Let's kick it off. There's a lot we want to get through here. For those in the room I haven't had the chance to meet, my name is Billy Fitzsimmons. Started at Piper at the start of 2026. I cover the hyperscalers, application software, and vertical software. We are incredibly excited to have Bill Magnuson, CEO, Co-Founder of Braze here. Bill, thanks for joining us.
Yeah, great to be here. I assume we're both Williams as well.
Yes.
Yeah.
We share that, yeah.
Didn't clear that up front.
Yeah.
Makes sense.
Maybe to start, you guys just reported earnings last week.
Yes.
Anything from the print you want investors in the room to kind of know leaving it? Can you just give a quick kind of pitch on Braze, the problem it solves for customers, and what you do?
Yeah, sure. I'll just start there. Braze is a customer engagement platform. We primarily help B2C brands, but brands of all kinds who are looking to better understand their first-party customer relationship, and take direct action on it in order to build stronger customer relationships over time. What that literally means is that we collect tens of trillions of first-party data points every year. We process those through a real-time AI-enhanced stream processor in order to then send, I think we sent between 4 trillion and 5 trillion messages last year on behalf of brands, all of which are being orchestrated and personalized using first-party contacts and first-party data and advanced AI. We're going to talk about a lot of that today. I think from the print, definitely an exciting quarter for us. [We beat] Braze, growing 26% year-over-year.
We had a record free cash flow quarter in the quarter as well. Operating income guidance, we moved up our operating income margin percent on the year up another 30 basis points, and pacing to a great full year result there as well. We had consistent bookings actually around the world for the first time in recent memory, which is great to see. We have been investing a lot in our go-to-market leadership organization since bringing on a new CRO last summer, and it has been great to see. We are a little bit over a year from him joining, and just got a strong sales leadership bench around the world, and we are seeing consistent results from that as well. So, a great quarter all around, and we are really excited about the back half of the year. We have got our customer conference coming up in just about two weeks.
It is called Forge, out in Las Vegas. It is going to be bigger and better than ever. We are going to talk about some of the AI roadmap as well, and I will be able to give you a bit of a preview of what is coming at Forge, but I will also probably drop more to come a few times in this chat. It has been an exciting year, so excited to dig into it.
There has been a lot of changes to the products. I have a lot of questions on AI. Can you also set the stage on some of the product innovation and how you have kind of enhanced the platform in this AI era?
Yeah. So high level, we are in a golden era, I think, right now in Braze product. Our product delivery velocity is moving faster than ever. We are in an incredible position as we have accelerated our own AI roadmap, and Braze already has proven scale. We have got the reliability, security, stability, performance at massive scale. I just mentioned some of those numbers before, and what that has meant for us over the last couple of years is that a lot of the really difficult distributed systems and scaling problems and all that, to be able to have smart pipes, to be able to drive a lot of this agentic orchestration acceleration through, was already there for us. We have been able running actually all the way back in 2011, about 15 years ago.
I took over the CEO seat about 10 years ago, and my technical Co-Founder, Jon Hyman, is still Braze's CTO today. Our SVP of Engineering and our Head of AI products have both been with the company more than 14 years. When I look at, I always chuckle a little bit when I read the founder mode articles and what have you, because we still have both of our technical co-founders and our most kind of tenured product and engineering leadership that have been around since the beginning. We went through and we were born in this last phase of disruption around the rise of mobile. Now, as we look at this wave of disruption from the rise of modern agentic AI, we are doing so with a founder mode team.
It has just been really exciting and awesome to be right at the frontier of change with us being able to leverage underlying science that is moving so rapidly, both in the LLM space as well as on the reinforcement learning side. To be able to combine that with a system that we have laboriously and lovingly scaled over the course of the last 15 years. Now, when you look at Braze's ability to just deliver customer engagement through smart self-optimizing pipes to our enterprise customer base, then be able to kind of build on top of that for much more advanced orchestration and personalization than was even possible before. We are able to deliver that combined solution, and it is moving faster than ever.
Let us talk about some of the individual products, because there are a few different ones here at different stages of maturity. So BrazeAI Decisioning Studio, BrazeAI Agent Console, BrazeAI Operator.
Yep.
How do they kind of work together to deliver a meaningful customer value to the end customer?
Yeah. Well, starting with Operator. Operator is effectively Braze Copilot, but it is much more than that. I am a believer that I think, the future of application software requires that you have bespoke intelligence like Braze Operator, that you can deliver to customers that is the best user of your software possible, right? Our litmus test on that is that we need Braze Operator to be the, I want Braze Operator in it, and it already is in the space, and it is actually accelerating, but we will continue to measure it by, what is the most state-of-the-art model from OpenAI or Claude when they read our documentation and try to use our dashboard product?
