Why Ecosystem Partnerships Are The Future In The Age Of AI | Bob Moore from Crossbeam

Yeah, I am so skeptical of business books. I think one of the first lines in this book is, "Generally, I think most business books could have been a blog post, and most blog posts could have been a tweet," and it kind of cascades on down from there. So when venturing into doing this, I really wanted to look at it from the perspective of there being a very genuine sense of market pull for the book and kind of the market being ready for it.

I got a stat from my publisher, Wiley, that the average first-time author whose books hit the shelves sells less than 300 copies of their book. So when you're out there seeing founders release books or these big kind of pontificators talking about new trends that exist inside of tech or new major revolutions that are happening, it's really interesting kind of to the layman encountering it in a Barnes & Noble. But the reality is that the product-market fit of a book is the same thing as the product-market fit of a startup.

Most of them fail. There are way more of them than should exist in the first place. And I think I went into this process with that knowledge and wanting to kind of bring a level of intellectual honesty to, hey, if we're going to do this, is it worth the time? Will there be an audience for it? And frankly, is there an interesting enough story to tell? And I think what happened around last summer when we started negotiating the book deal was just feeling like the confluence of all those things were finally happening and coming together.

So from an audience standpoint, we're up to 18,000 companies that use Crossbeam now. We're getting close to 100,000 users on the platform, which is pretty incredible. The number of people that engage with our existing content just through our digital channels is bigger than ever. And it felt like there was an opportunity to do more to pull together the stories and kind of the end-to-end narrative around this ecosystem-led growth movement into one singular piece that could persist not just for a post in a newsletter or something that shows up as a keynote at a conference, but instead could actually be this work that we could point to over a multi-year time period and say, if you're new to this topic, if you're curious about this topic, this is the thing that you should read.

This is the starting guide that'll take you all the way from what is this thing, why do you care about it, all the way into the actual playbooks.

Yeah, definitely. Another way of saying that VCs like to invest in repeat founders is that VCs like to invest in founders that have a chip on their shoulder, because a repeat founder that IPO'd their company and took $1 billion off the table may or may not actually be the best bet for the next $5 million check. But I think a founder that's gone through the full motions end to end and developed this really interesting blend of muscle memory and scar tissue that will kind of take them into their next business, knowing where to flex and lean into what they're strong at, but also knowing where to be cautious and be self-aware of their own weaknesses and build strengths around them.

That's a hell of a thing to start out with in terms of founding DNA. And I think that's, when I look back at my first two companies, RJMetrics and Stitch, precisely what kind of came out of them: a little bit of scar tissue and a little bit of muscle memory. So, to go into those stories really briefly, RJMetrics, I co-founded with a brilliant guy by the name of Jake Stein back in 2008, right in the heart of the Great Recession.

Great. So I worked at a venture firm. I worked at Insight Partners from '06 to '08. Jake and I quit on a Friday in September 2008, and on Saturday, Lehman Brothers collapsed. So we quit with all of this. We didn't plan to go out in a terrible market. Timing is everything. Timing, timing, timing. So, yeah, that business at its core, it was probably the earliest pure SaaS business in the business intelligence space. So dashboards, data pipelines, data warehousing, all in one siloed solution.

Most of our customers were e-commerce companies, and we would help them do things like suck in all of their payments data and their shopping cart data, and we'd tell them, hey, here's your customer lifetime value, here's a cohort analysis. Here's what your most valuable customers look like and how to get more of them. And it was a really interesting business because we were early, and it took a couple of years of kind of grinding it out with early adopters to really get the machinery working.

And then we had this beautiful, incredible window of product-market fit where it was flying off the shelves faster than we could even handle. And we took on venture money, and we scaled really aggressively, and we grew it into a pretty substantial business. And then just as quickly, we fell out of product-market fit as the modern data stack revolution happened. I know you had Tristan on recently talking about the modern data stack and the genesis of that over time. We were basically kind of systematically dismembered by the likes of Looker and Snowflake and the pieces whose sum was greater than what we could offer as a silo.

