10:32.609 It will change to more inside. 00:10:33.323 --> 00:10:37.193 When, um, uh, conversational interfaces, right? 00:10:37.193 --> 00:10:41.083 So I think that shift we're in the early days of making that shift happen. 00:10:41.433 --> 00:11:01.163 Um, as I said, uh, PLJ as a, you know, uh, as a standalone is, is one aspect of it, but I think you have to look at holistically in terms of your applications, the experience to the end users, um, and, and some of the metrics, I think, I think we are in a moving ship, um, here, especially with the AI coming into the mix, right? 00:11:01.163 --> 00:11:01.604 So. 00:11:01.744 --> 00:11:03.083 Still, still early days. 00:11:03.083 --> 00:11:03.364 Yep. 00:11:05.553 --> 00:11:05.903 Nice. 00:11:05.903 --> 00:11:06.124 Nice. 00:11:06.124 --> 00:11:08.354 That's, that's a good, good way to look at it. 00:11:08.474 --> 00:11:08.813 Right. 00:11:08.894 --> 00:11:17.259 Uh, Uh, but, but, you know, taking a step back and just like, you know, talking about, uh, that data that, that we're collecting like more on that, right. 00:11:17.259 --> 00:11:25.369 Uh, obviously cannot control, um, you know, the, the quality of data that you get from these third party platforms, right. 00:11:25.399 --> 00:11:41.714 But you, uh, generally like, you know, as you mentioned correctly in a PLG offering, right, um, where, you know, these, these are more open tools where You know, uh, there is a sort of fireality built into the picture and, you know, and, and these tools, like all the men, the ones that you mentioned, right. 00:11:41.714 --> 00:11:49.474 Even Slack or zoom or, you know, Miro and, you know, Figma and all of these, like they have a, they have an onboarding process, right. 00:11:49.474 --> 00:11:52.124 Or any PLG onboarding process. 00:11:52.163 --> 00:11:52.394 Right. 00:11:52.783 --> 00:11:55.744 Um, and, and, and during that onboarding process, you collect like. 00:11:56.298 --> 00:12:00.359 Uh, some information about like the user, right? 00:12:00.389 --> 00:12:03.678 Typically what's their role, like, where do they hear about you? 00:12:04.089 --> 00:12:11.668 Um, you know, what is the, what is the use case that they're trying to, uh, you know, uh, trying your product out for, right? 00:12:11.739 --> 00:12:13.808 Uh, what department do they come under? 00:12:13.958 --> 00:12:14.249 Right. 00:12:14.249 --> 00:12:16.889 And, and maybe some more right here and there. 00:12:16.969 --> 00:12:28.293 Uh, obviously they, you have their email, so you can figure out like what company do they work for, what industry, you know, how big is the company, what industry they belong to, and, you know, some more information there. 00:12:28.653 --> 00:12:41.859 But, uh, you know, I've seen often that, um, like companies do collect this information, but, uh, you know, they, they don't necessarily personalize the, Um, the product, right. 00:12:41.889 --> 00:12:49.649 Based on that information, just to give an example, if, uh, and for the listener as well, if you have signed up on Canva ever, right, like very easily. 00:12:50.089 --> 00:12:52.249 So they do have a very good onboarding, right? 00:12:52.249 --> 00:12:53.379 Like you sign up on Canva. 00:12:53.418 --> 00:12:55.269 They ask you like, what's the role they ask you? 00:12:55.269 --> 00:12:56.849 Like, what do you want to do with Canva? 00:12:57.269 --> 00:13:00.778 And, you know, some things and, and obviously they know what's my industry. 00:13:00.828 --> 00:13:04.589 They have all that information about my role, my responsibilities, my use case. 00:13:04.889 --> 00:13:12.119 But still, when I land on Canva, they still like show me, uh, the, the, there is nothing like personalized in the home screen. 00:13:12.129 --> 00:13:12.328 Yes. 00:13:12.369 --> 00:13:12.708 Right. 00:13:12.879 --> 00:13:19.839 For me versus let's say you sign up, like Satya, Satya signs up or I sign up or somebody else signs up. 00:13:20.168 --> 00:13:21.749 Like all of us see the same home screen. 00:13:21.788 --> 00:13:23.818 Then what's the point of collecting that data? 00:13:24.168 --> 00:13:24.479 Right. 00:13:24.528 --> 00:13:26.448 Uh, that's, that's what makes me think, right. 00:13:26.448 --> 00:13:37.823 So how can companies in your experience that they're like, how can

