The Exchanges

Q Ok, 10 per user per month. Now you sat on a lot of data at Facebook. I'm sure you were in maybe pricing conversations or you've at least studied it. You know, how do you come up with 10 bucks a month?

A Uh, one, you look at the market, and you, uh, that which gives you a very good understanding of what's the general willingness to pay, but also, uh, we survey our users, so we have, uh, you know, a large number of users that have tried Woven that are retained, that are using the product every single day, and we put, uh, um, a Van Westendorp survey in front of them, um, to get feedback on, um, You know, how much is too much? How little is too little? Uh, what do you think is fair? What would feel like a good, uh, value for you? And, uh, the union of those four questions helps to, uh, get you right to what the sweet spot is for the product.

AI assessment note: “we put a Van Westendorp survey in front of them”

Q Okay. Now, did you start building the early workings of Woven inside of Facebook and sort of decide to spin it out or was totally separate ideas?

A No, I, it was, um, there were, there was a tool that we had at Facebook called the, uh, the meeting tool and eventually became the calendar tool. And the innards of that, uh, were ultimately what, uh, inspired Woven. We had a bunch of problems or things that we wanted to solve at Facebook, which were time-related, starting with how do we allow two employees to meet with each other? But there were also even more interesting time problems when you got into some of the business functions, like for recruiting, how do they schedule large numbers of candidates without doing a huge amount of back and forth? For sales, how do we better prepare salespeople for meetings with customers? Even for facilities, like, can we get better data on how our conference rooms being utilized so we can plan out new buildings more effectively? So all these were different business problems that we had, where having something that the calendar doesn't do, um, but, uh, was integrated with the calendar, um, was, you know, sort of the big opportunity, and that's, that's what gave, um, Rise to Woven.

AI assessment note: “And the innards of that, uh, were ultimately what, uh, inspired Woven.”

Q Okay. Um, how many, I guess, Let's say you decide to put up the paywall after X engagement metric, right? What conversion rate are you going to optimize? Like what conversion rate do you want to hit on for people that you choose to show the paywall to?

A Yeah. Uh, so what we want to see is that for the, first off the, the engaged users that we, uh, are currently tracking those L five of sevens or heavy retained users, we want to see that we get at least a hundred percent conversion of those and that they're, uh, if anything, we're hoping that that, um, That definition has turned to be too strict, that there is actually a broader population of people who will pay for the product who don't use it as actively as, as those. So that's, uh, the first measure of success is, uh, you know, what percentage of our user base, uh, converts to paying relative to the version of the user base that is currently deemed to be heavy retained. Uh, the second thing is then for new users, where we'll care about is those busy accounts. So when we acquire a user who fits our target persona, You know, we want to see that, you know, we're converting them to paying users at least, uh, seven percent of the time, and ideally above that.

AI assessment note: “we're converting them to paying users at least, uh, seven percent of the time”

Q So, so talk to us now about the product today. Is it doing exactly these things, or what spin have you put on it?

A Yeah. So, I mean, at its core, Wolven is an intelligence layer for the calendar. So it makes, it completes the calendar. The calendar has a bunch of things that it is not good at doing. Um, you know, for example, when you schedule with people, your calendar can't automatically do that for you. You usually have to have an executive assistant or spend a lot of time in email saying, here's times that would work for me. What times work for you? Um, and so what we did is we built a bunch of extensions to the calendar to help it do that for you. Now, we, we were very clear that we did not want to replace the underlying calendaring system. So the things that Microsoft does and Google do, we didn't want to get into that business. So Woven is a layer on top of those systems. Um, but it does extend them, uh, pretty richly such that, you know, there's a UI for the product and there's a lot of automation that it can provide.

AI assessment note: “at its core, Wolven is an intelligence layer for the calendar.”

Q measure based off engagement, but finding that right metric is so difficult. Uh, I'm, I'm going off my memory here, but I believe Facebook came up with very simple and succinct, which was, I think like 10 friends in seven days, and that was like a really good thing to get done to, you know, drive lifetime value over time. Do you have that similar sort of statement for woven?

A Actually when Facebook had was what's called L five of seven. So did you use the product five out of the last seven days? And we're using something very similar or woven to track engagement. The 10 friends was basically the, if you got 10 friends, you were much more likely to stay retained as measured by L five of seven. And we have a similar objective there. We, we know that if our users create just a few events, Um, their probability of retention is significantly higher. And so that is the initial onboarding focus is get them through, get them properly set up and teach them how to use the product in some basic, simple way. And, uh, once we get them through that point, then, uh, you know, We can start exposing the more advanced features to them.

AI assessment note: “we know that if our users create just a few events, their probability of retention”

Q right now because you're pre revenue, but you think you're going to charge 10 bucks a month and you probably run some backwards calculations on what percent of the free years will convert. Uh, if, you know, if you paid 12 bucks to get a desktop sign up right now, based off your models, how many desktop signs do you need, you think, to convert one to a paid customer?

A Well, there's a big variable that we haven't been able to test yet because we don't have the paywall in place, which is that final step of what converts users. But what we're using is proxy measures based on engagement. One of the nice things about productivity software is your value proposition is you're helping people save time. So they're not going to use your product if it's not providing that, uh, that value. And generally, because we're going after professionals, busy people, their time is worth You know, six figures or more, uh, on an annual basis. So, um, the ratio there is pretty attractive for them. Uh, and therefore we've, we believe that, uh, you know, the, a moderate to significant engagement level is a, is a adequate proxy at this stage for, um, for paying user. And, uh, you know, so getting back to the, uh, you know, the unit economics, uh, Uh, you know, if it's going to cost us, if we can get those CACs down below three bucks a user, then we can afford, you know, uh, more time to either onboard the user or, Um, you know, we don't have to be as deliberate on, on targeting, uh, in order to get that LTV to CAC, uh, above three.

AI assessment note: “there's a big variable that we haven't been able to test yet”