And what happened at the end of that is we were fortunate that we were able to have a decent outcome at RJMetrics. We got acquired by Magento, which soon got folded into Adobe. Adobe bought Magento for $1.68 billion, which was on the order of closer to 100x than 10x of what we sold for at RJ. The $2.6 billion mistake, it's really that, right? Which is this level of strategic awareness on not just being able to pick an idea that's going to be durable, but being self-aware of where the market is going and strategically mapping to that, always with a few years ahead in mind.

And also just being able to do the raw execution so that when you're hot, you make hay while the sun is shining, position yourself in such a way where you have an incredible posture that can be defensible against whatever the next incantation of the space that you're in might be. So I think we did all three of those phases like first-timers at RJMetrics. And one of the things that—and this is what leads to Crossbeam—that we witnessed there was, when we started really getting our lunch eaten by Looker, one of the things that was happening was that we realized we were extremely weak in terms of having ecosystem DNA in the way that our company worked.

We were a giant silo. So if you bought RJMetrics, it didn't really matter what other tools you had. It didn't really matter what else existed in the fabric of your company's technology stack. We were, from an experience standpoint, very much a single-player-mode kind of product that was consumed and bought and considered and valued almost more like traditional license-and-maintenance-style software products, as opposed to part of kind of a fabric of technology tools. And when the modern data stack emerged, it wasn't really RJMetrics versus Looker; it was RJMetrics versus Redshift data warehouse with some data pipeline technology in the middle and LookML's modeling language feeding end results into Looker dashboards.

So when people bought the alternative to RJ, they were actually buying four or five different products. And one of the superpowers that we saw all those products have is that when one of them had a company in their sales pipeline, that meant all of them had a company in their sales pipeline. Nobody bought just one piece of the stack. So that meant there was this really interesting demand generation and customer retention and loyalty motion that these companies could build by effectively having a rich, rich, rich interconnectivity of not the products, but the go-to-market organizations and the way that they sold those products.

And this is where this ecosystem-led growth strategy started to become really clear and crystallized for me. People think about partnerships, especially in the modern SaaS era, and they think about tech integrations. They think about partner teams doing press releases and, how have I integrated with you, and what does that mean from a kind of value proposition standpoint? And all that's very relevant. ELG doesn't really come into the fold until you start thinking about the ways in which your go-to-market team is actually leveraging those facts, the ways in which your sales pipelines cross-pollinate, the ways in which your messaging cross-pollinates, the ways in which you retain customers together and grow those customers together.

And in our world with RJ versus Looker, we started losing because we weren't just up against Looker's sales team, we were up against Amazon's sales team, we were up against Fivetran's sales team, we were up against anybody else in the stack. And not having that was a big eye-opener. So interestingly, we sell RJMetrics, and immediately on the heels of that, we started a company called Stitch Data, which was basically an if-you-can't-beat-them, join-them kind of play, where we started this business that was a direct competitor to Fivetran existing in the modern data stack.

And ironically, our number one partner in that business became Looker. So our worst enemy became our best friend really quickly. And man, did it work great. It had all the stuff, right? It was a PLG business. We had kind of a really awesome free trial to low price point. It was kind of consumption-based in how much people paid, and it fit. It snapped right into this really awesome, fast-growing ecosystem. And that meant that as a small company of 20 people or so with no new incremental outside funding, we were able to grow a pretty substantial business in a really short amount of time, really on the backs of almost purely ELG and PLG having this perfect marriage in the way that the business got built.

And the number one source of our referrals was all those same people in the modern data stack. It was the data warehouse providers who needed to get that next incremental data source into their databases. It was the business intelligence and dashboarding solutions who needed to build the next chart or dashboard, but the right data wasn't available. All of them sent their customers to Stitch in order to solve that problem, and that became our entire sales pipeline. So in 2018, we sold that business to Talend and found ourselves in this moment where there was an opportunity to go after Crossbeam.

