In this conversation, a16z Growth general partner Sarah Wang speaks with Crossbeam CEO Bob Moore about his new book, Ecosystem-Led Growth: A Blueprint for Sales and Marketing Success Using the Power of Partnerships. They dive into how this approach can improve customer acquisition, account expansion, and access to new markets by leveraging partners and partner data. Sharing examples from Crossbeam, as well as his previous companies RJMetrics and Stitch, Bob explains the huge potential for enhancing GTM efforts with ecosystem-level thinking.
- [00:01:43] The birth of ecosystem-led growth
- [00:05:12] The data revolution in partnerships
- [00:12:46] Integrating ecosystem-level GTM motions
- [00:16:29] Bob’s founder history
- [00:24:30] Ecosystem-qualified leads and new opportunities
- [00:29:34] ELG as AI force multiplier
The birth of ecosystem-led growth
Sarah Wang: You’re a pioneer in the space of ecosystem-led growth. I wanted to ask you, why did you decide to write the book? And while you’re answering that, can you unpack the term a little bit for any listeners who are not as familiar with what ecosystem-led growth means?
Bob Moore: Yeah, I’ll start there and then I can talk a bit about the “why now?” around the book. So ecosystem-led growth at its core is just the practice of leveraging the data and relationships in your partner ecosystem to better attract and convert and grow customers. It’s basically the go-to-market side of partnerships and ecosystems, which in a lot of ways has historically been neglected or underserved or misrepresented in a lot of contexts. But we’ve arrived in a place in the last few years where there is just a new generation of playbooks that can be run that just weren’t accessible prior.
That is the answer to your other question, which is the big “why now?” I think a book about ecosystems or partnerships a decade ago, or five years ago, would have been hindered by the fact that the typical practitioner in this field a decade ago was really hamstrung by not having data at their fingertips. Some of the most important questions that get asked on the go-to-market side of the partnership world are questions like, “how many customers do we have in common and who are they?” Or, “are my sales reps selling into the same companies as your sales reps?”
And there’s been a real data revolution over the last several years here where we’ve gone from answering those questions through an archaic account mapping methodology, where once a year or once a quarter you email spreadsheets back and forth with every partner and try to figure out where there’s overlap, to a world where large scale automated, secure, privacy-compliant data sharing between companies has actually been made possible.
Right. And it’s been made possible by solutions like Crossbeam, but also at large, just the modern data stack hitting at the same time as the maturity of the API economy has just created a technological re-platforming of how companies think about sharing data across company lines. That has unlocked this new data layer that allows people to turn their ecosystems into data assets. Those data assets are extremely actionable, and they lead to new playbooks where you can generate new pipeline, you can convert deals better, you can grow the customers you have in a more informed way.
In terms of the book, there’s no reason to write a book unless people are going to show up wanting to read it. I’ve written my share of ambitious e-books and white papers and things like that along the way. This is different, right? This is like a book book that is published by Wiley. It’s on the shelves at Barnes & Noble. We got the news today that we were a USA Today National Bestseller.
Sarah Wang: Incredible. Congrats.
Bob Moore: Thank you. That stuff doesn’t happen unless there’s already a pre-existing market pool for it. Like, you can’t manifest those things into existence. And I think it just goes to reinforce that ecosystems and the playbooks that the book lays out, this is stuff that’s happening in real time in the market. And it’s really just about building the definitions and the plays and the stories of success around them into one spot so people know where to look. And that’s really what the book is designed to be and hopefully what we’ve achieved in putting it out.
The data revolution in partnerships
Sarah Wang: That’s incredibly exciting and definitely part of our investment thesis in investing in Crossbeam. I know you and I have talked about this a lot, but it reminds me of maybe 15, 20 years ago, the evolution that happened with inside sales and that pull, right? There was a time when everything was field sales. Inside sales felt like a foreign concept.
