(upbeat music) >> Hello everyone, welcome to this CUBE conversation. I'm John Furrier, your host of theCUBE here in Palo Alto, California in our studios. Got a great guest here, Rachel Obstler, Vice President, Head of Product at heap.io or Heap is the company name, heap.io is URL. Rachel, thanks for coming on. >> Thanks for having me, John. Great to be here. >> So you guys are as a company is heavily backed with some big time VCs and funders. The momentum is pretty significant. You see the accolades in the industry. It's a hot market for anyone who can collect data easily and make sense of it relative to everything being measured, which is the Nirvana. You can measure everything, but then what do you do with it? So you're at the center of it. You're heading up product for heap. This is what you guys do. And there's a lot of solutions, so let's get into it. Describe the company. What's your mission and what you guys do? >> Yeah, so let me start maybe with how Heap was even started and where the idea came from. So Heap was started by Matin Movassate, someone who was working at Facebook. And this is important 'cause it gets right at the problem that we are trying to solve, which is that he was a product manager at Facebook and he was spending a lot of money on pizza. The reason why he was spending a lot of money on pizza is because he wanted to be able to measure what the users were doing in the product that he was responsible for, and he couldn't get the data. And in order to get the data, he would have to go beg his engineers to put in all sorts of tracking code to collect data. And every time he did so, he had to bribe him with pizza because it's no one's favorite work, number one, and then people want to build new things. They don't want to just constantly be adding tracking code. And then the other thing he found is that even when he did that then it took a couple weeks to get it done. And then he had to wait to collect the data to see what data is. It takes a while to build up the data, and he just thought there must be a better way. And so he founded, he with a couple other co-founders, and the idea was that we could automatically collect data all the time. So it didn't matter if you launched something new, you didn't have to do anything. The data would be automatically collected. And so Heap's mission is really to make it easy to create amazing digital experiences. And we do that by firstly, just making sure you have all the data of what your users are doing because you would think you want to create a new digital experience. You could just do that and it would be perfect the first time, but that's not how it works and users are not predictable. >> Yeah, remember back in the day, big data, Hadoop and that kind of fell flap, but the idea of a data lake started there. You saw the rise of Databricks, the Snowflakes. So this idea that you can collect is there. It's here now, state of the art. Now I see that market. Now the business model comes in. Okay, I can collect everything. How fast can I turn around the insights becomes the next question. So what is the business model of the company? What does the product do? Is it SaaS? Is it a as a package software? How do you guys deploy? How do your customers consume and pay for the service? >> Yeah, so we are a SaaS company and we sell largely to, it could be a product manager. It could be someone in marketing, but it's someone who is responsible for a digital service or a digital product. So they're responsible for making sure that that they're hitting whatever targets they have. It could be revenue, it could be just usage, getting more users adopted, making sure they stay in the product. So that's who we sell to. And so basically our model is just around sessions. So how many sessions do you have? How much data are you collecting? How much traffic do you have? And that's how we charge. I think you were getting at something else though that was really interesting, which is this proliferation of data and then how do you get to an insight. And so one of the things that we've done is first of all, okay, collecting all the data and making sure that you have everything that you need, but then you have a lot of data. So that is indeed an issue. And so we've also built on top of Heap a data science layer that will automatically surface interesting points. So for instance, let's say that you have a very common user flow. Maybe it's your checkout flow. Maybe it's a signup flow and you know exactly what the major milestones are. Like you first fill out a form, you sign up, like maybe you get to do the first thing in the trial. You configure it, you get some value. So we're collecting not only those major milestones, we're collecting every single thing that happens in between. And then we'll automatically surface when there is an important drop off point, for instance, between two milestones so that you know exactly where things are going wrong. >> So you have these indicators. So it's a data driven business. I can see that clearly. And the value proposition in the pitch to the customer is ease of use. Is it accelerated time to value for insights? Is it eliminating IT? Is it the 10X marketer? Or all of those things? What is the core contract with the customer, the brand promise? >> That's exactly. So it's the ability to get to insight. First of all, that you may never have found on your own, or that would take you a long time to keep trialing an error of collecting data until you found something interesting. So getting to that insight faster and being able to understand very quickly, how you can drive impact with your business. And the other thing that we've done recently that adds a lot to this is we recently joined forces with a company named Auryc so we just announced this on Monday. So now on top of having all the data and automatically surfacing points of interest, like this is where you're having drop off, this is where you have an opportunity, we now allow you to watch it. So not only just see it analytically, see it in the numbers, but immediately click a show me button, and then just watch examples of users getting stuck in that place. And it really gives you a much better or clearer context for exactly what's happening. And it gives you a much better way to come up with ideas as to how to fix it as one of those digital builders or digital owners. >> You know, kind of dating myself when I mention this movie "Contact" where Jodie foster finds that one little nugget that opens up so much more insight. This is what you're getting at where if you can find that one piece