And we can talk more about that, but that's kind of the through line: that back-to-back of RJMetrics and Stitch, the scar tissue from RJMetrics, the muscle memory from Stitch, that teed up the Crossbeam story being possible.

Yeah, ecosystem-led growth at its core, it's really a go-to-market motion. It focuses on partner ecosystems as this primary way to attract, convert, and grow customer relationships. And it's distinct from the legacy universe of partner strategy in that it is extremely focused on the impact that the ecosystem can have on your revenue. The KPIs for ELG are the same KPIs for growing your business at its core, right? If you're a SaaS business, that means it's ARR, it's customer retention, it's pipeline generation, it's conversion rates.

It's all the things that matter in your sales, marketing, and customer success funnels. Those are the metrics that matter inside of ELG. And every playbook that you run in ELG maps directly to amplifying and improving those metrics. The other thing that's extremely important and distinct about the ELG plays is the role that data plays in being able to bring them to life. So there's a big why now here, right? Why couldn't this book have come out 10 years ago, or maybe even five years ago?

And all of that actually does have to do with data. If you think back to traditional ways of partnering across companies, there has always been this classic kind of game theory-style problem that has existed, almost like the prisoner's dilemma problem, where the questions you often want to answer are not difficult questions. They're things like, hey, partner, how many customers do we have in common, and who are they? Or are my sales reps currently trying to sell to any of the same companies as your sales reps?

But in order to answer those questions, you have to draw a Venn diagram between your data silo and your partner's data silo. And math is working against you. Mathematically, you cannot draw a Venn diagram and know what's in the middle unless you have all of the data from both of those silos. So this is where you get into the prisoner's dilemma, because I may love you as a partner, but I am not giving you my entire customer list just so we can find the sliver of overlapping customers, and vice versa.

This has created this data standoff situation historically between companies that leads to this kind of archaic practice known as account mapping, where companies email spreadsheets back and forth, and those spreadsheets get very often compiled by individual sales reps or partnership account managers with the goal of kind of finessing down the data into just the stuff that they think might overlap or might matter, and then maybe once a quarter or even once a year doing some complicated scheme of running VLOOKUPs in Excel and trying to figure out where they might have some collaboration surface.

And this process is terrible. I saw it at every single company of any scale that I had ever been at. And even setting aside the security and privacy implications of doing legacy account mapping, which are pretty terrible, you also get into just the practical implications of doing this on a very rare, spaced-out basis. You lose a lot of the value in knowing how and where the velocity and acceleration within people's sales pipelines and customer bases are actually happening. The most valuable signal of intent from your partner ecosystem probably happens in the 48 hours to one week after your partner closes a deal with someone that you're trying to sell to.

These moments of opportunity for very organic cross-pollination of sales conversations, they're fleeting, they're ephemeral. And if the data is not able to be exchanged on a more real-time basis and more fluidly and more completely, you miss out on the absolute best stuff. And that is a big part of why the scalability of these legacy partner practices has just been super poor. So getting back to the why now question, now the data is here, and this is what Crossbeam does, right? The core of our product is that we are kind of account mapping at scale.

We are this independent third-party solution that's a trusted party, almost like an escrow service for data, that sits in between companies who are collaborating and basically does all the hard stuff: ingesting the data from the systems of record, cleaning up that data, superimposing it onto a universal data model so the data can be compared across multiple companies, even if it looks very different at its origin source. And then, most importantly, placing this trust and security layer over the whole thing so that every company, A, retains ownership over their own data, and B, has absolute control over who can see what, when, and under what circumstances.

And what that does is it basically unlocks this massive account mapping matrix for companies who want to lean on their ecosystems to get much smarter about who to sell to, how to position their products, and the value proposition of themselves in the context of the fabric of technologies and strategies that are relevant to those buyers, and then ultimately grow and retain those accounts as part of that ecosystem and part of that fabric, which time and time again, we show the hard data of leading to better customers and higher close rates and faster deals.

