Crossbeam Revenue, Valuation & Funding (2021)

[00:00] Quick context. This was recorded March twenty eighth and twenty ninth. So a couple weeks ago at my live event, saasopen.com. We had a thousand software CEOs there. If you missed it, we hope to see at the next one, September fifth and sixth in New York City, sasopen.com. But for now, let's jump into the recording. [00:18] >> We started progressively missing the numbers worse and worse. So if you can't beat them, join them. And in twenty months, we got to as many paying customers as it took us eight years. Hey, [00:32] folks. If we haven't met yet, my name is Nathan Latka. I launched and sold my first software company back in 2015 and went on to write a book about it, which you guys made a Wall Street Journal bestseller purchasing over 30,000 copies. Thank you so much for that. After the book, I launched this show and one went on to create founderpath.com. I raised a large fund to do non dilutive deals with b to b software founders. [00:59] So far, we've invested in over 400 software founders totaling a $150,000,000. Here in 2024, we're doing three to four new deals per week. So if you're looking for capital and don't wanna give up equity, go sign up at founderpath.com for free to get your offer. Alright. Let's jump into the interview. [01:18] >> Appreciate it. Okay. Thank you. Hey, everybody. I know we're in, like, the part of the afternoon where it's nap time. So hopefully, we'll be able to keep everybody pretty entertained and engaged for this one. I love talking about this topic. Currently, I'm the CEO of Crossbeam. We'll kinda get to a little bit of the Crossbeam story, but that's not what most of this talk is about. Most of this is about my first SaaS company, which is [01:44] >> a business called RJMetrix, which I started back in 2008. And just the incredible ride that we had there and how ultimately, I think a series of decisions led to us missing out on what was probably a 2 to $3,000,000,000 market opportunity. So first of all, I just wanna acknowledge. Right? Like, there's there's some lights at the end of the tunnel as you look out at the market compared to, say, six months ago or twelve months ago. [02:09] >> But by and large, I think something thematically that you're probably hearing over and over again today is that there is a bit of the tail end of a SaaSpocalypse going on where it has just been significantly more difficult to sell, to generate leads, to convert, to function in a ROI positive cash efficient way than it was eighteen months ago, twenty four months ago, etcetera. And part of the reason why I like talking about the RJ metric [02:36] >> story is because I've kinda seen this movie before. And there are some moments in that story that remind me a lot of this moment that many founders are finding themselves in in this market right now. So let's hop in the time machine. We go back to 2008. I am wearing cargo khaki shorts among other things. That's me on the left and my cofounder Jake Stein. He and I worked at Insight Venture Partners, is now known as [03:00] >> Insight Partners, which is an awesome investor up in New York. We were junior junior junior employees, basically cold callers on the investment team. And a side effect of being do that job for a few years is we just got to meet a whole lot of companies and see what kind of needs they had. And we identified what we thought was a really cool idea, which was let's bring this idea of business intelligence, which at the time [03:24] >> was dominated by on premise physically installed software into the cloud, into this cloud movement that was happening. And they didn't even have SaaS as a popularized term yet. Things were called ASPs or application service providers, I think. I don't know if anybody remembers that era. But we decided to quit our jobs and launch this thing that basically followed the paradigm in the bottom right there. It's gonna do three things. It's gonna suck all the data out [03:50] >> of your databases where it's really difficult to to query and it's sitting in your production systems. It's gonna store it in this centralized data warehouse that we host and we maintain so that you can run analytical queries on it. And then we're gonna give you this awesome reporting platform on the front end where you can build dashboards and charts, etcetera. So we stood up and we quit our jobs on a Friday in September 2008. And on [04:14] >> Saturday, Lehman Brothers collapsed. And we found ourselves out in this market where we had planned to raise venture dollars. We'd come out of a venture fund. We thought at least, hey, maybe the fund that we were working for would back us. We got nothing. And we looked at our bank accounts and we said, let's attempt to bootstrap this thing. And that's precisely what we did for the first several years of the business. We were kind of [04:36] >> the tortoise in the tortoise of the hare story. Right? Like, we had some market pool, but we kind of were early to the market. And we iterated our way through just revenue driven growth, this glorious era of bootstrapping where we were able to kind of march along to get to our first several million dollars of ARR, mainly just through adding another customer, adding more dollars, using that to hire, and creating that cycle. And by 2011, the [05:03] >> market started to wake up, and we found that we were actually in a much cleaner place of product market fit. Like, the market was actually looking for and actively seeking out analytics tools like these, and we found this really awesome sweet spot. Of course, when the market wakes up on a category, if we were the tortoise, there were a bunch of hares as well. So what we saw, we raised about $25,000,000 in that, you know, 2011 [05:26] >> to $20.14 range. In that same era, a whole universe of other players directly in our space were raising an order of magnitude larger capital. You've got at least three companies here that raised at least $100,000,000 to do precisely what we