Then there’s obviously a lot of companies like the ZoomInfos of the world that popped up with data to support that sales motion. I think the ROI on inside sales has proven out to be quite high. Maybe just on that topic, how do you think about operationalizing this new data that is available now with the emergence of the modern data stack? There’s so many tools to your point on the “why now?” But how do you actually make use of that data that’s now available?
Bob Moore: Yeah, great question. And it’s great that you bring up predictable revenue. Actually, I was trading emails with Aaron Ross this morning because I think in a lot of ways that when I was an up and coming entrepreneur 15 years ago, that book landed in my hands and it felt like someone had given me the keys to the kingdom.
If you think about that moment, that was in the early 2010s, there was just this confluence of things that were true in the world at that time. You could get directly to executives through email as a channel. There was something to the scalability of being able to build out SDR teams—and actually Aaron Ross came out of Salesforce, right? And like the modern cloud based CRM system was somewhat novel and new.
If you had adopted that, this was a logical next step that you could apply, which again, was like a playbook that you could run over top of a new technological disruption that had happened. A lot of companies were able to read that book and understand, oh, wow, this is a moment where you have this confluence of technology, of data, and of process and company structure that’s all happened, that provides you with these inputs to actually now run plays that would have normally just been kind of nonsensical even a few years before.
So, the cool thing about ELG is that a decade later, it’s like a version of that story. I hope that’s not too presumptuous. But you think about what’s true now, to get at the answer to your question, is the data layer that I mentioned before. So you’ve got this new universe of data. You have a world where something really fundamental has shifted in the way people buy technology. A great way to look at this is through the lens of buyer psychology and the way that products and services get procured in the modern market.
People are not buying technology products and services in isolation anymore. They’re not replacing a license and maintenance-installed shrink-wrapped piece of software with that version, only in the cloud. It’s evolved into a place where these buying decisions are being made in stacks. The number one input to whether or not someone buys that next incremental piece of software or potentially engages that service provider is actually how well it interacts and interoperates with the other technology decisions they’ve already made to drive it some kind of bigger strategy. And that is absolutely transformational and huge.
What it means is buying decisions are being more heavily influenced by how your technology co-exists and cooperates with the partners in your ecosystem, how it fits into that mesh of the combined value proposition. The ecosystem argument is the argument that wins and the company that can make a really compelling case about how their product fits into the ecosystem and the mesh, or the stack of the tools and technologies that already exist or might exist inside of the company they’re selling to. Those are the ones that win. That’s a major, major moment.
So, then you have this question that you asked around operationalizing, which is the other piece of that tree. You’ve got the data. You’ve kind of got the motivation and like change in behavior or like the macro factors. Now it’s a question of can you actually bring it to life inside of companies? And this is where we get into the world of why didn’t we call this partnership-led growth? Right. Well, first of all, PLG was already taken and I didn’t want to fight over it.
Sarah Wang: I might have heard of that one, yeah.
Bob Moore: Yeah. The other factor that’s probably more important is that this is not just about partnership teams. And in fact, in a lot of ways, it’s explicitly not about the work that the partnership team does alone. It’s about the work of the go-to-market team. When we think about operationalizing these plays, the partner team is an important critical layer of connectivity between organizations. But the wins that actually happen, they happen due to the work of sales leadership and sales people. And in an increasing number of cases, marketers as well. But I think focusing on sales for the sake of this conversation will allow me to give you some good examples.
One piece that’s really crystallized here, you think about the buyer behavior shift that I just mentioned and think for a moment about the idea of intent and the signal that might matter that’s coming into your company from the market about who is a qualified buyer and who you choose to spend time and energy with in trying to generate sales pipeline. So we start at the top of the funnel. We think about SCR teams. We think about potentially account execs who are doing their own demand-gen or trying to activate accounts that have landed in their book of business. Maybe one of the most important questions that you can ask, given that buyer behavior shift, is “who has bought products recently that are directly compatible and very often bought alongside or in sequence with my product?”