Sure, yeah. And even more important criteria there might be, I'm only willing to share that customer with you if it's already in your data as a stage three opportunity or later. Or maybe if it's stage three or later, I'm going to share information about who the sales rep is on my side or some more material metadata about the deal. But if it's just in your prospects list or your leads list, or maybe it's just a stage one opportunity, maybe I'll just share the name of the account or some more kind of rolled-up information.

Maybe I'll just give you the number of accounts that overlap or something like that so that you can do the analysis of mapping out where there might be hotspots or a large addressable market for the partnership. But as things get more tangible and real, then you can really start to expose richer and richer data that actually drives behaviors and actions on a deal-by-deal basis. And look, we get the full spectrum, right? There's a whole section in the book about basic security and privacy strategies that we've seen deployed by different companies.

Some of them have ironclad partnership agreements with NDAs that specifically envision doing this kind of work, and they share everything, and they're extremely open. And they use a feature in our product called Greenfield Sharing, where they just kind of open the books. And if it's a company that they have a deep strategic relationship with, or in some cases even M&A, right after acquiring a company, when you just need a fast path to high visibility into where the data intersects before you integrate the CRMs.

It's a great solution for that. All the way on the other end of the spectrum, one of the neat things about Crossbeam is reciprocity is not required or guaranteed. So if you are a supernode or you're kind of at the center of your ecosystem and you have a lot of relationship leverage against the other parties, you might be able to ask them to share significantly more with you than you share back to them. So you might get full visibility into any time your customers or prospects or opportunities overlap, you get to know that.

But the only thing you share back with them is the numerical totals of how many are there. And you might say, hey, that doesn't sound like a very good deal. Well, it actually turns out to be a great deal in cases where the supernode in the center has programs to allow for co-selling, for incentivizing their reps to try and drive these cross-pollinations. And they just want to keep the control in their own wheelhouse because they're kind of the arbiter of the largest proportion of the data in that relationship.

And it ends up being absolutely great for the people in their ecosystem. It unlocks whole new motions for kind of leaning on a bigger partner they otherwise wouldn't have been able to get the attention of. So there's a lot of different ways to spin it, but at its core, it's this incredibly finite control over being able to say, I am interested in collaborating when these conditions are true. And they may be firmographic, they may be technographic, they may have to do with whether or not an overlap exists, but we can do it all.

And that's the beauty of the tech underlying our platform.

Yeah, this is a moment, right? And this is, I think, also why the market pull for the book has been so strong and what led us to actually put this together. Because I could have—10 years ago, the tech didn't exist, but five years ago it did. We were founded in late 2018, and we kind of had this vision. We could have written this book then. I think the difference between then and now is that we've gone through the full lifecycle of the ZIRP era.

And we've been able to have a lot of operators, founders, CROs, CMOs, sales leaders kind of get the ability to, at a very large and wide scale, when capital was inexpensive, run every single playbook under the sun into the ground. And this includes inbound, it includes outbound, it includes ads and ABM, it includes basically anything you can do to generate demand. When there's not as much emphasis on your LTV-to-CAC ratios and you can have a burn multiple that's up in the stratosphere, every incremental strategy seems great.

And in that era, I'd say we were kind of thrown onto the pile as one of those strategies, right? Or ELG was thrown onto the pile as one of those strategies. What's different now is there's been a huge wake-up call, again, with a confluence of multiple things happening at once, the most important and pertinent of which is increasing interest rates, the end of the ZIRP era, and this incredible seismic shift in the focus of what metrics matter inside of businesses, away from growth at all costs to growth at a very specific acceptable band of costs.

And it's not just about cash conservation or kind of the path to profitability. It's about Rule of 40, it's about burn multiples, it's about figuring out how to grow in ways that don't consume massive amounts of cash. Both things need to be true in order to have an interesting business. So ELG turns out to be a really awesome strategy for that because, at its core, this is not a strategy that requires you to hire incremental human beings in order to do the work better.