were doing. Domo in particular on the right was just raising as much money as they wanted at any given time. Josh James, the founder of Omniture, founded Domo and had a blank check from a lot of [05:50] >> VC firms. So we found ourselves out in this market where there were a lot more dollars, a lot more competition. And while the market was blooming, it wasn't blooming as fast as the dollars going into it were. [06:02] >> And then something else happened that ended up being even more important than all that, which is Amazon Web Services launched this product called Redshift. Amazon Redshift was the first player in the large cloud based scalable data warehouse market. It was great. Remember I mentioned that little middle circle in our product stack where all the data got stored and we would host it for you, and it's where you could run all the queries efficiently? This thing was [06:26] >> like that only, no lie, a 100 times better. We had forked my SQL and built all kinds of bells and whistles on it. This was like a completely re envisioned, replatformed way for people to analyze their data. And it was better than that chunk of our product. So it created this really interesting paradigm shift where the middle part of the stack died for us. And a whole new market of products like Redshift, like Snowflake, like Google [06:54] >> released a product called BigQuery, Microsoft has an offering in this area, came along and just changed where people physically wanted to store their data. And once people replatform their data into those data warehouses, well, a whole host of other companies came along that could sit right on top of those data warehouses, provide the charts, provide the dashboards, provide the data modeling, and not have to worry about all the hard stuff on the infrastructure side. So meanwhile, [07:18] >> we're still doing all three. We really have to solve these three big technical problems, but we know our middle piece is not gonna win. And right under our noses, we were watching this new thing emerge known as the modern data stack. And this movement, I didn't realize it at the time. It was happening in analytics, but it was happening everywhere. It was happening in marketing and sales tech, happening in HR tech, happening in cybersecurity, happening across [07:40] >> all these different categories where the emergence of not just the cloud, but also the API economy, the extreme ease with which you can build interoperable products, was basically taking the world into this massive unbundling cycle where you used to buy this one kind of monolithic solution that was a sweet solution. Now the better move, the preferred move was that you buy your software in stacks, and those stacks are interoperable with one another. And what happened as [08:07] >> a result of this movement is that we just saw our TAM get completely crushed at RJMetrix. Because on one side, all this money is pouring in to companies that are doing exactly what we are doing, which is making it really, really hard to scale in an economically efficient way. But on the other side, nobody even wants that stuff. They don't want it from us. They don't want it from GoodData. They don't want it from Burst. They [08:28] >> don't want it from Domo. That market kind of stinks because there's been a shift in how people buy software that we don't actually line up with. So we're getting crushed in the middle here. And the way I always like to talk about this is founders are constantly on the hunt for product market fit. Product market fit is the thing, the buzzword that you will hear all the time. And very often, founders think about this like, Okay, [08:50] >> the market is what the market is. And I don't control it. And it's kind of in one spot. And then where I have power and influence as a founder is my product. And I can modify my product. I can pivot. I can iterate. I can AB test. I can run the build measure learn cycle from the lean startup, and I can weave my way in until my product is overlapping with the market really solidly. And that [09:13] >> may be true to a point. And many markets steer like the Titanic where products can be iterated on relatively rapidly. But I think what happens that a lot of people don't appreciate is particularly in the markets that many of us operate in, and AI is a great paradigm to look at around this, the speed with which markets actually are moving as well is really, really underappreciated. So while you are pivoting your way to product market fit, [09:37] >> don't lose sight of the fact that the market itself is also drifting. And that's precisely what happened at RJ. You get this perfect, like, Goldilocks set of circumstances where we entered that market in 2008 early, And we had to bootstrap for a while. The market was not quite ready for the product. We had to have a really, really niche universe of buyers. And then we had this glorious window of product market fit. And as much as [09:59] >> I'd love to take credit and say we figured it out, we iterated the product, we built great product, what we really did was not die when we were too early and be there when the market actually drifted into the window for what we had built. And just as quickly, while we were in a hiring frenzy and while we were focusing so much on sustaining the demand that existed in that product market fit window, we weren't seeing [10:21] >> where the puck was going. And we eventually drifted out of product market fit after just three or four years inside that tight window. So this is like I've got a couple slides here that are just, like, brutal that I love sharing. This is our The red line is our targets in 2015, and the blue line is our actual MRR growth quarter by quarter. That is not what you want to see, and that is not what your [10:41] >> board wants to see. We really, really basically just hit a point where the business stopped for the very first time. Right? This is seven years into its life. Not only did we start missing numbers, we started