All right. Hi everybody. Thanks for joining us. I am honored to have the CEO of HubSpot, Yamini Rangan, with us. Yamini, thanks so much for making the time to come down to beautiful Newport Beach, and appreciate you joining us. I think I'll kick off with probably the question everybody's been asking you, which is.
Okay
the AI strategy at HubSpot is resonating. You had the Spring Spotlight, you showcased a lot of new products. What's the main message or two that you want us to take away from the Spring Spotlight, and what resonated most with your customers?
All right. Samad Samana, thank you so much for having us here, and it is a wonderful day. It's turning out to be beautiful outside. Thanks a lot for having us here. Spring Spotlight for HubSpot, and maybe just a step back, our AI strategy is to help our customers become much more productive with our platform and also deliver work for our customers. The second part, which is delivering work for our customers, comes in the form of many first-party agents that we deliver for our customers. At Spring Spotlight, we launched AEO. We'll talk about AEO Agent, we'll talk about Prospecting Agent and Data Agent. Just to step back, the set of first-party agents that we're delivering to our customers are, one, to help them resolve their support tickets.
Two, on the sales side, is to help them drive much better outreach so that they can continue to build their pipeline. Three, on the marketing side, is to build awareness using new tools like AEO. One of the things that we have learned is that you need to have great context. Context delivers better outcomes, otherwise, AI is just going to deliver an output that looks like a slop, and there's a big difference between AI outputs and AI outcomes, and that's the set of innovation that we delivered at Spring Spotlight. Where are we seeing adoption and initial kind of reaction?
I think AEO, we'll probably talk a little bit more about AEO. We announced a standalone product as well as capabilities of AEO built into Marketing Hub Pro and Enterprise. We're seeing a strong adoption in early trials there and continued adoption of the agents that we talked about, Customer Agent and Prospecting Agent.
Great. Then, I think there's a lot of either confusion or still learning where we are in terms of the relationship between the LLMs and between the existing software platforms. What relationship does Hubs have with the LLM? Sam mentioned you by name on stage. Dario was at UNBOUND last year. This year's lineup for UNBOUND, hopefully everybody's there. I forget what the rebranding of the name is.
UNBOUND.
UNBOUND. There we go.
Yes.
I just didn't want to screw that up.
That's all right.
You have all these disruptors. Is it a complementary or competitive relationship? Just help people understand what they're misunderstanding.
Absolutely complementary. I think for the last couple of years, every single time we've gotten the question about LLMs and HubSpot in particular, applications in general, we've maintained that it is a complementary relationship. Here's how I would say, if you think about an LLM, LLMs are trained on publicly available data on the internet, petabytes of publicly available data. HubSpot has the data and the context of how go-to-market teams operate in 300,000 customers. That is why it's complementary. You need the combination of LLM capabilities with the context of how sales or marketing or service and support functions within a company in order to deliver more growth and better leads. Ultimately, our customers don't care about AI for the sake of AI.
They care about more leads, better awareness, closed pipeline, and better customer outcomes, and you need that context from HubSpot to be able to drive that. Here's the thing. We are partners with all of them. We have a deep partnership with Anthropic, with OpenAI, with Gemini, and we continue to work very closely with them. We understand where they're going in terms of their roadmap. They talk about us as the SMB partner, and so it is a very complementary relationship. The question in the market and the broader narrative is, are LLMs going to get so good in terms of capabilities that they just destroy the value of the application layer? This is where we have a point of view. The point of view is that you need probabilistic output of LLMs, but you need deterministic output within sales.
If you are running sales and you are a CRO of a company, and you just run an LLM and you get a probabilistic answer of what your next forecast is going to be, it's not going to cut it. You're not going to be able to drive that. There's a combination of having both probabilistic as well as deterministic work. The other part is all of the workflows, the logic is within applications. What do I mean by that? You can go to an LLM and say, "Write me a blog." Pretty easy to write a blog.
If you go to an LLM today and say, "Draw a campaign for me across five different channels, YouTube, LinkedIn, pick your channel, and I'm going to give you $1 million, and I want you to run this campaign across 10 million contacts that I have, and then make sure that the campaigns are attributed with the right revenue that they're driving," it has none of those capabilities. That's the logic that exists within applications. LLMs can call on those logic, they call it really well, but they don't have that logic, and that's again why it's complementary. The third thing that I would say is LLMs are single player. It's easy for one person to use an LLM and to share that output. Applications that drive growth are inherently multiplayer.
