HubSpot, Inc. (HUBS) Earnings Call Transcript & Summary

It really is simple and easy for point solutions. It's harder when you're doing it across a platform. And let me give you a couple of examples of how we think about actual credit utilization. So our customer agent is a pretty easy example because it resolves the support tickets. There are a number of companies that are providing customer agent. And so you can actually look at the market and you can look at the metrics. And there are broadly 2 ways in which we look at this. Many of the companies out there are charging per conversation and some of them are charging per resolution. So one of the first decisions we made early this year is to tie it very closely to the outcome that agent delivers, which happens to be the ticket resolution. So that's how we picked it because it's much more customer-friendly. Then you pick a price point, which is not super competitive. We don't want to be always the top player, but we want to be a premium player in the market. So you pick a number. So I think like the way we think about agent rate cards depends on the outcomes that we drive and the number of channels that we drive and what is happening within the market. That is not the same with a data agent, which is enriching records and preparing the audience and the prospect match, that could look very, very different. But to simplify this back for a customer that is today buying apps. In the past, it was pretty easier. They were able to buy a seat. And now because software is delivering work, it has to be not tied to the seat, and therefore, there's a credit mechanism that is growing. And the industry now has a lot of conversations about, you call it tokens, you call it usage, do you call it credits, you call it outcomes. And we believe that there is just a clear model, which is a hybrid between seats and credit and credit consumes anything that is usage-based and tied as close to the value. And that's the philosophy that we have, and that's what we are driving in terms of how we position back to our customers.

I want to run a couple of hypotheses by you on the compare and contrast between larger customers and smaller customers? So one school of thought is enterprises have the resource and the talent. If they want to build stuff in-house, they can. If they want to put together complicated marketing stacks on top of Databricks, they can go ahead and do that. SMBs, they really need their trusted front office vendor to guide them a little bit more because they don't have the resources. Therefore, SMBs are more insulated from the DIY threat. Now the other school of thought is SMBs much more so than enterprises are more willing to take risks. They're more willing to experiment. They're more willing to try things with new vendors. Do you have a view on what's actually happening in practice?

Yes. I'll start with your first hypothesis, which is SMBs, in general, want it to be easy and want it to be fast. And we've seen this consistently throughout our 20-year history. When we got started as an inbound marketing company, it was easy. When we actually moved from that Phase 1 of being a marketing solution to a CRM platform, almost everybody said, well, it's going to be really difficult because why would an SMB now want to buy a platform and there are other platforms out there and other solutions out there. I think the approach that we took is to simplify a very complex technology and make it easy for SMBs to adopt because a typical company with a 50-person company or a 200-person company, they only have limited number of resources, both people as well as dollars, and they need to get to their growth goals. And so we got to be really good at democratizing complex technology and making it available for our customers. And that fueled the last 7 years of our growth, where we have gone from a single hub company into a platform company driving multi-hub adoption. So we proved that model. So the question is, is that what happens and plays out within AI? I talk to customers every single day and the ability for them to assess every new model that happens to come out every week and put together a whole series of agents is quite complex. And that's why we believe that we have a huge opportunity to take complex technology like AI and make it super easy for SMBs to access and get value from. And that is the value proposition that we have, and that resonates within our customer base. Having said that, I can see why you're asking the flip side of the question, which is DIY, it's pretty easy for people to string together a set of things. I think in the early stages of a platform shift, you will see a lot of fog and noise. And so there will be a level of experimentation. But beyond that phase, we do think that we deliver a value for the SMBs by making everything simple and easy, and that is going to continue to resonate.

Fantastic. Okay. We got a lot of questions on Salesforce and Qadforce. The question I want to ask you for your business is, what do you think a healthy relationship with the frontier lab company looks like? And is there a scenario where that type of agreement could also make sense for HubSpot.