I need Operator to be head and shoulders above that in capability, because Braze Operator benefits from proprietary use case libraries, from advanced skills that we built from the vertical integration into the dashboards like metadata environment, both from the metadata of the Braze objects in the development environment itself, as well as the first-party context that the customer has access to and the way that they have integrated Braze. What we have really benefited from, in particular this summer, is that we built Braze Canvas to be a proper programming environment. You have never heard me talk about it as like a workflow tool. It is a visual programming language. Braze Canvases, which are the primary unit that Braze customers build and configure their Braze environment in, it has full features with a runtime environment behind it.
It has got a full application context concept that is mutable and visible to the user. It has got debugging and observability capability. It has multi-user versioning and collaboration features. It really has been built as a full development environment, very similar to Cursor for marketers to be able to build out advanced marketing and customer engagement programs. As a result, when we built the bridge from the underlying foundational models, which of course have all been enhanced and optimized for coding, into really understanding the Braze development environment, the acceleration and capability has been tremendous.
What Operator has really done is unlocked all of the capability that was already there in Braze, but was being underutilized because a lot of our customer base just did not have the time or the resources or the full skill sets to be able to really use all the advanced and comprehensive capability that is in Braze. I think that is the first major unlock, and that gets into the headless conversation and such, and we can talk about that more as well. The critical starting point is that Operator is unlocking all of Braze for all of our customer community, and it is doing it rapidly. The stories that we hear back from customers who have adopted Operator are incredibly glowing.
I shared a stat actually during the earnings call that over the last 90 days, we've had almost 80% of our customer base has used Operator more than 10x . More than half of those have then gone on to use it more than 100x because it's addicting like that, right? Because of the value that it brings to you. We're seeing customers who are completely changing the way that they run their own day-to-day. It mirrors what we saw happen in the software engineering world, which is that as tools like Claude Code and Codex and such got better, and especially as they leap forward over the course of the last nine months, developers just don't write code by hand anymore, right?
They still need a development environment in order to be able to inspect the entire system and be able to test and review and have the right observability and all those other things, which are, by the way, all these things I just mentioned that Canvas has, right? You need a place to deploy all that, and the distributed runtime that automatically scales for you, which is effectively what lives behind Canvas, is the critical way that you take the programs that you build, and you get them running in the cloud, being driven by first-party data and doing so in a way that's secure and scalable and high performance. We're delivering all of that effectively as agentic harness for marketers to be able to build customer engagement programs and have Operator build it for them. That's Operator.
Then within each of these Canvas programs that I've been talking about, there are more and more advanced features that we're building. So before, your Canvas programs would have like, let's say you have a forgotten cart or abandoned cart campaign, and it's like, okay, the Canvas trigger is based off of someone viewing a product page, and then it starts a timer, and if the purchase is not made within the timer, then we send a message to try to motivate the sale, right? Simple forgotten cart concept. This exists across all verticals. Agent Console provides the ability to inject an LLM into that same flow.
An example that we've seen customers have driven really great performance uplift on is when you are about to send an abandoned cart message, you have the LLM rewrite the entire content of it to be like a custom compare and contrast of the last few, let's say for a hotel brand, for instance, that we work with in APAC, they had the forgotten cart message send a custom compare and contrast of the properties, the last few properties that the person had looked at, along with great vacation ideas and other sorts of inspiration that was tied to the context that they have about the user as to business traveler, luxury, for pleasure, romantic, et cetera.
By bringing in that first-party context about the user and about the recent user journey, they then rewrite the content so that every single email or SMS is completely one-to-one. That is just injected into the flow of the program that they were running before. That is an example of Agent Console getting used. Some of the really great use cases we have seen there are being able to drive personalization off of even sparse data points. Feeding them with first-party context about the user is obviously where a lot of the differentiation comes from. Braze manages all that for you and then invokes the agent call. Marketers are still experimenting with exactly what the great strengths and weaknesses of taking this agent or this LLM-based approach is.
Now, one of the places to bridge into Decisioning Studio that agentic personalization is not particularly good at is in discipline around discounting. If you think about your own interactions with these chat-based tools, they have been aligned to please us, right, and to stay in an engagement loop. They do not want to upset us. When something is the equivalent of a price negotiation, they do not hold the line and hold the tension. They just give up the discount right away, and they want to give you as much of a discount as possible because they are trying to please you. If you want to be able to do, that is the opposite of a lot of brands' goals right now, right? You do not want to train your customers to expect discounting, and discounting hits the bottom line.
