The Most Efficient Marketing Org In The World? (35 People, $350M) | Tim Rutten, CMO @ Backbase

Speaker A: Tim Brewton is the CMO at Backbase, one of the biggest digital banking platforms in the world and he's running a crazy efficient go to market organization completely built around AI.

Speaker B: I have 35ft in total running a book of business of 350 million and up ARR.

Speaker A: To learn that efficiency, Tim had to throw out the old org structure and rebuild the entire GTM function from the ground up. And in today's episode he shares a ton of what he's learned through that process.

Speaker B: Make sure you have a landing zone, a good architecture, really understand how to do this so you can make non engineers dangerous.

Speaker A: We also get into a bunch of other on topics which GTM workflows and tactics are going obsolete and how GTM roles need to be restructured for this new reality.

Speaker B: Certain parts are being hollowed out and that is not the career anymore.

Speaker A: The career is I loved this episode because as you know, I'm massively AI pilled and Tim really knows his stuff. I took a ton of notes and you gave me a lot to think about and I think you'll really enjoy this one. Welcome to the Revenue Leadership Podcast. A quick psa. There's a change coming to the Revenue Leadership Podcast. I've moved my show to a new YouTube account. So if you enjoy the show, I recommend you go subscribe so you don't miss any upcoming episodes and a bunch of other content that will be on there. And you can find the new channel@YouTube.com Kyle Norton the channel name is Kyle Norton the Revenue Leadership Podcast and that's where we're going to be posting all the future episodes, clips and a bunch of new stuff as well. And uh, if you're wondering, the audio podcast will still be in all of the same places that it's always been, uh, on Spotify, Apple, podcasts, snips and so on. So if you listen in audio only form, no need to do anything. But if you watch on YouTube now, you know where to find future episodes. And while I'm at it, I would love if everybody could support the show by liking, subscribing, reviewing, leaving a comment, uh, on whatever platform they listen on. It helps new viewers find the podcast. It's really helpful for us and I always forget to mention it. Uh, if you love the podcast or even like it a little bit, uh, do us a solid and go review it, comment, uh, share it with a friend and that would be most appreciated. And now let's get back to the show. Today's guest is Tim Rutten, CMO at Backbase uh, where he spent the last decade helping build one of the biggest digital banking platforms in the world. Backbase recently launched its AI native banking os and Tim has been building the same kind of AI native operating model inside the GTM function from the ground up. And so we're going to talk about what AI native leadership actually means day to day, why individual co pilots are table stakes and why we're really past them now, and what it takes to redesign a revenue org around AI principles and combining agents and humans. And we're going to unpack some really specific examples that I'm excited about. So we're going to get pretty tactical on this one. So Tim, thank you for joining and appreciate it.

Speaker B: Yeah, very welcome, Kyle. Uh, I'm excited to be on the show. Maybe for starters, uh, the more you say ainative, the more people will probably get a little bit iffy or maybe even a little bit annoyed by now. Like three or six months ago it was the cool thing, uh, in town. And by now people are probably getting a little bit, uh, you know, hesitant. And I hope that through the conversation we're going to show a little bit more and at least to be able to show some examples on what it really means and why it's not, it's not a buzzword, not at all.

Speaker A: Yeah, I'm, uh, excited to unpack. These are, uh, episodes that I always enjoy doing where we can really get into specific use cases with people who have been deep in the weeds building stuff themselves. I have a bunch of questions about what you previewed, uh, to me on our, on our intro call. So let's just start there. Like, what does AI Native mean to you? Like, it is the buzzword du jour. Every founder wants an AI native revenue leader. People want to be that. So how would you define it and what does that actually mean to you?

Speaker B: To be fair, I think the starting point of this whole AI native wave that we're currently in on, how to organize and even uh, our value propositions and AI native banking almost. What does that actually mean? It's more of a principle of architecture, almost like how did you build up your organizational model? How did you build up the roles that are working in that organization? How are they leveraging the technology that's around, uh, to do their work and to do it in a different way that is actually AI native. Meaning if you and I would start a company today, Kyle, I would do it vastly different versus starting that company two years ago. I would start with primarily. Okay, so there is AI. How do we organize ourselves in such a way that we get the maximum value out of that technology. And again, it's a container term. So there's a lot of different technologies and a lot of different ways to apply it accordingly. Um, but the essence of AI Native is that you are systems thinking by design from day zero. So if you then think about AI powered AI first, AI what have you, they're not going to get the full value or it is not going to get the full value out of what the technology actually can do for you. So AI native is really, it's really a thing, it's really a different way of looking at the same problem. I'll give you an explicit example. There's many sellers, many AES, BDMs, marketeers that use their own GPT instance, be it Claude, be it ChatGPT or Gemini, and they do certain workloads through that interface. It probably helps you do your work better, faster and sometimes even repetitive. But it's not systematic, it's just for the individual. And it's as good as the way you've organized and let's say set up your instance. It doesn't help the organization as much. So if you're the senior leadership of that group that's all playing with these different types of tools, how do you make sure that the quality is there? How do you make sure that silly work is taken out? How do you make sure that you have an end to end view on how the operation is actually working? And how do you make sure that you take out the stupid work? That's not what the individuals in the organization are going to do by themselves. So thinking AI native means top down with a very clear mandate. How are we going to completely rewrite the organizational and operational model? And that literally starts at we're going to throw away the org chart, we're going to look at the actual work that we need to do and we're going to re envision it from the ground up versus let's say stitching it on top of something that we already have running for many decades. And there's politics and hierarchy and what have you. So that's what it means to me. It's very, it's very transformative.

Speaker A: Backbase is an older company, like it was a pre AI company. And so you're going through this transformation and rewriting things from scratch. And so uh, what does that mean for the org chart? I think that's a really interesting example of thinking in this way. So how did you look at the jobs to be done in the org chart and rebuild things.

Speaker B: Yeah. So the first decision I took together with my CRO was if we follow the hypothesis that I just shared, or at least the philosophy of you either go all in or you don't go at all. Like in the middle, you're not getting the full value. But there's a lot of upheaval in New York with these tools and everyone needs to do their little thing. So we basically said we're going to go all in. So we're going to assume for a moment that this whole agentic workforce and rewriting your processes in org chart, that it actually holds true. So let's just go all in. Let's take the bet almost. Although it was a little bit more informed, of course, than what I'm sharing here. From that moment onwards, I could look at the organization as a system, as a engineering problem almost. How do we make sure that our value propositions, our ICPs, our segmentation, our go to market strategy, the intel that we have from dialogues in actual conversations in the field, how do we make sure that all that context is properly organized so we can start driving certain activities that immediately cross all boundaries of all teams. And that was literally the call number one. Like if he, if myself and the CRO are fully behind it, we're going to break the barriers of the typical team setup. Because if you drive go to market holistically, sales, marketing partner, sales, customer success, it's all the same. They are all working against the entity of the customer, the prospect or a partner in the other scenario and all motions around it. Yeah. Whether it's driven by the marketing craft or the sales craft or what have you, they all need to operate on the same foundation. And that basically became uh, the go to market os, which we'll talk about a little bit more, which is literally a system, an application that is running, um, on our premises, that runs the full operation front to back. Um, there's almost no exceptions anymore. What that does to the org chart is very interesting. I was reluctant to change it in the beginning because there's so much change already. People are very much fatigued with what's out there currently. All the hype, all the big buzzwords, all the, you know, massive posts on LinkedIn and people are replacing their marketing teams or sales teams with an agent, which is not true. Just so you know, we decided to slow down on the, let's reorg and really flip it to a, uh, supposedly a native model and actually bring everyone in on the learning journey. Um, and I think that part was was critical in making sure that we didn't lose our people. Because I think that's one of the main insights that I'm coming back to over and over again. You will need the human capability to drive the whole system into the right direction.

