The Metrics Brothers
00:11:50 · Dave Kellogg
[00:11:50] S0: Yeah. And knowing that Ray and also knowing Iconiq, two things. One, I bet if we went footnote diving, we could find the answer. And two, the obvious correction to that is you do the privates on net as well, even though they don't typically do it, right? Because you can't do the other correction because you don't have the data. But in any case, the trend here is the CAC payback periods, according to them, again, quartile is sixteen months, which frankly, think is a real good, particularly mid market and enterprise sales. In my mind, that's a real good CAC payback period. As mentioned, we've already talked about just how difficult these comps can be. The next slide I think we should talk about is rule of 40. I want to go to slide 25, Ray. The issue I have with these charts is they never show the overall average. They're just showing the segmented values. So you can't look at this chart and go, Oh, the median R40 for the whole set was blah. What they give you is the top quartile segmented. So it's a little tricky to read, but presumably if you work at a portfolio company, if you're listening to our podcast, you can go find the line that correlates to you. And if you're a $50 $100,000,000 company, you would find that the top quartile R40 is 36, which strikes me as a, for that size, it's a pretty good R40 score. Any thoughts on this chart, Ray?
00:13:15 · Ray Rike, Dave Kellogg
[00:13:15] S1: No, the only thing that jumps out at me is number one, the fact that 200,000,000 is at 57%. That's really, really good. And if the top quartile is 36% or 31%, even though it's good for the smaller companies, doesn't sound like a rule, sounds more like the goal. [00:13:32] S0: Yeah. So, okay. In any case, net magic number, they do. I agree with you Ray, by the way. On net magic number, it's the next chart there on slide 26. I don't love magic number. I view it as an inverted CAC ratio. People know that. Also same issue Ray raised earlier, privates versus publics. Privates tend to do it on new ARR, publics do it on net new or net new implied really. So what they're showing, it's around one, which is fortunate because when you invert one, you get one. And that's a really good... When you look at that magic number, I think of it as an extraction ratio, Ray. I think of CAC as how much you spend to get a dollar of new ARR. I think of that magic number as how much ARR you extract from a dollar of sales and marketing spend, right? Because I needed some visceral way to think of it. And look, it's around one if you want to be top quartile. So roughly consistent with the CAC payback period. ARR per FTE, Ray, on slide 27. Again, numbers strike me as high, right? Their smallest company segment is nearly 200 ks median. Now we flip to median in ARR per FTE and their big companies are three thirty. Any thoughts on this data or this metric? Again, look at the end, Ray, you're segmenting 63 people into four buckets.
00:14:58 · Ray Rike, Dave Kellogg
[00:14:58] S1: Yeah, very small. I guess what I would say is in the research we've been doing lately, we do see ARR per FTE has ticked up anywhere from 10 to 20% per segment over the last couple of years. And this looks pretty consistent, right? If I go from three zero eight to three thirty one for the few 100,000,000 plus, that's up what 23. So that's about eight, 9%. So this fits with what I'm seeing out there, Dave. [00:15:25] S0: Okay. Slide 32, they've got burn multiple. I don't think it's that useful with this course of segmentation. They're basically saying below 100,000,000 and above 100,000,000. Well above 100,000,000 burn multiple in my mind starts to be a little bit, it's never irrelevant, but it's not as important a metric. And below 100,000,000, it actually changes quite a bit with scale. Like there's a difference between burn multiple to 5,000,000 and burn multiple 50,000,000 or burn multiple at 90,000,000. Here they're showing the median. So now we're back to medians below 100,000,000 is 1.7x. This is generally consistent with my rule of thumb, which is you want your burn multiple to run around two or less, but if you're getting up into three, four, five, six range, it's getting awfully, awfully high. Any thoughts on burn, Ray? [00:16:11] S1: No, just exactly what you said. If you're a $20,000,000 versus $80,000,000 company in your VC back, it's going to be very different. Too coarse for me too.
00:16:11 · Ray Rike, Dave Kellogg
[00:16:11] S1: No, just exactly what you said. If you're a $20,000,000 versus $80,000,000 company in your VC back, it's going to be very different. Too coarse for me too. [00:16:20] S0: Yep. So on slide 33, they have some interesting kind of survey questions here, and this is just about priorities. I'm going to hit it real fast. But in order, the top priorities are: GTM acceleration, distantly followed by efficiency and NRR, At least in terms of what's our top priority, and then AI commercialization. And those top five are really, I guess they're top four, I don't know how they rank them, but those are the top four priorities that people are focusing on. Only an N size of 58. I just think it's interesting when they throw kind of non quant questions in. Any thought of this one, Ray, before we go over to look about AI? [00:17:05] S1: Yeah, I know we were talking about how great they are at defining things. I didn't see how they define GTM acceleration. Is that growth acceleration? Is it growth efficiency? I don't know what it is.
00:17:05 · Ray Rike, Dave Kellogg
[00:17:05] S1: Yeah, I know we were talking about how great they are at defining things. I didn't see how they define GTM acceleration. Is that growth acceleration? Is it growth efficiency? I don't know what it is. [00:17:16] S0: Yeah, really good point, Ray. Really good point. And it's kind of a weird presentation of it too, but good point. For a company that usually defines things really well, that's missing. All right, let's skip on to the AI section, the age of AI. They do some comparison contrast of SaaS and AI companies or traditional SaaS and AI companies. There's a little bit of kind of philosophy on slide 35, which isn't bad. They then talk about some of the new metrics people are tracking. Nothing that exciting here, Ray. They basically say, hey, metrics are in three buckets. Adoption, people are looking at stuff like user count, net new ARR, free to paid conversion. People have always looked at that stuff. Engagement metrics, wow to MAU ratio, okay, pretty normal. AI mentions, renewals. Okay. I didn't see anything particularly new here. They look at business outcomes, number of resolutions handled by AI, cost savings, CSAT, number of agents deployed. Anything strike you on the slide 36, Ray? [00:18:16] S1: I was at an event last week and we asked how many people measure the business outcomes of their customers, and it was less than 20%. It's like SaaS software was supposed to improve business outcomes, right? Increase revenue, decrease expenses, increase productivity. I'm glad to see that we actually think we're doing it now with AI.
00:18:16 · Ray Rike, Dave Kellogg
[00:18:16] S1: I was at an event last week and we asked how many people measure the business outcomes of their customers, and it was less than 20%. It's like SaaS software was supposed to improve business outcomes, right? Increase revenue, decrease expenses, increase productivity. I'm glad to see that we actually think we're doing it now with AI. [00:18:35] S0: Yeah, true. I mean, we're always getting closer. It's kind of like a slow asymptotic convergence on business outcomes. I mean, even back in the day when we sold relational databases, the idea was that you could flexibly analyze your data to make better decisions to run your company better. And that dates back to my first day in the software industry. So we've been going after this for a long time and delivering along the way, but there's still plenty of work to do. They have a section on the age of AI. They talk about something we covered a few episodes ago, Ray, which is what we call it months to 100,000,000. They and I'm going to give them a little dig again. Sometimes they use years, sometimes they use months, sometimes they use quarters. They got to standardize on a metric here. I think we should standardize on months personally. And they talk about 11 labs that took two years to 100 and got there at 150 people. Cursor, one year to 100, got there at 20 people, which still blows my mind. Glean took three years to get to 100, got there with 1,000 people. And then lovable who took eight months to get to a 100 and got there at 45 people. So we've talked about this metric. Any, any thoughts on this Ray?