This is a force multiplier that sits on top of your existing go-to-market organization that just allows people to spend time with a more appropriate, pre-vetted, qualified universe of prospective customers. It allows you to qualify those leads and those customers using very rich, very proprietary data that's proprietary to your business. And then it allows you to actually pursue those accounts and win those deals in a way where there are more wins and the deals happen faster. And, of course, retain those accounts and minimize churn and maximize expansion.

This is not, hey, you need to go hire an army or fire all your SDRs and hire, I don't know, EDRs, right? Ecosystem development reps. You don't necessarily need to have a complete revolution and kind of burn-down-and-rise-from-the-ashes exercise to go through to deploy this stuff. This is really about taking this incredible amount of leverage that most modern companies already have, extracting the data that can allow you to actually benefit from that leverage, and then getting it in the hands of the operators in your company that are tasked with actually growing your business on the front lines.

And in this particular moment, there is just an enormous universe of companies that are kind of figuring that out and getting on board there. And the ROI is just absolutely through the roof because the cost basis is incredibly low and incremental. But the actual gains are so large that, again, ELG—there's an important word in there, which is using partner ecosystems as the primary way to attract, convert, and grow customer relationships. This, in so many businesses, has grown from this nice-to-have afterthought, kind of weighed down by the legacy of how partnerships used to be done, and evolved into this incredible: this is actually the tip of the spear that's responsible for the most efficient and most kind of high-volume growth in their business.

Yeah, I'm glad you asked that because there's two sides to this answer, right? I mentioned there's a confluence of factors. One of them is obviously the post-ZIRP era effects of a focus on efficiency, and that's where ELG shines. The other thing is this slow heat death of just about every other strategy that has worked in the last 10 years. And we can walk through a few of them briefly, right? You mentioned AI. When you think about inbound, I don't think inbound is dead.

I'm a big believer in inbound. I wrote a freaking book, right? I'm a believer in content marketing and the importance of category creation and brand and audience building for companies. However, I think the strategies and the human strengths that will provide companies with leverage in that world are being completely burned to the ground by AI. And I think about the content playbooks that worked back at RJMetrics and even at Stitch, and I could walk through them one by one. And I do a couple of examples in the book, just how, in a world where modern generative AI exists, these are completely undermined and would not even be worth the energy of experimenting with them in a hackathon, just because it's twofold, right?

You've got, obviously, sure, AI can generate content that has gone from laughably bad to better than the bottom 25% of marketing employees to maybe better than the bottom 80% of marketing employees, and on a trajectory to continue progressing there. But on top of that, the core underpinning of a lot of inbound marketing is discovery. And literally, the nature of inbound is that people are coming inbound. Well, how do they find you from an inbound standpoint? Search engine optimization and organic search is just a major component of that.

There's other methods, but it matters a lot. SEO and content generation and where it lands on these long-tail searches. And search itself is threatened by the presence of AI. And I think there's a whole cohort of humans that have already largely replaced a very large proportion of their traditional search behavior on search engines, from looking through these search engine results pages to just trusting the answers that come out of ChatGPT or whatever their preferred chat agent is. And that's a really big deal because it changes the nature of discovery, which, again, gets back to the old playbooks in inbound, just are going to be completely reimagined.

Okay, so it doesn't mean that inbound is gone, but it means that your ability to actually forecast growth in your business and deploy dollars against that strategy is completely undermined. And it's like, if you're looking at your 2024, 2025 growth plans, how much pipeline is going to come in from inbound? If that was highly predictable before, it's on shaky footing now. It's bad news. But you can kind of look across inbound. Obviously, AI is disruptive to it. You see these other ones, like outbound selling, right?