Speaker A: Is it the same people though? Like are, you know, you need people to drive this org chart, but not everybody is going to make that transition to being a systems thinker and like a love of being in tools. Have you had to change over much of the team to try to get to this place?

Speaker B: I would say that right now we're organizing more around logical units of work and collaboration. Uh, which means that I'm actually collapsing certain functions. So on the marketing side as I'm the cmo, classically you have a brand function, branding creative, then you have content, then digital product marketing, the field, ABM events. You can go on like it's all these different desks that have eight little pieces of the pie that has been collapsing every other quarter into quarter, let's say streamline buckets with one single leader. So eventually my leadership team also is going to have a way simpler setup because you can get a broader remit and basically bring responsibilities together that can logically be clustered now because you literally can, let's say the posts that are out there talking about the fact that certain roles are being challenged, it is true, certain roles are being challenged and some are just honestly redundant. Uh, it makes no sense to do desk research anymore. Why would you, right, just fire up an agent, um, utilize exa, maybe combine to fire crawl and you'll get the best damn research out there. There's just no better. No human can do it better. If I think about for instance the BDM role, certain parts are being hollowed out and that is not the career anymore. The career is okay. How do you strategically penetrate the top accounts? And how do you build a relationship using the infrastructure that's giving you the right signal and the right, let's say detail on a certain account that's warmed up with exactly the right angle to pick the phone up for or you know exactly where that particular stakeholder is. Maybe this individual is speaking at a keynote event. Make sure you are there with all the ammo of the research that you have coming from the infrastructure. Even though that sounds very logical and easy to say, still within the actual individuals that hold a certain role as being challenged, they feel a little bit scared. It is very overwhelming to see this whole machine rolling into the org and literally taking out the work.

Speaker A: Humans aren't great with change, like, it's not in our nature to be, it's not in our nature to be great at dealing with upheaval. You know, we're adaptable as a species, but on an individual level, like we crave consistency and stability. And so what you see is this, in many organizations, this thrash of like resistance because it feels, feels like change is being done to them and not with them. And so how have you gotten everybody to come along on this journey and change the nature of their roles or be collapsed into different units of work? What's been most important to get people to come along this journey, for what

Speaker B: it's worth, but what I did last year, and uh, we're talking end of Q3, this is the moment that I myself got into the terminal, fired up Claude code and basically the universe open to me. This is before it was hot and happening on the LinkedIn channels and what have you. Uh, because my CTO basically said, Tim, you have to get into it to get the understanding of what's coming. Ever since I was completely sold and it switched my mindset almost immediately. And I applied the exact same logic to my marketing leadership team and also very soon after to the Rev Ops team, like team. I'm going to get you a premium seat with Claude. Um, premium, which means you can go into the terminal, fire a plot code and just do whatever you would like to do. There's no boundaries, there's no target on it, no roi, whatever. Uh, I just need you guys to see what I'm seeing. And that's exactly what happened. So within two weeks, uh, people were building prototypes like crazy. They were connecting certain APIs and got analyses out that, you know, originally you couldn't because of systems boundaries or integration work or what have you. And something very interestingly happened. I was of course doing my part, which on the content side and strategy side was just top notch. Best quality I ever was able to deliver as an individual and next to working with my teams, of course. But the exact same thing happened to my marketing leadership team. So all the six, seven leaders that were running a classic operation saw the light, like, oh, okay, so this is coming, this is what it can already do today. And they completely rethought how they would love to operate their team going forward. Not having the answer per se, but at least the click was there. That this whole AI conversation is, is not just a high level boardroom type of thing to cut costs. It's a very real thing and it's actually incredible if you embrace it early and you you start becoming a leader that has this in their back pocket, just like you. And I can work with Microsoft Word and Excel and Google Sheets, whatever. It's like, like a standard skill at this moment. You probably still have it on your resume. Like I'm super AI native and I can do Claude. I think in six to 12 months it's not there anymore. It's just normal. If you don't have it, then you're really working in the wrong world almost. So that was the first part where I internally got a lot of momentum, uh, as in people started pinging me on Slack from the full organization. Like, hey Tim, can I get access to your repository? I heard you guys are doing some crazy stuff. And it just kept on going. So that initial getting people excited is not me telling them, it was actually me opening up the door and saying, hey, you're going to get this expensive account, go for it. And then we just basically went through the whole revenue organization. So we had around, um, 20 or 30 people overnight that were doing this type of work and it just seeded like crazy.

Speaker A: And what were the big structural challenges that needed to get solved? Because you give everybody terminal, you give everybody Claude code and then invariably they hit a bunch of these roadblocks where, you know, the quality of the output's not what you wanted or they can't do certain things. And some of those things need to be fixed at like a structural level. What were some of the pillars that you found you needed to put in place to get, to get the maximum benefit? Like you mentioned context engineering as one of them. But what were those other key things that you feel like needed to be in place place?

Speaker B: I would say that the, the real response to this question is this is not a great approach to scale it at all. This is a great approach to get people fired up and, and see the power which by now, by today it's, it's 100 times more powerful. So people just immediately see it, get the aha and off you go. The main move I was making is get people change ready and actually make them the champions, uh, to drive it forward. All that was not the answer. Uh, if anything, I wanted to get people out of the terminal ASAP again. So around two to four weeks, when we hit that mark, we basically put it to a halt and we got enough momentum and mental buy in from the full Org to start properly investigating. Okay, how do we bring this to life so that the full organization can run at the same level of quality so we can actually tackle the challenges that you would typically have like data consistency, who can access which data, who can actually create, read, update, delete on Salesforce. Like how do you really bring this together? Not via regular terminal instances and cloud cowork instances. That is simply not scalable in the context of how at least we run the business. So it's just the initial breakthrough which all the way ended up in the boardroom in the management team. Like okay, what you're doing in that corner together, um, can you please present it? I think it's uh, like very meaningful and maybe we should actually consider doing it across the business. It basically gave me enough confidence to aggressively invest in a landing zone where you could actually start building GTMOs like a proper system where people log in and with the rights and entitlements that they have can do their daily work. Whereas the majority of the work is actually driven by either agents or automations or workflows. Um, which of course is a bigger step to take. Right, that takes time. It takes also a bit of brevity to go through that door versus just going for the frontier labs that have let's say um, seed licenses and basically give individuals to a degree superpower.

Speaker A: And so this is what led you to build the GTM OS as the core harness and interface for all the GTM work, is that correct? Okay, so maybe just let's start diving into the operating system. So like what did you build? What is this thing?

