Episode 152:

3 Steps To Enhance Product Analytics

August 23, 2023

This week on The Data Stack Show, Eric and Kostas chat with Ken Fine, the CEO at Heap. During the conversation, the group discusses the unique auto track capabilities offered by Heap in the product analytics space. They also delve into Ken’s background, including his experience working on submarines and his transition to technology and startups. Ken explains the three elements of Heap’s solution framework, which include data capture, data science capabilities, and providing qualitative context. The conversation also covers the challenges faced by Heap in collecting and managing large volumes of data, their strategy and positioning in the market, the future of the data industry, integrating quantitative and qualitative data in product management and more.


Highlights from this week’s conversation include:

  • Ken’s background and journey to Heap (2:32)
  • Heap’s problem-solving approach (8:19)
  • Auto-capture and its significance in the marketplace (13:03)
  • Providing qualitative context: sessions and surveys (16:23)
  • Collection and storage of data (25:42) 
  • Challenges of real-time data collection (26:40)
  • The true gap in the market today (37:39)
  • Consolidation and aggregation of data solutions (41:58)
  • Simplifying the datastack (47:32)
  • A different approach in engineering and software development (51:12)
  • Skills and Stages in Company Growth (55:58)
  • Final thoughts and takeaways (1:02:52)


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RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.


Eric Dodds 00:03
Welcome to The Data Stack Show. Each week we explore the world of data by talking to the people shaping its future. You’ll learn about new data technology and trends and how data teams and processes are run at top companies. The Data Stack Show is brought to you by RudderStack, the CDP for developers. You can learn more at RudderStack.com. Welcome back to The Data Stack Show. We are talking today with Ken from Heap. He’s the CEO at Heap. And costus. I remember when maybe, I don’t know if it’s when he came out. But I remember really early days, he was a player in the product analytics space. And one of the things that was really unique about Heap was that they offered auto track capabilities, right. So instead of sort of, you know, anyone familiar with tracking data and a website or a product or whatever, he sort of instrument events and sort of track those, whatever, right. And he, you know, it’s like, almost like Google Analytics, where you just install the script, and it just collects all of that data, every single user interaction, but as raw data, you know, that, you know, you can visualize, there’s, you know, 20 reps, or whatever. And that is so interesting to me. Because having been in and around the space for a long time and even interacted with some autotrac functionality. It’s phenomenally difficult on a number of different levels. And Heap seems to be the only one who sort of emerged from the analytics, you know, fray. Who actually still does it, you know, and seems to do a really, I know, there’s a little background. But that’s fascinating to me, from a user experience standpoint, from a technical standpoint, from a cost standpoint. I mean, I just want to know how they did that. So that’s my burning question. How about you?

Kostas Pardalis 02:05
Yeah, well, the truck is very interesting. It will be interesting, like to hear from him, like, the story behind it. I mean, for me, like, first of all, we have outside of like the technical conversation that we can have with him, or like the product conversation we have, like, also like a very interesting person, in terms of like, personal, like, professional journey, right? We have like a person who did like crazy. I mean, he lifts like the Navy to go and get into like technology. That was before the.com, bubble, built companies went public, around public companies, and ended up going back to startups again. So it’s one of like these very rare, extremely rare, like cases where we have like a founder, who has done like the whole journey from beginning to end. And I think it will be very interesting, like to ask a few questions about that journey, and ask him to share some learnings with us.

Eric Dodds 03:16
100%. Well, let’s dig in and talk with Ken. Good. Ken, welcome to The Data Stack Show. Very excited to chat. Thank you. Great to be here. All right. And well, let’s start where we always do your career actually started working on submarines, which is absolutely fascinating. So take us back to the beginning and tell us you know, sort of what led you to Heap?

Ken Fine 03:41
Sure, be happy to do so. So to chart the path to Heap probably the best place to start is college. So I studied engineering in college when I was a young kid was trying to figure out how to pay for college. And one of the methods familiar avenues for me was a military scholarship. So went to college on a navy scholarship and then served within the Navy as an engineer and a physicist, as part of a team of people that designed propulsion systems for submarines. So during that time, I learned how to work in team environments to solve complex technical problems. These problems are complicated enough that you couldn’t go off by yourself and your cube and your to your desk and figure it out love doing that decided once my journey was complete with the military that I wanted to learn more about building companies that were based on technology based companies and products. So my wife and I got in our car. We lived in Washington, DC, I worked at the Pentagon back then. And we drove to San Francisco sight unseen. And I’ve been here ever since I end up going to business school out here at Stanford. And then if now spent the last 25 years in primarily b2b SaaS Companies all high growth, venture backed. And the common theme has been companies that are doing something interesting with large sets of data and applying some type of algorithmic IP on top of the data to help extract nuggets of value for the users. So that’s a little bit about me and my journey and what took me eventually, here to Heap.

Eric Dodds 05:24
Yeah, fascinating. Okay, I do have to ask, did you ever get to go on to the submarine? Or were you just designing the propulsion systems because you know, people say they’re cramped, I guess the new ones are a lot better than they used to be.

Ken Fine 05:37
I went to go, I went to see on submarines got to see the work that we were doing. And I got to go to the shipbuilders locations where they built the submarines. So a big part of my life was back in DC, at the Pentagon doing design work, but then we get out and go to Newport News, shipbuilding electric boats, where they built submarines and inspected and designed and test the work we did, we’d also get to work with the sailors themselves that operated these systems, these systems were remarkably complicated. So part of my job was to run classes, and teach people about these systems, and then go to see and and see them operate. So I was fortunate that I would do that for shorter periods of time, I get to eventually go back home, and sleep in my bed in DC, which my wife was happy about. Yeah. Yeah, I went to see, and, and also to the shipping locations.