progressively missing the numbers worse and worse. We ended up needing to lay off a bunch of people in our go to market team because we had an SDR team where the economics were just incredible, and it made perfect sense in [11:05] >> 2011 and 2012. And in 2015, everything just went upside down and underwater. And we realized we had the best SDRs in the world, but we didn't have the product market fit needed to actually make those phone calls resonate with people that were ready to raise their hands. And the SDR model stopped working for us. [11:22] >> This is the other thing that happened, which is like for the founders in the room, I I I feel like it's important to show this slide. These are glass door reviews about RJMetrix from that era. And it is just the most heartbreaking thing to look back on, but it was a really, really challenging environment, especially for salespeople in that universe because as the business was deconstructing, I think we were basically not self aware enough to get [11:46] >> out ahead of it. And kind of we were watching it feeling as though it was happening to us rather than taking control. I will point out that there's one or two of these that hate the company but approves of the CEO. So I have a little bit of a little bit of feather I can put in my cap there. Who do you think was building the terrible company? So these I always I I grab these screenshots [12:05] >> at the time. They're like vanquished from the Internet at this point, but, like, I keep these around because I think they're important to to remember in in doing this message. So we get in this world where it goes from, like, too good be to be true to too hard to handle. And that reminded me of, you know, 2021 to 2023 for most of these companies. Right? Forget about raising cash. A lot of these strategies that used [12:26] >> to make a lot of sense kinda stopped working. Ads got too expensive for us. Buyers weren't buying in the way that they were buying before. And it was kinda like Groundhog Day, you know, going through the last couple of years seeing a lot of a lot of SaaS companies experience that. So what struck me though was that the modern data stack, all these companies that had adopted that new paradigm where the software is composable and the [12:48] >> buyers are buying in stacks, they're buying software that kind of participates in an ecosystem, they weren't having these these problems. They had these incredible joint value propositions with the companies that they interoperated with and the products they were compatible with. The products were so sticky because if you rip these products out, all the other ones that are in that value chain are either less valuable or literally stop working. And the efficiency of their go to market [13:14] >> strategies just worked a lot better because they were able to share pipeline. Like a win for one of them is a win for everybody in the stack because it's pulling people into this paradigm of buying inside of an ecosystem. So if you can't beat them, join them. We got very fortunate with RJMetrix in that we had a pretty decent sized business that was working really well in the ecommerce sector. And we were just kind of stopping [13:38] >> seeing growth in all the other categories that we had previously played, SaaS, gaming, etcetera. But ecommerce was strong. And there was a company called Magento that had just spun out of eBay. They're the largest open source ecommerce provider. So shopping cart provider, like a direct competitor to Shopify. And they were private equity backed at the time. And they really needed a better analytics solution for their e commerce store customers. And we entered into a deal to [14:05] >> be acquired by them. But in the process, we cut a deal. We said, going to conduct this deal, here's what we want to keep. We want to keep 20 employees, and we want to keep the IP that allows us to pull in data from all those different sources that RJMetrix knows how to pull. Basically, that of the red arrow there that's going from various servers and SaaS tools into that data warehouse. And Magento didn't care because [14:32] >> they only cared about getting Magento data into the analysis tool. But we had built like 70 connectors. And we launched a product that was no longer supposed to be a suite solution. It actually fit into the modern data stack paradigm. So we went from being the enemy of this space to actually participating in it and living in this data integration ETL thing. So we had partners all of a sudden. Every single SaaS tool that you might [14:56] >> extract data from, that was a partner. We also had partners in the data warehousing universe. Snowflake became a big partner. Amazon became a big partner. And everybody else downstream from there, all the dashboard providers, etcetera. And we were able to actually find really fast aggressive product market fit. And in twenty months, we got to as many paying customers as it took us eight years to get at RJMetrix with almost no salespeople. It was a PLG ecosystem [15:22] >> led motion. Our biggest refers of customers were the very same companies that destroyed RJMetrix. It was Looker. It was Redshift. It was Snowflake. Sending us customers because we were the ones who could get the data into the warehouses so they could sell those value propositions to customers. So just 20 in, Talend came along. We hadn't raised any outside capital, and they kind of made us an offer we couldn't refuse. And we sold Stitch for $60,000,000 and [15:48] >> kind of had an interesting learning there. Oh, and by the way, my $2,600,000,000 mistake, which was the headline here, very shortly thereafter, Looker gets acquired by Google for $2,600,000,000. And Looker was the number one company that had basically won over all of the RJMetrix customers that kind of hopped away. So kind of missing out on that movement in the in the ecosystem led, in the modern composable data stack universe was kind of this this opportunity squandered [16:15] >> with with