Speaker B: So GTM OS or go to Market OS is basically our internal term for um, what we built in house to basically have a very robust landing zone where we can build any type of application, any type of agentic workflow, any type of agent that you can work with, be it conversational, uh, be it trigger based, to actually bring it into a landing zone so that we could deploy it properly to all of the people that we are collaborating with across uh, the go to market organization. So if you think about it, if you put it on the screen on the left there's a sidebar which uh, is actually mapped against capabilities and teams and programs. Um, and then on the right you properly have an interface that allows you to do uh, the type of activity that is part of that particular team or part of that particular program or part of the particular reporting angle that you're looking through. What is really critical here is that the creation of that landing zone was actually the biggest hurdle. How do you get compliance sign off in a non regulated business but working for regulated banks, that's uh, of course where we are playing in the industry. Making sure that security compliance is completely okay with the landing zone that you've chosen for and want to go for because it will get connected to your CRM. In our case, that's Salesforce. It will get connected to external data. We use a lot of, let's say, research tooling and, um, neural network search tooling, like exa. You bring all that data in, how do you make sure that that's completely, let's say, signed off, uh, in the sense that you're not doing anything silly in terms of the business or risky. The next step needs to be completely safe when it comes to authentication. So we actually leverage our enterprise authentication services to get people signed into GTMOs, so we know who they are and we give them access to this specific, specific apps and agents. And then from there onwards it's, um, it's just like you're using ChatGPT or Claude, honestly, it's actually using the same frameworks, but it's completely ours. So where typically you would, let's say, stand up your ChatGPT or Claude desktop environment or your coworker environment, you start connecting services, right? You start connecting Salesforce or HubSpot or your email or your calendar. We basically do that at a enterprise level. So we have these tools under the hood, fully connected, fully guardrailed and metered. So you can't go crazy and pull everything from Salesforce in one go so that everyone in the organization can do the work that's relevant to them at the highest quality level, while we can continuously keep a tap on what's actually happening, what's working well. And also some tools just change, like tools that are great for outreach today might be really not great for tomorrow. And we can easily swap out the tools and the actual team members working on top of the platform or the agents that are consistently working on the platform. They just pick up the new tool and that's it. In essence, you're looking for an AI native stack or an AI native landing zone, or I think Vercel calls it the AI native cloud. So where do you deliver and deploy applications and agents and everything with it? That is completely leveraging the AI native way of thinking. Vercel is a fantastic example for this. It's our standard on the GTM os and, um, we leverage the full stack of Vercel, which every other week is getting more and more complete, to hit our objectives and ambitions. So when we started, you could easily leverage Vercel to indeed deploy your application, which is next JS with TypeScript and React, and let's uh, say notice the package builder, all good. That's not really special. It's been around for many years. But it also introduced the AI gateway. So you basically have one API that gives access to all the LLMs out there for which most of them are zero. Data retention, policy driven, meaning you can actually bring more confidential data to the table, but you can do it at scale. It also gave a proper framework or a reference framework architecture, if I may, that basically helps you structure how do you set up your agents, how do you structure your prompts, how do you, how do you give agents tools, how do you manage those tools and version them, um, how do you make sure that you have all these ingredients properly set up? So you're basically engineering whatever the agent has access to, yes or no, and what the quality of those, let's say elements and parameters are. So with Vercel are basically getting more and more into that stack as an example. Also workflows became a new capability in their stack. I think it's two months ago, completely native, which means that also in your build you can actually build workflows, uh, with natural language. So it builds agents, it builds workflows, it builds tools. Those are all the critical things that you need to fire up an agent or any automation workflow. Um, and before you know it, we're now six months ahead into this game. I think we're now at 30 to 40% of completely automating and rewriting the way we operate as an organization.

Speaker A: With Vercel, do you get access to some of the capabilities out of the other tools like you know, in, in Codex and Claude code? Now you, now they can spawn off sub agents to do like a bunch of other tasks or do you get access all uh, to all of that through the gateway or do you have to build those primitives inside GTMOs?

Speaker B: So this is what uh, Vercel actually delivers because the gateways is the gateway. You basically have all APIs of all LLMs to hit them and get a response, right? So you need something in, in front to actually do that type of work. And that literally is the AI SDK from Vercel. So that basically does all the um, spawning of agents, sub agents, bringing them back. Uh, yeah, collating the outcome and then taking it forward. It's also not per se, only in that context because uh, for much of the work that's happening in GTM os, it is actually not per se AI. So if you look at the way we do reporting, which is completely consuming and headless from Salesforce, we built the reports with cloud code in a cursor setup within this architecture. However, if we then deploy it, it's just a next JS application which consumes the APIs from Salesforce. By now it has a bit of a data tier in the middle, like a semantic fabric that basically has all the latest and greatest data from Salesforce. So we can do it at rapid pace and performance. So we don't have hundreds of people hitting the Salesforce API, but that's about it. So there's nothing agentic or nothing per se AI in the actual interface that the individual users see. By now we're applying it on top. So we build it with AI, of course, like agentic delivery. By now we have all the data, all reporting completely in place. And then you can start to uh, bring in um, let's say reasoning loops to have a view on pipeline quality, um, pipeline discipline. How fast is the velocity of different regions, different types of opportunities? Uh, where do we see based on the patterns where a deal is being called but unlikely to happen because we see these patterns in um, let's say the conversations with the actual prospect. So I think also there, there's a bit of a misunderstanding that all of a sudden you need to do everything AI in the sense that everything needs to go through the LLM. I would say that almost, almost 70 to 80% of GTMOs is not LLM based. It's actually very deterministic. But there's automation and workflows below certain activities and there's agents running that do certain activities as well. And they hit, let's say LLMs, uh, through an AI gateway. So also there it's what's the actual balance of what you actually utilize? Interesting.

Speaker A: So how many tools did you deprecate or uh, how many tools could you like stop using? Because you've got this uh, solution in place.

Speaker B: So last week we deprecated a tool for 15k per year. Beginning of last. No, uh, this is Q4, we were able to not renew six sense in this uh, particular scenario because we completely built a signal engine bespoke that is not black box but white box. I know exactly what is happening there. We define the scoring rubric all the time. Like we're really tuning the model ourselves. And it's, by the way, it's our model. It's just such an, let's say, mind opener. The moment you have that capability to build it, to bring it to life and then connect it to the other building blocks that you, that you have a vision for initially and then you get takeoff because you have some foundational stuff in place that's actually it's going to give you alpha, like go to market alpha almost. So these are two examples already and uh, there's many more coming. They're on the list. So before the end of the year we're taking out at least, at least 100k extra.

Speaker A: And so when you're building new tools and applications, are you doing that in the Vercel experience and then deploying it into GTMOs?

Speaker B: So the actual build can be in any type of ide, like a developer environment. Uh, we leverage for the majority uh, Cursor. Uh Cursor for the reason that it has a fantastic debugging uh, flow or debugging capability, uh, which is using their composer model which essentially goes through the code very fast, puts debugging items into the code and then tests it and fixes it completely autonomously. But for the very same accounts we could just build in the terminal with cloud code on. So basically it's, it's the decision of those that are building within the uh, GTM OS repository. But uh, from there onwards it's, it's not more advanced than it's literally committing against a GitHub repository. There's a whole set of checks that are happening. Security checks, build checks, quality checks and once that's um, say pull request reviewed it automatically gets deployed on Vercel also does certain checks and then actually puts it uh, promotes it to production. So there's this whole pipeline by now that is uh, yeah making sure that we have quality that we bring to the table with every pull request that we uh, approve.

Speaker A: How many people are working on this full time?

Speaker B: That's a funny fact because full time it's actually only one individual today and then part time it's individuals like myself and two to three other builders that are spending sometimes in the afternoon but for the majority it's evenings and weekends because we're just really in there really excited to be able to build. But funnily enough that's where it ends today. And already you see that type of takeoff on what it achieves within the context of Backbase. We are in markets for more builders but if you think about that profile, it's like the wildest profile today to uh, to recruit for. So I think the beauty is mostly to uh, make sure you have a landing zone, a good architecture, really understand how to do this so you can make non engineers dangerous. That for us has been um, a major step.

Speaker A: It's you and two to three other People building part time are those like Rev Ops folks or those like business owners who, what's the profile of those other individuals?

