00:23:44 · Paul Todgham
[00:23:44] S0: So so I start with that. Right? Like, I actually feel like I have more time to focus on how do I partner, you know, with the business, with, you know, the key things that are gonna drive success for Greenhouse, which, you know, we're a tech company. So it's usually some combination of your go to market function and, you know, your product and R and D function. Know, how to... How can I best enable those those teams to do their best work, make the best database decisions, you know, where to allocate our investment and so on? So I start with a position of kind of optimism, and, like, it's a privilege to to get to do that. That said, it is challenging. I mean, I think balancing, you know, short term performance with long term strategic change is always hard, and it's super important to to do it well. You know? You definitely don't wanna be in either extreme, right, overly focused on the short term that you're blindsided by AI or other, you know, other changing technologies. And by the time you've got your act together, you know, your competitors are nine months ahead of you. And you can't just be kind of taking taking swings at the fences in the in the future, you know, for what's gonna what's gonna pan out. My my private equity investors would not keep me at my job very long if, you know, every forecast we missed or or, you know, we were constantly under delivering. I think decision making under uncertainty is super critical for finance during, you know, a changed dynamic or a time when there's this much change. But individuals comfort with that, and their skill with that can vary dramatically. I was kind of fortunate. Came of age as a consultant. You know, I spent the first eight and a half years of my career at BCG. So I learned that skill pretty early. We had to do a lot of making recommendations with both quantitative and qualitative factors, some of which were more certain, some of which were less. Accountants often maybe learn that skill later, if at all. And so being able to take your team through that, I think, is important. One of the things I tell my team, in the absence of us getting involved, a decision will get made. And almost certainly a less good decision will get made. We may have only a little bit of information. We may only have two hours or two days.
00:25:24 · Paul Todgham, CJ Gustafson
Maybe if we're lucky, maybe we have two weeks to work on this problem even if we think it's gonna take two months. My bar for them is not, are we getting to a perfect decision? It's, you know, in the time we had available to an... To to be involved, did we help make that decision better? And if the answer to that is yes, know, our teams feel great about what they're [00:25:40] S1: It's kind of a catch 22 for a lot of companies right now where they have this weird forecasting problem that they're super pumped that they're gonna launch this new product, but if something is actually that transformative, the historical data actually isn't that useful. So the question becomes, what do [00:25:55] S0: you forecast off of? I'm navigating that right now. Obviously, we just kicked off our budget cycle, and and we've got a couple technologies that I think are kind of right on that cusp of kind of inflecting and taking off. And I'm, you know, trying to navigate what's the best budget to build in that scenario. I don't think I have the right... All the answers. I mean, the approach I'm taking is put a little more confidence in kind of what I can bank and put a little more weight on that. And hopefully, the inflection is more upside. It starts with being in a fortunate position of my CRO and I and our team, you know, can get to a a good path that our board will be acceptable without betting on something that hasn't inflected yet. And then hopefully, that's the difference between us blowing the lights out versus having a good year.
00:25:55 · Paul Todgham, CJ Gustafson
[00:25:55] S0: you forecast off of? I'm navigating that right now. Obviously, we just kicked off our budget cycle, and and we've got a couple technologies that I think are kind of right on that cusp of kind of inflecting and taking off. And I'm, you know, trying to navigate what's the best budget to build in that scenario. I don't think I have the right... All the answers. I mean, the approach I'm taking is put a little more confidence in kind of what I can bank and put a little more weight on that. And hopefully, the inflection is more upside. It starts with being in a fortunate position of my CRO and I and our team, you know, can get to a a good path that our board will be acceptable without betting on something that hasn't inflected yet. And then hopefully, that's the difference between us blowing the lights out versus having a good year. [00:26:34] S1: Let's talk pricing. How the hell do you price AI right now?