companies go from, you know, just not just collecting data, but also like taking actions, uh, on using this data. 00:13:39.394 --> 00:13:39.744 Yeah. 00:13:39.783 --> 00:13:40.053 Yeah. 00:13:40.364 --> 00:13:43.403 As I mentioned, I think we're in the very, very early days. 00:13:43.494 --> 00:13:48.043 Um, I think you see a lot of, uh, uh, use cases in the B2C world, right? 00:13:48.104 --> 00:13:54.964 Um, I would, I usually, whenever, you know, people are looking at some trends in B2B, I point them to B2C. 00:13:55.153 --> 00:14:03.114 I would say look at what B2C has been doing because in a couple of years, B2B SaaS would very well emulate, right? 00:14:03.114 --> 00:14:10.484 So that's where even when you log into some of the social apps, whether it's Instagram, Access, you know, I think that's a personalized experience, right? 00:14:10.484 --> 00:14:11.524 So they're using data well. 00:14:12.313 --> 00:14:18.144 To really personalize the, um, uh, personalize the experience or, or the feed, right? 00:14:18.413 --> 00:14:26.094 Um, I would say in, in, in terms of SAS, we're still in early days of, of bringing that level of personalization. 00:14:26.344 --> 00:14:32.943 And I'm sure whether it's Canva or somebody else, they're deeply thinking about, Hey, what is the problem the user is trying to solve? 00:14:33.254 --> 00:14:35.193 How can we personalize the experience? 00:14:35.193 --> 00:14:38.783 And maybe, maybe the answer is sometimes personalization is not required. 00:14:38.783 --> 00:14:38.974 Thanks. 00:14:39.208 --> 00:14:42.489 Um, because he's trying to do an activity to derive an outcome. 00:14:42.509 --> 00:14:44.458 So let's go stay focused on that. 00:14:45.188 --> 00:14:47.219 Um, let's go stay focused on that. 00:14:47.219 --> 00:14:47.458 Right. 00:14:47.458 --> 00:14:50.609 So, so that is also, uh, possible, uh, right. 00:14:50.639 --> 00:15:03.788 Um, but, but I think more and more you will see, uh, applications, especially the borderlines of, uh, prosumer, um, um, you know, self serve and everything else that start to span over. 00:15:03.818 --> 00:15:07.129 And, and, uh, I think majority of the PLD companies. 00:15:07.244 --> 00:15:09.104 is not limited to a geo. 00:15:09.364 --> 00:15:12.443 Um, like zoom doesn't sell in us, right? 00:15:12.693 --> 00:15:15.614 It is used by users across the globe, right? 00:15:15.663 --> 00:15:16.803 Um, across the globe. 00:15:16.823 --> 00:15:27.714 I think when you see that huge diversity of, um, users coming from, uh, uh, various geos, various, uh, languages, et cetera, I think personalization would play a big role. 00:15:28.033 --> 00:15:33.533 So I think, yeah, the SAS apps would evolve just in the early, early days of that experience. 00:15:33.533 --> 00:15:33.823 Yeah. 00:15:34.793 --> 00:15:35.144 Got it. 00:15:35.283 --> 00:15:46.913 But, uh, again, like what, what, what is the problem that you feel, which is there, which hasn't allowed like SaaS apps to. 00:15:47.403 --> 00:15:49.453 Like, you know, be at that stage, right? 00:15:49.453 --> 00:15:59.043 Because like, see, Instagram has been doing these personalization, like a lot of the Instagram, but you know, the, the, you know, B2C or D2C platforms, right? 00:15:59.043 --> 00:16:00.423 Like even e commerce, right? 