That question was virtually unanswerable five years ago, and it’s really the advent of products like Crossbeam that have opened up a lens for companies to be able to know the answer to that question. By being able to do real time account mapping across your partner ecosystem, you can build up this aggregated set of signals across everybody that you sell alongside or win alongside. And that can be something that informs people in the sales org about which accounts are worth focusing on today? How is that different from yesterday? What does it look like in the quarter ahead?
You know, it’s like running a magnet over that long tail of a hundred thousand companies that ZoomInfo told you you might be able to sell into based on firmographic characteristics and being able to pluck up the ones that are specifically showing evidence of bringing a strategy to life that you could be a part of because what you’re offering fits into that fold. Operationalizing that then looks like asking “what’s the ecosystem positioning and what’s the ecosystem play in every single deal that gets looked at inside of your company?” That starts with sales leadership and sales management.
Really, the organizations where we’ve seen this work the best are the ones where it’s the pipeline review meetings, it’s the QBRs, it’s the kind of management sessions where the question is being asked, what’s blocked inside this pipeline? What’s got the highest chance of winning and kind of being a commit for this quarter? And what is the ecosystem play on all of those deals? What’s the positioning? Is there a way we can customize our messaging to better position the product to win? Is there a way that we can potentially just focus our time and our energy and our efforts better to be on the companies that we’re actually more likely to have a shot with because of what we can see out there in the ecosystem pouring in?
And in cases where it’s still relevant, is there a way we can actually enlist a partner? Are there back channel references, deeper information discovery? Those classic partner plays aren’t dead. It’s just that winning through ELG doesn’t mean you have to have a partner on the phone or even a partner manager from within your company on the phone on every single deal. Quite the contrary. That’s almost the exception, not the rule. It’s like the ecosystem is informing the strategy of who to talk to, how to forecast whether or not they’re going to close and why, and actually how to talk to them in the first place.
Integrating ecosystem-level GTM motions
Sarah Wang: This is not necessarily just the focus of a partnerships team, right? It’s really the full go-to-market team when things are firing on all cylinders. And maybe just to ask a follow up question on that front: partnerships can be a little bit of a loaded term. You know, people sometimes say it’s where things go to die. That being said, it’s probably been a tougher segment to be a part of in the last, call it, one to two years where there’s been a focus on efficiency and maybe some of this transition to ELG couldn’t pay off before the efficiency sort of took over the org.
So we’d love your thoughts on maybe just advice for companies rebuilding their partnership orgs or they’re developing their sophistication on the ELG front. What did you see being on the front lines of this? And when efficiency is now table stakes, how are companies actually leveraging their ecosystem to grow faster?
Bob Moore: Yeah, saying that partnerships is a loaded term is certainly accurate. But, you know, what’s being said between the lines there is that partnerships is kind of a toxic term in a lot of companies. And this is one of the things where, when we started working on Crossbeam, we knew that as soon as we brought up the concept of partnerships, if we were talking to someone that had built a career as a direct seller or kind of on the front lines carrying quota inside of a [inaudible], there was a high likelihood that they would have an immediate allergic reaction to the concept of doing anything that brings more energy and time and attention into the partnerships world.
Iit has been a challenging era where everybody that’s ever sold anything has been burned in some way by some kind of partnership experience along the way, where they feel like a partner is kind of messing around in their deals or injecting unnecessary risk. It leads to this very interesting co-op petition internally and politically inside of a lot of companies where there’s a sense that these partnership efforts are at their core a force multiplier and providing some kind of uplift. But the ability to actually measure and attribute and scale the practices behind that are really, really, really challenged.
This showed up in the SaaS-pocalypse. The thing that we saw in the layoff landscape was, yeah, partner teams got hit really hard. And it’s interesting to look at that through the lens of ELG, where these ELG plays, when they are run well and run right, actually lead to hiring more sales professionals than they do lead to hiring more partnership professionals. Because what they’re doing is increasing pipeline or increasing conversion rates, right, which ultimately are things that drive the ability for more quota capacity to be added, which should drive toward just adding more sales reps.