Eric Dodds 06:32
Very cool. Now, I may be drawing too close of a connection. But it seems like that experience, did that influence the way you thought about customers? Right? I mean, in many ways. The operators of the submarines, you know, in some ways are a customer of what you were designing and building. Did you take a similar approach when you got into software? In a way? I

Ken Fine 06:55
mean, effectively, if you look at my role, I described it as being an engineer, being a physicist, I mean, that was the technical part of what we did. So I was technically designing products, if you will. But if you really look at the actual work that we did, it was part engineer, part product manager, like there weren’t product managers in that world. So basically, the engineers, we were the product managers. Oh, interesting, that we were out with our customer means different environment, they’re not actually buying products, and then Vyper outward, which are the commanding officers of submarines, and the people in the who are actually operating the submarines, ensuring that we understood their requirements, and what they needed to operate and to do so quickly and safely and, and then we bring that learning back and say, Alright, here are the things we need to do with our technical requirements. And then eventually, we’d bring them back out and test our products with them to confirm they work. So you can think of that is almost the equivalent of user testing. Sure. We were PMS. We were designers. I didn’t know it a ton. Yeah, the atmosphere designers and we are engineers as well.

Eric Dodds 08:05
Sure. Absolutely. Fascinating. Well, before we go any further, could you give us just an overview of Heap, I just want to orient the listeners to like what Heap is what it does, and what problem we solve?

Ken Fine 08:19
You bet. So Heap is a digital insights platform. So essentially, what we do is we help our customers understand the digital journeys of their customers. So what are people doing within the a web application or mobile or other, and then quickly identifying the opportunities for improvement. So that’s, in short, what we do and we make those services available to companies of multiple different within multiple different industries. And multiple different sizes, from very small, higher growth digitally, native businesses all the way up to very large, Venerable enterprise companies that were started long ago, not digital and have since transformed their business to work

Eric Dodds 09:14
with digital footprint. And do you, you know, I know like heaps a big company and you serve a wide variety of different types of customers? Is there a particular set of personas or a persona that you serve who’s performing a particular job function? Or is it sort of all over the board,

Ken Fine 09:34
the persona and the the map, if you will, that we serve across the company has evolved over time. So one way you could think of it is anyone whom you would characterize as a we call them digital builders, and see anyone within within an organization that is responsible for the performance and efficacy of some digital experience. In the actual titles that would commonly show up as would be product manager is certainly common. And that would be more than half of our persona usage, there would be their marketers very common politically, and they’re responsible for customer acquisition, digital customer acquisition, we’re also finding now that customer success, customer support, or common users is they try to decipher what’s going on with their customers. And then if you think about this, is if we were very dryness on a whiteboard is like a hub and spoke, you could think of all those business owners that I just named as spokes. What’s one of the things that’s evolved over time with the advent of Databricks and Snowflake, are the centralized data and analytics teams. And we find that they have become consumers, a lot of the data that we capture, and they will look at it in different ways and do different types of manipulations, then let’s say a product manager would. So now we tend to work closely with that centralized team, the hub, as well as those different business owners, the spokes.

Eric Dodds 11:06
Yeah, fascinating. I want to ask about the approach. So the analytics space is fascinating in general, because it’s been around for a long time. There are some gigantic incumbents, and there are any number of ways to solve the problem. And so like, you know, as I think about it, if you put this on a spectrum, right, you have maybe more blank slate type solutions, right? So this is just a tool that allows you to write SQL on top of warehouse data and, you know, produce some basic visualizations, okay. I mean, it’s almost like a blank slate, right? Here’s a BI tool. And here’s an empty dashboard, you need to write sequel and fill it on raw data. Then on the other end of the spectrum, you have, you know, let’s say Google Analytics, for example, right? I mean, you can change some things, but largely, here, your charts. And this is what you get. What is heaps approach? Because, you know, arguably, there’s value for different use cases on either end of that spectrum. And they’re sort of everything in between. So where does he plan?

Ken Fine 12:21
That’s a good question. And it’s one that we’ve thought about a lot at Heap? And I’ll answer it in two ways, or a two different frameworks. The first I’ll start with the, the hub and spoke model that I just described. So in part the, I believe the answer to that question, Eric depends upon which of those persona you’re trying to enable. So the way we’ve approached it Heap is there’s a particular solution that we provide for the hub, the data teams at the center. That’s a little bit different. And the philosophy of a different than that which we provide to the business users, let’s say a product manager or marketer. So the way to think of Heap and the way we approach this, as we have three basic elements to our solution framework. The first is how we capture data. And that’s core to heaps, uniqueness and differentiation in the marketplace. So we have an approach to capturing data that the market refers to sometimes as Auto Capture or auto track, it was a big part of how the company was founded took years and years to get that technology to really work well. And you can think of it as like a digital vacuum cleaner, you install Heap, the vacuum turns on, we’re inhaling massive amounts of data, which is basically every digital interaction every click every swipe on a mobile device, and then bringing that into Heap. And then saving it as long as the customer like is to save it, and making it available in real time. So think of that as piece one. And that piece, we can send to either the hub or the spoke. So centralized data team, they want that data set, we’ll send it to them, and then they can do a lot of complex manipulations with it. Or we can send it to the spoke to a product manager or a marketer. Now, if we send it to a product manager, the marketer they need to be enabled. Now they’re not data scientist, or they’re not usually data scientists. So that leads to pieces two and three of our framework. Piece two, what are the things that we learned and this was something we implemented more when I joined and worked on start working when I joined, is it cap capturing everything or maybe put differently everything is a lot. You’re catching literally every single click and every single action that can be overwhelming to a product manager or marketer or customer success managers. So we have done to accommodate that. As we have built from the ground up. That’s set of data science capabilities that are natively built specifically to interrogate these massive datasets and look for evidence of user struggle. Anything that looks like there may be a reason to believe that a user is struggling to go from point A to point B. And a and b are defined by the product, the user say, Hey, I’m trying to understand this journey. Or we’ve created technologies now that hunt literally like like digital flashlights, they hunt through these datasets, and they look for some evidence that, hey, something seems either really bad or really good. And over time, our heuristics become progressively more sophisticated. And we surface that to the business users user and say, Hey, we’re gonna shine the flashlight over here, maybe a spot that you may have expected to be, you know, searching in, or maybe something completely out of your purview. Yeah, maybe a blind spot for. So now you’ve got a source of truth and data that gets sent to the centralized data team gets sent out to the PMs and the marketers, then you got the flashlight, that helps you identify to look at and then the third of three parts to our philosophy, and model are okay, once you shine a flashlight on something, essentially, what we’re doing is we’re we’re identify identifying a friction point, or perhaps a success point, you don’t really know what’s going on there, you just know, it’s something probably worthy of attention. That leads to part three for us, which is to provide qualitative context, my that will will do is we will serve up a personalized playlist of sessions or videos to watch. So now you can say, Okay, what’s going on at this point of friction, and we will serve up the sessions, we’ll fast forward to the point of session that you should be looking at. And then we’re also integrating within application surveys. So you can read, you can get sentiment and emotion around those friction points. by So essentially, now we’re building this broad digital insights platform. But to your question, we serve, let’s say a centralized data scientist, in a way that’s little bit different than how we would serve a product manager or a growth marketer. I’ll pause there.

