WEBVTT
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Welcome to Pricing Page Unpacked.
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I'm Rob Litterst and I'm joined by Ulrich Lirkoff Schmidt, pricing expert and CEO of Willingness to Pay.
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Each week we take a real company's pricing page and break it down the decisions behind it, the trade-offs, and what it tells us about how the company actually wants to grow.
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No slides, no scripts, just a real conversation between two friends who live and breathe pricing.
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Let's dive in.
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I'm excited for this one today, Ulrich, because we are talking about the poster child for outcome-based pricing.
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This company, I don't want to say that they were at risk of being a has been.
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I think their CEO kind of already telegraphed that in his blog post a couple of weeks back.
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Intercom has completely reinvented their platform.
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And as the kind of first mover towards outcome-based pricing, they have become kind of the poster child for where you can go with AI pricing and how you can kind of price the work rather than the software that does the work, right?
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And I think people have been extremely excited about intercom and their pricing over the last couple of years.
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I think it's they're probably one of the most referenced products in SaaS right now, just because of this change that they've made.
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I personally have probably written about Intercom's pricing three or four times.
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And I can tell you, like before that, I don't think I ever wrote about intercom's pricing.
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So that what they've done with Finn, I think, is nothing short of kind of completely rebuilding the company from first principles and really committing to going AI native.
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And it's super impressive.
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A few notes about Intercom before we get into it.
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So they were founded in 2011.
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They have a customer service platform with unified customer messaging and support.
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They have a messenger, an inbox or help desk, a help center, automation and chatbots, uh, outbound messaging, customer data platform, and importantly, an AI agent called Fin, which has basically become kind of like the new well, we'll get to it in a second, but they're they're more or less rebranding the entire company around their AI agent.
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They have over 25,000 customers used widely by SaaS companies, FinTech, and e-com companies for customer support and engagement.
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They are at about 400 million in annual revenue right now.
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And we'll get into the more the specifics there.
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Uh, they are a private company and they have around a thousand employees, I believe.
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Again, traditionally a seat-based license model, typical of most customer support platforms.
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Um, but what they've done with Fin's AI agent is they started out charging kind of a flat 99 cent per resolution rate.
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It really shifted to outcome-based pricing and have become kind of the the go-to AI agent for customer support.
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Like it's interesting to me why this is such a big deal.
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Like I think I think they sort of sort of like maybe that's just me being, you know, isn't it obvious?
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Like so there's one of these where like they, I think also Intercom has had like, but remind me if I'm wrong, like they've also had some sort of pricing based on list size.
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Like how many customers you actually had.
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I think there's one of these where you can price on many different metrics.
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Number of service agents or users is one, number of customers is another, one of the number of customer conversations is a third, and then you can have like resolved conversations at the end, which is ultimately what you want, right?
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So along with this sort of and and all of it just exists in the universe to just deal with customers.
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So like the job to be done for something like Intercom is is very straightforward.
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Like we are selling something, and for whatever reason our customers want to talk to us, and that's a problem.
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So we just have to deal with that part, right?
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So, and then obviously we want it, we want the resolutions to be good in the sense that either we want, you know, um complaints to be low or net promoter score to be high or refunds to be low or whatever it is, right?
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And then if it's gonna cost us money, we want it to cost as least as possible.
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And if we can get revenue out of it, we want as much as possible.
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So I think it's one of these where because of the domain that they're operating in, it's such an easy piece of math because it is, it's so it's uh it's so narrow a slice of the world where if you do something broader and more, let's say, harder to measure, like we talked about HopSpot last time.
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So it's like all marketing, automation, CRM, everything all in once.
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It's like, okay, I guess you could like you could measure something on revenue, but there are so many different aspects of it, not all of them inside of HopSpot, right?
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But with Instagram, it's like, okay, we own the entire process end-to-end, and you can sort of measure the inputs and we can sort of show you the outputs as well.
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Done.
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And then they just hook pricing instead of at the beginning with users, we're just gonna move the slider all the way to the right and just say, hey, we're just gonna price based on the outcome, which is the resolved uh conversation.
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And I think that makes sense, right?
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Um you and even if you look at the way that like uh call centers have worked, many of them have actually priced based on resolved conversations.
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So if you have like an outsourced call center that does something, like if you do sales and you go like, hey, we're gonna, we don't want to own the BDR process ourselves, we've got to hire someone to do the meetings for us.
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You pay per meeting booked.
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Right.
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Like even though it's humans and it's slower, like, but you still paid for the outcome.
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And then if they were good or bad at it, that was sort of on them, but you just paid for whatever actually was like got out on the other end.
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So it's not like it's a new idea, it's just that the idea just worked way better when because of AI and and sort of the tech progress, suddenly the metric that they were doing was suddenly being compressed massively, right?
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And I I was speaking to another, that sort of can't name them, but an operator of call centers that and they have like a lot of global customers, they do maybe a quarter billion of revenue in call centers, and they were pricing based on essentially an hourly rate on their operators.
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And the their problem was that a lot of AI actually makes the operators way more efficient, so suddenly they don't have to spend as much time.
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So they were faced with a similar conundrum as I'm sure Intercom was faced with, which is if we lean into the tech, suddenly the main driver of our pricing is gonna be reduced drastically.
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Right.
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So if we lean into the tech and we reduce the time needed from operators, because that's how we price, suddenly instead of like a quarter billion, we're just gonna make a hundred million.
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And that's obviously bad.
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So we're delivering more value for like a lower price, like doesn't match, right?
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It's perverse incentives, yeah.
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So then the pricing needs to change.
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Yeah.
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Yeah.
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So I think, and as so I think it's one of these where I think the reason that Insigom is so is such a poster child is that it's not so much that their solution is original and fancy, it's that everybody has this problem.
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Just like the pain is common a hundred percent.
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I I think and I think like to your point, I think customer support is one of those things that no company like loves doing, right?
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It's it's it's typically a job that you know is is very kind of menial.
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You're you're kind of like responding to people.
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If you plotted a histogram of all of the support tickets, you're gonna find that like a lot of them are the same, right?
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So it's it's like ideal, it's like fertile ground for AI to just take it over, right?
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Because it's it's gonna be doing a lot of the same jobs over and over again.
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And it's also something that humans have no problem handing off to an AI agent, right?
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Like most humans don't want to do this work.
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And I think like at that intersection where you have repeatable work that is very common and work that humans don't want to do, you are going to, it is naturally gonna gravitate towards AI agents taking that over.
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And that's what's happening in customer support.
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And I think Intercom was kind of the first company to like really go all in on that, um, at least in the kind of like SaaS tech space.
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Owen wrote a blog post on March 2nd, uh 2026, titled There's Exactly One Way That SaaS Can Be Saved.
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And it basically talks through Intercom's growth challenges, their kind of history of why they went all in on their AI agent, which they named Finn.
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And I think the the biggest takeaway from this entire blog post is just this chart that he posted with um the SAS, the kind of I guess median or average SaaS growth rate um quarter over quarter, going from Q4 2025.
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Yeah.
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And it shows and it maps out kind of like the median growth rate, and then it maps out intercom's growth rate.
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And the really interesting thing here um is oh Owen took it took some time away from Intercom, I believe, for for health challenges.
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And he stepped back into the fold um between Q4 2022 and Q1 2023.
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And when he stepped back into the fold, they were already going through um this process where their growth rates were declining pretty rapidly.
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And to paint a picture for people who can't see this, in Q1 of 2022, they grew 24% year over year.
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In Q2, they were down to 18% year over year.
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By Q4, they were down to 12%, 12% year over year.
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And then it keeps going down from there.
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So in Owen's first year back as CEO, their growth rate dropped to 4% year over year.
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Uh down to U3 at 37%, like a year and a half earlier.
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And this is compared to markets that are doing like 18, 20%.
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So this is where like internally, what happens here is that so you're you're gonna face a lot of board pressure.
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This is this is not the target.
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So you're not you're not at like 24% growth rate and then projecting that it's gonna drop to 4% in a year.
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So what's gonna happen is that you say, hey, we're gonna be at 24% now, then we're gonna get a week, like I know we were at 30 last year, so we're gonna get back to 30.
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So the the way that the board, the boardroom has looked is like, okay, we're gonna we're gonna grow like 35% next year.
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And then what happens is it just tanks down to four.
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And then that means that everybody knows that this is bad.
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Yeah.
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And then, but nobody knows how to fix it, right?
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And then one of the ways is like, oh, we'll just not sleep, we'll just work harder, whatever.
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And we'll like so, and then the sort of shit rolls downhill.
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Like the idea is like everybody suddenly becomes like in crisis mode.
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Everybody feels that they're doing everything wrong because it isn't working.
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So this is sort of a real sort of leadership crisis where you have to sort of figure out, okay, so is this the point where we just like we get back to basics and we just do everything a little better, and then we just incrementally improve the process we have, and that's really what's wrong.
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It's just like we got lazy, right?
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Or is it the time to truly come up with something new?
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And that's really the story that he's telling.
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He's saying, hey, we did something new.
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The fact that they dropped to 4% might actually have been what allowed him to get it through the boardroom.
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Like if he had followed like just a naturally declining slope of growth, like similar to the market, the boardroom might have been, yeah, you know, let's just like not do the drastic thing.
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Let's just like keep our model, it's work pretty good.
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Like we're still growing 15%, whatever.
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But because it's so obviously going wrong, like it's like, all right, like every every option is on the table.
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Who has great ideas?
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Like, let's let's give this like going another direction.
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You know, the saying, like, don't let a good crisis go to waste.
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That's a little bit like a really good crisis.
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And that also means that he gets to like swing for the fences and do something like radically new.
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And then that becomes let's the model is wrong.
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Let's do thin.
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Let's change the the the the essentially the business model and our relationship to our customs.
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Uh yeah, exact.
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I think you put that perfectly.
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Uh I'm gonna read from the post real quick because he says a couple of things that just drive home exactly what you just said.
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So Owen says, we had a lot in our favor that got us out ahead of the journey that everyone else must now take.
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For one, we were desperate for new growth.
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We were performing far worse than average.
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We had less to lose.
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That makes hard decisions less scary.
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Beyond that, we had a CEO change.
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It's far easier for someone new to come in and say they don't like how the last guy did things and ripped everything up.
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I think those two factors, to your point, are enormous here because it's a you have a new CEO, you have new leadership, it's really easy to kind of like turn the team around onto this new, this new focus.
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And secondly, the numbers obviously are not matching up.
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And if you can see this chart, uh when they were growing 4% year over year, that's that's 15% less than the median SaaS company.
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So the the typical SaaS company is growing 19% year over year.
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And then if you fast forward, and what we're gonna get into is kind of what they did and and how they're pricing Finn AI.
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But by by what is this, Q1 2026, the median SaaS company is growing 11% year over year.
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So it's down from 19%, and intercom grew 26% year over year.
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So they completely back up to uh to to 22.
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So 24.
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So yeah, I think I did a pretty comprehensive like mystery shopping exercise across like customer chatbots, I think around two years ago, so beginning of 2024.
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So this is at the sort of the bottom of where Intercom sort of bottomed out of like four or five percent growth.
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Yeah.
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So what we did was we basically mystery shopped within 10 different sort of competitors in the space because we had a client that was that was in there in that space.
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And then so what we would see was that everybody had started to sort of price, like fundamentally, like some form of like token-based pricing, like like hey, we have tokens at the back end, but like we're gonna price per conversation or per resolved conversation, or the different sort of versions of that.