00:16:00.423 --> 00:16:06.203 Like the more, like you go to Amazon and you keep buying like, you know, uh, more and more stuff. 00:16:06.224 --> 00:16:14.474 And then like the feed kind of keeps changing to, uh, to, uh, a more personalized feeds as to like what you would buy. 00:16:14.639 --> 00:16:15.019 Right. 00:16:15.219 --> 00:16:18.428 So my Amazon feed would look like very different from your feed. 00:16:19.114 --> 00:16:22.774 And, you know, that's the same aspect for any B2C platform, as you mentioned. 00:16:22.774 --> 00:16:23.053 Right. 00:16:23.053 --> 00:16:27.494 And, uh, and this has been there since like many, many years, right. 00:16:27.673 --> 00:16:30.173 Even before like AI came into the picture, right. 00:16:30.183 --> 00:16:42.494 So like now with AI into the picture, I think it should be more easier and more, you know, um, like, you know, easier to get basically more easier to get into, uh, doing something like this. 00:16:42.504 --> 00:16:48.933 So what, what is it that you feel in your opinion is stopping the companies? 00:16:48.943 --> 00:16:49.173 This is it. 00:16:49.948 --> 00:17:01.499 Um, like, because they don't have the right data, they don't have the right tools, they don't have the right, uh, you know, process or systems or like, what is your, uh, opinion on this? 00:17:01.509 --> 00:17:01.859 Yeah. 00:17:01.918 --> 00:17:02.328 Yeah. 00:17:02.408 --> 00:17:02.729 Yeah. 00:17:03.089 --> 00:17:12.709 I think this, uh, one of the secret sauce of many PLG companies is extremely strong foundation in data, like, like every, every bit and piece of data, right? 00:17:12.719 --> 00:17:13.828 So we talked about the funnel. 00:17:14.499 --> 00:17:19.239 We talked about, uh, um, funnel conversion, funnel stages, all of it, right? 00:17:19.239 --> 00:17:24.638 So a lot of that is, is, you know, experimentation, um, is driven by data. 00:17:25.038 --> 00:17:35.189 And, and, uh, one is, of course, you, you have the data about the user, um, the org, the corresponding org that he belongs to, um, and, and et cetera. 00:17:35.189 --> 00:17:42.739 Second thing is, I think the actions that user do, that's where I think tools like product analytics are very, very important because. 00:17:43.124 --> 00:17:57.124 What happens is when you get a first time user is actions in the app are very, very critical, you know, very, very critical because that tell you something that he or she might not have told you, um, in a, in a zoom call or something, right? 00:17:57.144 --> 00:17:58.294 Is he using the app? 00:17:58.294 --> 00:17:59.663 What features is he using? 00:18:00.009 --> 00:18:04.709 How deeply the feature is adopted, uh, why is he not

adopting the feature? 00:18:04.729 --> 00:18:05.019 Right. 00:18:05.058 --> 00:18:07.378 Um, is it a discovery problem? 00:18:07.598 --> 00:18:10.939 You know, is it a value problem, et cetera, et cetera, right? 00:18:10.939 --> 00:18:27.648 So again, you, you have to use that product data combined with the, um, uh, combined with his, uh, Uh, you know, behavioral data on the website combined with his formographic data, formographic means, you know, uh, again, the location, the org, um, where he belongs. 00:18:27.648 --> 00:18:39.479 So unless you combine all three types of data, you have to combine the formographic, the, you know, behavioral data that he exhibits, uh, on the website and blog and other sources or to your email campaign. 00:18:39.638 --> 00:18:40.878 The third thing is in app. 00:18:41.413 --> 00:18:47.273 And I think you have to combine all of it to really understand what is the user trying to accomplish, right? 00:18:47.324 --> 00:18:52.203 You know, why did why did somebody from a mastercard signed up on this particular? 00:18:53.013 --> 00:18:55.124 Application what is he trying to behave? 