Even when you use it as a tactic, you want to use it in a highly controlled way. A lot of times, especially around subscription, you also want to sometimes make the call to be raising prices on people when you get to an annual renewal or what have you. Those are all examples where we need a different science approach. The answer for it is constrained optimization using reinforcement learning, where the model is actually able to experiment with different discount levels and different price changes across the customer base, across the thousands or millions of interactions that you are having with other customers over time. Then the model learns over time where the points of resonance are and where it can take risks in order to optimize for something like decreasing discounting or increasing prices.
That is where Decisioning Studio comes into play, is when you have got a reinforcement learning need, where the model needs to achieve a more advanced goal than merely engagement, and where there will be benefits from it learning from all the other interactions that are happening across all the different messages that get sent. When you think about the context window, there is a lot of conversation around making sure that agents have the right focus and first-party context, and they can be non-deterministic if you do not have the right guardrails around them and what have you. All of those considerations are at play when we are engineering Agent Console. Part of the answer to that is keeping the first-party context that you deliver to the agent tight, right?
Part of the job of Agent Console in Canvas is we need to deliver the user context and other first-party data into that window in an efficient way so that when the agent runs, it has higher predictability, higher performance, there's less non-determinism, et cetera. You're getting value out of that first-party data, but without the context window potentially distracting or slowing down or ballooning the cost of the agentic call that you're about to make. That need also means that it's harder for that agentic approach to learn from the millions of other interactions that might be going on in your customer base. But we already have an approach for that. It's also how your TikTok feed personalizes itself over time.
By looking at the mapping between different user interests and different content properties and being able to find the points of resonance between them. So Decisioning Studio runs in effectively the same way, but adapted for these marketing use cases, where you're finding these points of resonance along with the content personalization options and using that to drive higher performance, just like your TikTok feed would personalize over time to drive higher levels of engagement. Those are the two major product families is the LLM-based approach, and then the heavier weight math-based approach. They can actually be combined together. So you can, for instance, use decisioning to drive your discount optimization strategy if you're trying to constrain discounting, to go back to that example before, but then still have the LLM rewrite the content in a fully personalized way before it gets sent out.
We have customers that are experimenting with combining those together as well. One of the great things about the Braze platform is that you can now string all this together where you go and you come into the Braze dashboard experience, you chat with Operator, and you say, "Hey, Operator." We just shared an example of this on the earnings call, actually, from our City by City Sydney hackathon. It was a coffee chain of cafes in Australia, and they are already using our survey tool in order to collect post-visit surveys from people. What they wanted to do was actually update the survey response to automatically adapt itself based on the content that someone put into the survey feedback. In Braze, that is extremely straightforward now. They had a quick chat with the Operator. They told it what they wanted to do.
It went into their Canvas environment. It built a multi-step response Canvas. So it's like, send the survey, process the response, and then depending on whether the customer sentiment is negative or positive, if it's negative, we want to send an SMS follow-up, and then the content of that SMS follow-up should be based off of what they complained about, basically, in the survey. Operator then went and built the agent that processed the survey response and built the personalized reply, and then pulled that back in, configured it into the Canvas flow, and set it up to automatically send those SMS. That was all done in under 15 minutes.
You start from just this goal, and Operator navigates around, it programs it does it in front of your eyes, so you can watch it, and you can have certainty that it's building the right thing. You can edit it and change it and observe it and everything afterward. Then launches and has this much higher level of personalization across these channels using these strategies that were being run before, but they're now being delivered at much higher quality.
I think it's not every day you get to sit down with a technical founder CEO, so I want to get your perspective on a couple different topics as it relates to AI. I think the foremost one is agentic commerce and where Braze sits in a world where there's agentic commerce. I think a lot of people in this room probably saw the announcements from Meta with Meta Muse.
Yes.
How do you think about the world evolving in that context?
Well, it's funny you ask this on the eve of our. We just had our 20th earnings call ever, and we're coming up on our five-year anniversary of our IPO, so I had the occasion of looking at my letter from the S-1 recently. If you go read our S-1 letter, in the middle, I talk about three different generational changes that we see that are going to continue to provide tailwinds for our business. The second one is that building a first-party relationship will be an increasing imperative for brands as they continue to be disintermediated by whether it's agentic retail commerce experience or a bookings aggregator or an online travel agent in the travel and hospitality space, or it's a delivery provider within the quick service restaurant space. Right?
These are all examples where someone comes in between you and your customer. Their play is one where they get to keep all the first-party data, and they get to build an ad platform that extracts your profits out of the customer relationship. It is the case that while those are often important sources of new customers for brands, we are already seeing this with ChatGPT referral traffic into e-com, the ChatGPT apps, and now with Meta Muse and what have you. Also include in that things like TikTok Shop and other sorts of Instagram shopping. There are all these other places where our consumer behavior and our attention gets aggregated into these big platforms, and then that drives shopping decisions.