that you didn't see before and bring it in and open it up and bring in that new data, it could change the landscape and lens of the entire data. >> Yeah. I can give you an example. So we have a customer, Casper. Most are familiar with that they sell mattresses online. So they're really a digital innovator for selling something online that previously you had to like go into a store to do. And they have a whole checkout flow. And what they discovered was that users that at the very end of the flow chose same day delivery were much more likely to convert and ultimately buy a mattress. They would not necessarily have looked at this. They wouldn't necessarily have looked at or decided to track like delivery mechanism. Like that's just not the most front and center thing, but because he collects all the data, they could look at it and say, oh, people who are choosing this converted a much higher rate. And so then they thought, well, okay, this is happening at the very end of the process. Like they've already gone through choosing what they want and putting it in their card and then it's like the very last thing they do. What if we made the fact that you could get same day delivery obvious at the beginning of the whole funnel. And so they tried that and it improved their conversion rate considerably. And so these are the types of things that you wouldn't necessarily anticipate. >> I got to have a mattress to sleep on. I want it today. Come on. >> Yeah, exactly. Like there's a whole market of people who are like, oh no, I need a mattress right now. >> This is exactly the point. I think this is why I love this opportunity that you guys are in. Every company now is digitalizing their business, aka digital transformation. But now they're going to have applications, they're going to have cloud native developers, they're going to be building modern applications. And they have to think like an eCommerce company, but it's not about brick and mortars anymore. It's just digital. So this is the new normal. This is an imperative. This is a fact. And so a lot of them don't know what to do. So like, wait a minute, who do we call? This is like a new problem for the mainstream. >> Yeah, and think about it too. Actually e-commerce has been doing this for quite a while, but think about all the B2B companies and B2B SaaS, like all the things that today, you do online. And that they're really having to start thinking more like e-commerce companies and really think about how do we drive conversion, even if conversion isn't the same thing or doesn't mean the same thing, but it means like a successful retained user. It's still important to understand what their journey is and where you going to help them. >> Recently, the pandemic has pulled forward this digital gap that every company's seeing, especially the B2B, which is virtual events, which is just an indicator of the convergence of physical and online. But it brings up billions of signals and I know we have an event software that people do as well. But when you're measuring everything, someone's in a chat, someone hit a web page, I mean there are billions of signals that need to get stored, and this is what you guys do. So I want to ask you, you run the product team. What's under the covers? What's the secret sauce for you guys at Heap? Because you got to store everything. That's one challenge. That's one problem you got to solve. Then you got to make it fast because most of the databases can't actually roll up data fast enough. So you're waiting for the graph forever when some people say. What's under the covers? What's the secret sauce? >> Well, it's a couple different things. So one is we designed the system from the very beginning for that purpose. For the purpose of bringing in all those different signals and then being able to cut the data lots of different ways. And then also to be able to apply data science to it in real time to be able to surface these important points that you should be looking at. So a lot of it is just about designing the system for the very beginning for that purpose. It was also designed to be easy for everyone to use. So what was a really important principle for us is a democratization of data. So in the past, you have these central data teams. You still have them today. Central data teams that are responsible for doing complex analysis. Well, we want to bring as much of that functionality to the digital builders, the product managers, the marketers, the ones that are making decisions about how to drive impact for their digital products and make it super easy for them to find these insights without having to go through a central team that could again take weeks and months to get an answer back from. >> Well, that's what brings up a good point. I want to dig into, if you don't mind, Rachel, this data engineering challenge. There's not enough talent out there. When I call data engineer, I'm talking about like the specialist person. She could be a unique engineer, but not a data scientist. We're talking about like hardcore data engineering, pipelining, streaming data, hardcore. There's not many people that fit that bill. So how do you scale that? Is that what you guys help do? >> We can help with that. Because, again, like if you put the power in the hands of the product people or the marketers or the people that are making those decisions, they can do their own analysis. Then you can really offload some of those central teams and they can do some of the much more complex work, but they don't have to spend their time constantly serving maybe the easier questions to answer. You have data that's self-service for everyone. >> Okay, before I get into the quick customer side of it, quickly while I have you on the product side. What are some of your priorities? You look at the roadmap, probably got tons of people calling. I can only imagine the customer base is diverse in its feature requests. Everyone has the same need, but they all have different businesses. So they want a feature here. They want a feature there. What's the priorities? How do you prioritize? What are some of your priorities for how you're going to build out and keep continuing the momentum? >> Yeah, so I mentioned earlier that we just joined forces with a company name Auryc that has session replay capabilities, as well as voice of customer. So one of our priorities is that we've noticed in this market, there's a real, it's very broken up in a strange way. I shouldn't say it's strange. It's probably because this is the way markets form, startups start, and they pick a technology and they build on