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Dynamic Paywalls That Drove Millions in New Revenue – Shawn Gong, Tinder
00:23:15
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- [00:01]
A
Welcome to the Sub Club Podcast, a show dedicated to the best practices for building and growing app businesses. We sit down with the entrepreneurs, investors and builders behind the most successful apps in the world to learn from their successes and failures. Sub Club is brought to you by RevenueCat. Thousands of the world's best apps trust RevenueCat to power in app purchases, manage customers, and grow revenue across iOS and Android and the web. You can learn more@revenuecat.com let's get into the show. Hello, I'm your host, David Bernard. Today's conversation is shorter than usual and will be featured in revenuecat State of Subscription Apps Report. Each episode in this series will explore one crucial topic and share actionable insights from top subscription app operators. With me today, Sean Gong, a product leader building monetization and growth engines for consumer unicorns, including Tinder and Grindr. On the podcast, I talk with Sean about how Tinder's machine learning powered paywalls drove millions in new revenue, the art of selling features a la carte without killing subscription revenue, and why Tinder select flopped despite users saying they'd pay for it. Hey Sean, thanks so much for joining me on the podcast today.
- [01:27]
B
Thank you so much, David, for having me. Such a pleasure. I always love your newsletter, podcast and all the content. So it's, it's what an honor to be here with you.
- [01:39]
A
Oh, thanks so much, man. Well, so I've had several ex Tinder employees on the podcast, but I'm super excited to get you on because I haven't gotten to talk to somebody who's been in the trenches the last few years. And Tinder's done a lot of cool stuff on the monetization front. So I want to dive into a project I know you, you worked a lot on and that's AI driven pricing. So what was the problem, what were you trying to solve and how did things go?
- [02:05]
B
Yeah, I'm very excited. So let me start with the core problem because that's really shaped everything we built at Tinder. So the user problem is decision overload. Simplify to put that way. So as you might know, Tinder has a lot of purchase options from multiple subscription tiers and then under each tier there is multiple plans like weekly, monthly and then we have a lot of a la carte products. So it's great, right, because we have so many products for user choose from. However we talk to users and we notice two things. At least one is some user. They bought the Platinum, the highest subscription tier we offer to everyone simply because that's the most expensive one. So their belief is that, well, I'm willing to pay the most and I'm going to get the best. So I'm going to buy Platinum, which is great. However, we noticed some of the users who bought Platinum, they only use the features under our second tier Gold. So they'll realize, oh, actually you bought something you didn't even fully use. You could have just bought our Gold subscription tier to save you money. Or maybe another issue can be like, we can do a better job to educate users. Hey, there is other feature from Platinum. You should take advantage of them. You haven't used them yet. Another thing we notice is some users, they didn't buy anything simply because they were overwhelmed. It's like, oh my God, so many options, I don't want to pick. And then also reality we learn is maybe David, you have your own experience. You know, when you look at particularly your revenue cat specialize in paywall. Right. So one common mistake a lot of company make is they describe all the features or benefits. The benefits are better than feature. Yes. However, do you think user going to read through them, make a decision? No, that's what we thought user going to do. Okay, let's compare Tinder's plus Tinder's Globe. Tinder is Platinum and then compare the other car and then make decision. No, user make decision within a second. So it's our job to help them to make decision better. So that's from the. Basically user problem is like decision overload and then so it's overwhelming. And then from the business problem is, of course that leads to relatively lower conversion. Right. Because for some user who wanted to buy, but they were overwhelmed, they decided not to buy and then that lower conversion. So that's a classic case of misalignment incentives. So we wanted to maximize revenue, but our UI experience made it harder for user to make a confident decision.
- [04:45]
A
Yeah, And I mean Tinder famously, I mean, this is why I have you on. And so many people in industry have talked about Tinder because it's led the way in these hybrid monetization. But having three different levels of plan and then, you know, y' all test it out a fourth level of plan. We'll talk about that toward the end, hopefully if we have time. But it does get super confusing. And then you have the super, the boosts and the other in app purchases and things like that. So then what did you do to actually to solve that and to make it more accessible to folks and getting people to the right plan.