Speaker B: Um, it's myself but I have an engineering background because I basically was running a web design agency for ten years, uh, when it was still uh, interesting. A very helpful indeed. Also uh, run web hosting businesses, have a bit of a systems engineering uh, view as well. So basically I have a head start. I know the business, I know the marketing craft. So with that me briefing someone to build on my behalf is actually already inefficient. But secondarily we have uh, a GTM engineer, like a proper one who's basically running the full show full time. Our director of RevOps is building, let's say part time, our director of content marketing is building part time. And we're now trying to get one or two more business people that also have the knack of systems thinking and building because they've been doing their own little prototypes for uh, for the past few months. We're trying to onboard them as well in the architecture so we basically get even more critical mass to make faster progress. Um, and it's showing already.

Speaker A: And so what does onboarding somebody look like? Do they have to learn, you know, computer science primitives and like start to understand how these systems operate? Is it about just learning to use the tools and giving them a cursor account and showing them how to like build an eval or a test or like what, what does that onboarding uh, look like to you?

Speaker B: I think first of all the people that you try to onboard, they should be naturally energetic to dive in and learn regardless of where they are in their, let's say, learning cycle. The second point would be under the assumption that your architecture is sound and it's properly set up for, let's say agent delivery. You don't need to be fully computer science level at all to build certain capabilities and requirements. Not at all. Because the repository itself will indicate to, in this case claude, which is our favorite LLM currently for building. Hey, this is how the architecture works. Hey, if you're making UI components, use this library. Um, hey, if you want to have access to Salesforce, use this particular tool. No other tool is allowed. So you basically have all harness in which the building happens. Which means that you're almost abstracting away all the complexity for any business, let's say person to just interface with Claude, build requirements, start planning, which you will do completely within the context of the architecture and off you go. So in my view, especially within the coming three to six months. There's going to be a moment that anyone could certainly build within our architecture that does assume proper architecture, proper setup and pipelines in how requirements plans, uh, test driven design, domain driven design, how all of these best practices actually come together and are abstracted away for those that know the business that would like to build, um, needless to say, still, you need to be comfortable in building. Right? Not an expert, but comfortable and interested and then you can learn incredibly rapidly.

Speaker A: Yeah, I mean you just ask whatever question you've got to your LLM of choice and be like, yeah, like explain, explain the frameworks we've chosen and why and explain, you know, like it could explain the entire code base to you just in a conversation.

Speaker B: And let's also have to look at all of this. So when I said you go all in or nothing at all and you remain in the middle with. Everyone has their own little instance and they build prototypes and they send HTML files to each other. It doesn't scale. But why the aha uh is there is that people that are running a role or an operation have the best ideas because they are the ones that have certain challenges and they basically get mind blown and think, hey, what would happen if I would do xyz, let's do a prototype, let's try to stitch it together. If you cannot, uh, have them land on GTMOs, you get that type of firepower to build an engineer and actually get it to production and it's the actual business leaders, I mean that is, that's IP to me, that's special. That's how you have a competitive advantage and how you go to market. In my world, that's exactly the thesis over invest in a great architecture and landing zone so that the people that know best what we could actually automate and you know, um, maybe have completely authentic processes in place as well, that they're actually the ones on the buttons that they can do it instead of knocking on the door of GTM engineering, which I don't think is a function on its own. It has longevity. Um, assuming what I'm sharing is true, which I see enough evidence of.

Speaker A: Wait, so say more about that. Why do you, why don't you think GTM engineering is, is a role with longevity?

Speaker B: Look, it will have longevity because with one individual you can go very, very far, but it will become the bottleneck. So if I look at my team right now, or the setup we have right now, there's one proper full time allocated engineer, but the only way we're going to continue scaling is the circle around it that is prone to build and can leverage that architecture. If you don't get that circle in place, you need 5 to 10 GTM engineers and it will become very, very slow. Why? Because they will not know the business. They are not running demand generation, they're not running outreach, so you need to brief them and then it goes back and forth. And even though it's agentic delivery, so it goes supposedly a little bit faster. You get meetings, you get slide decks, you get status updates, you get all the non AI native type of behavior. So you either know the business you can build or you don't. If you don't, to me, eventually it will not fly. Because the interesting fact is that the competitor next door, if they get that formula properly correct, they're going to go 10x compared to you in terms of speed. So I do believe in a GTM architect, like a heavyweight senior player that knows their stuff, owns and governs the architecture and the pipelines and everything with it. Maybe you have two to make sure that you always have redundancy and quality control and for that principle. But honestly I think those are the two I would invest in heavily and then I would make sure the outer circle is enabled to build and go have speed.

Speaker A: And so the GTM architect is basically building the harness in the platform and then the person building the tooling are the business owners, the agents, the workflows.

Speaker B: Correct, correct. And I think uh, a gtm, um, architect is always also more fair to what I'm seeing right now in actual daily execution. Because engineering almost seems like, yeah, they're just classic building. No, no, no, they're architecting the system in which it's being built by agents. A hundred percent of the GTMOS code is generated, which already on its own is a crazy fact.

Speaker A: It's a different riff on sort of like our approach and what I've been sort of espousing, which is, you know, a centralized version of AI and go to market. Because what I've, what I've generally seen is either, you know, people give everybody a Claude license and they just say go nuts. And then you end up with a ton of skills locked in people's individual cloud or chat instances and there's no scale to it and, and oftentimes those things aren't built very well. So I've preferred to build with a centralized team where people go and access that expertise that's going to build things, uh, and then deploy them generally to the entire rep population. But this is a, uh, different version of it. Where if your harness is good enough, you can actually turn all of the leaders in a business into builders. But understanding how to qualify or certify to get to building is still an interesting question. It is still quite centralized because it's only a handful of people.

Speaker B: But don't get me wrong, I'm not making a statement that everyone should be a builder and get into the repository and have cursor and start building. Not at all. You need a subset of top players, maybe five to eight over time that are committed to the whole deployed environment and they're basically looking after a business lines and making sure that certain capabilities get built out. And then a lot of, maybe not power, but a lot of innovation power gravitates to those individuals because they're going to get the hang of things and because they're so close to the business and the fire, they also know where they're going to make the business most happy because maybe it's even themselves directly or it's very close in their proximity. So as an example our director RevOps is building, um, is fully responsible for the reporting, let's say um, part of GTMOs, the reports. Also the dynamic, interactive and automated reports that we have by now they run the full pipeline generation and pipeline management cadence. Nobody's in Salesforce anymore. It took them three months in total

Speaker A: to build the whole reporting stack native into GTMOs.

Speaker B: Correct. But then fully in the way that we actually operate including automated signals, automated emails with end of week status reports, pipeline drift, what have you. Like the full operating model which you could have a vision on um, let's say a year or two ago that you would implement in many many years. We now implement it in three months. Just imagine that individual in that role completely fired up. The sky is the limit, whatever he can think of. Together with the team, I just saw them having their qbr. They will deliver next week and it's rolled out, is deployed and it's going to change the way we operate. Uh, all of our go to market teams and sales functions in the different regions and territories. Um, so with that one individual we got that 10x impact. I don't per se need two in the rev ops domain, one is enough. Same thing in my marketing org.

Speaker A: And how big is backbase total?

Speaker B: 2,000 2,200 individuals.

Speaker A: So still quite a lean team for that size of company.

Speaker B: I would even argue that I might even have the most lean marketing org uh at this scale f35ft in total running a book of business of 350 million and up. Uh ARR. So if you divide that headcount, uh wise we are very efficient and very effective. I would argue if I look at go to market efficiency or the magic number. So trailing 12 months new array, every dollar invested we generate 80 cents in ARR and ARR is 5 years so it's actually 8 times 5 is a ah, nice 4x uh impact.