If you have a sales team and the sales team has 50 reps, every rep's pipeline change every single day needs to be represented as a team pipeline roll-up, and that is not what an LLM does. Again, very complementary. Where I think is that LLM capabilities will continue to improve, but what HubSpot delivers on top of that is a growth context, knowing deep domain expertise within marketing, sales, and service, but also contextualizing it to the business that we serve. That's why it continues to be very exciting and also complementary.
Bless you. There's another thing that you'd mentioned in a meeting I heard earlier, and I think it's important, which you were asked about the pain points that HubSpot solves that maybe are underappreciated that an LLM can't. I know you mentioned the context side, but I was wondering if you could dig a little bit deeper into the pain points that HubSpot uniquely solves that maybe an LLM can't today, like permissioning.
Yeah, absolutely. The conversation that we were having is what are inherent value that HubSpot customers buy us for? The example that I would give is a marketing automation, right? Think about a team within marketing that has to figure out what are the set of campaigns that they need to drive in a particular quarter, across what channels, by allocating how much resources, and how many contacts is that going to reach in multiple languages across multiple regions. Think about the complexity of all of that. That is exactly what our solution provides, and that is not something that is easily extractable or easily done by an LLM. The second thing that I mentioned is also the permissions. You think about a sales rep that has access to run certain sequences and is part of a sales hierarchy. You need to know their role.
You need to know their permission. You need to know which team they belong. If they change roles and go to another team, you need to know where they're now going to roll up to. All of those permissions, the security, the governance associated with the actions they can take, that belongs to an application layer. You think about the content, the conversations that applications have. That's the growth context. You think about the workflows and the logic that applications have. That is not immediately replaceable. You think about the multiplayer mode that we support with the right permissions. You put all of that together. This is why, again, going back to the point of is it one day that there's just one application which is an LLM. That does not seem like the right approach to think about what is happening within the space.
Look, I'm old enough to remember when the hyperscalers are going to do everything, and I think we have more software than ever out there.
Exactly.
I think another area that's been a big focal point, and I think it was maybe even more so early last year, is on the disruption to SEO.
Yeah
agentic search.
Sure.
I know HubSpot recently, we mentioned AEO earlier. I was wondering if you could double-click into that.
Yes.
Explain why that's important, and how maybe customer behavior is changing and what the value of AEO is now in an LLM-centric world.
Absolutely. The leads that used to get generated were from people searching on Google, clicking blue links, and coming to the website, and the industry calls it content leads, leads based on content. That as a source for the top of funnel is actually declining. An interesting statistic for HubSpot customers, we track all of the content leads. This year alone, the top of funnel content leads have dropped by 27% across HubSpot's customer base. You can begin to see that the content leads declining, which is basically SEO, is having a huge impact. Every B2B marketing person out there is now looking to diversify their sources to other areas. That is the same playbook that we've been on. We've diversified our set of sources. We had even more content leads, inbound leads that were part of our top of funnel.
For the past two years, we've been on the journey to diversify our lead sources from inbound to using social media with YouTube presence, with LinkedIn presence, to newsletters, to podcasts, and now to AEO. The first big trend that's happening in marketing is diversification of top of funnel lead sources. That is the playbook that we launched with Loop. That is the set of products that we have within Marketing Hub. What is new and interesting is answer engine optimization, or AEO, which is how does a product or a brand show up in an LLM with citation? That is what AEO is. What we have found is that if you show up within an LLM answer, the conversion is 3x better. Not 3% better, like 3x better because the person that's actually doing the prompting is deeper in their search.
They have much more specific questions, and whatever the answers they get, they tend to act faster from that, and so the conversion rate is higher. That is the reason why we launched a standalone tool for AEO. This was part of our spring Spotlight release, and what it does is for our customers, it'll say, what's their AI visibility? Basically, give it a set of prompts, and it'll say, "What is your share of voice? If someone asks this question in LLM, how many times do you show up?" That's the first thing it does. The second thing it does is it'll give you recommendations. You're not showing up in Reddit or this community, so make sure that your content is actually available, and that's the second.