- [05:16]
B
So that was a challenging. But luckily we had A brilliant machine learning team. So when I talked to the team members and then they told me hey, we can try to build ML models and then use that to predict users willingness to pay and then we can surface the best product they might most likely to buy. So that's our solution because based on the insight we talk about is like people, customers don't need a lot of option, they need the right one. Right. Think about like Netflix, you know how many users like oh my God, I don't know what to watch. There are so many choices. They end up spending like hours scrolling and then not making watch anything. That's why Netflix have something like top picks for you, right. Based on your previous behavior and your ratings, they predict what you're most likely to watch. So solve that problem. So very similar. So this is a huge moment for us I think particularly even for Tinder for the industry is we shipped from stale pricing to like dynamic pricing. So we don't show the same paywall, same product to all the users. So you know, think about, let's say David, you are willing to buy Platinum, why we should we show you plus, right. I mean that's a problem not just from business perspective. We won't be able to maximize the revenue and then we can use that to reinvest for, to make product better for you. Another problem is you don't get to enjoy all the best benefits we can offer. Right. Simply because you chose the plus. So that's what the solution come within. We use a machine learning, we train the model and then we eventually release a model for that to predict and serve the best SKU for customers.
- [07:02]
A
How do you test that? Like what was the AB test? It was just the standard kind of deterministic, normal. Everybody gets this paywall at this part of the flow and then everybody gets that paywall at that part of the flow. And did you just do, do an a B test to prove that the ML decision model was better than what you had before?
- [07:25]
B
Yeah, spot on. Yes. But obviously to reduce the risk we couldn't just like start it from all the paywalls, right. Because we have a lot of different paywall and then the same times that's very expensive to and it takes a lot of time and then effort to train the model to, to test a model. So we start with something small so we test with a couple features and then just you know start with not all the scope. So we can see hey, based on this machine model to historically, you know like a version a control will be same, you know, like as we used to show the paywall and the products to the new one, this is dynamic. We will change based on the basically when they show a user, let's say for David and then our paywall will ask the ML model, hey, which paywall should we show? And then the paywall will decide okay, which product we should recommend to David. And then that's how we test this treatment. And then we'll be able to measure the conversion and then the total revenue.
- [08:30]
A
And how did it do?
- [08:32]
B
It's great. Yeah. So yeah. So based on our prediction, I think it's definitely multi million dollars annual increase for Tinder. I also want to clarify, you know, for that is like it's not just like maximum revenue, it's like an evil business plan. No, it's like the money we're going to use, we can reinvest to the product. Right. So we can improve our user experience and then build more like benefits for customers. That's why when I talk to a lot of startup founders, I always advise them, hey, don't wait to think about your monetization strategy. You know, you don't feel bad to charge your users. They want to to pay you and they deserve it because you're going to provide better experience for them, not mentioning to compete with your competitors and you'll be able to move faster and sooner.
- [09:22]
A
What about countermetrics? Was there anything you were watching to make sure things didn't go south in retention or user experience? That people weren't happy with the plans that they were presented?
- [09:34]
B
Tinder has a really good process in place. And then we required to also measure our contract metrics. This is great because you know, you cannot only focus on one area. For example for this case we cannot just only focus on the first time conversion and then only the revenue amount. Right. So we have to measure, hey, let's say David based on our model, maybe prior to that he will buy A plus. Now he bought Platinum. So we want to check, hey, is David going to come back? Is David going to buy Platinum again is going to cancel. So we definitely measure this long term success metrics to make sure this model really served correctly. Because that's nothing. We wanted to continue to work on like most product features, right? It's not like launch and down. That's it. Like we have to optimize iterate. So we wanted to learn, hey, we actually have different models, we built a different ones. We want to compare so we want to see how they perform between those models same time how they perform compared to the paywall without the models so we can make sure we focus on long term success. For the takeaway for this example is the real unlock wasn't just better pricing, it was a better decision design. Right. Helping users to choose a product they truly fit them. So for your product, any founders or startups out there, even you don't have a ML team yet. Um, I think maybe the simple way you can do is design three products or tiers for the customers. Why? Because that's a very common or easy way for you to pick from right? Someone want to buy the most expensive one, some of them just want to buy the cheapest one and then someone don't know how to make decision, they buy the middle one. So that's usually the simplest way for you to design your product here because actually change how decision are made. Let's say David, if you only offer one product so your decision is should I buy or not? But if a day we show you three products now you are thinking which one should I get? So you bypass the yes or no and it's more likely to convert.