Speaker A: You're in back to back meetings all day, you're trying to stay present but you're also worried you'll forget the decision, the action item, um, the important next step. And that's where granola comes in. Granola is an AI powered notepad for meetings. You jot down rough notes like you always do and in the background granola transcribes and turns them into clear useful notes. When the meeting ends, no bots joining your calls, no distractions, just a clean notepad that helps you focus. Use code therevenue for three months of granola for free. So you can head on over to granola AI/therevenue to learn more. Again that is the revenue for three months of granola for free. Check it out at ah Granola AI slash therevenue. What else did you look at besides Vercel as this like hosting platform? What are the other options that somebody could think about to build this like harness operating system slash ux?

Speaker B: Uh, we were looking into Google GCP and uh Vertex so that we are on the Google suite as a company. So it was pretty logic for logical for us to look into that stack. However they were standalone components and the SDKs that then built with and deploy were not as, let's say fluent as we found with Vercel. So can you actually in your agentic building environment have the agents work against the environment to deploy certain things, to set certain cron jobs, to deploy the database migration script and actually execute on it, run the tests? That wasn't as uh, mature with GCP uh at that moment. Um, and then very soon after we just landed on Vercel because the prototype was just flying already. Um, and the main challenge we had to go through is hey, can we get sign off on this? Because it's not a very enterprise grade stack, at least not at that moment. Uh, but by now Vercel is certainly becoming or very rapidly becoming an enterprise stack. Um, so that was really. Those were the two options at the table. Um, there were maybe a few others in the long tail but they didn't pass my table.

Speaker A: And did you look at buying any solutions like this in the market? Are there really solutions in the market that purport to be this harness and system for sure.

Speaker B: So if you, if I rewind back the time to January last year when our CEO said we're going to go risk on, um, we're going to go all in on AI internally and in the product proposition, I started doing my runs like, okay, what's out there currently? What could we potentially consume? So I had many pitches, I had many demos and with all of these demos and pitches, I felt this makes no sense pricing wise, like hundreds and thousands of dollars. And it only did one little vertical of the actual vision I would love to get, get to. So in essence I would overspent on ip. That was not really changing the game. And it was almost overnight expensive because it was AI. So there was just a mismatch between what am I looking for, what am I going to pay for? And by the way, you're a startup and clearly you have no traction. So no, thank you. So with the majority of the ones that we were testing out, we felt there's no moat for these guys, so why would we buy into it? And the more we went through that cycle, I felt you can think as a CMO and stay hands off. And no, no, no, I've got this portfolio to manage and I'm a leader. I'm like, no, we're going to get into the terminal because I think we can actually build certain things on our own and probably get to a breakthrough level within three months. And that's exactly what happened. And the first problem that we tackled was, uh, look, there's a renewal coming up from the six Sense platform. It doesn't work for our motion. It's a great company, but it doesn't work for our motion for a variety of reasons. Let's just not, uh, renew it. So we terminate it and we have an issue because it informs our campaigning, it informs certain parts of the engine. So within two months we had to fix the issue. We need our own signal engine. And that's exactly what the team stepped into and, uh, started building. And I think we got a lot of conviction out of that build, uh, after those two months. And then we just kept on moving forward in that, uh, direction.

Speaker A: So you said that you reorganized the team around logical units of work. Can you explain that? And like, how does, how is the team organized today?

Speaker B: So when I took over, I reorganized a team in brand creative and product marketing. Like a strategic column, More of an hq. How do we bring it globally to life? And then in the middle of the organizational model, we have um, digital doing the always on the digital campaigning, the content function and the ABM function which are essentially the regional marketing teams. And then all the way on the right we have marketing operations which we actually clustered into sales and marketing operations like the rev ops, move the bucket in the middle, that one is collapsing. So digital content ABM to me are one thing. Um, because you, you classically have high level demand generation activities happening and then low level ABM execution happening. And that divide or that connect between has been very hard to execute in a human LED model. With an AI LED model it can actually run as exactly one thing. So that's where we're seeing that collapse uh, happening.

Speaker A: And so then you'll basically have two pillars. You'll have brand creative, product marketing and you'll have digital content and abm and those will be the two correct like nodes of the team.

Speaker B: And my, my current vision is brand creative is really a pillar because that is a certain craft and it's very much out there. Different types, let's say different types of levels of storytelling that need to happen there. I think product marketing is mostly evolving into the go to market portfolio function. How do you bring all of these different go to markets, products, value propositions and so forth together while being super close to the customer? And actually that's going to be 80% of your job. You're going to be on the phone all day long with customers, executives, buyers to win loss analysis. So whatever you bring into your go to market execution engine, which typically was slide decks and documents and what have you, you're generating that with the intel that you have. But then you better get the intel. So that's, that's a certain twist on that craft. Uh, to go deep on, you need executive exposure. Um, if anything you almost yourself need to be an executive to be able to level with these people. Because we sell at the board level and then in the middle. Indeed, to your point, that collapses if you ask me, into a, uh, it's basically a demand gen and EVM function ran as one and not multiple layers with multiple activities, multiple roadmaps. No, no, it's one single roadmap and

Speaker A: it's one engine, one customer journey, one funnel, different. Different channels.

Speaker B: Correct. Which also at the same time is very demanding. Um, at least for those listening. If you're uh, you know, in the position that you can run that full portfolio across all elements of those crafts. How do you drive digital properly, how do you drive content properly, how do you drive ABM properly across the full stack? Uh, and Strategically, you know, leading the team and having depth across everything while doing this in an innovative way. I don't know if there's many people out there that have done that before, but uh, if you're excited you should call me.

Speaker A: And they're continuing to collapse and continuing to have one person be able to orchestrate more just because of the tools you've put in their hands.

Speaker B: No, absolutely. And maybe to give an explicit example, currently we're probably towards end of July, I'm going to have it uh, available to the teams. We're basically scanning the 3,000 accounts that we go to market for. It's a very specific TAM that we have and they're mid to large scale banks with the GTMOs. We're now in a position where we can actually scan those accounts practically 24 7, map them against our ICP, where the ICP we can also refine all the time based on what we learn in the market and where we see deal velocity and what have you across all segments. And using that we can actually put these accounts in certain buckets. Strong fit, moderate fit, cooling down, no fit. So from there onwards we have a subset of accounts specific to an ICP match with our solution lines, which we can then outside in research, which also is almost instant. Um, and I'm talking hundreds of accounts still, if not thousands to understand where are they investing, where are they on their journey combined with the signal that we have from our own signal engine, are they interacting with our channels, with our sellers, with our partner ecosystem, what have you. And using that information, to me it's like super proprietary data. Uh, we could campaign against them appropriately, either at the highest, uh, let's say level more one too many, but then cluster based. So we don't do one too many to 3,000, we do it to 30 accounts based on a thematic cluster that we've found and basically that continues throughout the whole flow of our demand generation engine, which has nothing to do with how the team is structured. Like nothing. It's basically outside in how do I want the operation to function? By the way, first principles, what is logical to do? Well, listen to the market, find whatever is out there, combine it with the conversations you're having, which are practically stored in our CRM20, uh, 47 and start making a hypothesis and philosophy on what themes and what clusters are these accounts investing in. How does that then map against our value proposition? And then all of a sudden I've got these 3,000 accounts nicely bucketed, nicely themed, mapped against where we can actually enter with the highest propensity to buy. And then I have campaigns that all of a sudden become super targeted, super specific. There's no way that I could have done that or that I can achieve that. Just going through the human collaboration model, it's literally impossible. You can't research 3,000 accounts at that level of quality. You then can't bucket them, you know, with individuals in their spreadsheets. So it's really systems thinking and engineering to solve a go to market issue. If I may. Um, and then running this every week for around $500 at max, I think we're going to get it to 50 very soon. That's just out of this world.