SDR teams. At RJMetrics in 2011, I got a hold of the book Predictable Revenue by Aaron Ross, and I felt like someone had handed me this secret pamphlet that had been passed down through the ages that was like, this is going to change everything. And funny enough, it did change everything in that era where we had amazing product-market fit for RJ. It was like we had a giant boiler room full of SDRs, and that machine was working. But I think what we've seen happen is you literally go through the entire maturity curve of that.

And we've had SaaS platforms that have emerged that have basically allowed people to, at a very large scale, systematically, basically, for lack of a better term, cold solicit or spam people in extremely high numbers because you can buy email addresses from third-party data brokers, of which there are a bunch of companies, some of whom are public, where you can kind of match A and B together and just be super aggressive about not hyper-personalized, relatively cold outbound email campaigns. And unfortunately, this is worse than a zero-sum game.

As more and more people use it, it's actually a negative-sum game because while the number of companies that might be able to send these emails can grow nearly infinitely, there's no cap on that market. The amount of attention span that the average recipient can allocate to their inbox and the amount of skepticism that exists are finite, right? So the average email—yeah, to say the least, right? So the skepticism index is higher than it's ever been. The attention span index is lower than it's ever been, which means that the incremental value of a sent email, even with a personal touch to it, even with a little extra flair, has basically bottomed out.

And the ROI on these SDR programs, because of the negative-sum game factors and the fact that everybody decided to do it, has basically flipped. It's flipped upside down. And again, I don't say this to say that outbound is dead. I just again say it to say, similar to inbound, the whole thing is going to get burned to the ground. And there are even playbooks in the ELG book that are specifically related to SDR teams and outbound. But the important thing is that you've got the why now of it, and SDR teams, they seem to work magically when products have great product-market fit, and they seem to not work well when they don't.

And spoiler alert, it's not because you hired a better or worse SDR leader. It's because cold outbound doesn't generate demand. Cold outbound generates awareness manually among otherwise highly targeted folks. So if you just blast the world, sure, if you have great product-market fit, it'll probably all add up. But the question ought to be, how are the emails that are going out being kind of pre-filtered so that they're likely to hit the right companies at the right time with the right message?

And a lot of the modern intent data and personalization platforms and things kind of get at that indirectly or by proxy or through third-party data sources. ELG, because of this data layer that comes out of Crossbeam, gives you way richer intent for the reasons I said before. You know who to reach out to when, with what messaging, because you basically have this lens into what's going on from the buyer side around them deploying strategies and deploying technology inside their own organization. And that's a really powerful thing.

So, yeah, I could go on. Ads being affected by GDPR and the duopoly and the mobile operating system and the impact that has on the ROI and efficiency of ad buying and ABM. We go through a lot of these in the book, but it's rough times out there. People are looking for something new, and I think that's a big part of the why now.

Yeah. So the cool thing with Crossbeam is that there is a pretty robust, very low-lift free tier that is basically the entry point. And a very low-lift thing that any company can do is get onto the platform and get connected with your first wave of partners. And depending on the maturity of your business, you may have a fully developed, mature partner ecosystem. And the second you log in, Crossbeam will tell you who's already on Crossbeam, who's not on Crossbeam.

If they're not on Crossbeam, it gives you a really easy mechanism to invite them in. They can use the free tier as well. And within that free tier, this is where you can do things like do that initial analysis of, if we did unlock this data layer in a systematic way, how many of our accounts would actually be instantaneously enriched by data from partners in our ecosystem? How much more would we be able to learn about every company that's in our sales pipeline right now?

Which of those companies would we have more information on? And we actually let you get really far in that journey on the free tier, really on purpose. It's really once you want to operationalize that data by pushing it back into Salesforce or Dynamics or HubSpot, or adding a bunch of users into the platform, that's when you enter into those paid tiers. But long before that, it's this discovery, this validation. I'm a huge data nerd, if it hasn't come across already. And my answer to these questions is always, show me the data.