RJ. But seeing it work in Stitch and work so fast, I felt like the work was not done. And we had this learning, which was basically the most valuable collaborations that we had were ones where we did this thing called account mapping, which is pretty much, hey, let me compare the data in my CRM to the data in your CRM. Well, main problem was that you're basically trying to draw a Venn diagram between two [16:40] >> things that were really not meant to be Venn diagrammed against each other. All kinds of technical challenges. If you wanna answer questions as simple as, hey, partner, how many customers do we have in common, who are they? Or are my sales reps currently selling to any of the same companies as your sales reps? Math is working against you Because you can't draw a Venn diagram unless you have all of the data from both of the sets. [17:00] >> So if you want to know what's in the middle, you actually have to overshare with the partner. You have to give them your whole customer list or your whole sales pipeline. And that's a nonstarter for most people. So most companies either don't do this or they do it by emailing around spreadsheets of these very, very fragmented subsets of data, and it's kind of a losing strategy. But what we figured out with the partners we had at [17:20] >> Stitch was if you can crack this and do it at scale, you can basically unlock this incredible fire hose of data. That's almost like you getting a little peephole into the CRM systems of every other product that is compatible with your product in some way. And it unleashes this this thing that we call the account mapping matrix, which is if you think about your prospects, your opportunities, and your customers, and you compare them to any given [17:42] >> partner's prospects, opportunities, and customers, you get this really cool mind box where depending on what box you're looking at, there's a whole host of applications, playbooks, use cases where you can convert that data into go to market motions. So Crossbeam was launched as a product in early twenty nineteen to basically be that escrow service for data that sits in between companies who are collaborating with each other and provides them with this environment where they can compare [18:08] >> those data sets but have really, really tight controls over who sees what, when, and under what circumstances, And ultimately, we're taking ownership over their own data and control over those access layers. And it's been really, really exciting to work with. So we've grown very, very virally over the course of the last five years. There's over 17,000 companies now on cross beam. That includes the overwhelming majority of the Forbes Cloud 100, a ton that are in the [18:34] >> large publicly traded space. I think we've got over 80% of the Bessemer Cloud Index that now uses it. And what's really kind of most important that has come out of all that is the plays and the playbooks. So this book, ecosystem led growth, just came out two weeks ago from Wiley, and it really is that treasure trove of here are the ways in which all those 17,000 companies, and in fact, the best ones among them, are [18:57] >> using the data from the account mapping matrix, using this data from their partner ecosystems in order to build scalable growth strategies that convert into high ROI, high efficiency results. So I'll talk a bit more about that book in a second. But at the core is these playbooks, they kind of hit everywhere in the funnel. Right? Marketing, sales, customer success. And I don't have enough time to go through a huge number of examples here, but I do [19:21] >> wanna talk about one of them just because it's so it clarifies a lot of this value really, really efficiently, which is this concept that's been widely adopted known as ecosystem qualified leads. So if you think about that three by three matrix, this strip along the bottom is the universe of your prospects, which can be a very, very broadly defined list. Everybody whose badge you've ever scanned at a trade show, every business card that you've ever logged [19:47] >> into Salesforce in some way, everybody that you've accumulated on your email or content list, how does that intersect for each of your partners with their prospects, with their active open opportunities who are in the process of buying from them right now, and probably most importantly with their customers who are actually paying them? Now doing this with one partner is pretty interesting, but doing it with dozens of partners or many, many hundreds of partners as as many [20:12] >> companies do ends up providing you with this incredible, incredible fire hose that's very, very dynamic that can inform where buying activity is actually happening among the universe of companies that you care about and specifically what products and areas they're actually making those purchases in. And what that translates to is this really powerful kind of second party data that is a a very, very useful example of intent. Right? It shows you where the hand raisers are who [20:38] >> are bought into this idea that their buying decisions are actually governed by what tools are interoperable with the things they already have in their stack, which is probably the prominent buying determiner at this point for most software products, particularly in SaaS. Stripe is a big adapter to this. Pete Cummings, head of EMEA Stripe, has a section in the book where he talks about ecosystem qualified leads really being key. There's a bunch of really interesting playbooks there. [21:04] >> But the book came out on March 12. It goes through all these things. Just wanted to also plug that I've got a bunch of them here, you can have them for free. So if you go all the way down the end of that hallway, at the very end, there's kind of a book signing station. I'm going be hanging out there this afternoon. We brought a bunch of copies with us. If you want to geek out on [21:23] >> ELG, you've got anything you want to chat about in that universe, or you just want to share horror stories from the 2008 era, I am totally game. And appreciate the time. Twelve seconds left. We did it. Alright. Thanks, everybody.