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So we could just see that a lot of new players had just emerged.
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So I think what we'll see is that as intercom's growth is dropping, it's sort of like the inverse of that is that the number of startups that is trying to solve this with AI.
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So what I'm wondering is like, is Intercom's price dropping?
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Maybe because number of users is declining.
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So net revenue retention is actually our renewals are harder.
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Like, hey, you had a thousand seats last year, how many do you want next year?
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Like 800.
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Oh, okay.
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It's it's hard for me to get more money out of you.
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I actually have to like, I have to fight for the status quo, right?
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So suddenly what might have been a good net revenue retention rate suddenly becomes like negative, not because customers are churning, but because you have contraction, right?
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And so if you're fighting contraction, and then when you lean into like who's gonna buy like a traditional sort of seat-based customer support solution if everybody knows that AI is gonna do this in the like maybe not right now, but then at least next year, right?
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So that also means that new sales just becomes harder.
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So they have these sort of like dual headwinds from AI, and I think that's what's really gonna like drive it into the ground here, right?
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For context, I remember that a lot of the resolved conversations, like the price range for resolved conversations were between, not resolved, but conversations in general, but between like 50 cents to like two, three dollars.
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And I did another study for one of the largest system integrators in Europe.
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They do like well over a couple billion and they do a lot of actually a lot of like service help desks for larger companies.
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And they did a lot of outsized, uh outsourced service desks.
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And they were saying, hey, if we run the service desk in like with a European operator, the price for a ticket is is like 28, 29 euros.
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Like, so I call in for service desk, the actual cost is 29 euros.
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So what was that?
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35 bucks, something like that.
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If we do it with like an Eastern European cheap labor, like offshore Indian, whatever, like cheap labor, it drops to like six to eight bucks, something like that.
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Wow.
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So it's like if we do the cheapest possible labor that we think we can get away with, the price is still three to ten times larger than what the automated solutions were doing at that point in time.
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Yeah.
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So that's where when pricing is this like with the gap is this big in the market, that's where people, at least someone in the market, is like, I don't care that the quality is not 100% the same.
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We're gonna go with the cheap option, right?
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Yeah.
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Because for many, and we actually did this also where we had customers that like online gaming websites and a lot of these others, which were we don't have customer support because if we did, we would get like 20 million tickets a day.
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Like just doesn't work.
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But with AI, we certainly can get customer support.
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Like it's actually something we can add to the business, which we never had before because it just wasn't economically viable.
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Because, you know, eight bucks, thirty bucks with an operator model, suddenly with an automated AI model, we can actually offer this.
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So it actually opened up an entirely new market of customers that weren't there before.
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That's amazing.
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Yeah, and uh when you when you look at it like that and you kind of start to compare those price points, it starts to make a lot of sense.
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Why people, why there's such an appetite for 99 cents per resolution, which is what I think Intercom ended up going with.
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Yeah, I think it's so so it's also interesting that it's 99 cents.
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Yeah, like like an iTunes song, right?
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So it's like, hey, like not a dollar.
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Right.
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I wrote a book on pricing psychology maybe eight years ago.
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Uh and uh the number one question I got all the time was does it work B2B?
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I was like, of course it does.
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And you're just like like here, you have what are they doing, 400 million?
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And they're doing it on 99 cents.
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It's like 80%.
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Would they do 404 million if they did one dollar?
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Right.
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Who knows?
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But we're we're not making that bet.
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Like we're sitting at 99.
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For sure.
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100%.
00:17:01.840 --> 00:17:18.240
And I mean, like, even just looking at we we talked about HubSpot last time, and HubSpot's got that classic good, better, best, and it's like the compromise effect every time, but uh drive drive people to the middle, and I think like, you know, at least like 75% of SaaS companies use that strategy of like three packages, you know, try to drive people towards the middle one.
00:17:18.319 --> 00:17:29.519
The last thing to note here on Owen's blog post, so they are now at$400 million in ARR overall, with Finn about to pass$100 million itself, which I think is really, really interesting.
00:17:29.680 --> 00:17:31.759
I I forget who I saw post this.
00:17:31.839 --> 00:17:41.119
I think it might have been Jake Saper, uh venture capitalist from Emergence, but I think he said something about like hearing whispers that intercom might actually roll Finn out as its own company.
00:17:41.200 --> 00:17:43.680
And they've actually done some of like the groundwork on this.
00:17:43.759 --> 00:17:48.400
And they they have like an intercom specific pricing page, which I'll jump to right now.
00:17:48.559 --> 00:17:59.440
So if you're if you're not watching, um, we're looking at the intercom pricing page where you can see they're more conventional um plans from from their kind of traditional help desk, right?
00:17:59.599 --> 00:18:05.680
So they have four different plans, um, essential advanced expert and then fin AI agent.
00:18:05.839 --> 00:18:11.440
And then for essential advanced expert, you can actually layer uh fin's outcome-based pricing on top.
00:18:11.519 --> 00:18:14.240
They do a really good job of kind of making fin accessible.
00:18:14.319 --> 00:18:28.799
And then the other thing that I think is really, really interesting is up top they have a million dollar guarantee for Fin, which basically says if you sign up for Fin and are not 100% satisfied in your first 90 days, we will give you up to a million dollars of your money back.
00:18:29.039 --> 00:18:29.920
No questions asked.
00:18:30.160 --> 00:18:31.039
Really, really wild.
00:18:31.279 --> 00:18:38.079
And I think like that kind of addresses the biggest concern that people have with an agentic customer support platform is does it actually do a good job?
00:18:38.160 --> 00:18:38.880
Is it high quality?
00:18:38.960 --> 00:18:40.319
Will it actually resolve these tickets?
00:18:40.400 --> 00:18:42.079
They're kind of putting their money where their mouth is there.
00:18:42.319 --> 00:18:44.880
So they so they actually change this guarantee.
00:18:45.039 --> 00:18:45.440
Oh, let's say.
00:18:45.599 --> 00:18:47.359
I'm a big fan of guarantees.
00:18:47.680 --> 00:18:53.359
So in the original guarantee, the guarantee was that they would give you a million dollars if it didn't resolve.
00:18:53.440 --> 00:18:55.759
I can't remember what it was, like it is 50% of tickets.
00:18:55.839 --> 00:18:57.279
That was like a relatively low number.
00:18:57.440 --> 00:18:58.880
So now they don't do that.
00:18:59.039 --> 00:19:03.759
They give you a million dollars of your own money back up to Got it, got it.
00:19:04.000 --> 00:19:05.039
Big difference, right?
00:19:05.200 --> 00:19:09.039
Somebody came in with like, okay, I'm gonna like they can't help my customers.
00:19:09.759 --> 00:19:12.799
But I think actually, so they they I talked about that guarantee.
00:19:13.039 --> 00:19:17.839
And it was uh having looked at a lot of these data sets, it's like, of course you can resolve 50%.
00:19:18.160 --> 00:19:20.000
Like that's absolutely doable.
00:19:20.400 --> 00:19:29.519
So more more likely, like even with like initial solutions, you you usually resolve around 80% because most customer questions are like super simple.
00:19:29.599 --> 00:19:38.400
So if you're hooked up to like a relatively good database and you have like good data to train it on, yeah, 80% resolution is I guess that's a pretty sort of like low bar.
00:19:38.559 --> 00:19:43.519
So 50% resolution is almost like guarantee that they're never gonna pay it out, right?
00:19:43.599 --> 00:19:55.200
But maybe they did or legal got a hold of them and said, you know, we can't have like the liability of just having the potential of having to pay out a million for every customer is just like not something that we're okay with.
00:19:55.440 --> 00:20:05.200
I think something else that's going on on this pricing page is interesting, which is that the way that the position, so the first three uh tier series of essential advanced experts per says includes Fin AI agent.
00:20:05.440 --> 00:20:11.359
And then the last one, the Fin AI agent, as a standalone, says already have a help desk question mark.
00:20:11.519 --> 00:20:17.440
So what they're actually doing is they say, hey, what we found is like the old solution, we had to replace existing solutions.
00:20:17.599 --> 00:20:22.000
So we're fighting with, oh, we already have a provider and the contract runs for another two years, all that.
00:20:22.079 --> 00:20:28.720
And then we're saying, hey, no problem, you can just like latch on our AI agent on top of the thing you already have.
00:20:28.880 --> 00:20:31.119
The idea is that you actually sort of shift cost.
00:20:31.440 --> 00:20:40.400
So if I already run a help desk and I run a million tickets and I do them at, let's say, the outsource price of five bucks a tick, I have a five million dollar cost for my help desk.
00:20:40.480 --> 00:20:49.279
And then Fin AI comes in and says, hey, how about you just take all that flow and route it through us, and then we'll solve 800,000 of those, and then you're gonna pay 800K.
00:20:49.359 --> 00:20:54.319
And then it's only like the other 200k that you need to then get to your team.
00:20:54.400 --> 00:21:00.160
So you can fire like 80% of the team, keep the good ones, the ones you like, and they can then resolve the rest.
00:21:00.240 --> 00:21:03.039
And then blended cost becomes 1.8 million.
00:21:03.119 --> 00:21:04.000
That's the value proposition.
00:21:04.079 --> 00:21:06.079
And then people are like, oh, great, we'll do that.
00:21:06.240 --> 00:21:10.720
And then when the original contract runs out, then you can come in with your other plan.
00:21:10.799 --> 00:21:21.839
So the the original Instacon product becomes like the chaser that gets that gets sold like when the original other contract runs out, but the thin AI agent becomes something that could get sold today.
00:21:22.000 --> 00:21:22.559
I love this.
00:21:22.720 --> 00:21:28.559
I it honestly it this is kind of a weird parallel, but it reminds me of Shopify.
00:21:28.640 --> 00:21:34.880
Like Shopify has this SaaS revenue for when you are using the Shopify platform for your online store.
00:21:34.960 --> 00:21:39.920
But then they have all this merchant solutions revenue from when people actually process payments through Shopify.
00:21:40.000 --> 00:21:43.759
So they have kind of like the system of record more or less, and then this like system of action.
00:21:43.920 --> 00:21:48.079
And they launch this Shopi button that you can just drop on any site.
00:21:48.160 --> 00:21:57.359
And you don't need to be, you don't need to be using your hosting your store on Shopify, but you can still drop this shop pay button on your site and it allows them to generate some revenue there.
00:21:57.440 --> 00:21:59.839
And it's a way for them to kind of broaden their ecosystem.
00:22:00.079 --> 00:22:00.240
System.
00:22:00.319 --> 00:22:03.599
And I think what Intercom is doing here with Fin is kind of similar to that, right?
00:22:03.680 --> 00:22:07.599
It's like they have this system of record, which is their traditional business, their traditional help desk solution.
00:22:07.680 --> 00:22:19.119
The Fin AI agent allows them to plug into other companies, expand their ecosystem, get in front of other customers without having to do a complete rip and replace, which to your point is brutal and not something that everybody always wants to do.
00:22:19.200 --> 00:22:21.680
But it allows them to prove themselves with their agent.
00:22:21.839 --> 00:22:24.400
So when that removal comes up, it makes the case a lot easier.
00:22:24.480 --> 00:22:24.960
It's genius.
00:22:25.119 --> 00:22:25.279
Yeah.
00:22:25.440 --> 00:22:32.000
And if you see like the first feature they have in the fin AI agent is set up in under an hour on your current help desk.
00:22:32.160 --> 00:22:33.279
Like that's aggressive.