Speaker A: I want to break down some of those components. So like pillar one is you're just constantly listening to the market. And so what are you listening for and what is the system by which you're like bringing in that information and synthesizing it? Just like give me some of the architecture there.

Speaker B: If you think about the uh, type of account we sell into, they are mid to large scale banks and we sell to the C suite and we influence the C minus one layer and sometimes C minus two. But basically it's C and C minus one. Um, we're using tooling like exa, which is neural search. So it's not just a regular search API, it's actually agentic search, meaning you give AXA the objective to search for, let's say the senior leadership of a given bank, let's say JP Morgan Chase, and it just goes out there and it reasons and it challenges itself on the outcome until it has a, let's say high probability quality outcome and it goes lightning fast. So that's one of the tools where if you think about account research, account mapping, finding the DMU members and so forth, it's second to none at this moment. There's probably a reason why Andersen Horowitz is big, uh, as an investor into them right now. It's like the new, the future Google if you ask me at this moment. But then again you need a, you need to have the philosophy and the system to land it in. Otherwise you're going to just do account by account. So we do it in large scale sweeps. Uh, so we scale up the process, we hit many of these accounts, we then collate all that research and then given that you have let's say a thousand accounts and a thousand, let's say returns on uh, let's say that research data, you can start finding patterns. You can then apply a scoring rubric like okay, what are the parameters that are actually driving the match with the back base value proposition. So it's like a regression analysis almost. And typically you get four to five scoring parameters which you can then dial up and down to put accounts into certain themes and into certain buckets. Then all of a sudden you've got your own not mental model but let's say data model where you can just play where should these accounts land and why. And that's maybe the angle you were looking for. Like what do you then research? We research strategic projects. What are our thematic investments at board level that they're going all in on which are named, which are budgeted for and have an executive against them. It's like classic sales 101 almost. It comes close to the band uh, in a way or medic but basically that's the game for me at least in my motion at back base. So what that ends up happening. Let's take JP Morgan Chase. They have let's say five to ten thematic investments and a typical executive will have one or two of them in their portfolio and some will be cross functional. So there's multiple executives. But I'm now able to execute this at a level where on a theme level a particular bank can be in multiple clustered campaigns with different executive titles. So I'm actually campaigning against JP Morgan Chase from three different angles with three different Personas, three different campaigns, three different and so forth. Um, so I think that almost collapses the whole one to many, one to few notion because this is actually one to few already because you're making smaller clusters that actually globally hold true. So in a way I'm rethinking how demand gen can be done in a world where building and analysis and research is almost for free. It isn't for free don't get me wrong. But there's no limitation.

Speaker A: Just explain what Backbase does quickly because I want to ask some specific questions.

Speaker B: So Backbase is a white label banking platform and uh, recently we announced uh, that we're basically the first AI native banking operating system which means any bank that needs to either transform uh, their operation to become more effective, efficient or needs new web channels, digital channels, mobile channels or wants to bring agents to life. They need a certain stack to do this or on top of um, and that is Backbase. So Backbase typically enters mid to larger scale banks that have hundreds of legacy systems that they need to tie together to actually service the customer or help the employee in the customer, ah, call center do their work or the branch representative. Classically that's all stitched together, you've got these broken apps. With Backbase, you have one single operating system that basically orchestrates the customer experience, the agents as well as the human employee led interactions.

Speaker A: With that in mind, like what are the themes that you're watching for? Like, so you're, you're, you're watching the entire market for the strategic projects that are mentioned in the news or analyst calls or Twitter and trying to understand who has projects that are relevant for like the, the pillars of your product. Is that what I'm hearing?

Speaker B: Yeah, that's exactly it. I um, have to say though, this is the obvious one. You take the annual report, you take the press releases, analyst reports, what have you. But on top of that there's a lot of signal around. The individuals and the executive team take their linkedins, take where they are speaking, they are on podcasts, they are on certain podia, try to find those nuggets which is a combination of the Exit tool, Fire Crawl and a few others to basically get that surround sound view. Like what are they talking about at all time? What are they passionate about? What's, what's their, their focus? Uh, in that sense and what is beautiful with an LLM, once you have all that raw data in the reasoning is exactly the power of the LLM basically trying to find correlation and how can they basically score and weight what do we believe holds true? It will also give you garbage if you don't set those rules and scorings right. Once you've got that engineered. Yeah, I basically have a pipeline of intel that's uh, second to none.

Speaker A: It's a little bit like Palantir with external and internal data, trying to stitch it all together and then find the probable actions.

Speaker B: Honestly, I think anyone can build this. However, um, I think it's the vision, the philosophy that's going to allow you to stand out because eventually there should be software vendors that are going to do exactly what I'm describing right now because it makes sense to me like first principles thinking, of course this is going to happen eventually. So I think right now there's just 6 to 12 months m maybe 18. Let's see how fast the industry goes where this is an advantage because landing this in a larger organization is just, it's a people problem. Do you have enough buy in? Do you have the right people to actually do this? Um, is this not an on the side thing and it gets ignored. Can you really funnel investment and power to it? Yes. No. Um, so I think the actual challenge is mostly the human hurdle, not the building anymore.

Speaker A: If you want to know where Go to Market is going, you just have to look at where product development is today and that's where Go to Market is going to be in 18 months. And so the almost ad nauseam conversation in engineering is how do you build this dark factory or software factory and how do you do the context engineering, the harness, the loops all tied together in this system to be able to produce code at these like um, massive output levels. But all of that maps perfectly to Go to market. And you're one of the very few companies that I think have endeavored to do this with this level of sophistication. I only know like two other companies who are sort of their ramp, who's talked about it publicly with, with Glass, where they have spent massive amounts of time and money building the harness and an experience and using Salesforce in a headless manner and replacing their, you know, BI platform with homegrown tools. And I do think this is where people will end up.

Speaker B: And interestingly enough, the actual investment that I have been making and we have been making is, is very, very, very low compared to the impact that it's making. I can't share the number here publicly, but it's, it's incredibly digestible if you have the right talent, uh, to work with internally and right attitude, aptitude, culture, buy in, what have you. But let's say funding wise, I'm really uh, amazed to be honest. It's the best ROI I've seen in this full year.

Speaker A: Where is the ROI predominantly coming from? Is it pipegen conversion rates per rep, efficiency? Where have been some of the biggest uh, output areas?