Well, we've had that connector for over 15 months. We are the first CRM to have a connector with OpenAI that we launched in June 2025. We launched our connector with Anthropic and Claude in July 2025, and we have a connector with Gemini that we launched in September of 2025. We're the first CRM to have connectors across all 3 frontier models. So I can give you data based on the last 15 months of what the interaction patterns look like. And simplistically speaking, you have a conversational interface in any one of these frontier models that is now deeply connected to the go-to-market skills within your applications that enables the end user to just use natural language to ask about trends and also take actions within the application layer. And that is exactly what we have seen over the past 15 months in terms of the data. We now have over 350,000 users using each of these connectors every week. And so that has grown. And what we see in the data and the patterns of these users are that, one, they're using it for a number of insights, right? If I'm a campaign manager, and I have 10 million contacts and I want to drive a multi-language, multi-region campaign, I'm going to work in HubSpot all day long. But if I am a CMO and I'm preparing for a meeting and I want to create an artifact based on the campaign trends and the pipeline trends that I want to share with someone, then I'm going to use one of these interfaces to actually grab that information. And what we have found in the 15 months of data that we have collected across these connectors is that users of these cohorts engage more and they use HubSpot more and the retention is about 12 points higher than anybody else that is not using these interfaces. So there's a lot of dialogue in the industry of is this a good, who gets the value, if you lose the interface layer. And the way we think about it is that way back when browsers came on board, you needed to get your applications to show up on those browsers and you did. And right now, there is a new interface layer, which is any one of these conversational interfaces. And so your applications actually show up there, and it actually drives better engagement and better retention. And of course, as models and these layers get better, your job as a company and our job as a company is to provide additional value on top of that, which is exactly the pattern that we're seeing.

This is a really good point. So you have 15 months of data on some of these use cases. So if you were to think about the sweet spot of customer, let's say, 200 employees through to 2,000 where historically you've been able to take share from Salesforce. Based on the functionality you already have, and what you can see from your market intelligence, what you're hearing from customers, it sounds like you already have essentially the same functionality as what a Qadforce would offer. Is that connecting the dots too explicitly?

I mean we've had that. So I can't speak about a competitor product, and I'm not going to do a tear down of a product. But I will say that connectors do something very simple. They take the skills within your application and make it available to a new interface, and that is exactly what we have had. We have thousands and hundreds of thousands of users that are using it. And so I know that in every one of the conversations I've had today, I've gotten this question, I'm like, let me give you the data over the last 15 months of what we have seen because our product is generally available and being used every single day.

I would love to pick up on the conversation you and I started having on the earnings call and look, there is a lot of different things happening in terms of buying cycles right now, experimentation, et cetera. And there's a lot that HubSpot has already done to drive outcomes in this new business environment. If you were to put on your prognostication hat for the next 6, 12 months, how long do you think we're going to be in this type of environment that feels like it's pulling teeth a little bit versus when the momentum actually starts to compound?

Yes. That's a very difficult question to answer because I don't think that anybody here has like a crystal ball. But what I will say looking across our customer base is that we have seen some patterns of adoption. There's -- I'd say, early adopters within the customer base that are driving adoption from experimentation to full on scale. They turn on every agent, HubSpot delivers. They build custom agents on top of HubSpot's platform, and they're consistently beginning to use HubSpot. And so there's a whole portion of the customer base that is early adopters. I'd say there are what I would refer to from the technology adoption cycle as early majority and late majority. Now those customers are a bit more cautious, and they are experimenting. They are turning pilots on, and they will continue to move into kind of the spectrum of large-scale AI adoption, but it will take time, right? It's not even across the industries that we serve, and it's not even across the segment of customers that we serve. So as we look at it, and this is what happens during every platform shift. Across every platform shift, you don't get uniform distribution day 1 across your entire customer base. You do get pockets that become early adopters and then you get pockets that are waiting to see where it goes. And so our job is to look at that majority of customers that are cautious adopters and say, what's the friction point? What are the barriers in terms of large-scale adoption of AI within the segment that we serve? And it comes down to a few things. Number one is data. If they have the data consolidated in one place, if they have really high-quality data, then they are faster adopters of AI. So we spend a lot of time with our partner ecosystem to drive that type of data consolidation and cleanliness within the data. The second barrier is really the comfort with the new technology in the platform. Is this going to work in my environment is the #1 question we get. This is exactly why we took a number of deliberate actions to drive trials and then to seed those use cases so that the comfort level increases. And then once we've been exposed to agentic use cases, the third question is, is my cost going to be predictable? And can I get visibility and control over the spend? And there, again, we're driving a lot of customer-friendly policies. Every customer of ours can set thresholds and they can do it by use case, by the agent use case, and they can track their credit spend across their different use cases. And we have made it pretty friendly to really look at the outcomes that we are delivering to the cost and tying that as close as possible to that. So I think every cycle is going to go through this process. And our job is to help navigate the customers, look at the barriers and remove the friction in terms of AI adoption.