00:22:33.440 --> 00:22:36.880
Like you don't want to be, you don't want to be the current help desk.
00:22:37.119 --> 00:22:37.519
Right.
00:22:37.759 --> 00:22:40.480
Like then that's just like the truth, right?
00:22:40.559 --> 00:22:48.240
So so it's also one of these where that's a like from a packaging point of view, it's a brilliant move because you're using it sort of as a Trojan horse.
00:22:48.400 --> 00:22:53.279
Because like the other alternative would be, hey, buy our thing, it has the AI agent.
00:22:53.440 --> 00:22:56.480
And then people are like, ah, yeah, okay, but I have this current thing that I also like.
00:22:56.559 --> 00:23:02.720
And now it's just like, hey, have your current thing, but just like take our AI thing on top of that and then see what happens.
00:23:02.880 --> 00:23:04.480
And then you only pay if it works.
00:23:04.559 --> 00:23:06.000
And it takes just an hour to set in.
00:23:06.079 --> 00:23:07.359
So like, how easy is that to sell?
00:23:07.440 --> 00:23:11.759
And you even have a 14-day free trial just to like twist the knife, right?
00:23:11.920 --> 00:23:12.160
Right.
00:23:12.319 --> 00:23:13.119
So right.
00:23:13.359 --> 00:23:20.400
As opposed to like who wants like a full like help desk support setup with a like, I'm not going to trial that in 40 days.
00:23:20.480 --> 00:23:23.680
I have to train people, like they need to have user roles, like all that.
00:23:23.839 --> 00:23:33.200
Like, so the the other thing is just like it's so much easier to just like slide in right on top of the tech stack and then just try to sort of skim all the easy conversations off of there.
00:23:33.440 --> 00:23:33.839
It's awesome.
00:23:33.920 --> 00:23:42.799
Yeah, they they've definitely iterated a bunch on this pricing, but I I love where they're at right now, kind of allowing people to add it on top of their existing plan, add it to an existing help desk.
00:23:42.960 --> 00:23:48.240
And then you also see they have their own website for Finn, which is really interesting to me.
00:23:48.319 --> 00:23:54.079
And like I think the contrast, for those that can't see this, the um the contrast in pricing pages, I think is notable.
00:23:54.160 --> 00:23:58.319
They they the traditional intercom pricing page has kind of this beige background.
00:23:58.400 --> 00:24:06.640
It's very kind of like standard SaaS pricing page, nothing spicy, nothing like super uh eye-catching or eye-popping.
00:24:06.799 --> 00:24:17.359
You go to the fin AI pricing page, and there's kind of like this galactic theme where everything looks futuristic and kind of have like star like constellations in the background.
00:24:17.440 --> 00:24:25.680
And it's uh it's pretty clear that they're like kind of drawing this line between um intercom proper and and fin AI.
00:24:26.160 --> 00:24:35.839
And this is really interesting to me because they they they just they seem like they're kind of pushing towards kind of going all in with Finn and putting all their eggs in that basket, which I think makes a lot of sense.
00:24:36.079 --> 00:24:40.880
And actually interesting here, it's they're saying it's 99 cents per outcome.
00:24:41.119 --> 00:24:48.319
And so just you're using the word outcome as opposed to resolved customer conversation, which is, I guess, the outcome.
00:24:48.480 --> 00:24:53.680
So it's like so they have two, like they have resolved conversation and then like executes a procedure.
00:24:53.839 --> 00:25:01.920
So when your the original like chat market was like can the system just talk to the customer or can it also do things like issue and refund?
00:25:02.000 --> 00:25:05.119
Yeah, or like reroute a package or like restart a system.
00:25:05.279 --> 00:25:11.759
So I did a lot of these also with um in like IoT universes where people would do like uh electrical charges for cars.
00:25:11.839 --> 00:25:15.440
So it's like, can I call in and have the chat but like reset the charger if it doesn't work?
00:25:15.519 --> 00:25:16.160
So things like that.
00:25:16.319 --> 00:25:22.880
And there actually was like it was like an advanced feature that like the systems didn't get ready to do this until like 2025, really.
00:25:23.039 --> 00:25:26.079
So before that, they were mostly like, we'll just talk, right?
00:25:26.400 --> 00:25:37.759
But if we and then maybe we'll perform like simple actions, but anything that let's say requires security on money in any way or access to physical installations, we can't do it, right?
00:25:37.920 --> 00:25:41.039
So I think just having that extra part there.
00:25:41.200 --> 00:25:45.519
And actually, also it interestingly allows them to double dip, right?
00:25:45.759 --> 00:25:49.680
Because you can resolve a conversation by executing a procedure.
00:25:49.759 --> 00:25:50.079
Yes.
00:25:50.240 --> 00:25:52.799
Like I returned the package and it is now resolved.
00:25:52.960 --> 00:25:53.359
Done.
00:25:53.599 --> 00:25:55.039
198, right?
00:25:55.200 --> 00:25:55.440
Yeah.
00:25:55.599 --> 00:25:57.920
That what I think is super, super interesting about this too.
00:25:58.000 --> 00:26:02.240
Like we've talked about this a bunch with credits and even last week with HubSpot.
00:26:02.480 --> 00:26:05.359
But we we've talked about credits and outcome-based pricing.
00:26:05.440 --> 00:26:15.359
And I think some of the most important things with both of those are really defining what the customer is getting and making it very, very clear, like what they will be charged for.
00:26:15.519 --> 00:26:27.440
And I think one of the hurdles that a lot of companies have with credits is they have like a very janky credit model or a credit model that or a very janky credit table where it's not totally clear like how much you're paying and like what a credit is worth.
00:26:27.599 --> 00:26:37.519
And I think, especially with outcome-based pricing, one of the hardest things is having like agreed upon outcomes with your customers that you can actually attribute success to from your platform.
00:26:37.599 --> 00:26:37.759
Right.
00:26:37.920 --> 00:26:54.799
And so Intercom comes right out and says this for the people that can't see this, there's like one of those little, there's a there's a little clickable icon right next to per outcome on the fin pricing page that you can click on and it shows you that an outcome is counted when Finn resolves a conversation, as Ulrich was saying, or Finn executes a procedure.
00:26:54.880 --> 00:26:56.960
And then there's an asterisk next to procedure.
00:26:57.039 --> 00:27:01.359
This just kind of like illustrates how complicated all this stuff is and why you need to go to this level of detail.
00:27:01.599 --> 00:27:05.359
So a procedure applies only to customers using procedures.
00:27:05.519 --> 00:27:11.039
Procedures train fin to handle complex multi-step customer queries like subscription issues or refunds.
00:27:11.200 --> 00:27:16.640
And they call this out, I think, because people probably come to this page and they're like, well, what about the stuff that's not super easy?
00:27:16.720 --> 00:27:19.440
It's not just like a one-step answer from our knowledge base.
00:27:19.599 --> 00:27:22.160
What about the stuff that actually requires a little bit of back and forth?
00:27:22.240 --> 00:27:23.839
Like, can Finn actually help me do that?
00:27:24.000 --> 00:27:30.880
And they've kind of made clear with this little pop-up bubble, then they have this thing called procedures that helps you work through some of the more complicated issues.
00:27:30.960 --> 00:27:33.279
So I think that's super interesting and super smart.
00:27:33.359 --> 00:27:40.319
I think the better like communications and like how you actually show this stuff, I think is is way more important than most people realize.
00:27:40.480 --> 00:27:40.799
For sure.
00:27:40.960 --> 00:27:48.319
And and so what I'm wondering with with something like Intercom and the pricing model they have here is like where do they go from here?
00:27:48.480 --> 00:27:51.119
You fundamentally sort of have like a couple bets.
00:27:51.200 --> 00:28:00.720
So one is that as I said, like, hey, more people are gonna start using like having help desks because before they were not economically viable and now they are, so let's do that.
00:28:00.880 --> 00:28:10.240
So it's like it's one of these where uh where if the price of gas drops, total revenue of gas goes up because more people take trips that they wouldn't otherwise have had, right?
00:28:10.319 --> 00:28:11.839
So like a Yemon's paradox.
00:28:12.000 --> 00:28:29.119
But then ultimately, what will like the conversation that's gonna happen inside of Instacom and what happens in like because and I know because it happens in all of these sort of like chat companies uh and help this companies is that that there is a very uneven distribution of value between the resolved conversations.
00:28:29.519 --> 00:28:29.759
Right.
00:28:29.920 --> 00:28:43.119
So let's say that you run like a private bank for high net worth individuals, and you might have whatever, a thousand conversations a year, but each of those is why is with like a billionaire that wants something done right now, right?
00:28:43.359 --> 00:28:49.519
So those conversations are also only gonna cost 99 cents if I use Fin to resolve them, right?
00:28:49.680 --> 00:28:55.839
And then other like the gaming website that have 10 million things a day, they are extremely low value.
00:28:56.000 --> 00:29:02.640
So you're gonna have situations where at the extreme, it might even not even be worth a dollar for me to solve it.
00:29:02.799 --> 00:29:06.079
So let's say why doesn't Wikipedia have Fin AI?
00:29:06.240 --> 00:29:14.880
Well, because it doesn't make sense for Wikipedia to pay a dollar for people to have questions answered, because like that's a lot of dollars for Wikipedia and they have no value out of it.
00:29:14.960 --> 00:29:15.200
Right.
00:29:15.359 --> 00:29:24.240
So you're just you're gonna say, okay, we're gonna have this event that exists in the world, which is a conversation that gets resolved, and then we're just gonna decide that it's gonna be a dollar.
00:29:24.400 --> 00:29:27.200
And then everything that is above that is still a dollar.
00:29:27.279 --> 00:29:41.440
Even if it's worth$10,000 to the customer or more, we're still gonna charge a dollar because we don't know how to really, in an easy and straightforward way, articulate another model that would also monetize that value.
00:29:41.599 --> 00:29:43.839
So we're just gonna leave that money on the table.
00:29:43.920 --> 00:29:50.079
So the high net worth individual private banking guys, they still pay a dollar because we don't know how to charge those.
00:29:50.240 --> 00:29:55.200
And then anything below a dollar, we've just decided that we're not gonna serve that market, right?
00:29:55.279 --> 00:29:55.519
Yeah.
00:29:55.680 --> 00:30:06.319
So what's gonna happen is that unless you figure out another way to do this, somebody's gonna come in with a very similar product and gonna price it at like 98 cents.
00:30:06.480 --> 00:30:07.119
Yeah.
00:30:07.599 --> 00:30:10.640
And then you're like, ah, okay, are we are we now gonna do this?
00:30:10.799 --> 00:30:14.720
Like, are we now gonna like, okay, 80 cents, 70, 50, whatever.
00:30:14.799 --> 00:30:15.920
And then it just goes down.
00:30:16.000 --> 00:30:21.359
And we we we can sort of see this in sort of many of these sort of commoditized services that they they try to then break this down.
00:30:21.519 --> 00:30:35.359
So as you see more and more enter this kind of space, I think Finn has a head start, and they're like, they're now eating the the call centers, like the operator-led model with humans involved, and they're like, there's a lot of revenue there for sure.
00:30:35.839 --> 00:30:37.599
Way more than the 400 million that we have now.
00:30:37.680 --> 00:30:38.240
So that's great.
00:30:38.400 --> 00:30:44.079
At a certain point, people are gonna say, hey, my gaming website is resolving like 8 million of these conversations a year.
00:30:44.160 --> 00:30:45.359
Uh, that's still a lot of money.