just got done editing this interview you guys are gonna love it before i do that though i want you to know that i'm going to be in the comments for the next 30 minutes or so answering your questions if there's additional questions you want me to ask the ceo next time i interview them leave them below or if you're just loving the data points i get ceos to share click the thumbs up button below that's your way of telling me you're loving this stuff and i'll get you more of it additionally again i'll be in the comments answering any questions you have all right for 30 minutes enjoy the interview hello everyone my guest today is bob moore he's a ceo and co-founder of crossbeam a collaborative data platform that helps companies build more valuable partnerships he previously co-founded rj metrics which was acquired by magento and stitch data which was acquired by talent bob you ready to take us to the top yeah great to be here yeah okay so i want to have you on after i saw a linkedin post where you did a great post tomorrow on rj metrics now you came on the show again i want to say it was two or three years ago talking about rj metrics so for people that missed that episode just give us the quick overview what did that company do yeah so we were a data analytics platform that mostly sold into e-commerce companies so pretty much anybody who sells anything on the internet could have been our customer and we helped them do analytics on their transaction data so they could know things like customer lifetime value study cohort analysis and basically help find more customers like their existing most valuable customers okay and what year did you launch that that was 2008 uh the day lehman brothers collapsed we launched that company that's a heck of a time to launch yeah it was a ride you weren't ex lehman you lost your lehman job and said i'm going to rj metrics that wasn't your case right no i was uh my co-founder and i were in finance in new york we worked over at uh insight venture partners the veteran capital firm but uh the lima thing was a very unfortunate coincidence yeah all right so the reason i won't have you on is again you did a great post-mortem on this which ties into your new current company cross-beam related to kind of being a true platform versus just like you're not really a platform but you use the word market your platform in your marketing so talk to us what happened with rj you launched the company what happened over time yeah so we when we started that company you know sas was kind of in its infancy and the model at that time was very much around building these big monolithic platforms so you know you want to build something that will be a one-stop shop where the end user doesn't have to worry about all the uh the op stuff going on in the background you just kind of click a few buttons you get the value out and you move on and for analytics that's actually a lot of pieces so rj metrics to make it work we had to build a huge data pipeline infrastructure where we could extract data from all the places that people's data might live like their shopping carts and their advertising platforms and their backend databases then we also had to have a place to put that data so we had to build a data warehouse that would house all that data when we pulled it out that could live on our infrastructure that we could worry about scaling and making sure the queries were fast and then we had to build all the stuff you think about when you think of analytics which is the dashboards and all of the components that allow you to make custom reports and have alerts and things like that so it was kind of this three-part stack and it worked great in certain industries particularly in e-commerce but as the market evolved and because we were a company that was bootstrapped at first and around for for a pretty long time we kind of got almost lapped on the technology side in that there were pieces of that stack where entirely new generations of technology came out that actually broke the stack apart and made the best practice completely different from an implementation standpoint and probably the biggest thing in that world was this thing called amazon redshift which is a data warehousing platform that makes it really easy to quickly and scalably store large amounts of data in the cloud um redshift and later google bigquery and companies like snowflake innovated on this model and basically if you think about that data warehouse as being like in the middle of the stack what rj metrics was it broke our stack right in half because no one wanted to use our little piece of a data warehouse that we built for analytics they wanted to have their data in their redshift cloud in aws or in their their gcp cloud um you didn't build those integrations yeah so what we ended up doing um is really kind of uh hunkering down and what we watched happen was we really just started selling just to the markets where they didn't have as much as a of an investment in owning that stack themselves and it took us from a world where we were trying to sell to every company that sold anything on the internet down down down down down down to like specialty retail by the time uh you know we ended up selling the company and look that's a big vertical uh but you know it was not a multi-billion dollar outcome so to give people context what year did you sell rj metrics to magento 2016 that deal happened okay what month do you remember june 2016. okay so guys to give you context episode 233 of the show robert or bob came on this was february bob of 2016. this is way back in the day so yeah and you know i probably wasn't talking about any of this stuff uh no you weren't but i wanna i think perspective is important to understand history right so when you came on the show then you had shared that you had about um 11 oh