Speaker B: So if I talk about roi, it's the, the obvious one is hey, we've been, we've been able to strip out certain infrastructure components and that's just an immediate P and L gain because the cost is not there anymore. The other one is time, like literally preparing quarterly business reviews globally, regionally, per ae. Incredibly costly. I think all setters in the conversation here, they recognize that most likely that it's just, it's, it's pulling teeth to get a deck together that is completely automated. With us now the only thing they need to do, anyone who does a QBR in the sales column, uh, they basically get their talk track right. Like why, why are we looking at this data? Why is your call in Q3 not fully uh, locked in? What's the challenge and how are you going to fill the gap? So they're now using their brain to strategize. Okay, what's the next Step versus spending two to three weeks. I'm not kidding to prepare the QBR to begin with and that was percolating across the full go to market function. Just the gravity of we need to hold each other accountable. I think that gain on its own is a multimillion gain already in time. Productivity and focus and happy sellers like literally uh same thing towards uh our investor right. They want all the detail, all the double clicks, everything now it's there, it's real time. Whatever you would like to have, we can actually deliver it to you almost instantly. We just need to export the PDF or the uh, the CSV and off you go. I think their operational excellence and reporting and accountability was a very big push that's practically solved for. And then thirdly I would say the interaction with the market and that's really demand gen pipeline generation. Account based marketing. How many one to one accounts can you execute if you only have 14 at this moment? Account based marketing managers in the regions. So my regions are just abm. They just do ABM and ABM only two years ago when I took over the role they were all up until here with workload like I have too many accounts need to handle. There's ah no way for me to do uh you know five accounts at the same time what have you. Right now one one to one account and I think of demo due to workflow that we have running from account research to mapping a DMU to then doing the strategic narrative on the account, the messaging track, the engagement strategy, the frequency, the actual content, the outreach plan, whatever. 15 minutes right. And it's ridiculously high quality.

Speaker A: So 15 minutes of the ABM manager going back and forth with the model to uh iterate.

Speaker B: This is typically together with the ae so they actually go through it together and the system drives the full workflow and then the output that is there is to all of them like yes, let's tune maybe a few things in terms of language because that's always, I think that's a good thing to humanize it a bit more. Although with the prompt card reels we have the majority of that already in check. But within 15 minutes you have a super strategic plan on a one to one account level. Prior that was weeks of work. So right now one to one campaigns to me are almost cheap. Like we can do a one to one it's not an issue also with the outreach, also with the warmup of the account, not an issue. The limitation then becomes how many can you personally as a human digest to also drive uh relationship building to do social selling, uh, to basically have real dialogues in the comments and do outreach over email, have conversations. You can now do this at 50 accounts at the same time. So BDMs and AES are in my world at least becoming supercharged networkers less busy with. We need to do all this account research. I need to deliver an account plan, it needs to have this format. So I'm just putting it into this format like you're putting the most expensive people on your payroll that need to deliver quota in slide X.

Speaker A: So have you seen an increase in pipe, uh, gen per person or pipe gen overall?

Speaker B: Pipe gen, overall, Absolutely. Yes. We are massively increasing the effectiveness against the market as we speak. I would be happy to come back towards the end of the year to really make the call and say we did it. Uh, but the initial uptake I've seen since beginning of this year While bringing these GTMOs workflows live, it's clearly on the rise. There's also a little bit of seasonality. Right? Q2 for us is event season. So it's always high. We should be tapering off now, summer holidays kicking in. But yes, I see way higher activity, way higher penetration and way more new business conversations. And then I'm not even yet having fully live the vision that I just shared with you of outside in demand generation and cluster campaigning and everything. So I think personally we're onto something. At least there's a strong hypothesis and belief we have the effort to do it. It's basically weeks of building, refining, getting into production and uh, seeing whether it flies yet or no. My take is that it's very likely

Speaker A: to uh, fly with the last 10 minutes here. What do you think is the most interesting use case to share something that you're eager to talk about?

Speaker B: ICP is always a little bit the holy grail. Who owns the icp? Who can make the final call on where we go to market and why. And then sales is actually in the front line. Marketing is not really in the front line as much. Does it sit with product marketing? Does it sit with solutions engineering? Is it at the CRO or CMO level? We're just systematizing it. So an ICP to me is nothing more than a set of parameters against which you can score an account. And that's influenced by your intelligence, meaning you've got experience, you get certain nuggets you win, certain deals you lose. There's dynamics around that ICP that change every other, I would say month by now. So making the call to systematize it and build an ICP engine which is when you think about it, it's a set of parameters with qualifiers, disqualifiers and certain dissatisfiers. So if this holds true, the account is out. I'll give you an example. If they just acquired a uh, competing uh, vendor solution, they're just out of the mix. We're not going to go for them at this moment in time like they're committed, right? Or if they are a mid tier organization and they just got announced to be acquired by another probably larger institution and we're not yet in dialogue with them, ignore because they're going to be busy with the M and A, uh, movement. So there's a whole set of pretty direct and clear parameters uh, that score an account which change all the time because we just announced the acquisition of Casisto. It's an agentic banking company, conversational banking. With that we can now actually go lower into a specific ICP because we have a different solution capability we can bring. So we tune the icp, campaigns are being tuned and off we go. And to me it starts at the. Yeah, if you think of it as a factory line, the ICP to me is very strategic. After that you can do account research within that ICP context, then you can do campaign building within that ICP context and so forth. And I think also there sometimes you just have to crack the hardest problem. For us last year that was the signal engine which is literally driving our engagement matrix. Like is an account called does it show first signal or is it highly engaged in a sales conversation? That's the horizontal axis. The vertical one is no opportunity, unqualified opportunity, qualified opportunity and customer. And if you visualize that you basically we're playing a game to move non engaged accounts all the way to the right bottom of that matrix so that they're highly engaged next to becoming a customer. It also allows us to see hey, there's a qualified opportunity but they're not engaged with us anymore. So that, that is a fake unqualified opportunity or we need to do effort to actually bring it back in, drive the dialogue or maybe take another angle and penetrate more. So by solving those hardest problems first. Signal engine, ICP engine, then you can get to the exciting stuff. So the exciting stuff is almost the hardest stuff first. And after that you can get to the exciting stuff. And I'll give you a final example. Why didn't I yet build an outreach orchestration agent that just hits all these accounts and hits them on their LinkedIn and sends the right message at the right time, because I need the foundations to be super, super strong and only then with the right strategic nature and the right intent behind it. I am willing to consider to do certain agentic outreach or certain nudges, but limited because I think it can hurt your brand like incredibly fast at the moment you turn it on and it's just too loud or too annoying or too multichannel. You're out of the mix. And we are selling to the C suite, C minus one. We're in essence in the same room with McKinsey. So we're like serious as a brand. So that's where foundation first be super strategic. And then based on how we work the account or where the account sits in its life cycle, there will be gentle moments in time where automation takes over and we actually get 100x the interaction with the market. That's what I'm working towards.

Speaker A: And what are those nudges for backbase. If, you know, selling into the C suite at a bank. It's not cold email.

Speaker B: No, not at all. They don't check their email. Spoiler alert. They're also not per SE on LinkedIn. Although there's this very interesting tactic I'd love to share with you. It's called love letters. Um, nutsy hats. It's crazy. I think everyone smiles always when I share this one. But basically love letters are a public charade of the executive team of the account. So you really champion the work they're doing, the philosophy that they have, the strategic moves they're making. As in, hey, for instance, DBS in Singapore. It's a fantastic, very digitally native, uh, organization and we want to get into the C suite and have a dialogue with them because we believe we can deliver value. So we basically do a bit of a long read love letter where we spec out our strategy, why we believe it's a great strategy, which by the way, it is. We directly tag the executive team members and why we um, champion them and why we believe you should follow them when they are on their journey. And if you do this, we're already doing this now for give or take, six months. Let's say 40% of the actual executives respond because you give them a massive confirmation of their work. You took the, let's say, um, we went through the hassle to write a real piece. Some of it is researched and driven by AI, but it's actually they're human written to make sure that they come across properly editorial and people feel special, they feel heard, they feel seen, even the executives. And in the mix of back based. That works because, you know, the higher you get up until the value chain, um, ego and, you know, prestige and confirmation, they, they are important, especially in the banking context. So that little tactic gets us into executive conversations, Kyle. It's crazy.

Speaker A: What else are you wrapping into an ABM campaign to, to get to that seniority of person?