00:30:45.440 --> 00:30:51.680
Like I like, and then if somebody comes in and says, hey, we can do the same thing, we can just do it for half, then we're gonna do that, right?
00:30:51.839 --> 00:30:59.839
And then suddenly you get into a price war, and the price war really sort of just makes it the distance towards the private banking conversation just becomes larger and larger.
00:31:00.000 --> 00:31:00.160
Yeah.
00:31:00.240 --> 00:31:10.480
And that where I think the next level is someone someone is gonna figure out either to verticalize them so that the support agents become really good at certain types of customers or conversations.
00:31:10.640 --> 00:31:11.039
Totally.
00:31:11.200 --> 00:31:14.079
Or we're just gonna see like a commoditization of this.
00:31:14.319 --> 00:31:19.440
So it's one of these where I don't know how like future-proof this model is.
00:31:19.680 --> 00:31:21.839
It works right now, it's probably gonna work in three years.
00:31:22.000 --> 00:31:23.119
This is gonna work in 10.
00:31:23.359 --> 00:31:23.839
Don't know.
00:31:24.000 --> 00:31:24.319
Yeah.
00:31:24.559 --> 00:31:25.039
I love that.
00:31:25.359 --> 00:31:26.720
I love that uh analysis.
00:31:26.799 --> 00:31:33.519
And I think like one area that I like one thing that I was thinking about is kind of how day AI has been pricing.
00:31:33.599 --> 00:31:37.519
They're this AI native CRM started by the guys that started HubSpot CRM.
00:31:37.599 --> 00:31:41.440
And they have this pricing approach where they give away their CRM for free.
00:31:41.599 --> 00:31:46.480
They charge just a monthly license for agents that you can layer on top of the CRM.
00:31:46.559 --> 00:31:52.640
So like they have an agent that does pricing research or that does like prospecting research for like$75 a month.
00:31:52.799 --> 00:31:58.400
They have an agent that does um like post-call follow-up for a certain amount per month.
00:31:58.559 --> 00:32:01.519
And you have like all these different jobs that these agents do.
00:32:01.759 --> 00:32:12.720
And to your point, I wonder if that's kind of like where this goes around like verticalizing these agents or kind of like giving them different jobs and having like a dedicated budget for you know each of these different agents.
00:32:12.880 --> 00:32:14.400
That obviously gets more complicated.
00:32:14.480 --> 00:32:26.000
I think the beauty of where Intercom is right now is they're using that blended model that you talked about, like somewhere between that like gaming company and the kind of high net worth individual, and they're just blending it out, making sure that it works, keeping it simple.
00:32:26.160 --> 00:32:31.039
But to your point, I think it's inevitable that there will be price wars and it's gonna be really fun to see how this evolves.
00:32:31.279 --> 00:32:48.000
So right now, Intercom is at a like because they're replacing a legacy model, operator-led call centers, they're they need their model to be simple, and also they need their model to be like really generous, even when I'm reading the fine print, right?
00:32:48.240 --> 00:32:52.960
So if Thin had like a lot of fine print where like there's a lot of gotchas in the pricing.
00:32:53.039 --> 00:33:02.240
So I sign up and suddenly I'm priced like three times 99 cents for a call because of you know the asterisk of the footnote, suddenly the market pulls back.
00:33:02.400 --> 00:33:09.039
However, when you start winning, they and the growth starts to decline because they've already sort of eaten like the legacy market.
00:33:09.279 --> 00:33:27.039
That's where you can then start to actually do these kinds of things to say, hey, we're now gonna have a model where depending on how many procedures you run and how they're stringed together and how complex they are or what kind of security levels they have, or whatever it is, or we're gonna also price the infrastructure or the API calls and the back, whatever.
00:33:27.119 --> 00:33:31.519
Like you can actually start to build complexity into the model that is less generous.
00:33:31.759 --> 00:33:43.279
And that's where you can say, hey, our enterprise grade thin AI can handle things that your startup can't, and that's why we're pricing in this other way.
00:33:43.440 --> 00:33:51.039
And so, yes, their resolved conversation might be 50 cent, and ours is, let's say, also 50 cent because we're matching that.
00:33:51.200 --> 00:33:56.799
But then we also have this platform fee and this all the infrastructure fees and all the other things that then make up the rest of the pricing.
00:33:56.960 --> 00:34:02.720
And I think that's sort of fundamentally the direction that they want to go in.
00:34:03.039 --> 00:34:07.279
Not now, not 2027, but when growth starts to decline again.
00:34:07.519 --> 00:34:09.679
So, Ulrich, what are we giving intercom?
00:34:10.000 --> 00:34:10.880
Are we buying?
00:34:11.039 --> 00:34:11.840
Are we holding?
00:34:12.079 --> 00:34:12.719
Are we selling?
00:34:13.039 --> 00:34:16.000
I think I'm uh it's a it's a stronghold.
00:34:16.159 --> 00:34:26.000
Like so I I'm really like I think I think I think it's one of these where I I really want to see for something to like we gave HubSpot a buy last week, right?
00:34:26.079 --> 00:34:33.119
Because I think I think what they're fundamentally doing is making it more likely that will they will grow even more in the future, right?
00:34:33.280 --> 00:34:33.440
Yeah.
00:34:33.599 --> 00:34:38.000
And I think what Finn has right now is a is a really good right now model.
00:34:38.079 --> 00:34:43.119
But I'm not sure that it's gonna be a really good like 2028 or 2029 model, right?
00:34:43.360 --> 00:34:52.239
So I think I'd write it, I'd get the most out of it, but I think they do need to innovate something on the model for it to sort of really like secure the market they have.
00:34:52.400 --> 00:34:57.039
So I think they're open to competition down the road uh with the current model they have.
00:34:57.199 --> 00:34:59.840
So I think they've gone they've done a really great job.
00:35:00.000 --> 00:35:06.320
So maybe I'm too not generous, too stingy with the recommendations here, but it but I think it's a whole.
00:35:06.400 --> 00:35:07.199
I think it's a whole.
00:35:07.360 --> 00:35:08.400
Um I like it.
00:35:08.559 --> 00:35:09.039
I like it.
00:35:09.119 --> 00:35:10.320
You're you're a tough creator.
00:35:10.559 --> 00:35:17.679
And I think like, you know, their their pricing, it is hard because it's like this pricing model and this new product literally saved their company, right?
00:35:18.079 --> 00:35:25.119
But to your point, like you can you can forecast out like pretty easily and see that this pricing isn't gonna hold forever.
00:35:25.199 --> 00:35:27.519
And it's unclear kind of where it's going from here.
00:35:27.679 --> 00:35:36.000
I think that I think what we were talking about before with procedures might be kind of like a step in the direction of like where they can go towards more complexity.
00:35:36.079 --> 00:35:38.559
But I'm really excited to follow that and kind of see where it goes.
00:35:38.639 --> 00:35:39.760
I I like the stronghold.
00:35:39.920 --> 00:35:45.280
And I I'm betting you, I'm betting you that they have a lot of like internal discounting going on at volume.
00:35:45.440 --> 00:35:46.000
Oh, for sure.
00:35:46.159 --> 00:35:59.920
So there's a lot of salespeople with different contracts and different like all all sorts of things going on that like so when they when they renew at a certain point, like it's gonna be all the work all over again to renew at and so forth.
00:36:00.000 --> 00:36:03.280
So it's like one of these where scale isn't built into this.
00:36:03.360 --> 00:36:09.199
So I'm paying 99 for the first conversation and 99 for the milling conversation, unless I call sales, right?
00:36:09.280 --> 00:36:16.800
And then so I think they just have there there's a big risk of like operational inefficiencies uh when you're doing it like that.
00:36:16.960 --> 00:36:20.960
So so that's that's part of the reasoning as well for for the stronghold here.
00:36:21.199 --> 00:36:21.519
Love it.
00:36:21.679 --> 00:36:24.000
That's it for this episode of Pricing Page Unpacked.
00:36:24.079 --> 00:36:30.320
If this was useful, subscribe to the podcast and join the discussion on the pricing SaaS community, LinkedIn or YouTube.
00:36:30.559 --> 00:36:34.239
And remember, your pricing page isn't just a page, it's a strategy statement.
00:00:03.279 --> 00:00:04.959
Welcome to Pricing Page Unpacked.
00:00:05.040 --> 00:00:09.839
I'm Rob Litterst and I'm joined by Ulrich Lirkoff Schmidt, pricing expert and CEO of Willingness to Pay.
00:00:09.919 --> 00:00:17.440
Each week we take a real company's pricing page and break it down the decisions behind it, the trade-offs, and what it tells us about how the company actually wants to grow.
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No slides, no scripts, just a real conversation between two friends who live and breathe pricing.
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Let's dive in.
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I'm excited for this one today, Ulrich, because we are talking about the poster child for outcome-based pricing.
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This company, I don't want to say that they were at risk of being a has been.
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I think their CEO kind of already telegraphed that in his blog post a couple of weeks back.
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Intercom has completely reinvented their platform.
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And as the kind of first mover towards outcome-based pricing, they have become kind of the poster child for where you can go with AI pricing and how you can kind of price the work rather than the software that does the work, right?
00:00:56.320 --> 00:01:01.359
And I think people have been extremely excited about intercom and their pricing over the last couple of years.
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I think it's they're probably one of the most referenced products in SaaS right now, just because of this change that they've made.
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I personally have probably written about Intercom's pricing three or four times.
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And I can tell you, like before that, I don't think I ever wrote about intercom's pricing.
00:01:15.359 --> 00:01:24.480
So that what they've done with Finn, I think, is nothing short of kind of completely rebuilding the company from first principles and really committing to going AI native.
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And it's super impressive.
00:01:25.680 --> 00:01:28.159
A few notes about Intercom before we get into it.
00:01:28.319 --> 00:01:30.560
So they were founded in 2011.
00:01:30.959 --> 00:01:35.120
They have a customer service platform with unified customer messaging and support.
00:01:35.200 --> 00:01:53.519
They have a messenger, an inbox or help desk, a help center, automation and chatbots, uh, outbound messaging, customer data platform, and importantly, an AI agent called Fin, which has basically become kind of like the new well, we'll get to it in a second, but they're they're more or less rebranding the entire company around their AI agent.
00:01:53.599 --> 00:02:00.480
They have over 25,000 customers used widely by SaaS companies, FinTech, and e-com companies for customer support and engagement.
00:02:00.640 --> 00:02:04.400
They are at about 400 million in annual revenue right now.
00:02:04.480 --> 00:02:06.480
And we'll get into the more the specifics there.
00:02:06.560 --> 00:02:10.319
Uh, they are a private company and they have around a thousand employees, I believe.
00:02:10.479 --> 00:02:16.400
Again, traditionally a seat-based license model, typical of most customer support platforms.
00:02:16.479 --> 00:02:23.039
Um, but what they've done with Fin's AI agent is they started out charging kind of a flat 99 cent per resolution rate.
00:02:23.199 --> 00:02:30.479
It really shifted to outcome-based pricing and have become kind of the the go-to AI agent for customer support.
00:02:30.719 --> 00:02:33.520
Like it's interesting to me why this is such a big deal.
00:02:33.680 --> 00:02:38.560
Like I think I think they sort of sort of like maybe that's just me being, you know, isn't it obvious?
00:02:38.719 --> 00:02:48.159
Like so there's one of these where like they, I think also Intercom has had like, but remind me if I'm wrong, like they've also had some sort of pricing based on list size.