The function of the partner team becomes in a lot of ways somewhat akin to almost what RevOps has been in the predictable revenue era, which is this incredibly necessary enabling layer that holds these strategic keys to how you unlock the data, how you enable people on that data, and how you build the playbooks on top of that. But ultimately, the work product and the jobs to be done and the benefits end up accruing in the go-to-market organization.
It allows for linear partner teams. It allows for higher ROI from the work of those partner teams. It allows the people on those partner teams to actually get the recognition they deserve and be higher leverage in the org. But at the same time, it does require things burning down a bit.
Ironically, this last wave of people cutting their partner teams has, I think, been something that’s caused them to look at ELG more seriously because they have to, because they can’t just run these monolithic full stack partner organizations anymore. They have to say, “how do we take the great work that this core partner team has done and parlay it into something that’s more horizontal inside the org and creates more value?” And that’s the ELG playbook: it unlocks precisely that.
Bob’s founder history
Sarah Wang: Yeah, I could not agree more. This is a little bit of moving away from the book directly: Bob, you’re a three-time founder. And the reason for even starting Crossbeam, I think, came from your own scar tissue as a founder. I’d actually love to dive into that. Maybe dive a little bit more into your experience, whether it was at Stitch or RJMetrics, etc., on when and where not going down this path of ELG actually proved to be, I don’t know if you go so far as to say a mistake. I won’t put words in your mouth, but maybe share more about that experience and how it changed your perspective.
Bob Moore: I’m a—you know this better than anybody—I’m a huge data nerd kind of by training and by DNA. And this is my third SaaS company. The first two were much nerdier. They were both in the analytics and data infrastructure universe. In 2008, I started a business called RJMetrics, which was basically the first SaaS analytics platform. We were a full stack. Basically you connect your backend database to us. There weren’t really SaaS products or APIs at that point. We were ingesting data mostly from MySQL and Postgres backend databases. We would ingest it for you. We would pull the data out. We transform it. We would warehouse it in a proprietary warehouse that we built because this was before Snowflake or Amazon Redshift. And then we would deliver it in dashboards and charts that were also constructed on technology we built. Right?
So it was a very ambitious, very extremely broad, full stack, suite solution, a one stop shop for getting your analytics done. Then by 2011 or 2012, some of the tech components of the Great Recession had started wearing off and the market started waking up. We found that we had this just incredible product market fit. From late 2011 until maybe 2015, RJMetrics was… the leads were flying in and falling off of our desk more days than not. It was a super exciting era.
We always credit product-market fit as being this thing where the market kind of sits in one place and then you iterate and pivot and learn and you modify your product, and then eventually you move the product into where that real sweet market spot is and then you win. I think we had this hubris in that company to believe that that’s what we did, when frankly, I think our product didn’t move very much and what happened was that the market actually evolved and moved into the zone of where our product was. That was exciting when it worked. But we missed something. And what we missed was that the market didn’t stop moving.
By the early 2018s, Amazon had released Amazon Redshift, which was kind of the first big player in the cloud data warehouses space. The emergence of the modern data stack was upon us. And we started losing deals. But we weren’t losing deals to companies that felt like competitors. Like people would say, “Oh, we’re not going to buy RJMetrics because we bought Amazon Redshift or because we bought Looker.” And we look at these products, and we say, well, Looker is not a competitor. All they do is they’re like Tableau. They just sit on top of complicated data infrastructure. We do the warehousing, we do the data ingestion, we do the data cleansing. They have a little modeling layer in LookML and then they do charts and dashboards, but it doesn’t feel like apples to apples. We kind of ignored that writing on the wall.
We viewed our competitive set as folks like GoodData and Domo and Burst and all these other, you know, largely single stack players. And in 2016 and even in 2015, we just started missing numbers. We went from crushing our numbers every quarter to literally coming in at like 20%, 30% achievement. When we popped our heads up and really had the space to be curious about what was going on in that market, what we realized was that we had an incredible software product, but we got defeated by the modern data stack movement, which allowed for an ecosystem of best of breed solutions to actually be superior in summation to the value proposition that we could offer as a silo.