Speaker B: The podcast. So outreach we do with the Banking Reinvented podcast, which is funnily enough, the fastest growing podcast of 2025, we were able to create a platform and a forum for bankers to talk about transformation. That wasn't really there. So I was also lucky in the sense that it was so niched that it started working and we've been upgrading that motion to only have C suite conversations and nothing else. So right now I'm saying a lot of no to people who would love to be on the show, but it's C suite only. You need to be the operator, you need to have the number or you need to have the actual heat to transform the bank. I'll give you an example. I did an outreach last week to the chief AI, uh, officer of Westpac in Australia. She just got confirmed. Immediate response. Would love to be on the show. I'm going to be very busy for now, but let's just connect, um, with my EA and let's get it booked in. And I cannot do that at scale. The two examples that I'm giving you, and that's really one of the things I'm very passionate about, are super lightweight, they're super cost effective versus the classic let's go abm. Put a massive elephant of a campaign onto the account and run it for three months and then look back. No, no, we're doing this weekly depending on where an account sits, whether accounts are moving or executives are moving. Minor outreach. And um, I think also with the brand building we've been doing for one and a half years straight now, we're getting way more top of mind position. So people actually feel like, hey, ah, yeah, cool. Uh, I'll briefly respond like, like why I'm here, Kyle.

Speaker A: And then I would imagine you've got like a big research brief for the podcast, that's all. AI no, I don't.

Speaker B: Of course I do a sweep on hey, us as executive and whatever, but I want a real dialogue. Otherwise I think it's a really uninteresting show if it's scripted or if it's directed. And hey, uh, we're only going to talk about this. Don't touch that element. It's same to what you and I are now doing here. We uncovered a few concepts in our pre call, and then we actually dive deep on whatever is, let's say, most resonating on the day. I do it with the bankers too, because the moment they get energetic, I know this is where I'm going to go. And then I got a good show, and it's going to be super interesting because they're clearly passionate or they're going through something or they want to share something that they're passionate about. And that's not how they initially get into the show because they're uncomfortable. But the moment they get to that space, we're good to go.

Speaker A: But without the research, how do you find what that thing is? Are you doing a pre episode?

Speaker B: Okay, pre call. It's like, what's most top of mind to you? It's one question. What is most top of mind to you and why? And that's almost the episode. And then you can always fan out and come back and draw certain conclusions. But I listen to many, many podcasts and I try to find the formula of why am I hooked on certain. Like the moonshots one. Why am I hooked on that one? Because it's always stretching your mindset or it's always energetic and it's always a different angle. So if it's scripted and it's just banking and hey, we did a mobile app for the past four years. Yeah, nobody really gets excited about that anymore.

Speaker A: Yeah, yeah. I tried to be a lot more prepared and organized when I started the podcast. And I would, like, send a docket ahead of time, but then people would start putting notes in the docket of, like, the answers they wanted to give. And those episodes were the worst. And so now somebody's like, hey, I don't have a. I don't have like, an outline for the podcast. Like, are you going to send me something? And I have to reply. I'm like, no, I'm like, I'm not going to send you anything. We're going to spend 10 minutes at the beginning framing out some ideas, and then I just want to go, because those. Those tend to be by far the most interesting because, like, I. I do this job every day, so I don't need to do a bunch of research to know what interesting questions to ask. The whole point of the podcast is like, I'm just going to ask the questions that are interesting for me, and I'm going to, like, count on those being the things that are interesting for listeners. And.

Speaker B: And so, uh, so coming back to Your question, uh, what is really, you know, breaking through in the ABM context? The example of a simple love letter and the example of a podcast. And around that there's a lot of activity and little nudges, outreach, warming up, connecting them to an event. We have closed door community closed dinners where only C suites are invited. So we make them feel very special all the time in every touch point. And that's, that's it. Because maybe it's very simplistic thinking, But I have 3,000 banks. They typically have a C suite of five to eight individuals. So it's three, 3,000 times five to eight. I need to know those people. So my main m motions are for those people and the rest is just to support it and then you can, you can get creative. And uh, yeah, being a C suite myself is helpful because I know what's coming into my inbox is typically not relevant to me or it's not even standing out or it's, it need automated. So I think also it's um, it's a wonderful time to um, have this deck, have this type of setup in the remit to actually just be creative and um, create breakthrough.

Speaker A: Very cool. All right, I want to move us to the quickfire to get you out on time. What do you think separates a good CMO from a truly great one?

Speaker B: Deep business and subject matter expertise, understanding of the company that you are representing. That really is the thing that makes for breaks in my world at least. The cmo, uh, seat.

Speaker A: What advice do you most commonly give to first time CMOs or first time marketing leaders?

Speaker B: You need to be well rounded. So if you come in just looking at your little swim lane or your craft that you are leading, you already making the number one mistake. Marketeers are coming in as a specialist, not as a generalist. If you ever want to, let's say, climb the ranks and become an executive that has a PNL level conversation or strategic conversation with your CEO or your CRO. You better get out of that mindset very fast. You're a business leader, number one. Number two, you're a crafts leader. And those two need to go together. And if they don't, then indeed the CMO will not have a seat at the boardroom and it will be just AVP marketing that is tactically executing whatever they cook up at the other table.

Speaker A: Yeah. What's the hardest lesson you've had to learn in your career?

Speaker B: Knowing what I didn't know and going through the pain of not knowing, trying to figure it out and hitting the wall multiple times until I got it. And that tends to happen, uh, every other year. Uh, Kyle, what's the best thing you've

Speaker A: read in the last year or two?

Speaker B: It sounds silly, but there was this book from, um, Tiago Forte, I believe, about how to structure your second brain. I'm one of those idiots that is really into, okay, how do I structure my thoughts so I can work with them appropriately and store it accordingly. This is pre AI, by the way. But Thiago Forte's book was about structuring an obsidian vault, which is a graph database in structuring notes. Uh, that book really triggered simplistic thinking and organizing of everything that's going around in my life and in my role.

Speaker A: Have you been able to make that work? I've tried this twice.

Speaker B: Yeah, it works, but, um, you have to be forgiving to yourself. So what I actively manage is projects and areas. That works. And that's it. So the other parts are just like a big dumpster fire, uh, part where all my old stuff is. And with, you know, file search I can find them. But, uh, project projects and areas works very well.

Speaker A: Tim, this was awesome, man. I'm really, uh, excited. We could get together on it and get into some like, real weeds. I'm so impressed with what you've built. Like, I talk to people about this stuff non stop. I'm constantly talking to people either on the podcast or outside of it to learn about what, what the most cutting, uh, edge folks are doing. And, and I haven't seen a lot of people that have built this level of sophistication. So, uh, big congrats, uh, and, and thanks so much for sharing all of this because I learned a ton. I have a ton of notes, uh, that I'm going to be taking back to my team and, and I'm sure folks listening are to learn a lot, learn a lot themselves.

Speaker B: Thanks, Kyle. Happy to share. I hope it was helpful, uh, for anyone listening in. And unfortunately it's audio, so I couldn't demo it or show it. That really speaks to the heart and mind usually. Um, but then again we're also just on a journey, so we're still learning and I think that's also the main takeaway.

Speaker A: Yeah, maybe we'll do a webinar or something. You can download some stuff later. Uh, lots of options.

Speaker B: Cool. All right, thank you so much.

Speaker A: Thank you for listening to the Radio Revenue Leadership podcast. If you enjoyed it, don't forget to subscribe and you can find a link in the show notes and be sure to leave a five star review. Share it with your network and please join me next Wednesday for another great conversation.