00:02:48.560 --> 00:02:50.560
Like how many customers you actually had.
00:02:50.719 --> 00:02:55.680
I think there's one of these where you can price on many different metrics.
00:02:55.919 --> 00:03:09.520
Number of service agents or users is one, number of customers is another, one of the number of customer conversations is a third, and then you can have like resolved conversations at the end, which is ultimately what you want, right?
00:03:09.680 --> 00:03:16.479
So along with this sort of and and all of it just exists in the universe to just deal with customers.
00:03:16.560 --> 00:03:21.199
So like the job to be done for something like Intercom is is very straightforward.
00:03:21.360 --> 00:03:26.319
Like we are selling something, and for whatever reason our customers want to talk to us, and that's a problem.
00:03:26.479 --> 00:03:29.120
So we just have to deal with that part, right?
00:03:29.599 --> 00:03:41.120
So, and then obviously we want it, we want the resolutions to be good in the sense that either we want, you know, um complaints to be low or net promoter score to be high or refunds to be low or whatever it is, right?
00:03:41.199 --> 00:03:45.199
And then if it's gonna cost us money, we want it to cost as least as possible.
00:03:45.360 --> 00:03:48.639
And if we can get revenue out of it, we want as much as possible.
00:03:48.800 --> 00:04:06.639
So I think it's one of these where because of the domain that they're operating in, it's such an easy piece of math because it is, it's so it's uh it's so narrow a slice of the world where if you do something broader and more, let's say, harder to measure, like we talked about HopSpot last time.
00:04:06.879 --> 00:04:10.319
So it's like all marketing, automation, CRM, everything all in once.
00:04:10.400 --> 00:04:17.920
It's like, okay, I guess you could like you could measure something on revenue, but there are so many different aspects of it, not all of them inside of HopSpot, right?
00:04:18.000 --> 00:04:24.240
But with Instagram, it's like, okay, we own the entire process end-to-end, and you can sort of measure the inputs and we can sort of show you the outputs as well.
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Done.
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And then they just hook pricing instead of at the beginning with users, we're just gonna move the slider all the way to the right and just say, hey, we're just gonna price based on the outcome, which is the resolved uh conversation.
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And I think that makes sense, right?
00:04:39.600 --> 00:04:48.240
Um you and even if you look at the way that like uh call centers have worked, many of them have actually priced based on resolved conversations.
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So if you have like an outsourced call center that does something, like if you do sales and you go like, hey, we're gonna, we don't want to own the BDR process ourselves, we've got to hire someone to do the meetings for us.
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You pay per meeting booked.
00:04:59.759 --> 00:05:00.079
Right.
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Like even though it's humans and it's slower, like, but you still paid for the outcome.
00:05:05.759 --> 00:05:12.240
And then if they were good or bad at it, that was sort of on them, but you just paid for whatever actually was like got out on the other end.
00:05:12.399 --> 00:05:26.560
So it's not like it's a new idea, it's just that the idea just worked way better when because of AI and and sort of the tech progress, suddenly the metric that they were doing was suddenly being compressed massively, right?
00:05:26.800 --> 00:05:44.160
And I I was speaking to another, that sort of can't name them, but an operator of call centers that and they have like a lot of global customers, they do maybe a quarter billion of revenue in call centers, and they were pricing based on essentially an hourly rate on their operators.
00:05:44.319 --> 00:05:51.040
And the their problem was that a lot of AI actually makes the operators way more efficient, so suddenly they don't have to spend as much time.
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So they were faced with a similar conundrum as I'm sure Intercom was faced with, which is if we lean into the tech, suddenly the main driver of our pricing is gonna be reduced drastically.
00:06:01.600 --> 00:06:01.839
Right.
00:06:02.000 --> 00:06:11.199
So if we lean into the tech and we reduce the time needed from operators, because that's how we price, suddenly instead of like a quarter billion, we're just gonna make a hundred million.
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And that's obviously bad.
00:06:12.639 --> 00:06:16.560
So we're delivering more value for like a lower price, like doesn't match, right?
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It's perverse incentives, yeah.
00:06:18.160 --> 00:06:19.680
So then the pricing needs to change.
00:06:19.839 --> 00:06:20.000
Yeah.
00:06:20.079 --> 00:06:20.160
Yeah.
00:06:20.399 --> 00:06:32.800
So I think, and as so I think it's one of these where I think the reason that Insigom is so is such a poster child is that it's not so much that their solution is original and fancy, it's that everybody has this problem.
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Just like the pain is common a hundred percent.
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I I think and I think like to your point, I think customer support is one of those things that no company like loves doing, right?
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It's it's it's typically a job that you know is is very kind of menial.
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You're you're kind of like responding to people.
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If you plotted a histogram of all of the support tickets, you're gonna find that like a lot of them are the same, right?
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So it's it's like ideal, it's like fertile ground for AI to just take it over, right?
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Because it's it's gonna be doing a lot of the same jobs over and over again.
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And it's also something that humans have no problem handing off to an AI agent, right?
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Like most humans don't want to do this work.
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And I think like at that intersection where you have repeatable work that is very common and work that humans don't want to do, you are going to, it is naturally gonna gravitate towards AI agents taking that over.
00:07:25.839 --> 00:07:27.439
And that's what's happening in customer support.
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And I think Intercom was kind of the first company to like really go all in on that, um, at least in the kind of like SaaS tech space.
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Owen wrote a blog post on March 2nd, uh 2026, titled There's Exactly One Way That SaaS Can Be Saved.
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And it basically talks through Intercom's growth challenges, their kind of history of why they went all in on their AI agent, which they named Finn.
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And I think the the biggest takeaway from this entire blog post is just this chart that he posted with um the SAS, the kind of I guess median or average SaaS growth rate um quarter over quarter, going from Q4 2025.
00:08:11.839 --> 00:08:12.000
Yeah.
00:08:12.240 --> 00:08:17.920
And it shows and it maps out kind of like the median growth rate, and then it maps out intercom's growth rate.
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And the really interesting thing here um is oh Owen took it took some time away from Intercom, I believe, for for health challenges.
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And he stepped back into the fold um between Q4 2022 and Q1 2023.
00:08:31.920 --> 00:08:39.519
And when he stepped back into the fold, they were already going through um this process where their growth rates were declining pretty rapidly.
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And to paint a picture for people who can't see this, in Q1 of 2022, they grew 24% year over year.
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In Q2, they were down to 18% year over year.
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By Q4, they were down to 12%, 12% year over year.
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And then it keeps going down from there.
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So in Owen's first year back as CEO, their growth rate dropped to 4% year over year.
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Uh down to U3 at 37%, like a year and a half earlier.
00:09:02.080 --> 00:09:05.679
And this is compared to markets that are doing like 18, 20%.
00:09:06.240 --> 00:09:15.360
So this is where like internally, what happens here is that so you're you're gonna face a lot of board pressure.
00:09:15.919 --> 00:09:17.679
This is this is not the target.
00:09:17.919 --> 00:09:24.080
So you're not you're not at like 24% growth rate and then projecting that it's gonna drop to 4% in a year.
00:09:24.240 --> 00:09:31.200
So what's gonna happen is that you say, hey, we're gonna be at 24% now, then we're gonna get a week, like I know we were at 30 last year, so we're gonna get back to 30.
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So the the way that the board, the boardroom has looked is like, okay, we're gonna we're gonna grow like 35% next year.
00:09:37.519 --> 00:09:40.559
And then what happens is it just tanks down to four.
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And then that means that everybody knows that this is bad.
00:09:44.399 --> 00:09:44.559
Yeah.
00:09:44.720 --> 00:09:47.759
And then, but nobody knows how to fix it, right?
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And then one of the ways is like, oh, we'll just not sleep, we'll just work harder, whatever.
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And we'll like so, and then the sort of shit rolls downhill.
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Like the idea is like everybody suddenly becomes like in crisis mode.
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Everybody feels that they're doing everything wrong because it isn't working.
00:10:01.679 --> 00:10:14.000
So this is sort of a real sort of leadership crisis where you have to sort of figure out, okay, so is this the point where we just like we get back to basics and we just do everything a little better, and then we just incrementally improve the process we have, and that's really what's wrong.
00:10:14.159 --> 00:10:15.919
It's just like we got lazy, right?
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Or is it the time to truly come up with something new?
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And that's really the story that he's telling.
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He's saying, hey, we did something new.
00:10:23.759 --> 00:10:30.639
The fact that they dropped to 4% might actually have been what allowed him to get it through the boardroom.
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Like if he had followed like just a naturally declining slope of growth, like similar to the market, the boardroom might have been, yeah, you know, let's just like not do the drastic thing.
00:10:40.399 --> 00:10:42.559
Let's just like keep our model, it's work pretty good.
00:10:42.720 --> 00:10:44.720
Like we're still growing 15%, whatever.
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But because it's so obviously going wrong, like it's like, all right, like every every option is on the table.
00:10:51.279 --> 00:10:52.240
Who has great ideas?
00:10:52.320 --> 00:10:55.360
Like, let's let's give this like going another direction.
00:10:55.440 --> 00:10:58.320
You know, the saying, like, don't let a good crisis go to waste.
00:10:58.559 --> 00:11:00.879
That's a little bit like a really good crisis.
00:11:00.960 --> 00:11:05.840
And that also means that he gets to like swing for the fences and do something like radically new.
00:11:06.000 --> 00:11:08.799
And then that becomes let's the model is wrong.
00:11:08.879 --> 00:11:10.159
Let's do thin.
00:11:10.399 --> 00:11:14.879
Let's change the the the the essentially the business model and our relationship to our customs.
00:11:15.200 --> 00:11:15.840
Uh yeah, exact.
00:11:16.240 --> 00:11:17.440
I think you put that perfectly.
00:11:17.519 --> 00:11:22.639
Uh I'm gonna read from the post real quick because he says a couple of things that just drive home exactly what you just said.
00:11:22.799 --> 00:11:27.679
So Owen says, we had a lot in our favor that got us out ahead of the journey that everyone else must now take.
00:11:27.840 --> 00:11:30.159
For one, we were desperate for new growth.
00:11:30.320 --> 00:11:32.559
We were performing far worse than average.
00:11:32.720 --> 00:11:33.840
We had less to lose.
00:11:34.000 --> 00:11:35.919
That makes hard decisions less scary.
00:11:36.080 --> 00:11:38.000
Beyond that, we had a CEO change.
00:11:38.080 --> 00:11:43.360
It's far easier for someone new to come in and say they don't like how the last guy did things and ripped everything up.
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I think those two factors, to your point, are enormous here because it's a you have a new CEO, you have new leadership, it's really easy to kind of like turn the team around onto this new, this new focus.
00:11:53.840 --> 00:11:56.720
And secondly, the numbers obviously are not matching up.
00:11:56.799 --> 00:12:04.240
And if you can see this chart, uh when they were growing 4% year over year, that's that's 15% less than the median SaaS company.
00:12:04.320 --> 00:12:07.600
So the the typical SaaS company is growing 19% year over year.
00:12:07.679 --> 00:12:13.519
And then if you fast forward, and what we're gonna get into is kind of what they did and and how they're pricing Finn AI.
00:12:14.320 --> 00:12:23.440
But by by what is this, Q1 2026, the median SaaS company is growing 11% year over year.
00:12:23.519 --> 00:12:27.840
So it's down from 19%, and intercom grew 26% year over year.