When that happens, the brands that can run the highest profit margin businesses and be able to operate with higher levels of strength as a brand are the ones that double down on building those first-party relationships with their customers. The new way of shopping becomes a source of new customers, but you need to get that first-party data on them. You need to get the ability to communicate with them. You need to train that customer to not start their customer journey through someone else's platform, but to actually come directly to you more often than not. If you achieve those goals, that customer is way more valuable to you.
I think the most critical takeaway for brands from the Meta Muse moment is that in the future, and again, I think this is already the case, but it will continue to become increasingly the case, the bifurcation between brands of those that you know the name of the brand, you care about them, you trust them for something that is precious to you in your life or to bring you great experiences around things that you are passionate about, et cetera. These are luxury brands, they are brands for your kids, they are brands for your favorite hobbies or what have you. The special moments in your life, the things where you know and care about that brand, and you have a first-party relationship with them.
Then there is going to be the companies, and I would not even call them brands, but the companies that deliver goods to you through your agentic commerce experience. If we go look at the Amazon Marketplace as an example today, there are already untold numbers of these that will sell you phone cases and chargers and all sorts of random stuff in the Amazon Marketplace. What has happened as Amazon has built the giant ads business in the marketplace is that luxury brands and a bunch of these other types of brands that I am talking about, they have actually doubled down on their own first-party ecosystem more to make sure that they are able to keep the first-party data from the interactions they have with customers and to make sure that they can build a customer relationship where they get to keep their margin.
They do not have to pay it all to whether it is Amazon Marketplace ads or it is Meta Muse's payment processing or Apple's in-app purchase rake that they have or what have you. We actually already saw this in the QSR space with Braze specifically because the same thing effectively happened with all the delivery platforms. When you go look at Braze's customer base today, we dominate the major quick service restaurant brands around the world. The reason for that is not because they all woke up one day and decided that they were going to be super sophisticated customer engagement like marketers. It is because they are responding to the disintermediation that happened to them because of these delivery apps, right? They started out originally like, "Okay, great. This gets me marginal demand." Then they wake up six months later and they are like, "Well, shit.
My most loyal customers have retrained themselves to buy through DoorDash now, and that means that my loyal customers, I am getting no data on them. All my margin is going to the delivery platform, and I have effectively lost control of demand," right? Then you look at how do they respond. McDonald's, as an example, responds in the exact same way that Marriott and Delta did 10 years ago dealing with the same problem with the online travel agents, where they go in, they redesign all their stores, in-store kiosk experience, they are reading license plates in the drive-thru, they are personalizing the menus. They revamp the mobile app in order to provide enhanced takeout options and a stronger mobile loyalty program and mobile wallet.
They essentially rebuild their entire digital ecosystem around getting primacy of that first-party connection with the customer so they can get the first-party data back, so they can get the ability to communicate with the customer back, and they can get their profit margin back, right? The airline and hotel industries had to do the same thing when online travel agents came into the picture. Luxury brands and other brands, as they have navigated this modern retail and commerce environment, have had to do the same thing, too. I think that this kind of Meta Muse agentic shopping moment is actually just like, it is retail's turn to go through the same thing that travel and hospitality and restaurants already did.
When you look at the impact on Braze in those categories, it is great for us because when brands have a stronger incentive to invest in their first-party relationships with their customers, that is when they become great Braze customers as well. I do think you are going to see some brands kind of die as they are unable to compete as these faceless commodities downstream of agentic shopping experiences, much like I am sure is already happening in the Amazon Marketplace, right? The brands that thrive and the ones that are great Braze customers are the ones that are going to double down on knowing and continuing to build that direct relationship with their customers. Also probably a great time to remind everyone how diversified the Braze customer base is.
Retail and consumer goods is actually only 22% of our business because this problem of building first-party relationships with your customers is one that is broad-based across every vertical. I think that's why we have the experience of having seen this same effect play out across multiple categories over time. We've got a strong track record of it really benefiting us. I can point to all the other categories that have had this aggregator disintermediation in the past. Yeah, it's obviously a big moment. I think that more and more customer behavior, much like has happened with e-com over the past 25 years, will shift to buying in these ways. It actually, I think, just doubles down the imperative for brands that want to survive to make sure that they're investing heavily in their first-party ecosystem.
It's a great answer. We'll wrap there. Bill, thanks for joining us, and really excited for Forge-
Yeah.
in just a couple of weeks and some-
Absolutely.
big announcements there. So thank you.
Yeah, thanks for having me.