00:18:55.124 --> 00:19:11.239 So the foundation for a lot of stuff Um, and that's where I think you probably need a data store or whatever, so that you're able to bring in all this data together and then understand the account or the user. 00:19:11.479 --> 00:19:14.989 And then you can think about personalization or, or anything like that. 00:19:14.989 --> 00:19:20.788 So I think the secret sauce to a lot of PLG, very successful PLG companies is data, massive data. 00:19:20.818 --> 00:19:21.088 Yeah. 00:19:21.088 --> 00:19:22.249 Nice. 00:19:22.429 --> 00:19:22.828 Nice. 00:19:23.048 --> 00:19:23.068 Yeah. 00:19:23.068 --> 00:19:25.338 I think that's, that's, um, that's, that. 00:19:26.128 --> 00:19:26.568 For sure. 00:19:26.568 --> 00:19:34.898 I mean, like for any, any, uh, uh, like motion, whether it's PLG or sales led or, you know, some, something else, right? 00:19:34.909 --> 00:19:39.808 Like I feel like data is like at the core of it for, uh, for everything, right. 00:19:39.818 --> 00:19:42.588 Like, uh, to help you take informed decisions. 00:19:42.598 --> 00:19:42.888 Right. 00:19:43.249 --> 00:19:49.338 So, uh, and, and I, I know we talked about, and you also mentioned a little bit like, you know, a bunch of tools, right. 00:19:49.368 --> 00:19:54.659 That, um, That generally makes all of this possible, like makes all the PLG possible. 00:19:54.769 --> 00:19:55.028 Right. 00:19:55.028 --> 00:20:02.009 So, um, what do you think are some of these platforms or what do you think some of these tools that, you know, help. 00:20:02.759 --> 00:20:06.298 Companies put together this whole PLG motion, right? 00:20:06.378 --> 00:20:18.659 Uh, we say like data and then data tools is like one aspect or one category, but then I'm sure like, you know, there's like a whole lot of other, uh, platforms that are required. 00:20:18.659 --> 00:20:25.888

And I'm sure like, you know, get Beamer like Beamer and user flow are, um, you know, like, you know, part of that tool stack. 00:20:26.449 --> 00:20:31.499 So yeah, maybe you can share more, uh, on the tools that enable PLG. 00:20:32.683 --> 00:20:33.903 Sure, sure, sure, sure. 00:20:34.364 --> 00:20:35.943 Yeah, no, uh, absolutely. 00:20:35.983 --> 00:20:40.773 Um, I think, uh, I would broadly classify into two buckets. 00:20:40.794 --> 00:20:42.124 Um, uh, right. 00:20:42.163 --> 00:20:49.644 Especially for product teams that is new to drive, you know, better, better activation, better engagement, better conversion, right? 00:20:49.913 --> 00:20:54.534 Um, there is the quantitative side of the house and then there is the qualitative side of the house, right? 00:20:54.614 --> 00:20:58.153 And combining both is super, super important. 00:20:58.554 --> 00:21:07.608 And on the quantitative side, I would put something like product analytics, which is a must have, uh, for every B2B SaaS, uh, PMs, right? 00:21:07.909 --> 00:21:15.679 Um, uh, and then you have things like, you know, user onboarding or, you know, product adoption and product engagement platforms. 00:21:16.058 --> 00:21:32.818 Um, the third is what I call experimentation, you know, and, and feature flagging, because you would want to run experimentation, um, within your own application, uh, to say, hey, you know, um, Are our people signing up converting better through path number one or through path number two? 00:21:32.848 --> 00:21:35.528 So experimentation is a very big piece of it. 00:21:35.838 --> 00:21:50.048 Um, fourth thing is the feedback I think the ability to collect qualitative feedback Um, you know about the experience itself what is working what is not working etc, etc So I would classify this toolkit into four or five pillars one. 00:21:50.058 --> 00:21:55.028 There is the Usage aspect of it, which is driven from, uh, product analytics tool. 00:21:55.038 --> 00:22:14.618 There is the experimentation aspect of it is, which is driven by, um, experimentation, feature flagging, all, all of those, um, uh, tooling, um, third is you have, uh, you know, the engagement side of the house, which is where we play, um, onboarding, you know, feedback, um, you know, uh, driving, driving, uh, adoption. 