00:12:27.919 --> 00:12:31.919
So they completely back up to uh to to 22.
00:12:32.080 --> 00:12:33.120
So 24.
00:12:33.200 --> 00:12:42.639
So yeah, I think I did a pretty comprehensive like mystery shopping exercise across like customer chatbots, I think around two years ago, so beginning of 2024.
00:12:42.720 --> 00:12:47.759
So this is at the sort of the bottom of where Intercom sort of bottomed out of like four or five percent growth.
00:12:47.919 --> 00:12:48.159
Yeah.
00:12:48.320 --> 00:12:56.879
So what we did was we basically mystery shopped within 10 different sort of competitors in the space because we had a client that was that was in there in that space.
00:12:56.960 --> 00:13:12.960
And then so what we would see was that everybody had started to sort of price, like fundamentally, like some form of like token-based pricing, like like hey, we have tokens at the back end, but like we're gonna price per conversation or per resolved conversation, or the different sort of versions of that.
00:13:13.120 --> 00:13:16.559
So we could just see that a lot of new players had just emerged.
00:13:16.639 --> 00:13:26.320
So I think what we'll see is that as intercom's growth is dropping, it's sort of like the inverse of that is that the number of startups that is trying to solve this with AI.
00:13:26.480 --> 00:13:30.399
So what I'm wondering is like, is Intercom's price dropping?
00:13:30.639 --> 00:13:34.399
Maybe because number of users is declining.
00:13:34.480 --> 00:13:37.279
So net revenue retention is actually our renewals are harder.
00:13:37.360 --> 00:13:40.799
Like, hey, you had a thousand seats last year, how many do you want next year?
00:13:40.960 --> 00:13:41.600
Like 800.
00:13:41.759 --> 00:13:42.399
Oh, okay.
00:13:42.480 --> 00:13:45.120
It's it's hard for me to get more money out of you.
00:13:45.200 --> 00:13:48.559
I actually have to like, I have to fight for the status quo, right?
00:13:48.639 --> 00:13:55.919
So suddenly what might have been a good net revenue retention rate suddenly becomes like negative, not because customers are churning, but because you have contraction, right?
00:13:56.000 --> 00:14:10.000
And so if you're fighting contraction, and then when you lean into like who's gonna buy like a traditional sort of seat-based customer support solution if everybody knows that AI is gonna do this in the like maybe not right now, but then at least next year, right?
00:14:10.080 --> 00:14:12.399
So that also means that new sales just becomes harder.
00:14:12.480 --> 00:14:18.480
So they have these sort of like dual headwinds from AI, and I think that's what's really gonna like drive it into the ground here, right?
00:14:18.639 --> 00:14:31.919
For context, I remember that a lot of the resolved conversations, like the price range for resolved conversations were between, not resolved, but conversations in general, but between like 50 cents to like two, three dollars.
00:14:32.000 --> 00:14:36.480
And I did another study for one of the largest system integrators in Europe.
00:14:36.639 --> 00:14:42.080
They do like well over a couple billion and they do a lot of actually a lot of like service help desks for larger companies.
00:14:42.159 --> 00:14:45.200
And they did a lot of outsized, uh outsourced service desks.
00:14:45.279 --> 00:14:54.639
And they were saying, hey, if we run the service desk in like with a European operator, the price for a ticket is is like 28, 29 euros.
00:14:54.799 --> 00:14:58.000
Like, so I call in for service desk, the actual cost is 29 euros.
00:14:58.080 --> 00:14:58.799
So what was that?
00:14:58.960 --> 00:15:00.080
35 bucks, something like that.
00:15:00.240 --> 00:15:08.320
If we do it with like an Eastern European cheap labor, like offshore Indian, whatever, like cheap labor, it drops to like six to eight bucks, something like that.
00:15:08.480 --> 00:15:08.720
Wow.
00:15:08.960 --> 00:15:21.759
So it's like if we do the cheapest possible labor that we think we can get away with, the price is still three to ten times larger than what the automated solutions were doing at that point in time.
00:15:22.000 --> 00:15:22.240
Yeah.
00:15:22.480 --> 00:15:33.200
So that's where when pricing is this like with the gap is this big in the market, that's where people, at least someone in the market, is like, I don't care that the quality is not 100% the same.
00:15:33.279 --> 00:15:34.960
We're gonna go with the cheap option, right?
00:15:35.039 --> 00:15:35.200
Yeah.
00:15:35.440 --> 00:15:48.960
Because for many, and we actually did this also where we had customers that like online gaming websites and a lot of these others, which were we don't have customer support because if we did, we would get like 20 million tickets a day.
00:15:49.120 --> 00:15:50.960
Like just doesn't work.
00:15:51.120 --> 00:15:54.399
But with AI, we certainly can get customer support.
00:15:54.559 --> 00:16:01.360
Like it's actually something we can add to the business, which we never had before because it just wasn't economically viable.
00:16:01.519 --> 00:16:09.360
Because, you know, eight bucks, thirty bucks with an operator model, suddenly with an automated AI model, we can actually offer this.
00:16:09.519 --> 00:16:13.519
So it actually opened up an entirely new market of customers that weren't there before.
00:16:13.759 --> 00:16:14.399
That's amazing.
00:16:14.639 --> 00:16:20.399
Yeah, and uh when you when you look at it like that and you kind of start to compare those price points, it starts to make a lot of sense.
00:16:20.559 --> 00:16:25.919
Why people, why there's such an appetite for 99 cents per resolution, which is what I think Intercom ended up going with.
00:16:26.240 --> 00:16:29.519
Yeah, I think it's so so it's also interesting that it's 99 cents.
00:16:29.600 --> 00:16:31.360
Yeah, like like an iTunes song, right?
00:16:31.440 --> 00:16:33.679
So it's like, hey, like not a dollar.
00:16:34.639 --> 00:16:35.200
Right.
00:16:35.840 --> 00:16:38.960
I wrote a book on pricing psychology maybe eight years ago.
00:16:39.120 --> 00:16:43.679
Uh and uh the number one question I got all the time was does it work B2B?
00:16:43.759 --> 00:16:44.879
I was like, of course it does.
00:16:44.960 --> 00:16:48.960
And you're just like like here, you have what are they doing, 400 million?
00:16:49.120 --> 00:16:50.720
And they're doing it on 99 cents.
00:16:50.960 --> 00:16:51.600
It's like 80%.
00:16:52.159 --> 00:16:54.960
Would they do 404 million if they did one dollar?
00:16:55.200 --> 00:16:55.600
Right.
00:16:55.759 --> 00:16:56.480
Who knows?
00:16:56.720 --> 00:16:58.639
But we're we're not making that bet.
00:16:58.879 --> 00:17:00.000
Like we're sitting at 99.
00:17:00.720 --> 00:17:01.039
For sure.
00:17:01.200 --> 00:17:01.600
100%.
00:17:01.840 --> 00:17:18.240
And I mean, like, even just looking at we we talked about HubSpot last time, and HubSpot's got that classic good, better, best, and it's like the compromise effect every time, but uh drive drive people to the middle, and I think like, you know, at least like 75% of SaaS companies use that strategy of like three packages, you know, try to drive people towards the middle one.
00:17:18.319 --> 00:17:29.519
The last thing to note here on Owen's blog post, so they are now at$400 million in ARR overall, with Finn about to pass$100 million itself, which I think is really, really interesting.
00:17:29.680 --> 00:17:31.759
I I forget who I saw post this.
00:17:31.839 --> 00:17:41.119
I think it might have been Jake Saper, uh venture capitalist from Emergence, but I think he said something about like hearing whispers that intercom might actually roll Finn out as its own company.
00:17:41.200 --> 00:17:43.680
And they've actually done some of like the groundwork on this.
00:17:43.759 --> 00:17:48.400
And they they have like an intercom specific pricing page, which I'll jump to right now.
00:17:48.559 --> 00:17:59.440
So if you're if you're not watching, um, we're looking at the intercom pricing page where you can see they're more conventional um plans from from their kind of traditional help desk, right?
00:17:59.599 --> 00:18:05.680
So they have four different plans, um, essential advanced expert and then fin AI agent.
00:18:05.839 --> 00:18:11.440
And then for essential advanced expert, you can actually layer uh fin's outcome-based pricing on top.
00:18:11.519 --> 00:18:14.240
They do a really good job of kind of making fin accessible.
00:18:14.319 --> 00:18:28.799
And then the other thing that I think is really, really interesting is up top they have a million dollar guarantee for Fin, which basically says if you sign up for Fin and are not 100% satisfied in your first 90 days, we will give you up to a million dollars of your money back.
00:18:29.039 --> 00:18:29.920
No questions asked.
00:18:30.160 --> 00:18:31.039
Really, really wild.
00:18:31.279 --> 00:18:38.079
And I think like that kind of addresses the biggest concern that people have with an agentic customer support platform is does it actually do a good job?
00:18:38.160 --> 00:18:38.880
Is it high quality?
00:18:38.960 --> 00:18:40.319
Will it actually resolve these tickets?
00:18:40.400 --> 00:18:42.079
They're kind of putting their money where their mouth is there.
00:18:42.319 --> 00:18:44.880
So they so they actually change this guarantee.
00:18:45.039 --> 00:18:45.440
Oh, let's say.
00:18:45.599 --> 00:18:47.359
I'm a big fan of guarantees.
00:18:47.680 --> 00:18:53.359
So in the original guarantee, the guarantee was that they would give you a million dollars if it didn't resolve.
00:18:53.440 --> 00:18:55.759
I can't remember what it was, like it is 50% of tickets.
00:18:55.839 --> 00:18:57.279
That was like a relatively low number.
00:18:57.440 --> 00:18:58.880
So now they don't do that.
00:18:59.039 --> 00:19:03.759
They give you a million dollars of your own money back up to Got it, got it.
00:19:04.000 --> 00:19:05.039
Big difference, right?
00:19:05.200 --> 00:19:09.039
Somebody came in with like, okay, I'm gonna like they can't help my customers.
00:19:09.759 --> 00:19:12.799
But I think actually, so they they I talked about that guarantee.
00:19:13.039 --> 00:19:17.839
And it was uh having looked at a lot of these data sets, it's like, of course you can resolve 50%.
00:19:18.160 --> 00:19:20.000
Like that's absolutely doable.
00:19:20.400 --> 00:19:29.519
So more more likely, like even with like initial solutions, you you usually resolve around 80% because most customer questions are like super simple.
00:19:29.599 --> 00:19:38.400
So if you're hooked up to like a relatively good database and you have like good data to train it on, yeah, 80% resolution is I guess that's a pretty sort of like low bar.
00:19:38.559 --> 00:19:43.519
So 50% resolution is almost like guarantee that they're never gonna pay it out, right?
00:19:43.599 --> 00:19:55.200
But maybe they did or legal got a hold of them and said, you know, we can't have like the liability of just having the potential of having to pay out a million for every customer is just like not something that we're okay with.
00:19:55.440 --> 00:20:05.200
I think something else that's going on on this pricing page is interesting, which is that the way that the position, so the first three uh tier series of essential advanced experts per says includes Fin AI agent.
00:20:05.440 --> 00:20:11.359
And then the last one, the Fin AI agent, as a standalone, says already have a help desk question mark.
00:20:11.519 --> 00:20:17.440
So what they're actually doing is they say, hey, what we found is like the old solution, we had to replace existing solutions.
00:20:17.599 --> 00:20:22.000
So we're fighting with, oh, we already have a provider and the contract runs for another two years, all that.