That was very, very disruptive from a value prop standpoint, but actually more importantly, it was disruptive from a go-to-market standpoint. Because this entire fabric of the modern dataset came out and all of these companies were basically heavily incentivized to help each other succeed and sell each other’s products. If you wanted to have the awesome dashboards in Looker, you couldn’t do it unless it sat on top of a data warehouse. We sold RJMetrics in 2016 to Magento, which quickly got folded into Adobe. It was a base hit deal, right? We were glad to have an outcome there. And I think we kind of hit our apex as a company and sold when it was appropriate. But a couple of years later, we woke up to the headlines that Looker had been acquired by Google for $2.6B.
So when I talk about my $2.6B mistake, there’s a lot of lessons there, right? It all links back to what I was talking about earlier? Looker, or Snowflake, or what’s now Fivetran and others, whether they call it this or not, they were running the ELG playbooks and they were running it beautifully and it led to this incredible N-squared creation of pipeline. Because as soon as one company got in the pipeline of the warehouse, it was also going to be in the pipeline of the BI tool and the ETL platform.
And, you know, the three co-founders of DBT were on the team at RJMetrics and saw this firsthand as well.So Tristan, Connor, and Drew were absolutely brilliant in their market observation and, you know, where that puck was going and how DBT would move into that fray. [I] throw them in the mix as well in that story, right, of being part of that big movement.
So after we sold RJMetrics, Jake and I looked at each other and said, Hey, if you can’t beat them, join them. And we very quickly spun up a company called Stitch Data, which was basically designed to participate in the modern data stack as an ETL platform. So data pipelines, moving data from all these various SaaS applications into the data warehouses, but being fairly agnostic about where it was going or coming from.
Stitch in about 18 months grew to almost the scale of revenue that RJMetrics got to in seven years. It had all of the cool stuff of the moment. It was PLG. There was a self-serve free trial, like really rapid high velocity user onboarding and conversion and the NRR numbers were crazy. But it was also ELG in that, in the grandest irony of all, our biggest partner at Stitch became Looker because we were winning together. And we were winning together with the warehouse providers and we were winning together with the people further up in the stack and the BI world. That really just created this incredible momentum around Stitch.
The company was around for less than two years because Talend offered us an offer we couldn’t refuse for a company that young, and we sold to them in a cash deal at the end of 2018. It was in that moment that I finally had an opportunity to pop my head up and look around, and do some pattern recognition on the last decade. A normal person would have gone and sat on the beach and sipped a margarita, but I, for some reason, immediately started another company. It was Crossbeam for all the reasons that I’ve talked about it here.
There’s still, it’s a little bit of this like, Crossbeam is like my Goldilocks. RJMetrics, I think we held onto it for too long. We had this beautiful product-market fit window and I think we squandered it in some ways by being immature operators and learning on the job. Then Stitch, I think we sold it too soon. It was convenient that somebody, not to be crass, but somebody backed a dump truck full of money up to my and Jake’s driveways. And we’d been working really hard for a really long time, particularly in that space, and it felt like an opportunity to really, you know, have a celebrated moment to take a breather. We decided to go for it.
Then we pop our heads up, we look at our number one competitor at Stitch, which was Fivetran, which has obviously gone on to… We sold Stitch for $60M and Fivetran’s firm unicorn status. Crossbeam has been really fortunate to… I get to show up for work with this combination of scar tissue and muscle memory. It’s been a really fun ride and the meta conversation around the founding is always a great one to get into.
Ecosystem-qualified leads and new opportunities
Sarah Wang: I think one of my favorite sections of the book is where you actually go into quite a bit of detail on what this looks like, whether that’s prioritizing partners or defining what is an ecosystem qualified lead, right? EQLs, it’s going to be as common of a term as MQLs.
I don’t want to give away the keys to the kingdom because everyone should buy this book and read it. That being said, maybe if you go to that section of the book and list out the real nitty gritty of how you put this into play. What are some things that you’ve seen the best companies do in terms of these playbooks, in terms of how they spike? What’s one thing that even companies who take this seriously might miss or get wrong?