- [11:54]
A
Yeah, that's fantastic advice. I. I do wish we all had ML teams. Maybe that's something at revenuecat we should be working on to help folks build those kind of sophisticated things into their own apps. But anyways, I did want to move on to this idea of monetization unbundling is that you know, not everybody wants the full subscription. Not everybody is going to fit into the gold, the platinum. So yeah, how do you think about unbundling that?
- [12:19]
B
We actually, you probably know we had some bundling products already a la car such as like Boost, super likes. I think a couple thing you want to think about like besides one issue you've already highlight is not everyone want to have a subscription, right? Subscription is great. I mean don't get me wrong because you get a package it's much easier than you have to decide which features you wanted to pick from and then to buy that way. It's painful, right? It's easy to buy a whole package and then repeat it renew automatically. So you have to think about twice. But there are some use cases think about like Boost. I think it's perfect a la carte product. Why? Because you don't. It depends on when you want to use it, right? When you don't receive enough likes so you want to boost yourself when let's say you swipe during the peak hours so you wanted to use that so that makes sense. Super likes too right? Because it's like well I don't know how many super likes I'm going to send? It depends on who I see in the app. Right. And another use case we decided to try is the travel mode also AKA passport mode. It's allowed any users to see anyone globally. So so cool. You know like let's say you can go to like Paris, go to Spain, wherever you want to go based on your needs can be basically you want to travel there or you want to just meet people there. And the thing about that feature is like you may not everyone wants it, right? Maybe because it's very special case for needs for certain people. In that case that feature is a great candidate as like an unbundled feature standalone. So that's how we think about what features make sense to be unbundled and design for our customers.
- [14:01]
A
How does the passport mode work? Is it a one time purchase for a limited time, Is it an add on subscription or how does it work exactly?
- [14:09]
B
Yeah. So historically this feature is part of a subscription so you have to get a subscription to use it. And then but like we talk about, the challenge for that is for some users they only want to use it when they plan to travel or to meet people outside of their home city and then they might not want to buy a subscription. Right. Because they just don't want to have that commitment or they don't want to, they don't feel like they need to use other features. So that's a perfect candidate for them. So we test out the features so that helped us able to capture non subscribers for the users who just want to buy other car for this passport feature and then they can use it for like one day, three day a week based on the offers we provided and then they can enjoy that benefit to meet people anywhere in the world.
- [14:57]
A
So how have things been going with the passport feature and it being unbundled from the subscription? Have you seen uptake?
- [15:05]
B
Yes, actually I think it's so fun to talk about this because it was not launch and then success story. That's it. Not that simple. We learned a few things based on our test. First thing is when we launched this standalone a la car passport feature and then the conversion went crazy. It was great. However we noticed like oh, that hurts our plus subscription because some user decided only by this feature now to buy subscription. But that can be a couple of things. So one is oh maybe we price too low, it's too affordable and then so it's like no brainer to user use it. The second thing we did is I hope we should increase the price for auto cart and to see how that would change the conversion and then subscription cannibalization. So we definitely noticed like the subscription cannibalization reduced as we expected and then the conversion reduced a little bit, but total revenue actually went up. So that was very positive. And then we continue to test because we wanted to minimize the subscription cannibalizations. So and then we increase the price again and then also have upsell on the paywall. Basically we show, hey David, you can buy tax Passport standalone a la carte, but also you can buy plus subscription. And then we use a very interesting, like a kind of psychology thing basically for our seven day passport feature a la carte, the price is the same as seven day plus subscription. So that case, David, you think like, oh duh, then the seven day plus subscription is a better deal, right? I can get it more than this feature. So that also helped of course, means like reduce the cannibalization, also increase the conversion revenue, but it's still not good enough. So we decided, hey, let's use our perfect ML model again. So we will show users subscription first. If you don't want to buy subscription, we show you other car. So we gave you a second chance.