sorry 400 customers 800 a month average acv had just passed about 4 million bucks in arr about 100 people all that sounded about right 22 million raised yeah i think we were um the arr was a couple million bucks higher by the time we sold it but yeah that's that's kind of the ballpark okay so you you so two things are happening here you're exiting to june in 2016 and there's another thing called stitch that comes off this so i want to get to stitch in a second but wrap up the magenta story was it public what what the sale price was uh it was not public now okay so it's private but you had raised 24 million to a date correct yeah yeah okay and doing somewhere between four and 10 million are at that point yeah yeah yeah so it was i would say uh a classic like base hit uh didn't knock it out of the park but we were we were happy with the outcome yeah not not you don't own a private jet but uh you're you're not also worried about where you're getting food from tonight yes there you go very good all right stitch what what is it how did it kind of spin out yeah so if you think about that stack i was talking about before so we had the data pipelines we had the data warehouse and then we had the cool charts and graphs when the data warehousing industry shifted what we had were kind of like the two other ends of the thing and one of them was this really cool data pipeline infrastructure that if you think about what data pipelines are at their core it's really just a way to get data out of certain systems and placed into other locations in a way that scales where the end customer doesn't have to worry about you know rewriting a script every time somebody's api changes we built a system that allowed us to support that kind of data connectivity for dozens and dozens and dozens of sas tools and when we sold to magento they weren't particularly interested in getting data from dozens and dozens of sass tools nearly as much as they were about getting data from magento um you know we became magento business intelligence there and really focused in on helping magento customers get better analytics out of um out of their e-commerce data so we were we had this leftover piece of technology that was the data pipeline stuff should people think about that like like a version of mulesoft or zapier or yeah it's a great i mean uh the fact that talend was the acquirer is is a really great kind of proxy for that which is kind of in that data integrations universe so making the plumbing that allows all of your cloud connected systems and uh on-premise systems to talk to each other to have data parity be correct and to help your analytics have data flowing in the right places at the right time uh so if you're a good example too is like if you're using a tool like looker as a business intelligence platform the question is how did the data get to looker because looker doesn't actually you know go out and you know manage hundreds and hundreds of little scripts that run and pull data out of all your little uh services and locations that's the kind of thing that an etl tool will do and that's extract transform load and that's what stitch is stitch is this kind of core etl um so yeah so we really we had this leftover thing from rj metrics and we were able to negotiate in the magento deal that we retained ownership of it so we actually just took that uh rebranded it as stitch data jake took over as ceo while i was at magento for the earn out and uh basically built this business up um off of this core technology and team that we had started with so did you have to give any of that so the stitch roll out you said negotiated kind of this piece your own did magento have any equity in stitch no they didn't did your early investors that put 24 million bucks into rj did any of them have equity in stitch yeah they did so you could almost think of it as like you know everybody owned that i everybody that was in the cap table uh for rj had had kind of a stake in stitch because it was effectively you know a mini rj that was kind of carbon copied out so um yeah the cap table of stitch ended ended up looking a lot like the captain uh rj at the time the upsell and what did give me can you give me a general sense of what you scaled stitched to total number team i think you stayed bootstrapped right uh yeah we didn't put any new net new capital into the business uh when it got acquired i think the team was in the 30s um and from a like magnitude standpoint it was almost the same size that rj metrics was when we sold it so 28 months we grew stitch up to about what it took eight years to get to get rj so again somewhere between four and ten million bucks in ar yeah yeah okay interesting and then talon 2018 they did publish this was kind of a 60 million dollar cash deal correct yep that is correct yeah yeah okay good so then after you've learned all this platform ecosystem how does stuff get in the look or how is it not you got a text message when looker sold to google for 2.6 billion dollars what was the text message yeah uh uh jake said uh uh i forget who said what but one of us texts the other like hey looker just got bought for two 2.6 billion and the consensus was basically like uh we up yeah they they won uh hands down um with with that number yeah and that was jake that was obviously a co-founder at rj yeah stitch sends you that june 6th at 10 22 a.m and you're going now we messed up so so you've learned all this now you've launched crossbeam right so are how you launched crosstalk in 2018 you raised i think 12 15 million total mainly from first mark what are you building a cross beam that rj didn't have yeah so crossbeam really is a platform to help companies build a go-to-market layer on top of these technology partner ecosystems that they have so um you know what rj didn't really have was an ability to hook into other platforms in a way where it helped create value that flowed in both directions rj was almost like where your data went to die like we pulled data in from all these