Eric Dodds 17:14
Yeah, I mean, the first thing that really strikes me about this approach is that it’s a smell, let’s call it a suite based approach. And so maybe the most blunt way to put it would be that in each of those categories that you described, there are entire companies who just focus on that single sub. That’s right. And so interested to know, the thinking behind this wheat based approach, like, you know, I mean, the data capture to the different personas makes a lot of sense. But yeah, can you explain this wheat based approach? I don’t know what you call it? That’s just my term? Yeah, sure.

Ken Fine 17:58
I think a suite is a is certainly a fair way to describe it. You know, we like many companies, we refer to it as a platform. So a digital insights platform. So the approach and the thinking behind it, is this as we so Heap, originated, if I were drawing here, I drew a little Venn diagram. And I would draw as one circle, product analytics. So that’s one part of the data analytics stack, often. That’s classic, you know, analyzing journeys, and where people get stuck. And that’s where Heap was born. So if you do, if you’d go look up a report, an analyst report and product analytics, you’ll see Heap there, you got another circle, and the Venn diagram would be session replay, and associated tools there. And there’s organizations that grip with. And then the third of three circles I would draw would be voice of the customer or within application feedback, being a basic capturing commentary. So those are three important and here to four distinct, different way to understand interrogate what’s happening digitally, and I in a previous life was a head of product at Medallia voice of the customer. Business. So I understand that that space. Well, so the observation from Heap was, we started in product analytics. And of course, we use our own product and we observe our customers using our product and here’s what we observed. People would use Heap and then eventually Oakley, they would find something that’s worthy of understanding better and say, okay, something’s happening here. There appears to be either an issue meaning a lot of people are getting stuck or the the conversion rate is low in this journey, or perhaps it’s really high. Wow, look, people are flying through this use case, but we don’t know why. We do not have any call did have context so people would leave here. So if you will log out of Heap or step out of Heap, and move into a session replay tool or go over into a voice of the customer product, and then start hunting, for sessions to watch, if they’re in session replay, hey, let’s find something here. Can we link these together, or combing through quotes and feedback from surveys, or maybe also sites trying to find something that matches this pain point, they found a product analytics. And the time that we were spending ourselves at our own product team was spending trying to stitch that together was ours, literally exporting files, hey, let’s see if we can match up this pain point, this friction point with these quotes or, or with these sessions. So then we started interviewing our customers. And we found they’re doing the same thing. And then either or they had tried it. And it was so onerous that they stopped doing it. So we developed the point of view that okay, the way the industry started was these were hard enough problems that they required separate companies that from the almost a Darwinian standpoint, shouldn’t do it all at once. But now we’ve reached a point where I think the actual optimal solution that actually is a one plus one is more than three where you need context, you need some way to identify friction points with analysis. And then you need to quickly and seamlessly be able to understand the context of the friction point that you’re observing, which is large, watching a session watching an experience, and then hearing sentiment. So the way we approach that was through acquisition. So we then said, Alright, we’re very strong in this product analytics category. We’ve got great technology there. Let’s go find a company that we believe has excellent best in class technology, and one or more of the other areas, with compatible integratable code and a strong team. And we did that by about a year ago, now acquiring company named auric. They were strong in session replay, they were strong in within application feedback, that team came to Heap very great technical capabilities. And we’ve now been integrated into a single platform. Last comment. And part of our philosophy is we’re not trying to replicate every feature in a session replay platform, or every feature in Qualtrics are Medallia or pick the market leaders in voice of the customer. We’re looking at the subset of things that we think provide mutual context. So that you have, you know, truly the ability to interrogate a data set and get the context of the things you’re finding quality, quantitative,

Eric Dodds 22:44
fascinating. I want to go a little bit technical and jump back to the one of the first things that she made, that she made a comment about the first category, so Auto Capture, or auto track. And this is interesting to me as a user, because, you know, when this came out, you know, I remember, you know, testing Heap out very early on, a number of other players had auto track Auto Capture solutions. And it’s a pretty gnarly thing, because, as you said, it’s a lot of data. You know, it’s, you know, you’re definitely going to get something that you want. But you’re also going to get a lot of things that you don’t know, if you want, and then certainly a lot of things that you probably will never want. How did and you know, I’m not, you know, I’m sure there are other solutions out there doing Auto Capture. But from my perspective, you know, having used a lot of the tools, most of the major players in the market have abandoned it. And Heap is really the only one that I know of that sort of emerged, as, you know, a market leader who does Auto Capture. So what’s the secret? Because I mean, it’s a little bit gnarly to use or was like very early on, but I can’t imagine what it’s like under the hood. You know, from your standpoint?