Get me into this environment where I'm able to actually see, oh wow, this is material and this matters for my business. The other thing I'll say is if you're an earlier-stage company and maybe you don't have any form of service partner relationships or technology partner relationships, you don't feel like you have a partner ecosystem yet. I would ask the question: don't think about this from the standpoint of your product. Think about this from the standpoint of your buyers and their experience.

And are there other products out there that, when they are used side by side with yours, the value proposition is just better? And that does not always necessitate a technology integration or the data flowing between tools, because more often than not, most companies now are living in these ecosystems where there is some kind of common data transference environment that exists. And if you're in sales technology or marketing technology, it might just be Salesforce, right? Are you both in the Salesforce app ecosystem?

Well, even if you don't integrate directly, your stuff probably coexists and commingles inside of these Salesforce instances. And there is a really profound better-together strategy and story. If you're in analytics, SQL is the great equalizer across everything. At Stitch back in the day, we didn't actually have hard integrations into Looker at all. It was the data warehouse that sat in between, where we deposited data and where Looker pulled the data out. There was no physical integration between us and Looker.

But Looker was our number one go-to-market partner because in the value chain of how people exercise and get value out of these other tools, we were a necessary component of that chain. What value chains are you in? Everybody that you can partner with is not just the people who are immediately connected to you, but everyone that's any number of degrees of separation, as long as the way in which that value comes together involves you as a potential step. So you may have an extremely interesting, robust partner ecosystem from a go-to-market standpoint for co-selling, for co-marketing, for cross-qualifying leads.

Even if you don't have any technology integrations and your product doesn't even have APIs yet, if you integrate into any major platform in some way, then chances are there's a story there. And I think that intellectual exercise is really interesting, and it allows you to go and potentially do initial connections with a universe of companies, like-minded companies with similar ideal customer profiles, and actually put the cart before the horse and get the go-to-market benefits before you even do the product investment work. And what a great way to validate that if you do build those integrations or you do go into a deeper strategic partnership, it's going to be worth it and it's going to generate returns.

Why not have the data first? And that's always where I would start.

Yeah, I make this joke in the book that my team is tired of hearing me answer questions in the form of a two-by-two matrix. But if you read the book, you see there's a good handful of these two-by-two matrices in there. And this is a two-by-two matrix answer, right? Like, is ELG right for me? When is ELG right for me? The two dimensions in this particular case that end up being important: one of them is the overall scale and state of maturity of your business.

And the other one is how much ecosystem DNA is inherently built into your company's value proposition. So the scale one is easy and somewhat obvious, right? If you're small, then you are less likely to have the gravitational pull of other companies, which means investing in a partnership will more often than not require heavier lift from you than it will from the partner. You're not necessarily going to be able to compel a huge universe of people to build into your platform or to show up and back up a truckload of really interesting data and hand it over to you. There's going to be some kind of investment-style lift that happens.

But as you get larger and larger, your customer base grows, your audience grows, the value of someone cross-pollinating their universe of prospects and customers with yours gets higher and higher. The center of that Venn diagram will inherently be bigger, and that will mean that the math for the potential partner will start to flip, and you start to be the Goliath in that David-and-Goliath. And this whole world of ecosystems is just like the little fish being eaten by the slightly bigger, by the slightly bigger, by the slightly bigger.

Everybody's got someone that's bigger than them and someone that's smaller than them. And where you are in your journey is just a question of where you are in that chain. So that's one dimension. But the other dimension of this ecosystem DNA question ends up becoming super important because if you think about a company like my first company, RJMetrics, we had very little ecosystem DNA. We were, by definition, a siloed, walled-off product that was a single-player, one-stop shop. We didn't really have that better-together value story.

We just aimed to do it all. If you think about my second company, Stitch, we were on the exact opposite end of the spectrum. You literally could not use Stitch unless you also had one of the 70 SaaS tools that we pulled data from and one of the five or six data warehouses where we deposited data, and frankly, the other ones farther down in the value chain in the BI space as well. So there was no such thing as a buyer putting yourself in the buyer's seat, making a purchase of Stitch that didn't also involve the purchase of other products.

