00:22:39 · Sandy Diao
I I think a lot of that, you know, early insight data really pointed us in this direction of saying this is going to work. But I think the lived reality is that when you actually run the experiment, users behave very, very differently. When I kind of reflect on that experience, our biggest learning here is that different types of users have different expectations about the value of these different pricing models. What we found when we segmented the data is if is that businesses or more of that b to b customer had value from that reverse trial because they could essentially kind of leisurely go back to it and test it out over time. And they didn't really have this, like, day to day DAU, WAU distinct need for it. So it felt more aligned with their value model. But for a lot of the consumer, prosumer creator type users using the platform, it didn't make sense to them. Right? Because they just didn't have the two week bandwidth or timeline in their minds for making a decision around a video editing tool. They're like, I need to cut a TikTok short right now. I need to cut a YouTube video right now. It's like, okay. I'll get this thing done, and then I'll disappear, I'll never come back. It doesn't... It didn't really help reinforce the idea of the value of the product. So, you know, we ultimately ended up reverting back to our original pricing model, but that's an example of how that early data really pointed us in the wrong direction.
00:23:58 · Guillaume Moubeche
[00:23:58] S1: Interesting. Yeah. And for me, you know, like, I've seen it multiple times that whenever you want to optimize for certain metrics, you might also, like, compromise a whole other sets, you know, of metrics. So if you want to optimize, let's say, for customer expansion, you're gonna reach out more to existing customer trying to oversell something, and some might actually, like, not like it and churn. So your churn might increase, but your expansion... So you have always, like, side effects. So I'm curious to know, like, from your perspective, whenever you have like a marketing budget or a growth budget, what do you... How exactly do you split it between these are the experiments where I wanna measure, like, a clear outcome versus, you know, like, these are, let's say, belief we have, and kind of like moonshot projects, where we know it's gonna go in the right direction, we know we're gonna not be able to measure exactly, you know, like the impact it has on the growth, but we believe that it's aligned with our brand, who we are, and we have a high conviction that it's beneficial for the company.
00:25:09 · Sandy Diao
[00:25:09] S0: Yeah, great question. Generally, for the growth teams I've led, there are two types of budgets. One is the time budget, and the second is the financial budget. For the time budget, almost all the time, we want to over index or shift most of our time resources on big bets or big swings. Right? We basically wanna find the power distribution channels that will drive the majority of our growth. And if we don't take enough of those swings to find what those channels are, we're really gonna suffer because we're gonna be testing, micro optimizing, you know, focusing on a a local minimum rather than the ultimate global maximum. And then there's the financial budgets. I I think for the financial budgets, we wanna be indexing most of our financial investments in primarily the things that we know are giving us somewhat measurable unit economics. So channels like paid ads, for example, influencer marketing, sometimes SEO if you have sort of this, like, content... The cost behind content creation, although, of course, that's changing a lot with AI these days. But I I think, you know, we generally think about our our budgeting in that way. And then for more of the time investments, we want directional outcomes. Right? We want essentially it to show some results that this is working at a scale or a magnitude that we care about and that's gonna drive a ton of users for us. And then we can get a little bit more detailed and nuanced about how much incrementality or the precise attribution method to use to properly attribute growth there. But the... The, you know, more of the financial budgeting, we probably wanna be pretty precise about that from day one, because the input or the growth lever there is gonna be budget. So that's just gonna be able to scale in the manner that we're able to prove out there's LTV to CAC and that there's actual ROI and return on ad spend. So that's how I've traditionally thought about it.
00:26:58 · Guillaume Moubeche
[00:26:58] S1: And I think you talked about, like, the the big swings at Descript. I think you you did something that generated, you know, like, a lot of the the revenue with... Can you please, like, share a bit the the growth loop that that you put in place there?
00:27:16 · Sandy Diao
[00:27:16] S0: Yeah. Descript is a, again, an audio video editing product. A lot of the users there are gonna be content creators, whether that's an individual content creator or a business content creator. And if you really think about the product without referencing any data, if you really try to have intuition about it, you'll figure out that the product itself is really meant for users who have their own built audiences. So this idea is essentially that every user that you acquire, that content they produce is meant to be seen by many, many other people, which means that in theory, there's this opportunity or this network effect, a user to user network effect, where one user can always equal n equals, you know, more than one user. So in order to make that true, we need to figure out how do you tap into the distribution that your users already have. And so one of the pieces of intuition I had here was we can essentially figure out how to get content creators to sell Descript on our behalf. But how do we do that without me reaching out to every individual content creator setting up some kind of content or partnership agreement there. And so one of the experiments I ran, even despite not having a lot of firm data around this being a growth lever for us, was setting up an affiliate program. And the affiliate program was very simple. We used a third party tool. We didn't build anything special or in house. And we essentially used this tool where people could sign up for a tracking link. That tracking link would measure the conversion of a Stripe event, meaning that somebody paid, and they would get a, you know, a percentage or a kickback on the referred paid subscriptions that they drove to our business. And we would just send this out to new users who signed up. In our onboarding emails, you know, not even that openly, but in the footer, it'd say, here's the affiliate program. You can go to our website. There'll be affiliate program. And when we first launched, we obviously made an announcement email, allowed for us to get our first, base of affiliates. And we launched it, and we said, let's keep the cost minimal, the build minimal, and see what happens. And it turns out a lot of people were interested. We opened the program to everyone at the time, so anyone and everyone could become an affiliate. We learned a lot of lessons along the way.
00:29:20 · Sandy Diao
Some affiliates are really bad. They do things like bid on your brand terms through Google Ads, which is terrible, and we eventually banned that, of course. But then there were also really great affiliates. As soon as we launched it, we saw organic content just pop. Right? If you went on YouTube, you saw a lot of tutorial content. You saw a lot of videos. We thought this is great. When you actually looked at the results and the data, you saw that they actually did refer paying customers because they're so... Creators understand the product. Their job of content creation, when we talk about the product, it comes across very authentically. And so by putting the link to the product there, even the creators themselves don't feel like they're running too much from an ad per se because they're running these workflow videos. So that ultimately netted us in realizing that content creators themselves, whether through affiliates or even eventually paid placements through influencer marketing, was gonna be a massive channel for us. And that eventually, you know, drove, you know, upwards to a third of the the business for, quite a number of years. And that's something that would not have come about if we were to have diligently looked at our dashboards, our traffic sources, and try to, you know, be overly analytical about what was going to work. It just really came from this understanding of who our users were and this intuition about the space itself.