top of it. So as a result, the way the market has formed is that you have analytics tools like Heap, and they look at very quantitative data, collecting all sorts of data and doing all sorts of quantitative cuts on it. And then you have tools that do things like session replay. So I just want to record sessions and watch and see exactly what the user's doing and follow their path through one at a time. And so one is aggregating data and the other one is looking at individual user journeys, but they're solving similar jobs and they're used by the same people. So a product manager, for example, wants to find a point of friction, wants to find an opportunity in their product that is significant, that is happening to a lot of people, that if they make a change will drive impact like a large impact for the business. So they'll identify that using the quant, but then to figure out how to fix it, they need the qual. They need to be able to watch it and really understand where people are getting stuck. They know where, but what does that really look like? Like, let me visualize this. And so our priority is really to bring these things together to have one platform where someone can just, in seconds, find this point of opportunity and then really understand it with a show me button so that they can watch examples of it and be like, I see exactly what's happening here and I have ideas of how to fix this. >> Yeah, something's happening at that intersection. Let's put some cameras on. Let's get some eyes on that. Let's look at it. >> Exactly. >> Oh, hey, let's put something. Let's fix that. So it makes a lot of sense. Now, customer attraction has been strong. I know it's been a lot of press and accolades online with when you guys are getting review wise. I mean, I can see DevOps and app people just using this easily, like signing up and I can collect all the data and seeing value, so I get that. What are some of the customer value propositions that are coming out of that, that you can share? And for the folks watching that don't know Heap, what's their problem that they're facing that you can solve, and what pain are they in or what problem do they solve? So example of some success that's coming out of the platform, enablement, the disruptive enablement, and then what's the problem, what's the customer's pain point, and when they know to call you guys or sign up. >> Yeah, so there's a couple different ways to look at it. When I was talking about is really for the user. There's this individual person who owns an outcome and this is where the market is going that the product managers, the marketers, they're not just there to build new features, they're there to drive outcomes for the business. And so in order to drive these outcomes, they need to figure out what are the most impactful things to do? Where are the investments that they need to make? And so Heap really helps them narrow down on those high impact areas and then be able to understand quickly as I was mentioning how to fix them. So that's one way to look at it. Another use case is coming from the other side. So talking in about session replay, you may have a singular problem. You may have a single support ticket. You may have someone complaining about something and you want to really understand, not only what is the problem, like what were they experiencing that caused them to file this ticket, but is this a singular problem, or is this something that is happening to many different people? And therefore, like we should prioritize fixing it very quickly. And so that's the other use case is let's start, not with the group, like the biggest impact and go to like exactly some examples, let's start with the singular and figure out if that gives you a path to the group. But the other use case that I think is really interesting is if you think about it from a macro point of view or from a product leader or a marketing leader's point of view, they're not just trying to drive impact. They're trying to make it easy for their team to drive that impact. So they're thinking about how do they make their whole organization a lot more data driven or insights driven? How do they change the culture, the process, not just the tool, but all of those things together so that they can have a bigger business impact and enable their team to be able to do this on their own? >> You guys are like a data department for developers and product managers. >> Essentially, like we are the complete dataset and the easy analysis that really helps you figure out, where do I invest? How do I justify my investments? And how do I measure how well my investments are doing? >> And this is where the iteration comes in. This is the model everyone's doing. You see a problem, you keep iterating. Got to look at the data, get some insight and keep looking back and making that product, get that flywheel going. Rachel, great stuff. Coming out here, real quick question for you to end the segment. What's the culture like over at Heap? If people are interested in joining the company or working with you guys. Every company has their own kind of DNA. What's the Heap culture like? >> That's a great question. So Heap is definitely a unique company that I've worked at and in a really good way. We find it really important to be respectful to each other. So one of our values is respectful candor. So you may be familiar with radical candor. We've kind of softened it a bit and said, look, it's good to be truthful and have candor, but let's do it in a respectful way. We really find important that everyone has a growth mindset. So we're always thinking about how do we improve? How do we get better? How do we grow faster? How do we learn? And then the other thing that I'll mention, another one of our values that I love, we call it, "taste the soup". Some people use to call it dogfooding, but we are in Heap all the time. We call it Heap on Heap. We really want to experience what our customers experience and constantly use our product to also get better and make our product better. >> A little more salt on the sauce, keep the soup, taste it a little bit. Good stuff. Rachel, thanks for coming on. Great insights and congratulations on a great product opportunity. Again, as world goes digital transformation, developers, product, all people want to instrument everything to then start figuring out how to improve their offering. So really hot market and hot company. Thanks for coming on. >> Thanks, John. Thanks for having me. >> This is theCUBE conversation. I'm John Furrier here in Palo Alto, California. Thanks for watching. (gentle music)