Ken Fine 24:06
Yeah. It’s a good observation. And it. I chuckled because it’s, I reflect on my journey to join a Heap. So I’ll answer it in this way. So when I got it, I got a call from someone who was an executive coach of mine years ago and was also the executive coach of the founder. And at the time, the CEO, he said, Hey, I, my previous company been acquired, and I was thinking about what’s next and said, Hey, what do you think about potentially looking at this company and working with Heap and said, I know if he and so let me refresh my memory took a look at the company at that time, which was 2018 19. And my takeaway was, I literally went to the website and thought, what an amazing dataset, like what an amazing thing having come from being a pro Got a manager and a head of product, and at companies that were very data centric, so wouldn’t extraordinary data set. And at that time, though, and this is going to be part of the answer the question, the offering was quite different. It really was just the, if you think about the description, I just gave the framework, it was just the vacuum cleaner part. So it was just auto track or Auto Capture part. We hadn’t built the rest of the platform yet. And, and it was a big part of the vision of the founders, which was, hey, there must be a better way to capture information than to be manually collecting it. So they spent a good six, seven years, just working on the vacuum cleaner. And the keep the tough. The difficult part. You said, You’re right. It’s gnarly. When you get under the hood, difficult parts were one, how do you collect this advice, size of data, this volume of data in an economically viable way? Like without just tanking, the financial metrics of your condo was

Eric Dodds 25:57
That was my next question. So as a little, you know,

Ken Fine 26:02
humorous anecdote in the early days of Heap, I think our gross margins were negative, they weren’t.

Eric Dodds 26:10
That’s a little bit scary. And SaaS,

Ken Fine 26:12
yeah, so it wasn’t that they weren’t yet best in class, you know, 70% 7580, we actually cost us more to save the data than it did to serve a customer. But the team at that time was quite resolute and coming up with different data compression methodologies, and is our former CTO at that time said, hey, it was no, it was not one single breakthrough, it was years and years of attacking the problem. Finally, to figure out how you get to a point now we have very good margin structure, and we can save that data. And then the other Ted, part of the technical problem was, how do you do that while making the data actually available in real time, all that data? So it’s not just you claim Auto Capture, but you want to do an analysis and you set up a query and it takes an hour? Like, no, it has to be there in real time? And how do you do it in a way where it’s you’re respectful of privacy and security requirements? If you want to serve large financial institutions and organizations like that? Sure. And how do you do it in a way that doesn’t slow down the host application. So just a whole raft. So the learning was, it’s a full stack technology problem, it is not a feature you turn on, you almost have to build from the ground up with that ambition, you don’t retrofit auto track, or Auto Capture. And it literally took a whole company, you know, five to seven years to get to a point where that was viable. Now, to just fast forward, though, when I joined, there was still a problem, which was Okay, so we’ve made a lot of progress on how do you capture that data and store it in an economically viable way and in a way that respects PII. But you still had the issue of that’s a lot of data for a product manager, manager to interrogate. And I came in, that’s when I began to assert the point of view of, hey, let’s move this company from being a vacuum cleaner, to adding the digital flashlight and start building out the data science capabilities, so that we can start to proactively surface the right insights. And then that took us to the next piece, which is Hey, now let’s integrate session replay. And within application C back to provide the context. So it’s this dual responsibility, you want to capture all this data, so you have a source of truth. So as you said, you’re not missing anything, because you’re not sure what to collect, what not to collect. But then you have to provide an assist, through analysis and data science to quickly find the needle in the haystack quickly find the hidden gem, so that you’re not searching aimlessly. And collecting data you don’t need.

Eric Dodds 28:41
Absolutely fascinating, what a story around the vision of the founders, just committing to making, you know, Auto Capture work, and then actually doing it over a multi year period. I mean, you know, anyone who’s been involved and SaaS knows, that’s not just the technology that’s, you know, dealing with investors trying to grow the business at the same time. I mean, I can’t imagine so what a story while I’ve been holding the mic hostage. So Costas, please jump in. I know you have a bunch of questions.

Kostas Pardalis 29:19
Yeah. Thank you. So again, I’d like to go through like the evolution of Heap like, since the day like the company started, and until today, and you mentioned already like some like very fascinating like details about that, like the obsession of like the founders from like, how to compress the data, how to store the data, how to manage the information, from the infrastructure point of view, and how you go from there to the product that would like the margins also start to appear, right? Like it’s not solving a thing. called problem anymore. It’s also like solving gladwyne gossamer problem. But it’s been a while. And it’s been like a very interesting period of time, right? Like there are many things have happened like in these past like 10 years, right, like 910 years, and especially like in this industry that has to do with data. So can you take us a little bit like through the journey of Heap together with like, what happened in the industry? And how what happened in the industry affected? Also Heap and the decisions that around like, where the product? And the company should go?

Ken Fine 30:35
Yeah, it’s a fascinating question. It can be a full day off site. And I’ll give you the short, the short version, the concise version, and then we could dig in as you like. So I think the original vision at at founding was not ultimately, that different from where we are meeting, the idea was, hey, let’s capture a source of truth, and ultimately enable and empower the business users. So that, you know, a high level has been consistent compass for the company. I think that as far as how that has evolved, and how it has in the context of the evolving data ecosystem, is, I think a few things one, going back to the question Eric asked it, I think it was a more challenging technical solve. And then in concert with that business solve that had been anticipated, like this is a really hard technical problem. And you’ve got to build a business in concert with that. So there was just a time a duration of investment. But the initial focus was, hey, let’s go after the product manager first, where we thought that was the primary landing persona, if you will. And then one things we saw in the industry over the last several years, as Databricks started to, and Snowflake and that whole world started to evolve, we saw one, a change within organizations where now a whole new set of Persona started to emerge, which were the centralized data analytics teams, which back when Heap was founded, in many cases just didn’t exist, it’s now you’ve got a new group, and a group that’s quite influential. So they may or may not be buyer for Heap. Sometimes they are sometimes they’re not. But they’re a key influencer and trying to create homogenous and scalable data practices across an organization. So they became for us now a key partner in the process of what you want to talk about economic selling, or just enabling the success of the organization. And they became a consumer in that in addition to starting, as I said, with the focusing on product managers, we learned like, Okay, we’re gonna have to really evolve to this hub and spoke model. Because there’s a desire on the part of some very sophisticated users to take the data, take our data, which they find to be very valuable, this source of truth on what’s happening from a digital interaction standpoint, and they want to join that data with their own data, transactional data, demographic data,

other signals.