The third, which is coming pretty soon, is you can immediately use our content management tools to be able to drive content actions based on those recommendations. That's the AEO solution. There is going to be a time where every B2B company has to have AEO solutions. There's no other way because that's where the industry is going. We have a standalone tool, so customers can start with that, and it's also available as part of our Marketing Hub Pro and Enterprise. We think that this could be a new front door where people get started with AEO, and then when they begin to need to take like content actions, they can upgrade to Marketing Hub Pro and Enterprise. Pretty excited about that opportunity.
Thanks for that. We've had all this technology transformation. You guys are driving the charge, but it's also led you some bold choices on the business side.
Yes
business decisions. Let's talk about the pricing model change.
Yeah.
There's been a lot of conversation around that. What were you seeing that drove you to move to outcome-based pricing for your Agents, and what are the early results that you're seeing from the change?
Just to provide context to our pricing model, we have a combination of seats and credits. That's our pricing model. What we do is for anybody that gets a core seat or a sales or service seat, we provide included credits, which means you begin to start using AI features as well as agents. In addition to that, they can buy packs of additional credits. Credits could be bought in packs of like 100,000 and beyond. The way we think about credit consumption is that our first-party agents, the agents that HubSpot builds and we deliver work as outcomes, will be based on outcomes that we deliver. Makes logical sense, right? If our Customer Agent is resolving tickets, then we'll only charge when we resolve a ticket. That is literally the change that we made.
If our Prospecting Agent is delivering a qualified lead, we will only charge when we deliver a qualified lead. I think that basically shows the confidence that we have within our product strategy and the level of outcomes that we are delivering for our customers based on that product strategy. We made those changes to really align our pricing mechanism to the product strategy and where we are going. In addition to that, there are other parts of our product as well as platform that will consume credits. A number of our customers build custom agents on top of HubSpot. Those are all just going to be based on credits consumed. Again, you'll have seats that have included credits. Credits can be consumed by first-party agents that deliver work. They can also be consumed through second-party and custom agents and other things within the platform.
The combination of core seats and credits are kind of the emerging way in which we are monetizing AI across HubSpot.
Yeah. Along with the model change, and I think there's just a lot of movement happening.
Yeah
you guys made some choices on the go-to-market side.
Yeah
concurrent with that.
Yeah.
Why was April the right time to invest in that?
Yeah
sales enablement, how should we think about that helping to accelerate AI traction in the quarters ahead?
Yeah, absolutely. Let me unpack what we did and why that has a near-term impact in terms of net new ARR, sets us up. Coming into this year, we in general, we plan for a virtual kickoff in the first quarter and enablement in the second quarter. That has always been the model. It's not a new timing in terms of April. What was different this time is that we spent a lot of time transitioning our go-to-market teams to be able to position agents and therefore credits and really driving the AI adoption. There is a sales motion change that's happening, and we're ahead of that. Specifically, if I unpack it, in Q1 this year, we saw our customers really wanting proof of value of agents that are delivering work. What does that mean?
It means that if they want to use Prospecting Agent, they want to make sure that the emails that the Prospecting Agent are writing is really what their BDR or their sales rep or their team would actually write, and they want to feel comfortable with that output. What we did was we actually gave a 28-day trial period for those types of agents. AEO Agent, Prospecting Agent, Customer Agent, we've given them trial periods. It has an impact of elongating sales cycles in the short term, but we intentionally made that choice in order to seed our customers with these agents so that they can continue to grow. That's like choice number one.
The second thing is we did take our sales team, we drove a lot of enablement and helping them communicate the value of agents and credits, and that has a short-term impact in terms of the quarter. Also getting them to understand the pricing model and where we are going with outcome-based pricing and getting comfort in that. Now, look, it has an impact near term, and we are very transparent and clear about that. The rationale is this, we are in the very early stages of AI transformation. It's like mile three in a 26.2 mi of like a marathon race that you're running, and what is important at this stage is removing blockers for customers, seeding the right agentic use cases, and making sure that the outcomes that we deliver are super clear.
When we do this in the early stage, we know that the right adoption will result from the changes that we're making. A set of intentional changes to drive AI adoption over the following quarters.