We're big believers in this idea of taking AI and applying it to business-specific context. I would love to understand a little bit of the nuts and bolts on how you do that. What is your internal AI strategy? How do you think about mix of frontier model versus open source model? How do you think about applying SLM to context data in a way that is not a headwind to gross margin long term?

Yes. That's a great question. So just to peel back the question, how are we thinking about the internal LLM usage? And how does that impact the gross margin of our business? I will say that we've talked about our platform as being an advantage for many years now. And you've asked me many questions about primary colors within platform and how we actually leverage.

I have them here.

You do. And I will bring that back into context, Gabriela, because one of the things about HubSpot is that we have a platform-centric approach to building. And what we have done in the last 3 years is we have created an internal agentic harness for go-to-market context. What does that mean? Lots of words there. I mean, context, everybody understands. It is what is specific about a customer's company, business, their end customers as well as their teams. That is the context graph that we can construct for each of our customers. What we have then been able to do is to actually take the agentic coding available outside and create an internal go-to-market harness that becomes our infrastructure. That means the evaluation loops that we run are much more efficient than off-the-shelf agentic coding platforms that you can build. That has our libraries, our skills, our connectors that is optimized for our developer environment. And the reason this type of a platform approach is important is that we can be the Switzerland of AI, and we are the Switzerland across LLMs. What that means is for a specific use case, we can optimize the use of an LLM. We might decide that a content use case, there is a certain LLM that performs better, and we are able to switch across LLMs. And the way we do that is to focus on 2 things. What is the best cost optimized approach and what is the best outcome optimized approach. So we look at 2 things across each of the use cases, and we're able to switch. The reason that's important is also why we can optimize gross margins, right? Because we have the infrastructure and we have the go-to-market harness that can go across LLMs. We're now able to optimize in terms of gross margins. For example, we've switched to open weight models, and that has brought the cost down across multiple use cases while keeping the quality of the outcomes high. In the past 6 weeks, there are a number of models that are now at 80% of the cost of other inference models, and we've been able to switch that. And so I think the industry is still very early in the cost optimization phase across LLMs, but we have now the capabilities and the neutrality across the LLMs to be able to drive that. And that helps us to really change the gross margin trajectory of the business, even though, of course, everybody is going to have some level of pressure because of how the transformation works within AI.

Any clues you can give us into INBOUND a (sic) [ UNBOUND ] next week that we should be paying attention to?

Well, it is going to be exciting. As many of you know, next week is our conference. We call it UNBOUND, because we've gone to the new era from SEO all the way here. And so yes, it is exciting. It's fully sold out, 15,000 customers. And of course, we have an Analyst Day. And there are probably a few areas. I think in terms of our product we talked about the intelligence layer, the action layer as well as the interface layer. And we are clearly driving that vision of being a system of context as well as system of agentic action. So a number of launches that furthers that vision and accelerates that vision. We talked a little bit about how marketing is changing for B2B. And we think that we have a huge opportunity ahead in terms of being the omnichannel marketing platform for our customers. And so we'll share vision in terms of how we are accelerating that. And of course, things are changing from a core CRM and how users get to interact with CRM. So we'll talk about that. So yes, it will be action-packed.

I still remember your Dario interview from last year. Yamini, it's always a pleasure. Please join me in thanking Yamini for her time.

Thank you so much.