00:22:14.818 --> 00:22:24.913 And fourth is, you know, there are There are tooling like, you know, session replay that allows you to watch user sessions and, and figure out a, which users have, have stuck through the journey, et cetera. 00:22:25.134 --> 00:22:36.673 So usually there are four or five pillars, um, that are, that are, uh, that, that are quite important, especially in context of B2C, B2B SaaS, I think B2C is a whole different world, um, because the level of. 00:22:37.019 --> 00:22:41.048 Uh, infrastructure and the tooling they require to personalize the experience. 00:22:41.358 --> 00:22:44.499 Um, I think that that has evolved significantly. 00:22:44.499 --> 00:22:46.259 I think B2B SaaS is just catching up. 00:22:46.298 --> 00:22:46.509 Yep. 00:22:47.798 --> 00:22:48.179 Nice. 00:22:48.179 --> 00:22:48.499 Nice. 00:22:48.548 --> 00:22:49.888 Very, very interesting. 00:22:50.409 --> 00:22:55.538 Uh, and, and I think great, great set of tools, um, and, and good pillars. 00:22:55.628 --> 00:22:59.118 Like I've not PLG motion. 00:22:59.558 --> 00:23:04.419 Um, just fairly worked with a few companies, uh, on the side for that. 00:23:04.449 --> 00:23:06.538 And, uh, we did have. 00:23:07.183 --> 00:23:19.574 Like same pillars, I would say, um, you know, that, that would help us like, you know, enable, um, the PLG or drive the PLG motion, uh, for the product. 00:23:19.703 --> 00:23:19.963 Right. 00:23:20.003 --> 00:23:22.183 So, uh, great stuff there. 00:23:22.193 --> 00:23:33.064 So before we move on to the last section of the episode, which is the lightning round, um, you know, I, I know we, you touched upon like, Hey, we are in the very early stages of the whole PLG. 00:23:33.588 --> 00:23:34.729 Uh, scenario. 00:23:34.979 --> 00:23:35.318 Right. 00:23:35.358 --> 00:23:43.689 So what, what do you feel like, what's next for PLG then, like, you know, in your opinion, like, okay, what's, what's the future of PLG lies in the next few years? 00:23:44.798 --> 00:23:46.638 Yeah, yeah, no, absolutely. 00:23:46.939 --> 00:23:55.259 I think, uh, uh, uh, again, uh, companies like Atlassian probably, you know, two decades ago kicked off this trend, right. 00:23:55.288 --> 00:23:59.989 And more and more, uh, companies have been adopting this, uh, trend. 00:24:00.078 --> 00:24:00.098 Yeah. 00:24:00.098 --> 00:24:00.169 Absolutely. 00:24:00.229 --> 00:24:08.298 I think a broader, uh, thesis is if you look at, um, a typical sales lead, um, they have high CAC and high LTV, right? 00:24:08.328 --> 00:24:10.219 You know, that's how sales lead works. 00:24:10.608 --> 00:24:16.788 Uh, if you take a typical PLG, probably they have lower cac, um, but have medium, uh, LTV, right? 00:24:16.818 --> 00:24:20.239 Uh, sometimes they might get lucky and have very high LT. 00:24:20.568 --> 00:24:27.358 So that's the fundamental limits of, you know, whether you go sales led or, or a PLG and both are phenomenal. 00:24:27.798 --> 00:24:36.699 Um, I would feel that a vast majority of the PLG companies, uh, go, go into a hybrid mode, hybrid mode, in a sense, they would have. 00:24:37.128 --> 00:24:39.459 Product led growth and product led sales. 00:24:39.878 --> 00:24:42.618 So which is, which is like a sales assistant motion, right? 00:24:42.628 --> 00:24:52.229 You might not have sellers on the street, on the field, uh, but you might have sellers within, you know, within, within your own office or work remotely to serve the customer. 00:24:52.229 --> 00:24:55.669 So I would say 99 percent of the PLG companies. 00:24:55.828 --> 00:24:58.028 Uh, go, go into a hybrid model, right? 00:24:58.249 --> 00:25:02.009 Where you have product led growth, which,

