Build an operating model to show what must be true

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 16:43

   Like at both, um, both Stripe and Vercel, one of the first things I come into a business when I do is build what I call an operating model, which is effectively the math behind what needs to be true to pick whatever number the founders picked. If you sort of build that spreadsheet, then we can all look ourselves in the eyes and say the degree to which we think that can be true, either because history would suggest it, or there's an assumption we're willing to make that would suggest a different trajectory. So like at Stripe, when I came in, we'd picked a nice, large, round, top line number. And I owned like the new business, which it turned out sort of the existing customer base revenue was relatively forecastable. And the new was the delta between the nice large round number and the forecastable existing business. You know, and so I went and I

2. 17:38

   built, you know, the model of like, here's what would need to be true. And you could see really rapidly that like that that was not likely to occur. Again, a company was growing like gangbusters. It just picked a very big number. And then you can have a conversation, actually a strategic one, which is like, guys, like we don't have enough pipeline. Actually, that was the case at Stripe, also the case at Rizal, I'll get back to, to do this.

3. 18:02

   So we have a couple options. Like we had one marketer at the time. We could invest in more marketing, put more dollars behind that. We could invest in outbound. We could move up market and get a larger ACV. So, you know, we could have a conversation about that and then also be able to say, you know, I don't see line of sight to this number unless the following things change that may or may not be possible to change on this time horizon.

4. 18:25

   It's the same thing in Vercel. Like, everybody at Vercel knows lead to revenue model. That's what I call it at Vercel. So it's the same thing. It's your full funnel math by segment, by geo. When we first built it, the data was messy also because I changed the segmentation. So in a lot of cases, like, it's not like I had clean data. But, like, you can put in a reasonable assumption as well. So also, if we do or don't hit the number, we know why, like, which underlying driver was different than we thought.

5. 18:54

   So my first board meeting was, like, a handful of weeks after I joined the company. And I was already able to take to the board at that meeting. Our current, you know, trajectory in these areas does not imply an ability to hit this number. In fact, we'll start falling off. Like, we were going to be just fine for the first two quarters, and then the way it was meant to ramp, there was just no way at that point it looked like we could hit through core.

6. 19:19

   Yeah, exactly. Like, we weren't creating enough pipeline. We had not created enough pipeline in Q4 before I showed up and weren't in Q1 when I showed up to get you to where you needed to go by Q3. That was also super helpful, right? Because, like, if I wasn't showing the trajectory of the whole year, I could have had the first two quarters be good and everybody be like, oh, this is great, and then just, like, falling off a cliff.

7. 19:39

   But actually, you could already predict that in the business if you did math. So, you know, and then again, we could have a conversation. So like I prioritized, I needed to hire a number of leaders. The first one I hired was the CMO because it was immediately obvious. Our top issue is marketing, pipeline, top of funnel. Got him in within 90 days. And by the time.