00:20:22.079 --> 00:20:28.720
And then we're saying, hey, no problem, you can just like latch on our AI agent on top of the thing you already have.
00:20:28.880 --> 00:20:31.119
The idea is that you actually sort of shift cost.
00:20:31.440 --> 00:20:40.400
So if I already run a help desk and I run a million tickets and I do them at, let's say, the outsource price of five bucks a tick, I have a five million dollar cost for my help desk.
00:20:40.480 --> 00:20:49.279
And then Fin AI comes in and says, hey, how about you just take all that flow and route it through us, and then we'll solve 800,000 of those, and then you're gonna pay 800K.
00:20:49.359 --> 00:20:54.319
And then it's only like the other 200k that you need to then get to your team.
00:20:54.400 --> 00:21:00.160
So you can fire like 80% of the team, keep the good ones, the ones you like, and they can then resolve the rest.
00:21:00.240 --> 00:21:03.039
And then blended cost becomes 1.8 million.
00:21:03.119 --> 00:21:04.000
That's the value proposition.
00:21:04.079 --> 00:21:06.079
And then people are like, oh, great, we'll do that.
00:21:06.240 --> 00:21:10.720
And then when the original contract runs out, then you can come in with your other plan.
00:21:10.799 --> 00:21:21.839
So the the original Instacon product becomes like the chaser that gets that gets sold like when the original other contract runs out, but the thin AI agent becomes something that could get sold today.
00:21:22.000 --> 00:21:22.559
I love this.
00:21:22.720 --> 00:21:28.559
I it honestly it this is kind of a weird parallel, but it reminds me of Shopify.
00:21:28.640 --> 00:21:34.880
Like Shopify has this SaaS revenue for when you are using the Shopify platform for your online store.
00:21:34.960 --> 00:21:39.920
But then they have all this merchant solutions revenue from when people actually process payments through Shopify.
00:21:40.000 --> 00:21:43.759
So they have kind of like the system of record more or less, and then this like system of action.
00:21:43.920 --> 00:21:48.079
And they launch this Shopi button that you can just drop on any site.
00:21:48.160 --> 00:21:57.359
And you don't need to be, you don't need to be using your hosting your store on Shopify, but you can still drop this shop pay button on your site and it allows them to generate some revenue there.
00:21:57.440 --> 00:21:59.839
And it's a way for them to kind of broaden their ecosystem.
00:22:00.079 --> 00:22:00.240
System.
00:22:00.319 --> 00:22:03.599
And I think what Intercom is doing here with Fin is kind of similar to that, right?
00:22:03.680 --> 00:22:07.599
It's like they have this system of record, which is their traditional business, their traditional help desk solution.
00:22:07.680 --> 00:22:19.119
The Fin AI agent allows them to plug into other companies, expand their ecosystem, get in front of other customers without having to do a complete rip and replace, which to your point is brutal and not something that everybody always wants to do.
00:22:19.200 --> 00:22:21.680
But it allows them to prove themselves with their agent.
00:22:21.839 --> 00:22:24.400
So when that removal comes up, it makes the case a lot easier.
00:22:24.480 --> 00:22:24.960
It's genius.
00:22:25.119 --> 00:22:25.279
Yeah.
00:22:25.440 --> 00:22:32.000
And if you see like the first feature they have in the fin AI agent is set up in under an hour on your current help desk.
00:22:32.160 --> 00:22:33.279
Like that's aggressive.
00:22:33.440 --> 00:22:36.880
Like you don't want to be, you don't want to be the current help desk.
00:22:37.119 --> 00:22:37.519
Right.
00:22:37.759 --> 00:22:40.480
Like then that's just like the truth, right?
00:22:40.559 --> 00:22:48.240
So so it's also one of these where that's a like from a packaging point of view, it's a brilliant move because you're using it sort of as a Trojan horse.
00:22:48.400 --> 00:22:53.279
Because like the other alternative would be, hey, buy our thing, it has the AI agent.
00:22:53.440 --> 00:22:56.480
And then people are like, ah, yeah, okay, but I have this current thing that I also like.
00:22:56.559 --> 00:23:02.720
And now it's just like, hey, have your current thing, but just like take our AI thing on top of that and then see what happens.
00:23:02.880 --> 00:23:04.480
And then you only pay if it works.
00:23:04.559 --> 00:23:06.000
And it takes just an hour to set in.
00:23:06.079 --> 00:23:07.359
So like, how easy is that to sell?
00:23:07.440 --> 00:23:11.759
And you even have a 14-day free trial just to like twist the knife, right?
00:23:11.920 --> 00:23:12.160
Right.
00:23:12.319 --> 00:23:13.119
So right.
00:23:13.359 --> 00:23:20.400
As opposed to like who wants like a full like help desk support setup with a like, I'm not going to trial that in 40 days.
00:23:20.480 --> 00:23:23.680
I have to train people, like they need to have user roles, like all that.
00:23:23.839 --> 00:23:33.200
Like, so the the other thing is just like it's so much easier to just like slide in right on top of the tech stack and then just try to sort of skim all the easy conversations off of there.
00:23:33.440 --> 00:23:33.839
It's awesome.
00:23:33.920 --> 00:23:42.799
Yeah, they they've definitely iterated a bunch on this pricing, but I I love where they're at right now, kind of allowing people to add it on top of their existing plan, add it to an existing help desk.
00:23:42.960 --> 00:23:48.240
And then you also see they have their own website for Finn, which is really interesting to me.
00:23:48.319 --> 00:23:54.079
And like I think the contrast, for those that can't see this, the um the contrast in pricing pages, I think is notable.
00:23:54.160 --> 00:23:58.319
They they the traditional intercom pricing page has kind of this beige background.
00:23:58.400 --> 00:24:06.640
It's very kind of like standard SaaS pricing page, nothing spicy, nothing like super uh eye-catching or eye-popping.
00:24:06.799 --> 00:24:17.359
You go to the fin AI pricing page, and there's kind of like this galactic theme where everything looks futuristic and kind of have like star like constellations in the background.
00:24:17.440 --> 00:24:25.680
And it's uh it's pretty clear that they're like kind of drawing this line between um intercom proper and and fin AI.
00:24:26.160 --> 00:24:35.839
And this is really interesting to me because they they they just they seem like they're kind of pushing towards kind of going all in with Finn and putting all their eggs in that basket, which I think makes a lot of sense.
00:24:36.079 --> 00:24:40.880
And actually interesting here, it's they're saying it's 99 cents per outcome.
00:24:41.119 --> 00:24:48.319
And so just you're using the word outcome as opposed to resolved customer conversation, which is, I guess, the outcome.
00:24:48.480 --> 00:24:53.680
So it's like so they have two, like they have resolved conversation and then like executes a procedure.
00:24:53.839 --> 00:25:01.920
So when your the original like chat market was like can the system just talk to the customer or can it also do things like issue and refund?
00:25:02.000 --> 00:25:05.119
Yeah, or like reroute a package or like restart a system.
00:25:05.279 --> 00:25:11.759
So I did a lot of these also with um in like IoT universes where people would do like uh electrical charges for cars.
00:25:11.839 --> 00:25:15.440
So it's like, can I call in and have the chat but like reset the charger if it doesn't work?
00:25:15.519 --> 00:25:16.160
So things like that.
00:25:16.319 --> 00:25:22.880
And there actually was like it was like an advanced feature that like the systems didn't get ready to do this until like 2025, really.
00:25:23.039 --> 00:25:26.079
So before that, they were mostly like, we'll just talk, right?
00:25:26.400 --> 00:25:37.759
But if we and then maybe we'll perform like simple actions, but anything that let's say requires security on money in any way or access to physical installations, we can't do it, right?
00:25:37.920 --> 00:25:41.039
So I think just having that extra part there.
00:25:41.200 --> 00:25:45.519
And actually, also it interestingly allows them to double dip, right?
00:25:45.759 --> 00:25:49.680
Because you can resolve a conversation by executing a procedure.
00:25:49.759 --> 00:25:50.079
Yes.
00:25:50.240 --> 00:25:52.799
Like I returned the package and it is now resolved.
00:25:52.960 --> 00:25:53.359
Done.
00:25:53.599 --> 00:25:55.039
198, right?
00:25:55.200 --> 00:25:55.440
Yeah.
00:25:55.599 --> 00:25:57.920
That what I think is super, super interesting about this too.
00:25:58.000 --> 00:26:02.240
Like we've talked about this a bunch with credits and even last week with HubSpot.
00:26:02.480 --> 00:26:05.359
But we we've talked about credits and outcome-based pricing.
00:26:05.440 --> 00:26:15.359
And I think some of the most important things with both of those are really defining what the customer is getting and making it very, very clear, like what they will be charged for.
00:26:15.519 --> 00:26:27.440
And I think one of the hurdles that a lot of companies have with credits is they have like a very janky credit model or a credit model that or a very janky credit table where it's not totally clear like how much you're paying and like what a credit is worth.
00:26:27.599 --> 00:26:37.519
And I think, especially with outcome-based pricing, one of the hardest things is having like agreed upon outcomes with your customers that you can actually attribute success to from your platform.
00:26:37.599 --> 00:26:37.759
Right.
00:26:37.920 --> 00:26:54.799
And so Intercom comes right out and says this for the people that can't see this, there's like one of those little, there's a there's a little clickable icon right next to per outcome on the fin pricing page that you can click on and it shows you that an outcome is counted when Finn resolves a conversation, as Ulrich was saying, or Finn executes a procedure.
00:26:54.880 --> 00:26:56.960
And then there's an asterisk next to procedure.
00:26:57.039 --> 00:27:01.359
This just kind of like illustrates how complicated all this stuff is and why you need to go to this level of detail.
00:27:01.599 --> 00:27:05.359
So a procedure applies only to customers using procedures.
00:27:05.519 --> 00:27:11.039
Procedures train fin to handle complex multi-step customer queries like subscription issues or refunds.
00:27:11.200 --> 00:27:16.640
And they call this out, I think, because people probably come to this page and they're like, well, what about the stuff that's not super easy?
00:27:16.720 --> 00:27:19.440
It's not just like a one-step answer from our knowledge base.
00:27:19.599 --> 00:27:22.160
What about the stuff that actually requires a little bit of back and forth?
00:27:22.240 --> 00:27:23.839
Like, can Finn actually help me do that?
00:27:24.000 --> 00:27:30.880
And they've kind of made clear with this little pop-up bubble, then they have this thing called procedures that helps you work through some of the more complicated issues.
00:27:30.960 --> 00:27:33.279
So I think that's super interesting and super smart.
00:27:33.359 --> 00:27:40.319
I think the better like communications and like how you actually show this stuff, I think is is way more important than most people realize.
00:27:40.480 --> 00:27:40.799
For sure.
00:27:40.960 --> 00:27:48.319
And and so what I'm wondering with with something like Intercom and the pricing model they have here is like where do they go from here?
00:27:48.480 --> 00:27:51.119
You fundamentally sort of have like a couple bets.
00:27:51.200 --> 00:28:00.720
So one is that as I said, like, hey, more people are gonna start using like having help desks because before they were not economically viable and now they are, so let's do that.
00:28:00.880 --> 00:28:10.240
So it's like it's one of these where uh where if the price of gas drops, total revenue of gas goes up because more people take trips that they wouldn't otherwise have had, right?
00:28:10.319 --> 00:28:11.839
So like a Yemon's paradox.