systems and we analyzed it but the outputs of our system didn't really go anywhere you just kind of came to rj and consumed what rj created in the modern sas economy that is kind of a very antiquated way to look at things stitch on the other hand is almost the exact opposite right it's all ecosystem no glory stitch pulls data in from one sas tool and pushes it out to another sas tool and it's just kind of like the plumbing in between and ironically we ended up building you know a lot more value at a much more rapid clip uh at stitch and it's because the maturity of the api economy and all the interoperability of all these sas platforms has created basically a new channel a new growth channel for sas businesses which is selling through and building market through your partners and all the different companies that your technology integrates with that you help make their products more valuable and they help make your products more valuable through integrations uh and through you know being able to basically play nicely with the data that uh you create and with uh the people that you work with so uh the question that was always at hand for us was how do you actually scale that um you can say a lot of really good things about partnerships and how companies work together but how do you actually point back and say 36 of my revenue last quarter was driven by my partner ecosystem how do you make sure that you're turning these relationships into new leads you're accelerating opportunities you're making your existing customers more sticky and making those accounts grow what crossbeam is doing is unlocking the data layer that can allow companies to do that you can almost think of us as like an escrow service for data where if you want to answer questions like how many customers do we have in common and who are they or are my sales reps currently selling to any of the same companies that your sales reps are selling to that's a really hard set of questions to answer in the past without over sharing data and over sharing data is not something that companies are very interested in doing right now uh nor should they be what crossbeam allows you to do is we are we're kind of like switzerland we sit in between two companies that are partnered both sides connect their data and they can actually analyze the combined data set to find these overlaps to find these opportunities to collaborate while keeping all the rest of their data private and secure very interesting that just unlocks all these new motions so follow up question on that first but scale wise today i mean are you talking like you're early and like five beta customers enterprise or you're at like 5 000 people using a freemium model yeah somewhere in the middle so we've got over 100 companies that are like fully onboarded on the platform hey they're paying a chunk of them are paying us now uh so like there's there's real revenue there um you know when we raised the series a we were pre-revenue still so that was just three months ago um but uh we've got a we've got a really solid quarter on converted customers and we're kind of like firing up the revenue engines now but it's it's still early days yeah yeah i mean do you think what do we have we have like 10 days left in 2019 yeah what did you guys set a revenue target for this year i'm trying to get a sense of how fast you go from zero to a million ar yeah i so if we hit our targets it'll be six-ish nine-ish months total will get us from zero to a million okay and i think we're based on this quarter we're pacing toward that so it'll be like april next year or something like that uh yeah that would be i mean it'd be a slam dunk but i think it's doable i think that that's like uh if things you know continue to go according to plan fair enough okay so let me let me just tell a story real quick so there's a lot of bd teams that like salesforce uh or or intuit that use their app exchanges to basically go after m a targets right if you do really well in their app store they'll buy you but those companies let's use the smart sheet into intuit example mark mader right from smartsheet doesn't want to share all this data with into it without knowing if it's actually going to happen is that a good use case intuit and smartsheet will use you to see where they overlap yeah this literally happened to me when we were selling rj to magento because we had other potential buyers and all the buyers wanted to see our full customer list before they gave us a term sheet but they don't care about the whole customer list they just want to know about the ones that overlap with their customers so that exact use case so m a corp dev due diligence even during venture investments it's a whole category of use cases um that's in the solutions part of our website um i don't love it as much as the day-to-day sales enablement stuff because it's less recurring but the value proposition is is very real yeah really okay wait so just to be clear i mean as you scale do you think you're going to build a bigger business selling into bd teams and potential aquatic companies being acquired or it's the sales it's the sales use case i think it's the go to market i think it's the whole go to market funnel so it's it's marketing sales and kind of account management customer success um there's really clear-cut recurring use cases for all of those uh that sit within our stack so i think that's really that's the that's the primary uh method of growth in the big recurring use case interesting okay um for people listening right now that wanted they you know in their seed deck they said we're a platform we're a platform because i thought it would juice their evaluation but they know deep in their hearts they're not actually a platform yet and they're going how do we actually build what we just sold how do you actually become a true platform not just one that you use in your branding yeah and i would like i would also just challenge the idea like do you want to be a true platform like bill