Bob Moore: That’s a great question. I’ll grab onto that ecosystem qualified lead example that you brought up because it’s just so relevant to so many companies right now. If you walk around to a bunch of board rooms, or I’m in a bunch of angel investments, and I think “pipeline” is the word of the year.
Sarah Wang: Absolutely.
Bob Moore: And demand generation, right? So ELG has a lot to do with demand gen. The biggest factor there gets back to, what are the strategies that are actually working in demand gen that are ROI positive right now?
There’s a lot that you can point to that worked a decade ago that is troubled right now. I’m sure you’ll ask me about AI or you’ll have your VC card revoked, right? But you know, the classic strategies of inbound and SEO are being burned to the ground by AI. They’ll be rebuilt, and inbound has its place, but AI just like… it’s making it difficult for people to predict exactly how that will drive demand in their businesses.
Outbound has been really successful for a lot of companies, but it does suffer from a negative sum game problem in terms of inbox fatigue. The amount of specialization and customization required to do that well is quite high. And then AI again, it’s going to enter into that fray to create a noise factory there.
The ads ecosystem has been challenged a lot by regulatory regimes and the duopoly and the mobile operating system, keeping cookies suppressed in certain cases, right? The ads are still there and the companies like Facebook have been very innovative in making sure the inventory still exists and it’s targeted. They’re expensive. It’s not just that these things aren’t working as well. It’s that they’re more expensive than ever in a universe where cash is king.
When you look at the ELG plays for demand gen, what you’re really doing is looking at intent and looking at signal that exists out there in the buyer universe. What if you could know who’s buying before they tell you who’s buying? And instead of it being just by the law of large numbers and sending out large amounts of outreach or ads, you could actually target and cultivate these conversations in the moment when they’re most likely to stick and hit and never miss one. I think the missed opportunity on demand gen is the scariest bit. How many customers are in your leads database that end up buying a competitor’s product any given quarter and you weren’t even in the conversation?
So ELG and ecosystem qualified leads solves for that because it allows you to perpetually be running the magnet, the proverbial magnet over that haystack and pulling up all the needles in the haystack. Say, okay, in this particular week or in this particular quarter, here is how you can reduce that hundred thousand names and companies down to a finite list that cuts off at the working capacity of the people who can reach out to them or target them or spend time on them. It’s all in the context of who’s buying, what are they buying, and what happens when they buy what they’ve just bought or in the process of buying..
The account mapping data from Crossbeam allows you to identify where there’s movement and the movers and shakers, the buyers, the players, the folks that should not be ignored out in that universe. But it also allows you to run the analysis of like, okay, we have 100 tech partners and 300 service partners. Who are the top 10 that when we coexist in a customer stack, actually we close the deals faster. Actually we have a higher conversion rate. Actually the ACVs are higher.
You can score every single account that exists in your giant lead list in terms of the level of signal that is showing up and the recency and relevancy of their potential buying indicators against how good is our ELG story here if we go outreach to them. Are they buying products from a company where we very, very commonly are bought together? When we are bought together, the value story is better.
That’s a really actually extremely simple exercise to go through. It is not rocket science. And this is what you get spoon-fed by Crossbeam. You can have notifications like a fire hose that says, look, we’ve got these criteria we care about, which is when someone goes from being an opportunity to a customer of this partner that is strategically aligned with us that we co-sell with frequently. The second that happens, we want the rep that owns that account to have visibility into that change, right? And whatever metadata that partner has been willing to expose in those situations. It’s a really, really, really big deal. It changes the way people think about ABM strategies, about SDM strategies, about even embedding things like into product experiences from a PLG standpoint. You can walk up and down the funnel, depending on your use case, but that’s a huge one.
ELG as AI force multiplier
Sarah Wang: Yeah, great. I definitely agree with your first comment. I think solving the pipeline—I don’t know if “problem” is the right word to use there—but for a lot of companies with product-market fit, still generating that pipeline remains the big albatross in the business. I think that’s really exciting.