uh, addresses yourselves or users. 00:25:02.249 --> 00:25:10.459 And then you have a product led, um, sales motion where your, uh, sales and solutioning team is helping customers, uh, resolve the problem. 00:25:10.699 --> 00:25:14.909 So I would frame that model as something like an inbound led sales assist, right? 00:25:14.909 --> 00:25:17.259 So you're still driving the needs inbound. 00:25:17.614 --> 00:25:19.973 But you're using the sales assist to do it. 00:25:19.983 --> 00:25:22.433 It's not for say classic PLG. 00:25:22.534 --> 00:25:25.993 Um, it's not self serve, but I would call it product led sales. 00:25:26.003 --> 00:25:31.663 So I think at the intersection of, uh, uh, PLG and PLS is where the future lies. 00:25:31.663 --> 00:25:31.953 Yeah. 00:25:34.179 --> 00:25:44.038 Yeah, I think, I think this, and this is something like a lot of companies, um, like have come into this specific category, which enable, uh, product led sales as well. 00:25:44.259 --> 00:25:44.648 Right. 00:25:44.709 --> 00:26:12.483 Um, uh, and, and, and I think that that would be, uh, like, you know, I, I'm not, I've not been like very far, like Mary was following these companies, but I'm sure like they've, they've already been using AI to, uh, to, You know, drive, uh, or like, you know, precisely dark or with precision, tell you like, Hey, these are, this is the next company, uh, or prospect, uh, account that, you know, that, uh, is going to upgrade. 00:26:13.163 --> 00:26:13.413 Right. 00:26:13.413 --> 00:26:19.144 So why not give them a little nudge, uh, and, and, you know, take them to the finish line. 00:26:19.364 --> 00:26:19.604 Right. 00:26:19.604 --> 00:26:20.263 So, yeah. 00:26:22.973 --> 00:26:23.263 Great. 00:26:23.304 --> 00:26:23.594 Great. 00:26:23.614 --> 00:26:24.064 Satya. 00:26:24.144 --> 00:26:28.483 Uh, so, you know, moving on to the lightning round, the last section of the episode. 00:26:28.894 --> 00:26:38.473 So, uh, I've already asked you like too many questions, but you know, I'm just going to ask you like three more and, uh, then you have to answer like with whatever is on top of your mind. 00:26:38.604 --> 00:26:38.884 Right. 00:26:38.884 --> 00:26:42.413 So, uh, starting with the first one, right. 00:26:42.423 --> 00:26:44.993 What's the one thing that you wish you would have known? 00:26:45.118 --> 00:26:47.638 Yeah. 00:26:48.719 --> 00:26:57.469 I think, uh, if I had, uh, a coach or a mentor, um, uh, when I first started, I think I would have been the next more successful. 00:26:57.979 --> 00:27:10.288 I think, uh, people vastly underestimate the impact of a, of a very solid, um, you know, CEO coach or a founder coach or someone, someone who actually went through your shoes. 00:27:11.064 --> 00:27:25.584 A couple of years, uh, he'd write again, maybe those people are also busy, but if I had a coach that could mentor me from, from my very early days of starting, because I took a lot of time to learn, um, uh, maybe, maybe it's my very