gates has this definition of a platform that i like which is that the economic activity that gets generated as a result of you existing um in your partner ecosystem is actually greater than the economic activity that you generate for yourself like at that point in time you kind of like the fulcrum has tipped and you are in platform territory but in reality you know a platform in theory is this this fundamental layer that stuff gets built on top of what we're seeing out in the market is that the big success stories you look at companies like zoom companies like slack you know the latest ipo crop in sas i would argue that very few of them are like true platforms what they really are is super nodes inside of a really healthy ecosystem uh it's not that people are just building on top of them it's that they are building on top of people who are then building back on top of them and there's a fluidity and a bi-directionality to how that works that makes the whole platform metaphor kind of moot and kind of outdated and i think honestly vcs are getting skeptical of that like you see the platform word in a seed stage deck and i think an eyebrow gets raised yeah yeah i keep an eye out for that okay let me let me try and let me try and learn something here so let's stick with video conferencing for a second uh you would call slack a super node or sorry zoom a super node right build a healthy ecosystem can you name a platform company that you think is a platform company in the video conferencing space oh that's a great question um i honestly i i jumped to saying no and the main reason is like when you think of that bill gates definition the question is are there companies that would literally would not exist if not for having completely been built on top of uh you know uber conference or or you know you go to the big enterprise side kind of the go to meetings side of the universe and in that regard i think almost by definition the vertical of video conferencing is inherently an ecosystem-centric product because what you have is sales enablement on one side and then you have kind of communication and team collaboration on the other it's not that you build a whole collaboration suite sas product on top of a single video conferencing platform almost anybody that wants to build on a video conferencing will build on multiple of them and therefore none of them can be the platform with a capital p that they're built on top of um when i think of platforms i think of like you know amazon web services i think salesforce is a platform uh because their their app store is a true true uh there are companies that are entirely existent because the only thing they do is provide technology on top of salesforce and that those are platform powered companies on that bob let's wrap up here with the famous five number one favorite business book oh uh i really right now am actually a big crossing the chasm fan like it holds up it like it keeps coming back in my life that book will not go away it's the only one by the way it's the only one that is like in that space where you look at it and you go this little orange cover and this crossing again it is still valuable two decades later it's still true it's not fluff yeah number two is there a ceo you're following or studying oh uh i'm a huge uh elon musk fan uh i think for many many reasons but i like the idea of like you know in your second or third generation of companies like really taking that step back and saying what will my great great great great grandkids be glad i worked on um and i you know i think about that a lot uh and i think he's he's taken probably one of the most awesome approaches to that number three besides juron what's your favorite online tool for building crossbeam oh i have rejoined um uh i'm like a superhuman addict to be honest uh superhuman is big in in my day-to-day i think it's probably the tool that i spend the most time in but the the has also somehow saved me the most time uh simultaneously and that's an interesting paradox number four how many hours of sleep you get every night uh i just i have a three-month-old at home uh so it's a bad time to ask me that question uh but i still like six is kind of the gold standard for me less than that i'm not super functional so six to eight okay and you got a little okay so one kid two how many kids well that's my first uh my daughter annie she is uh yeah three months old wow okay so married a kiddo how old are you i am 36 as a couple weeks ago amazing all right take us home what do you wish your 20 year old self knew oh uh listen more um and be as intellectually honest as humanly possible uh because the things that aren't going right are the things that will probably turn into the best things you can possibly uh you know have his outputs a couple years down the road i thought you were going to say that looker would sell for 2.6 billion start looker whatever you do just just start looking well guys on that note it sounds like it did again bob moore launched 2000 2008 rjmetrics waste raised 24 million bucks eventually acquired by magento in june of 2016. they were doing somewhere between four and 10 million bucks in ar they spun out a piece of tech uh magenta didn't value called stitch boots dropped that up to about 30 people another 4 to 10 million bucks in ar acquired by talland in 2018 for 60 million through all this he learned the importance of what it means to build a healthy platform ecosystem or ecosystem around your tool and built cross beam essentially escrow service for data launched in 2018 about 15 million raised 80-ish to 100 paying customers hoping to hit a million bucks in ar by april 2020 bob thanks for taking us to the top hey thank you good round up these ceos rarely give these kinds of interviews i hit them hard i get the data and i want to do it more so if you want to get more of this stuff make sure you subscribe up here and then additionally go check out one of my other ceo 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