Ken Fine 33:28
So now we’ve spent a lot of time putting that data into a format that can be easily consumed by those teams that your years ago didn’t exist. So you’re now your your PhD data scientist, working with a data engineer connecting our data to other you know, their own data, and then doing some really powerful analysis that would be beyond the scope of what you could do with heaps UI, and what you would expect to see from let’s say, a product manager or marketer, let me pause there.

Kostas Pardalis 33:59
Yeah, I Okay. I would like to talk a little bit about you mentioned two terms. You said, like be in first, and you wanted like, like, the strategy at the beginning was to go after like the BM persona, right? But many times, like, when you talk, you use the term like business user, right? Which obviously, like it’s a much broader persona inside the organization. And it makes me like, what happens in my mind is that the next thing that I think of when I hear the term like business user is bi, right? Like there’s always there was always the needs since we had like technology out there like to share with the business with some kind of like insights and reports and like all that stuff, like to help drive like the business and that’s like what I always think of like the business user show. What I want to ask you and this is like more of a question of trying like to time travel ourselves back to the beginning of the ship, not today. It’s wasn’t like it’s thought back in like a box, like a foot, like in the back of the mind. So like the people that what we’re actually doing here is actually BI. And the Pm is, let’s say, our vector to get into like the BI space because that’s like the opportunity today, right? But at the end, that’s what we want like to go and dominate out there, right? Like, we want to become like the next innovative solution of like how business intelligence is delivered to, to the business out there, or it was something else completely different to that

Ken Fine 35:45
ascension question, I think we’re BI is an interesting term. And I’ll share how we think about it. And then we again to it. So I’ve got two comments, one Kosis, you picked up on the where I use the term PM, and then also use the term business user. So one in the context of this evolution, it’s an astute observation that, you know, let’s say seven, eight years ago, we really were focused on PMS. And now the world of, if you want to call it digital transformation, digital ubiquity of the importance of digital experiences, has evolved in a way where, seven, eight years ago, we were focused on product managers. Now, there’s so many different persona that have responsibilities for digital experiences, marketers, now, customer supports involved customer success that we have evolved from that more narrow Bullseye for it to a broader one. But as it relates to be I, here’s where we draw the line. And not everyone in our space, I think, would draw where we draw, we think there’s one level of capability, which is more around I’ll call it standard performance metrics and dashboards. And when you say, Ei, or if you are at Heap and a product team meeting and use the word bi, we would lean, we would infer that something more in that camp like, Hey, give me the KPIs for this journey, and what the, where’s it performance. And usually, in that case, you can identify the steps in the journey you care about, and what kind of urgent targets you have and create dashboards for that. And certainly, he can do that. But Heap is not uniquely good at that, in our opinion. So we have decided we do not aspire to be a classic dashboarding BI tool, which by the way, is very valid strategy, there are competitors bars to take that strategy, the approach we’ve taken is that the true gap in the market, the true hole that that’s been out there is the ability for the I’ll use again, the word digital owner, that people who own the experience, to be able to with very low friction, interrogate that experience, and find friction points, wherever they may be. And often, they live in places that you didn’t expect. So unlike a BI tool, which is a more of a standard, you know, a longer journey, hey, how many what percent of people get to steps ABCDE, and F, we’re almost helping people look around that and off the tributaries, that journey, where we would have never thought to look, and we’ve got a system that’s designed to hunt through all those journeys and surface, the ones that matter. So perhaps in your definition, that could still be I d be bi, but that’s the separation we met, we’re not gonna we’re not gonna be a world class executive dashboarding tool, at least not in the near term. But we are going to be best in class and enabling a digital owner to find what matters within his or her digital experience.

Kostas Pardalis 38:57
Yeah, there is a reasonable I’m also asking, and it’s, I’m thinking also, like a bit provocative, to be honest. Because, like in this past, like 10 years, there are many helps a lot of like cycles and different tooling around the data, right? Like we had the moment of BI in a way when like luchar, both acquired, and then added, like, everything went like white silence. And if you think about it, we’ll leave LA right now, like at the beginning of I don’t like like, let’s say like, another revolution around like data. And a lot of things happening in pretty much every aspect of, let’s say, data industry, if I can call it like that, but bi still silence outside of like a couple of like, companies that they are trying to figure out how to merge. Let’s say the experience between notebooks and SQL are like dashboards and these kind of things that still they don’t get enough attention compared like to other things that are happening. Since what happened Like with nuclear and the consolidation there, like in the BI space, I haven’t like, at least heard something like happening. And I’m like wondering like where the next wave of like innovation is going to come there? Because there is like a lot of opportunity, right? Whenever, like the fundamental change, everything follows, right. So like visualization of the way like people like their face with data is like, very important. Anyway, that’s just like to add, like a little bit more context on my question here. But something else that I, I shared, during like your conversation with Eric, you talked about, like the difference between like the platform, and having, let’s say, like, one platform that serves like many different, let’s say, functionalities, and having like a more disaggregated, kind of like, architecture, that’s like, to me like, it’s really interesting, because if we again, go back to the data industry, we will see like these byproducts of like, the modern data stock assets, like gold, let’s say were pretty much what the data warehousing infrastructure for a company was, like 20 years ago, has been broken down into like, very small pieces, right. And every piece of that is like a product category with I don’t know how many vendors right now, to the point that like the data professionals out there that have started like experiencing, like fatigue around that by how do I choose? Well, I’m going to use here like, it’s tough, like crazy, right? And my feeling, and I’d love to hear like your opinion of that is that we will start seeing like a reverse in a sporadic and start seeing like things coming by together. At some level, at least, not necessarily like everything would be a huge monolith. But some things will come like again, together. What’s your opinion on that? And how do you feel like these platform isolation, let’s say of Heap was actually also affected by this whole experience of like disaggregating? Like the whole data infrastructure this past couple of years? Yeah.