I’m going to wrap it and I don’t want to get my VC card revoked. So, I am going to mention AI in this last question. The title of your last chapter is the future of ELG. I’d love for you to share how you think ELG evolves, what that future looks like, but also we are in the midst of a new platform shift with AI. You’ve alluded to some ways that AI is changing the broader go-to-market space. I’d love to hear your thoughts on how it changes, whether it’s ELG or your product in particular, but excited to get your thoughts and end here.
Bob Moore: I’m so excited by everything that’s happening in AI. How can you not be as a technologist, and even one that’s like, you know, undergoing the threat of having my very job and my value to the world being disrupted by it, right? I’m still excited. It’s just that cool. When I look at the work going on in the ELG universe through the lens of all that, the thing that occurs to me is just that this ecosystem data layer is distinct and unique from many of the other data sets and data assets that exist out there for the typical company.
If you look at the data you might get from a third party intent provider or a third party data broker, there’s a lot of ways in which you can kind of spend dollars to acquire information and knowledge that is inherently commoditized. If the next company or your next competitor is willing to spend the same amount of money, they can buy literally the same exact data. Part of what’s happening with these LLMs, as the parameters in these models get bigger and bigger and bigger, is that the proportion of data that’s out there in the world that’s like that in nature, that has actually been consumed and incorporated by these AI models, is getting higher and higher.
The cost is going to come down. The applications that sit on top of those data layers are going to become more commoditized. The ability to differentiate by cleverly deploying data that anybody can get their hands on is going to go to zero. Then the answer to what’s left is data that’s proprietary to you and your business, data that only your company can see, and your ability to, if not train, then certainly inform these AI models and agents and include in the context window of how these agents make recommendations and take action [based on] the things that only your company can see.
Now, historically, your own data, sure, that’s one great example, right? Like your CRM system, only your company has access to it and only your company can see it. That’s a great thing for your model to have some context on. But if you just have that first party data, it’s as blind as you are to what’s going on out there in the rest of the world. When we talk about the changing buyer dynamics and you talk about the importance of the interoperability of systems, it’s just not enough.
What ELG brings to the table, and what the data model that folks can get out of Crossbeam brings to the table, is this access to second party data in a very tightly controlled fashion that is inherently proprietary. Crossbeam is not a co-op where everybody throws their data in and you all get some result back out. It’s not a marketplace where people go and horse trade data and pay money to buy some data asset. This is a direct company-to-company mutual opt-in situation. And as a result of that, any given company’s data asset they get out of Crossbeam is uniquely proprietary to them and only accessible to them. It will never be scraped by an outside-looking-in large language model. It is also not a data asset that can be bought by some LLM builder or provider with an unlimited checkbook.
It is your company’s and only your company’s because your specific partner ecosystem has proactively opted in and authorized and turned all the dials and knobs in such a way that it meets their compliance and strategy needs to expose it to you.
If you’re on one of these networks like Crossbeam, all of a sudden you have a data asset that’s extremely proprietary and scalable and will not be reproduced or dismantled by AI. It actually becomes a force multiplier for the AI agents that you are using because it provides the additional context to allow them to make smarter recommendations, customizations, decisions and basically operate better. This is squarely in the zone of this differentiated data asset space that’s going to end up mattering a ton in the years to come as people start deploying these AI agents at scale to run their businesses.
Sarah Wang: And I think what’s really exciting is that the future you just painted is closer than we think it is. And so…
Bob Moore: Oh, yeah. We may already be in it, actually.
Sarah Wang: Exactly. We may already be in it. Thank you so much, Bob, for spending time with us. Congrats again on the book. We’re thrilled by how it’s done. But more importantly, just the thinking that you’ve done to advance the space. It’s been extremely impactful, even in our own portfolio as ecosystem-led growth is more than just a term. It’s really a movement. So, thank you so much, Bob. Great to have you.
Bob Moore: Thank you, Sarah. Always a pleasure.
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