nature. 00:27:25.584 --> 00:27:27.673 I took a lot of time to learn, but had a coach. 00:27:27.693 --> 00:27:33.384 See, uh, one thing you have to know is even people in the top one percentile in their field requires coaches. 00:27:33.433 --> 00:27:34.163 Look at Olymp. 00:27:34.354 --> 00:27:41.003 You know, people who go to Olympics or people who are like top one percentile in their sports, they also have absolute coaches, right? 00:27:41.003 --> 00:27:43.253 So, um, are they not talented? 00:27:43.253 --> 00:27:43.894 Are they not skilled? 00:27:44.054 --> 00:27:47.663 No, I think, uh, there are certain, uh, behavioral aspects. 00:27:47.663 --> 00:27:51.483 There are certain, uh, uh, aspects that a coach can double. 00:27:51.513 --> 00:27:57.374 So I would say if I go back, uh, decades ago, uh, I would love to, you know, get, get a mentor or a coach. 00:27:57.374 --> 00:27:57.624 Yeah. 00:27:58.973 --> 00:27:59.273 Nice. 00:27:59.273 --> 00:28:00.864 That's, that's a very interesting take. 00:28:00.864 --> 00:28:18.784 I'm, uh, I'm sure I'm Uh, like, you know, there's few people who after listening to this, like would go and, uh, you know, find a coach or maybe they already, if they already have one, they, they would have their faith reinstated on, uh, if, if they are losing in like, why should I have a coach and all of that? 00:28:19.423 --> 00:28:22.594 Um, nice, uh, onto the last one, right? 00:28:22.594 --> 00:28:26.013 So what's the one book recommendation that you have for the audience? 00:28:28.023 --> 00:28:40.308 Um, I think, I think I'm assuming your audience, uh, um, you know, you're, you're, you're, um, you know, your audience are mostly founders, CEOs and, and, uh, SAS, um, operators. 00:28:40.638 --> 00:28:47.794 I think recently I read a book, um, which is, um, Uh, thinking fast and slow by Daniel Kuhn. 00:28:48.163 --> 00:28:54.483 I think it's an amazing book that everyone should read because sometimes we make very fast decisions. 00:28:54.493 --> 00:28:56.013 Sometimes we make slow decisions. 00:28:56.054 --> 00:29:00.483 I think there is a rational why we make fast decisions and why we make slow decisions. 00:29:00.933 --> 00:29:10.794 Um, I felt it's an amazing book that gives you a deeper, um, underpinnings of why we, you know, why we think fast and make decisions and, you know, sometimes why we move slow. 00:29:10.794 --> 00:29:11.713 It's an amazing book. 00:29:11.733 --> 00:29:13.884 It's a strongly recommend that everyone reads that. 00:29:14.003 --> 00:29:14.023 Yeah. 00:29:14.584 --> 00:29:15.104 Nice. 00:29:15.104 --> 00:29:15.294 Nice. 00:29:15.663 --> 00:29:15.874 Yeah. 00:29:15.874 --> 00:29:28.044 I've, I've read it personally and like, definitely it's, it's a, it's a great one for, for everyone that either, like, not just like operators or SaaS founders or anything, but, uh, I think it's a, it's a good read for in, in general. 00:29:28.644 --> 00:29:30.054 Um, all right. 00:29:30.374 --> 00:29:30.773 Awesome. 00:29:30.894 --> 00:29:46.878 Uh, thanks again for coming on and, you know, sharing all these insights with us and, you know, um, like everything about PLG and I'm sure like, You are at the, uh, you know, you're, you're building like two companies, which enable PLG, uh, to the core. 00:29:46.999 --> 00:29:47.358 Right. 00:29:47.388 --> 00:29:50.909 Um, so, um, great, great stuff there. 00:29:50.919 --> 00:29:56.509 So thanks again, once again, to, uh, to come on the show, take some time out and share all these insights. 00:29:58.038 --> 00:30:02.419 No, really appreciate your time and thanks for the opportunity Sunil. 00:30:02.469 --> 00:30:12.919 Um, I think in our case, we are building, you know, one company, of course, like every other multi product, a SaaS company, I believe most successful SaaS companies become multi product. 00:30:13.249 --> 00:30:22.739 So we are a single company, two product lines, um, and looking, looking forward to, you know, building, um, a product adoption and engagement platform. 00:30:22.749 --> 00:30:25.548 We are very specifically focused on SaaS product teams. 00:30:25.558 --> 00:30:26.219 So, so that's. 00:30:26.534 --> 00:30:29.223 Kind of where we serve, but, uh, really thanks for the opportunity. 00:30:29.223 --> 00:30:29.523 Yeah. 00:30:29.523 --> 00:30:30.778 Awesome.