We were inherently—100% of our deals were as part of a larger ecosystem strategy and technology purchasing decision on the buyer side. So where do you fall on that map? Well, the average modern technology company is going to be certainly past the 50% mark, right? There's very few tech companies that exist, at least in modern cohorts, that are still that siloed and that much of a single-player universe in the aftermath of the API economy coming to life in such a big way over the last decade or so.

So chances are you've got a good amount of ecosystem DNA, and it's just a question of whether it's a force multiplier for your business or it's something that is inherently, existentially necessary for you to deliver your value proposition. But in either case, you can two-by-two these things and you can say the range from no to exclusively ecosystem DNA in how our value proposition gets realized, and the range from zero customers to large publicly traded enterprise on the scale side. And you can four-box it, right?

And depending on where you fall in the bottom left, the answer is probably ELG is not right for me yet, right? It's kind of a wait quadrant. Like, I'm small and I'm siloed, and it's going to be forcing it in order to try and make these ELG playbooks work in that particular business. You've either got to gain scale so that you have leverage to potentially have an ecosystem form around you, where you can start running these playbooks, or you've got to modify and evolve your value proposition in a way that it folds more organically into an ecosystem play for your buyers and fits into an ecosystem that your buyers have bought into.

And then you can do some interesting things. The top-right quadrant is also easy because it means that you are big and you have ecosystem DNA. This is all of our marquee star customers that are out there in Crossbeam land. It's the people who the case studies are about in the book. These are the people that have it figured out and are just radically transforming the way that their businesses operate using this stuff. But most companies fall in one of the other two buckets, which is you have a large scale, but you do not have a lot of inherent ecosystem DNA.

So a lot of companies that have existed for more than 10 years have achieved some level of scale, but generally were founded in an era when the modern API economy was not as front and center in the way that value propositions got created and brought to market. This is a really interesting area where companies can potentially be investing energy in starting to run these ELG playbooks at the top of the funnel and understanding, like I was talking about before, even prior to major product investments on creating APIs and building your own ecosystems, let's look at the go-to-market implications of if we were able to work more closely with companies X, Y, Z that probably are knocking on our door to figure out ways to collaborate and partner.

Do the go-to-market stuff first and start to do the co-marketing, do the co-selling, just on the basis of, hey, we may not integrate directly, but these two products—and this works for services companies as well, right, that are highly trained or not on bringing products to life and servicing and supporting them inside of joint customers—we are better together for all these reasons, and therefore we're going to cross-pollinate pipelines, we're going to cross-pollinate intelligence around the intent that exists within the buyer universe for this shared ICP.

That ends up being a really awesome way for you to validate and potentially justify big investments to do that ecosystem transformation inside of your legacy business and really bring it into that top-right quadrant. And then the bottom-right quadrant is we're not big, we're a small business, but, oh boy, do we have a lot of ecosystem DNA. And I'd say that this is basically any company that was founded in the last seven years. Unless you are a total rocket ship and you've just shot out into the top 10% of companies, chances are you're in this bottom-right quadrant.

And this could not be more exciting because it means this stuff is going to work. The existence of the partner ecosystem is implicitly guaranteed for you. Those partners are out there and they exist, and these playbooks will work. And the question is just a matter of how much can you scale it and deploy it and implement it in your business in order to just move your dot up on the scale side of that axis and make you one of those top-performing companies.

So for folks thinking about reading this book, I'd say the people in the top-right and the bottom-right quadrants are the ones where it's most exciting, because that ecosystem DNA just makes this such a high-probability win for you. But it's also really fun working in that top-left quadrant with legacy businesses that want to turn this stuff on. And believe me, there's a lot of them out there popping their heads up, looking for it, and there's a place for them as well.

Thank you, man. Always a pleasure. Great to be here.