00:26:38 · Paul Todgham
[00:26:38] S0: So we have a phenomenal director of pricing at at Greenhouse. He's one of the people outside of my org I speak with most frequently, Scott. Hire a really good pricing person. I'll start with that. We have a great pricing person. I think it... I also then would say start with really understanding your unit economics and your long term strategy. So we have a lot of AI features within Greenhouse recruiting where the economics still look a lot like traditional software. Like, yes, there's some token costs, but, you know, they've come down pretty dramatically. When you put engineers on it, they're able to optimize pretty well. So, you know, as long as users don't really abuse the feature, you're kinda fine. Right? So, you know, customers hate feeling nickel and dime. So if the marginal costs aren't breaking the bank, we're much more likely to build, like, a healthy amount of usage into one of our, you know, product tiers and then only go after consumption for that upper tail of the distribution. And folks who are in that upper tail often know it and are sort of more understanding and willing. Now, are other products where costs are higher and they scale pretty linearly with usage. So that model might not work. I'll give one example. AI interviewing, we we we talked a little about. We have this amazing product. I think it's the best in the market. And the return on it is a really easy case to make. Right? You could go one of two ways talking to customer. You could go the time saved argument. The average recruiting team spends about fifty minutes to an hour for a given twenty to twenty five minute phone screen. And we can bring that time down to about five minutes to carefully review the output of an AI interviewer. Right? It's pretty easy to value that time saved. A different way, the second approach, which we prefer to take, is go the kind of better talent argument. Point our vase... Voice AI tool at three times as many candidates you might have previously previously seen. So if you're gonna screen 20 personally, have the AI screen 60. And at least our our data so far suggests that your likelihood of finding strong talent deeper down your list is actually really strong. And and those folks are also more likely to get overlooked by other companies who are using superficial resume screens to choose talent.
00:28:25 · Paul Todgham, CJ Gustafson
That's an example where if you can kinda make a pretty clear ROI, price for it. And and and, you know, don't be afraid to be kind of aggressive about it. When there's market skepticism, which there is, you know, some for voice AI. Right? Maybe due to bad experiences with earlier tech or just the optics of AI interviewing. We focus on, again, communicate the value, but then make trial as easy as you can. We're very clear about what our, you know, our pricing is and how much our implementation might be costing. But then for certain customers and certain dynamics, are we willing to give a free trial or a free pilot or a discount for a three month period just to get folks using it? Or start with one department before you roll it out elsewhere. I would say every product is different, starting with, again, what are the unit economics? And how close does this sort of tie to some some unit of value that the customer understands. [00:29:08] S1: You have such a clear value metric, I think, is what you're describing across those different products, which makes it easier to price. I mean, pricing something in the AI is never necessarily easy, but at least you understand how the end user looks at value or how they quantify it. I guess related to that, how do you begin to think about willingness to pay?
00:29:26 · Paul Todgham
[00:29:26] S0: On the plus side, I think we can communicate value really well. The challenge we often face is that we're talking to talent teams who are often managed as cost centers, by their CFOs. So I'm grateful actually to have a chance that if there are sort of CFOs listening to this, which is your primary audience, just to sort of say, it takes effort to kind of really build a strong partnership between your people function and your finance function to kind of really think about the ROI of your hiring and and not just, you know, again, how much cost, you know, the the cost equation only. But it's worth that effort because, again, our people are so important. The cost of a bad hire is so high. I'll start with just kind of that, just an appeal to your audience, which I'm I'm I'm grateful to have the chance to connect with today. In terms of willingness to pay, again, I think it depends particularly on on the tool. But, I mean, ultimately, people see a lot of the value in these tools. They just need to know that it's predictable. And, again, if it's incremental to what they were paying before, there's gotta be incremental value. So, you know, I talked about AI interviewing as one example, but another one that we've launched is AI note taking. You and I are having this conversation and someone's transcribing the notes. If it was with Greenhouse, we'd be doing a... You'd doing a structured scorecard, right, questions to test certain competencies. And your scorecard would be populated, you know, after the fact. It's a pretty easy thing to predict. Right? You can predict how many interviews a company will conduct in a given year. We have data on that. So forecasting usage of it is pretty straightforward. And then there's just a question about, you know, value and willingness to pay. And, again, you can go straight to kinda how much time do think someone's spending writing up their notes after an interview. What's that worth director and up level? That's worth an awful lot, and you can quantify that.