- [17:06]
A
I love hearing these kind of stories because I just think Tinder's been around so long and so many of us in the industry look up to it as a pioneer in monetization in apps. And I just think so many apps are leaving money on the table by not experimenting with these kind of things and not trying all the different, you know, three tiers. And you know, and like we said, it can lead to confusion. It's not the right thing for every app, but there's, there's just so much opportunity to meet users where they are and charge them. And then speaking of which, Tinder select was a really interesting experiment. So I'd love to hear from you how that went. You know, just, it sounds fantastic and we've, we've talked about on the podcast many times before, you know, add a super premium tier. You know, we talked about it earlier like some people will just buy the most expensive thing. So sounded like a really interesting idea. How did, how did things go with Tinder Select?
- [18:01]
B
Yeah, I think Tinder select is a very interesting case study in terms of like the product market alignment and the behavior science. So let's start with the hypothesis. You know, basically the opportunity Tinder was exploring and why Tinja created Tinder select. Because Tinder has a massive global user base and then we also have some whales, right? You know, Basically the user who are willing to spend a lot of money, they buy a lot of highest subscription and then ala card they spend a lot of money with us to maximize the outcome. So we definitely saw hey, there is a waiting use to pay out there. Let's try if we have an even higher end tier and then provide even better service and product, would the user buy them not. So that's basically the hypothesis that we had. And then so that would be different segment we're targeting obviously. Right. And then same time for the. I think the challenge are two things. One is the identity fit and the brand positioning. Right. You know, because for any product not every company can just offer a high end. It's really rooted into like what is your brand is and then how customer thinks like a good fit or not. Right. I think for Tinder because we catering for the massive population. And then in that case it's a little bit difficult to. For some of you that feel like oh you know it's a good fit for me to use it. Think about that, David. If we tell you hey, you're going to pay $499 a month for subscription and then on Tinder and then when you come to Tinder you see people you might be able to see even just use a platinum subscription now you might think like okay, I don't feel that special here. Maybe you give me some special treatment, other benefits but I don't feel like the environment, people here that are special. Right. That's just difficult. It's very common. You know, think about like a club too. Right. It's like exclusive one, very expensive one versus just like a regular one. But you have expensive table. So think about that scenario. I think that's why make it challenging. It's like a luxury offering. But people might not feel the benefits meets their expectations.
- [20:11]
A
What's the future of Tender Select? Is it going away? Is it going to evolve?
- [20:16]
B
Yeah, I think we're just not going to expand much further and then just gradually scale down for this one it's not like a fail. It's basically like we learned what we initially wanted to and then we realized it just not worth the continual investment and the maintenance for us to have this feature. It's not the best fit for us. We have better bets to continue.
- [20:42]
A
Well, I think it's super cool that you all tried and a great lesson as always that people don't always do what they say in a user survey or a user interview. So always a great lesson to remember but you should try and that's the great thing. And then you did and you learned. Is there anything you want to share with the audience as we do wrap up?
- [21:02]
B
I think the most important thing I want the audience to realize or understand is don't treat your users as they are logical human beings. So what I mean by that is you cannot think, you cannot be like, oh, let me design this product and the user gonna read everything on the screen. They gonna to make decision based on information I share with them and then make a purchase decision. Whatever they want you wanted them to do. Right? No reality. They don't do that way. They don't follow your user experience. They don't just read everything to make decision. So they are like us, we make emotional decisions. I think that's very important. So that's why we have to really observe how they behave and talk to them and to really truly unlock the emotion behind that. Otherwise you are going to make wrong decisions.
- [21:57]
A
Where can people find you? Or are there any jobs you wanted to shout out at Tinder?
- [22:02]
B
Oh yeah, I think Ginger definitely still growing has a lot of fun opportunities. So encourage people to check out. It's one of the best places I've been because it's really fun culture, very progressive and then really care about people's lives. When I go to the cafeteria every time I get emotional because I see the wall. We show all the users who wrote letter to us show their wedding pictures. Oh, you know, like our work made a difference, you know, made people having a new family together. How lovely that. And then sometimes also if you wanted to grow your product and without hurting your retention. And then I can help you to unlock user emotions and design defensible growth loops. And then you can find me on LinkedIn and then mentor cruise.
- [22:49]
A
Awesome. Thank you so much for joining me. This was a really fun conversation.
- [22:53]
B
Yeah, thank you so much, David. It's always my greatest pleasure talking to you.
- [22:59]
A
Thanks so much for listening. If you have a minute, please leave a review in your favorite podcast player. You can also stop by chat.subclub.com to join our private community.