Very helpful. I know we've talked a lot about the innovation that you're doing on behalf of customers.
Yes
The company itself is internally transforming.
Yep
how you operate, and I know Dharmesh also speaks a lot to this in different forums as of you. I'm curious, just as you guys are thinking about drinking your own champagne.
Yes
I'll use that terminology, what type of transformation are you seeing from an internal perspective from AI? What tools is HubSpot unleashing for its own workforce?
Yeah. As much as we have driven AI transformation within the product and the platform, we are driving internal transformation with AI, and we're probably bleeding-edge adopters of AI. That has two benefits. One is as we experiment, iterate, and scale with AI, those translate into product in terms of the feedback directly in go-to-market. Second, we are much more authentic in communicating what works and what doesn't work with our customers. That's why we are constantly on the bleeding edge. Specifically in go-to-market, I would point to a handful of areas where we have seen really good results. The first is in the marketing side, if you go to our website, 82% of web chats are handled by AI, and we have increased that percentage pretty significantly over the past few quarters.
That is something that, again, translates into product that we can deliver for customers. The second is AEO, which we talked about even before we launched this AEO product. For the last probably six quarters, we have experimented with AEO. Our leads coming in from AEO has really grown. Pretty small base, but grown significantly over the past few quarters, and we have continued to be number one in CRM in terms of AI visibility. Those two are areas. In sales, Prospecting Agent, using intent sources and then driving emails so that our BDR teams and sales teams can actually have broader outreach as well as assistance in terms of deals closing, as well as summarizing. All of that we are doing, which has increased the productivity on the sales side pretty meaningfully over the last few quarters.
On support, I'll say that we've not hired a single tier 1 support agent since 2024, and we've been able to use that productivity in other areas of the business. In go-to-market, we are really in the bleeding edge of leveraging both agents as well as assistants on top of HubSpot's agentic platform to drive our own productivity. I'd say more importantly, three and a half years into leveraging AI on the bleeding edge, it's one thing to get individual productivity with AI, right? We are seeing that. You're using an LLM. I use LLM to write a whole bunch of things, and of course, that improves it.
There's a difference from going from individual productivity to institutional productivity, and that requires reimagining teams, reimagining workflows, providing a common context and a set of areas that we can go deep, and that's the learning, and that's kind of the vision of where we are going next. We are driving the next level of transformation internally within HubSpot to be able to reimagine how we get work done.
Listen, it's something that we're grappling with every day.
Yeah
a little side chat.
Yes
We've gotten five new tools, but the hours in my day haven't increased, and I think that's something we're all kind of struggling with right now to unlock that productivity.
Exactly
We are seeing, you mentioned not having hired in support for some amount of time, and certainly the leverage is there, right?
Yes.
Recently, the company took up its full year margin forecast. How are you thinking about the productivity gains and letting that margin flow through versus reinvesting, given just the big opportunity ahead? How do you balance that from the CEO's perspective?
Yeah. You balance it very carefully. I think the way we think about it is that our revenue growth continues to grow, and there's a gap between net revenue growth and headcount growth. We've been very, very disciplined in terms of where we hire and what kind of talent we hire, and how do we drive a level of AI fluency within the talent so that they can drive higher productivity, and that's exactly what you saw. At Analyst Day last year, we provided a midterm target range in terms of non-GAAP operating margin of 20%-22%, and we reached it a year ahead. That is at the same time when we are investing in agentic coding and our gross margins have gone down, right? We mentioned in Q1 that our gross margin went down.
At the same time, we increased the full-year operating margin guidance for the year, and that is what we are doing. The way we are accomplishing it is we'll continue to invest in AI innovation. We'll continue to invest in agentic coding that drives our developer productivity. At the same time, we're going to find through disciplined hiring as well as transforming our internal productivity and tools to get the operating margin leverage, and we're more confident now in terms of setting better operating margin targets for both the midterm as well as the longer term, and you'll hear more about this at our upcoming Analyst Day at UNBOUND.
Awesome. Well, Yamini, thank you so much for joining us. Like you mentioned, I look forward to seeing you in a couple of months at UNBOUND. In the meanwhile, we hope that HubSpot does really well. We're big fans.
Thank you.
Thank you for joining us.
Really appreciate the support. Thank you, everyone.