Ken Fine 42:00
Yeah, interesting. So here are my thoughts in and I’m going to take your question, Kostas, and I’m going to also link it to the current macroeconomic environment. So I think there’s also a link between Prolift times of proliferation, and times of consolidation, and aggregation. So I think we’ve gone through a period up until, let’s say, about a year ago, where there was, there’s a lot of investment in technologies, you know, capital going into companies like Heap developing, in many cases, point solutions. So lots of different ways of contributing to the, the patchwork quilt. And in the case where I live the data stack, the modern data sack, or digital analytics, data stack, or digital analytics data stack. And what I see now is, I think now we’re going to be shifting into a period for some period time of either seeing natural points of aggregation, consolidation, where there are adjacencies, that makes sense, and where because of the macroeconomic pressures, it doesn’t support the same number of smaller point solutions, who are all trying to make a kind of a very localized value proposition or did just not sufficient, there’s not sufficient demand. I do think that one of the points of consolidation and this was part of our thesis that led to our business strategy was the idea around, okay, there’s a set of solutions out there that are quantitative, meaning they take data, they do analysis, and they give you some type of quantitative output that says, X percent of people or users get from move across this journey in this way. And then there’s another set of solutions out there that are qualitative in nature, when I say qualitative, they are giving you information for human interpretation such as, hey, watch this session, or watch this experience or read these quotes when I was at Medallia, that’s quantitative analysis applied to qualitative content. And I think what we’re gonna see going forward is the the consolidation and aggregation of quantitative and qualitative and seeing data science and AI starting to sit on top of

Kostas Pardalis 44:28
that set of quantitative and qualitative data to provide synthesis. So start to bring those together. It makes a lot of sense. Okay, one last question about the industry and then I’d like to change the theme a little bit. So Heap was part of like with a wave of companies that they came out of the SAS revolution law chain, right like was the time that like, have certification, let’s say of the word right. Yeah. And we had Heap we had Mixpanel, we had the amplitudes. Right? Yeah, I’m mentioning like these three, because there is like a comun like pattern there. In terms of like the persona that is addressed, like, all three of them, they try like to go after like the PM, or the need, like for product people who have like insights, track data from mobile was also like, like this enormous amount of data coming from mobile at that time. So amplitudes went public. How do you see these group of companies like what’s their future? Let’s say? Not so like, in by the way, I don’t say, and I’d love like to hear your opinion on that, because that’s going to give us to the bridge to the next questions. It’s not, let’s join public life and the journey, right. So it doesn’t mean that because amplitudes made it to the public markets means that like, they have figured out everything, right, right. They’re still like trying to do that. So there’s like, everyone’s trying like to figure out, like, their unique value proposition there. But where are these, let’s say, category of products go into based on like your experience? And, okay, obviously, like with the amount of bias that you can have being like the CEO of one of these companies, right, but it would be great to hear, like, how you see things for this category of products? Yeah.

Ken Fine 46:29
Also, good question. So, my thought, I mean, I’ll bring my answer back to the previous theme around natural points of aggregation in the space. So I believe there will be pressures in this space, in part because it’s better for the user, and more economic for the buyer, particularly during a time of macroeconomic pressure, to bring together some of what historically a Heap, or an amplitude, and and Mixpanel have done in the category, often called product analytics, and quantitative analysis with what other companies have done in the area of qualitative in session replay and, and voice of the customer. And I think what you’re gonna see is pressure to bring those into single platform so that customers do not have to have multiple purchases, multiple products, and, and for the user, that’s expensive. And then for the user, it’s cumbersome, as I said, because you’re floating, you’re basically trying to piece together a puzzle of digital journey. What can I get out of amplitude, or Mixpanel, or Heap? And then what do I get from these other platforms, I think you’re gonna see constant pressure to bring together again, this kind of quality, classically, quantitative analysis with qualitative. That’s piece one, the other pressure, I think you’re gonna see is pressure from the marketplace to pardon the punch in advance, but to decrease the friction of finding friction, like, it just needs to be easier to collect the data and interrogate the data. If you’re looking at total cost of ownership, as a customer, you’re looking at the whole thing you’re looking at, you know, not just what happens once I have the data, well, how do I get the data. And let’s say in your case, that you pick Mixpanel, and amplitude, that’s usually a separate solution. So it’s, you need Mixpanel amplitude and something else to collect the data to give to them, then you’ve got them to do the analysis. And then you’ve got, as I said, probably some qualitative tools to understand it. So you’re trying to simplify that data stack. And decrease the investment you need to make in your own internal resources, your own engineers to piece that together. I think that’s going to be the nature of the pressure that we see in this and we see in this market. Yep.