00:28:12.000 --> 00:28:29.119
But then ultimately, what will like the conversation that's gonna happen inside of Instacom and what happens in like because and I know because it happens in all of these sort of like chat companies uh and help this companies is that that there is a very uneven distribution of value between the resolved conversations.
00:28:29.519 --> 00:28:29.759
Right.
00:28:29.920 --> 00:28:43.119
So let's say that you run like a private bank for high net worth individuals, and you might have whatever, a thousand conversations a year, but each of those is why is with like a billionaire that wants something done right now, right?
00:28:43.359 --> 00:28:49.519
So those conversations are also only gonna cost 99 cents if I use Fin to resolve them, right?
00:28:49.680 --> 00:28:55.839
And then other like the gaming website that have 10 million things a day, they are extremely low value.
00:28:56.000 --> 00:29:02.640
So you're gonna have situations where at the extreme, it might even not even be worth a dollar for me to solve it.
00:29:02.799 --> 00:29:06.079
So let's say why doesn't Wikipedia have Fin AI?
00:29:06.240 --> 00:29:14.880
Well, because it doesn't make sense for Wikipedia to pay a dollar for people to have questions answered, because like that's a lot of dollars for Wikipedia and they have no value out of it.
00:29:14.960 --> 00:29:15.200
Right.
00:29:15.359 --> 00:29:24.240
So you're just you're gonna say, okay, we're gonna have this event that exists in the world, which is a conversation that gets resolved, and then we're just gonna decide that it's gonna be a dollar.
00:29:24.400 --> 00:29:27.200
And then everything that is above that is still a dollar.
00:29:27.279 --> 00:29:41.440
Even if it's worth$10,000 to the customer or more, we're still gonna charge a dollar because we don't know how to really, in an easy and straightforward way, articulate another model that would also monetize that value.
00:29:41.599 --> 00:29:43.839
So we're just gonna leave that money on the table.
00:29:43.920 --> 00:29:50.079
So the high net worth individual private banking guys, they still pay a dollar because we don't know how to charge those.
00:29:50.240 --> 00:29:55.200
And then anything below a dollar, we've just decided that we're not gonna serve that market, right?
00:29:55.279 --> 00:29:55.519
Yeah.
00:29:55.680 --> 00:30:06.319
So what's gonna happen is that unless you figure out another way to do this, somebody's gonna come in with a very similar product and gonna price it at like 98 cents.
00:30:06.480 --> 00:30:07.119
Yeah.
00:30:07.599 --> 00:30:10.640
And then you're like, ah, okay, are we are we now gonna do this?
00:30:10.799 --> 00:30:14.720
Like, are we now gonna like, okay, 80 cents, 70, 50, whatever.
00:30:14.799 --> 00:30:15.920
And then it just goes down.
00:30:16.000 --> 00:30:21.359
And we we we can sort of see this in sort of many of these sort of commoditized services that they they try to then break this down.
00:30:21.519 --> 00:30:35.359
So as you see more and more enter this kind of space, I think Finn has a head start, and they're like, they're now eating the the call centers, like the operator-led model with humans involved, and they're like, there's a lot of revenue there for sure.
00:30:35.839 --> 00:30:37.599
Way more than the 400 million that we have now.
00:30:37.680 --> 00:30:38.240
So that's great.
00:30:38.400 --> 00:30:44.079
At a certain point, people are gonna say, hey, my gaming website is resolving like 8 million of these conversations a year.
00:30:44.160 --> 00:30:45.359
Uh, that's still a lot of money.
00:30:45.440 --> 00:30:51.680
Like I like, and then if somebody comes in and says, hey, we can do the same thing, we can just do it for half, then we're gonna do that, right?
00:30:51.839 --> 00:30:59.839
And then suddenly you get into a price war, and the price war really sort of just makes it the distance towards the private banking conversation just becomes larger and larger.
00:31:00.000 --> 00:31:00.160
Yeah.
00:31:00.240 --> 00:31:10.480
And that where I think the next level is someone someone is gonna figure out either to verticalize them so that the support agents become really good at certain types of customers or conversations.
00:31:10.640 --> 00:31:11.039
Totally.
00:31:11.200 --> 00:31:14.079
Or we're just gonna see like a commoditization of this.
00:31:14.319 --> 00:31:19.440
So it's one of these where I don't know how like future-proof this model is.
00:31:19.680 --> 00:31:21.839
It works right now, it's probably gonna work in three years.
00:31:22.000 --> 00:31:23.119
This is gonna work in 10.
00:31:23.359 --> 00:31:23.839
Don't know.
00:31:24.000 --> 00:31:24.319
Yeah.
00:31:24.559 --> 00:31:25.039
I love that.
00:31:25.359 --> 00:31:26.720
I love that uh analysis.
00:31:26.799 --> 00:31:33.519
And I think like one area that I like one thing that I was thinking about is kind of how day AI has been pricing.
00:31:33.599 --> 00:31:37.519
They're this AI native CRM started by the guys that started HubSpot CRM.
00:31:37.599 --> 00:31:41.440
And they have this pricing approach where they give away their CRM for free.
00:31:41.599 --> 00:31:46.480
They charge just a monthly license for agents that you can layer on top of the CRM.
00:31:46.559 --> 00:31:52.640
So like they have an agent that does pricing research or that does like prospecting research for like$75 a month.
00:31:52.799 --> 00:31:58.400
They have an agent that does um like post-call follow-up for a certain amount per month.
00:31:58.559 --> 00:32:01.519
And you have like all these different jobs that these agents do.
00:32:01.759 --> 00:32:12.720
And to your point, I wonder if that's kind of like where this goes around like verticalizing these agents or kind of like giving them different jobs and having like a dedicated budget for you know each of these different agents.
00:32:12.880 --> 00:32:14.400
That obviously gets more complicated.
00:32:14.480 --> 00:32:26.000
I think the beauty of where Intercom is right now is they're using that blended model that you talked about, like somewhere between that like gaming company and the kind of high net worth individual, and they're just blending it out, making sure that it works, keeping it simple.
00:32:26.160 --> 00:32:31.039
But to your point, I think it's inevitable that there will be price wars and it's gonna be really fun to see how this evolves.
00:32:31.279 --> 00:32:48.000
So right now, Intercom is at a like because they're replacing a legacy model, operator-led call centers, they're they need their model to be simple, and also they need their model to be like really generous, even when I'm reading the fine print, right?
00:32:48.240 --> 00:32:52.960
So if Thin had like a lot of fine print where like there's a lot of gotchas in the pricing.
00:32:53.039 --> 00:33:02.240
So I sign up and suddenly I'm priced like three times 99 cents for a call because of you know the asterisk of the footnote, suddenly the market pulls back.
00:33:02.400 --> 00:33:09.039
However, when you start winning, they and the growth starts to decline because they've already sort of eaten like the legacy market.
00:33:09.279 --> 00:33:27.039
That's where you can then start to actually do these kinds of things to say, hey, we're now gonna have a model where depending on how many procedures you run and how they're stringed together and how complex they are or what kind of security levels they have, or whatever it is, or we're gonna also price the infrastructure or the API calls and the back, whatever.
00:33:27.119 --> 00:33:31.519
Like you can actually start to build complexity into the model that is less generous.
00:33:31.759 --> 00:33:43.279
And that's where you can say, hey, our enterprise grade thin AI can handle things that your startup can't, and that's why we're pricing in this other way.
00:33:43.440 --> 00:33:51.039
And so, yes, their resolved conversation might be 50 cent, and ours is, let's say, also 50 cent because we're matching that.
00:33:51.200 --> 00:33:56.799
But then we also have this platform fee and this all the infrastructure fees and all the other things that then make up the rest of the pricing.
00:33:56.960 --> 00:34:02.720
And I think that's sort of fundamentally the direction that they want to go in.
00:34:03.039 --> 00:34:07.279
Not now, not 2027, but when growth starts to decline again.
00:34:07.519 --> 00:34:09.679
So, Ulrich, what are we giving intercom?
00:34:10.000 --> 00:34:10.880
Are we buying?
00:34:11.039 --> 00:34:11.840
Are we holding?
00:34:12.079 --> 00:34:12.719
Are we selling?
00:34:13.039 --> 00:34:16.000
I think I'm uh it's a it's a stronghold.
00:34:16.159 --> 00:34:26.000
Like so I I'm really like I think I think I think it's one of these where I I really want to see for something to like we gave HubSpot a buy last week, right?
00:34:26.079 --> 00:34:33.119
Because I think I think what they're fundamentally doing is making it more likely that will they will grow even more in the future, right?
00:34:33.280 --> 00:34:33.440
Yeah.
00:34:33.599 --> 00:34:38.000
And I think what Finn has right now is a is a really good right now model.
00:34:38.079 --> 00:34:43.119
But I'm not sure that it's gonna be a really good like 2028 or 2029 model, right?
00:34:43.360 --> 00:34:52.239
So I think I'd write it, I'd get the most out of it, but I think they do need to innovate something on the model for it to sort of really like secure the market they have.
00:34:52.400 --> 00:34:57.039
So I think they're open to competition down the road uh with the current model they have.
00:34:57.199 --> 00:34:59.840
So I think they've gone they've done a really great job.
00:35:00.000 --> 00:35:06.320
So maybe I'm too not generous, too stingy with the recommendations here, but it but I think it's a whole.
00:35:06.400 --> 00:35:07.199
I think it's a whole.
00:35:07.360 --> 00:35:08.400
Um I like it.
00:35:08.559 --> 00:35:09.039
I like it.
00:35:09.119 --> 00:35:10.320
You're you're a tough creator.
00:35:10.559 --> 00:35:17.679
And I think like, you know, their their pricing, it is hard because it's like this pricing model and this new product literally saved their company, right?
00:35:18.079 --> 00:35:25.119
But to your point, like you can you can forecast out like pretty easily and see that this pricing isn't gonna hold forever.
00:35:25.199 --> 00:35:27.519
And it's unclear kind of where it's going from here.
00:35:27.679 --> 00:35:36.000
I think that I think what we were talking about before with procedures might be kind of like a step in the direction of like where they can go towards more complexity.
00:35:36.079 --> 00:35:38.559
But I'm really excited to follow that and kind of see where it goes.
00:35:38.639 --> 00:35:39.760
I I like the stronghold.
00:35:39.920 --> 00:35:45.280
And I I'm betting you, I'm betting you that they have a lot of like internal discounting going on at volume.
00:35:45.440 --> 00:35:46.000
Oh, for sure.
00:35:46.159 --> 00:35:59.920
So there's a lot of salespeople with different contracts and different like all all sorts of things going on that like so when they when they renew at a certain point, like it's gonna be all the work all over again to renew at and so forth.
00:36:00.000 --> 00:36:03.280
So it's like one of these where scale isn't built into this.
00:36:03.360 --> 00:36:09.199
So I'm paying 99 for the first conversation and 99 for the milling conversation, unless I call sales, right?
00:36:09.280 --> 00:36:16.800
And then so I think they just have there there's a big risk of like operational inefficiencies uh when you're doing it like that.
00:36:16.960 --> 00:36:20.960
So so that's that's part of the reasoning as well for for the stronghold here.
00:36:21.199 --> 00:36:21.519
Love it.
00:36:21.679 --> 00:36:24.000
That's it for this episode of Pricing Page Unpacked.
00:36:24.079 --> 00:36:30.320
If this was useful, subscribe to the podcast and join the discussion on the pricing SaaS community, LinkedIn or YouTube.
00:36:30.559 --> 00:36:34.239
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