Kostas Pardalis 48:56
Makes total sense. But like some great insights. All right. I have to a little bit more personal questions. Okay. And then I’ll give the microphone back to to Eric, first, I was listening to you like telling your story about like, how you started with being like, like in the Navy, doing engineering there. And then moving into like Silicon Valley and like, into like, building like software businesses and God being like a person who has gone through the transition of being an engineer and then trying like to get into business. And I know like the struggle that like an engineer can have, because engineers like especially like good engineers out there like solving like real complex problems, right? Like they need to build like a very structured in the very, let’s say, predictable way of thinking right like there is a cause and effect there. There is a system and we need like to define the system and make sure that there is no or, like, they mentioned that we haven’t, like, understood well, right and put like into like our designs. Yeah. And I bet that building propulsion for like, submarines is like to the extreme of these problems, right? Like, you have to be very detail oriented, like you deliver something and this something needs to work, right? Like you cannot send your sailors down like into like the water and an MVP, right? Oops, doesn’t work. Okay, let’s iterate on it. Like, no, you can’t iterate on that. So I’d love to hear like how you experienced lots transition from being, let’s say, in South like a serious, let’s say, engineering environments, going to the MVP, to the extreme of like, fake it till you make it’s kind of modality that like software gives us the opportunity to have right because it’s safe to make mistakes. They’re like, No one is going to die. In many cases, book cases, at least not always. So I’d love to hear like how you experienced that and how you did it? Because it’s hard, like it can be mentally painful?

Ken Fine 51:12
Wow, that’s an awesome question. And I don’t think I’ve ever been asked it before. And I have a point of view on it. So you’re right. So in my first life, designing propulsion systems, the whole organization, the whole culture, was very different than Silicon Valley in the sense that people’s lives actually were at stake, like it was real was not hyperbole, if we made mistakes, people could get hurt. This is nuclear technology being operated on a submarine at sea. So as a result, we had incredibly high standards for technical quality. And lots of processes, you can call them forms of quality assurance, I suppose. But lots of reviews and ways of inspecting our work to decrease the likelihood that you made a mistake. So and take put that in context, when I worked at that first job. I think the time from beginning the design of our product, which was a submarine would to launch was a decade and he years to launch your product. And then when it came, so now fast forward to your question, I came out here. And wow. Okay. First of all, for the most part, lives aren’t actually at stake. I mean, someone’s not going to be injured when they’re using most b2b SaaS platforms if there’s an issue meant and perhaps in some, but most not, and the need for speed and agility and iteration was much different. So my learning there came at my first company as company Financial Engines, who was founded by a professor that I studied when I was at Stanford Business School. And the learning there came through failure. So we started ultimate being a successful company in the first several years. We started as a b2c platform and had some really interesting ideas for features and capabilities that came from his research that ultimately were not successful or not successful enough in the market. And what we learned was, and this is a learning for me, I was the head of product there, the we weren’t sufficiently agile, and that we weren’t, and that I needed to stop specking everything with, you know, 100 page, read marketing requirements, docs and product requirement docs, and needed to basically embrace a completely different approach, which we call Nayland scale, which was okay, there’s a stage when you’re figuring things out when you’re in the blueprint stage, and need to be hyper agile. And you need to be basically testing before you had before you write code. So a lot of that’s more mainstream today. But it required a change in culture, attitude skill set, a complete revamp of our whole product development process, to kill all the waterfall stuff that was in existence back then. And then it basically turned on its head. So it did require for me to almost reinvent myself. Because all the things that had worked for me for so many years, actually, not only wouldn’t work for me, but could create the opposite outcome. You could drive an organization to be too slow and too rigid and unable to be successful.

Kostas Pardalis 54:27
Yeah, 100%. Okay, that was like, super interesting. All right, my last question, and then I’ll give the microphone back to. That’s great. So, again, it’s a question about like your journey. You’ve done pretty much every thing that someone would say cannot see like as part of like being an entrepreneur, right, like you. You started, companies from scratch went public, like how to be part of like running a public company and then went back to a private company. People usually especially like people who haven’t done But like probably they think that like, this is like a very like near thing, right? Like the Holy Grail is like to go ring the bell, and then you’re done. You retire, blah, blah, I don’t know, like, whatever, then you are like you did it. But my intuition and like, Okay, I haven’t done it but like, like, that’s how I think about it’s based on your examples of like things are not as linear or simple as we would like to think. So can you share? Because you have like a very unique experience without rates? Can you share, like some insights about like, what’s, I don’t know, anything that you find, like interesting to share with the people there from just like, what’s the difference between, like being part of a public company or like starting a company, or what made you personally from being like in a public company, going back to a private company, anything that you think like, could help anyone who is starting a company like just like being interested in enterpreneurship,

Ken Fine 55:58
I’d be happy to I have developed in that point of view, your point of view on that. So I would characterize it this way. So the first big chunk of my career like it is for many people was more organic, meaning I didn’t sit down my guidance counselor when I was 15, and said, I’m gonna go serve it, I’m gonna go to Silicon Valley, like, you know, obviously, that would have been a unusual map to know ex ante. But here’s the learning, that the point of learning for me, and then the direct answer your question, so when I got to a point in my journey, where I was at this company, financial engineers, they were public. And it was time for me to decide what to do next, I guess we could see your point could stop working, could go on to another large public company and continue the journey, you know, could to something larger. Or I could, you know, I could start a company or go to a growth stage company, here’s the learning that I came to By talking to a number of people. And then I decided to make an explicit or intentional part of the rest of my journey. And that learning was this, that there were two things that I was solving for. One was a learning that the types of skills that you need to be successful, effective as a regardless whether you’re an individual contributor, or a leader, in companies at different stages are actually different. It is different, being in super early stage, call it pre product market fit, pre revenue growth, that skill set, I think of as a detective skill set, your job is to find the market calls it today product market fit, like that’s what you’re on a mission. And you’re interrogating and hypothesizing and experimenting and finding hints and signal. And it’s a really unique and powerful and hard to come by skill set. There’s another skill set that I’ll call growth stage, which is, hey, there’s something here, like we have something repeatable maybe to certain not to different degrees, but we have some level of a product that can be offered in a certain way, like with a value proposition that people purchase. And then they use to a degree of success. Now we need to improve and scale that. That’s a different set of skills. I mean, there it’s not that there’s no overlap between the skills, but it is different. And then that set of skills, start to get into pattern recognition. Like you’re growing so quickly, that it helps to have developed some notion of, you know, what does it look like to have a SaaS company with professional services? Or what does it like to sell to large enterprise or to do product led growth, like having seen it before matters? So my learning was this, that led me to actually go from a public company back to smaller was, I decided I needed to make two decisions. One was, what’s the stage that I want to become good at? What’s the skill set I want to reinforce? So I made a personal decision, which resulted in me not responding to a lot of inbound opportunities for jobs was, I want to get really good at growth stage. I liked that stage. There’s some little product market fit. Now I got to scale it. So I’m going to go I’m going to hunt for that opportunity. The second is, what’s your functional area of expertise? Do you want to be great at product sales marketing? So think of that as like a x, y axis? What’s your functional bullseye? What’s your stage bullseye, and then try to get really good at that quadrant? That’s that was my learning. And then that and that’s why after the first company, I’ve done bursts stage over and over again, trying to get better each time.

Kostas Pardalis 59:33
Yeah, that’s an amazing framework, by the way, and like, thank you so much for sharing that like it’s going to be valuable like for me, also. So thank you so much, Eric. All yours.

Eric Dodds 59:44
Yeah, that is an amazing perspective can appreciate that so much. We’re right at the buzzer here. But one thing I’m really curious to ask you just on a personal level, you know, you’ve worked you have an interesting perspective because as you said, very beginning of the chat, you know, all the contexts that you’ve worked in, have been sort of using some sort of AI layer to uncover insights from large datasets. And, of course, you’re excited about Heap. But what other new things out there excite you in sort of the analytics space in general. I mean, LLM is is sort of an obvious one. But you bring a perspective of, you know, 2025 years, sort of doing this, and they’ve really seen the entire industry change. So I’m personally curious what, what things get you excited?

Ken Fine 1:00:41
Yeah, I don’t mean to be underwhelming. But LLM is what’s interesting about it, rather than just picking it to me is, and the way we’re looking at it is the challenge and a big challenge in the data space, certainly for us, has been, it’s essentially, it’s the application layer. How do you make? How do you enable people who aren’t data scientists to make sense of data. And that’s hard is really hard if everyone were had some combination of a computer science degree. And data science degree, yeah, you just a bunch of data and as query tools and say, knock yourself out. But that’s not the world we live in. So we’re looking at AC LLM as a way to enhance amplify assist, or maybe even eventually replace the classic UI layer today, and just rethink how you interrogate datasets. Sure. So that’s what our team is working on. And then the other for me I’ve mentioned is, I think the other trend is the world of hardcore quantitative analysis. And just looking at journeys and attrition rates and the world of qualitative context, and user testing and human judgment have largely been separate. They’ve been separate jobs. They’ve been separate tools, companies, or companies that you’re right. And I used to back actually forget about when I was a product manager, I used to give talks on the integration of quant and qual and say, Hey, if you’re a great product manager, it’s just back then the tools were quant was, you know, pull some log files and do some analysis and qual was go interview people. And by the way, think interviewing people is great. But now, technologies are rising. And I think the other trend you’re gonna see if we fast forward about two years is the basically integrating those datasets together and extracting wisdom from the combination of the two.

Eric Dodds 1:02:40
Love it. Okay, this, this time flew by amazing conversation. Thank you so much for giving us a few minutes of your time.

Ken Fine 1:02:49
Eric and Costas, thank you very much for having me. It was great to be here.

Eric Dodds 1:02:52
Okay, fascinating show with Ken from Heap. Costas, I think my big takeaway was his answer to my burning question around auto track. And the story around the Heap founders commitment over a long period of years, to actually make that a viable solution with all the engineering problems involved was a fascinating story, right? I mean, you have security concerns, because you’re auto capturing all of this data, you have severe gross margin problems because of the amount of data that you’re actually collecting, right? When you have to put that somewhere. And so compression issues, and I mean, pretty wild. And then you actually have to make it usable for someone and you’re talking about, you know, truly a firehose of data. They’ve done it really well. And that was just a great story. Of course, the engineering parts are interesting. But the commitment to a vision by the founders, especially the technical ones, was really a great story. I mean, definitely a must listen to.

Kostas Pardalis 1:04:08
Yeah, if I look was saying, it was a very, like, extremely fascinating conversation that we have many different levels. I agree about auto drugs. And you know, like, auto track is like one of these interesting problems, because it’s extremely easy to implement. I mean, at the end, like, all you need to do is like, just capture every change on the DOM tree. But it’s extremely hard to make it work at the end for the user. Yeah, like it’s, there’s like, a huge distance between, like the basic engineering work and what needs to be done on top of that, in order like to make these feature like something that actually works for everyone. So that’s one thing. The other thing that’s, I keep, like from the they’re like, Dwolla Things of like I keep like friendly conversation with him. One is the part where with this, we were discussing about this whole process of going to a market states in the past couple of years where like, we have like this boom of like, all these tools coming out there, and how now we’ll be getting into lighting environment where we will see more integrated solutions instead of like, all the difference, like every possible feature being the product on its own, and to give us like some very interesting insights there. And the other thing is what he she talks about, like the different types of people that are needed, at different stages of the building of a company, right. And I think that resonates a lot also, like with the founders of Heap, right. And that part was, like, extremely interesting. I’m not getting to share more, because I think like, that’s like the least interesting what he has to say. But that part was, like, very insightful for me.

Eric Dodds 1:06:11
Yeah, I agree. Also, a great tidbit for anyone interested in what it would be like to be a product manager slash engineer for submarine technology. That was a really crazy story. So definitely take a listen, subscribe if you haven’t, lots of great episodes coming up. And we’ll notify you and your favorite podcast app. Tell a friend and we will catch you on the next one. We hope you enjoyed this episode of The Data Stack Show. Be sure to subscribe on your favorite podcast app to get notified about new episodes every week. We’d also love your feedback. You can email me, Eric Dodds, at eric@datastackshow.com. That’s E-R-I-C at datastackshow.com. The show is brought to you by RudderStack, the CDP for developers. Learn how to build a CDP on your data warehouse at RudderStack.com.