# How Mojo Increased ARPU 60% In Just Five Months – Michal Parizek, Mojo

Channel: Sub Club by RevenueCat
Video: https://www.youtube.com/watch?v=plEgbUsVzV8
Duration: 21 min
Language: English
Words: 4025
Transcript page: https://viewrankai.com/tools/youtube-transcript/plEgbUsVzV8

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[0:00] Maybe one surprising thing was something what we tested particularly in Asia. It was actually in Japan. It was like a long scrolling pay wall with a lot of information like there was lots of social proof reviews. There was like a good comparison between the free tier and the pro tier. And that design actually worked incredibly well in Japan driving I think 20% lift in revenue. But actually the same design kind of failed in the US. actually in the US more sort of kind of easy to read or more kind of cleaner design with like a slider and the videos in the background and just very punchy messages work much better

[0:38] actually than the sort of very in-depth and very descriptive design for Asia. Hello, I'm your host David Bernard. Today's conversation is shorter than usual and will be featured in Revenue Cat 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, Michael Paris,

[1:02] senior growth product manager at Mojo. On the podcast, I talked with Michael about the experiments behind Mojo's 60% lift in average revenue per user, why a winning payw wall in Japan completely failed in the US, and why not relying on day one for most of your revenue is actually a strength. Hey, Michael, thanks so much for joining me on the

[1:23] podcast today. Hey, thanks for having me. you wrote a blog post uh on the revenue cap blog a while back about how Mojo increased average revenue per user 60% in five months and I've been wanting to have you on since reading that blog post because there are so many little things in there I think people could take away from and not everybody's going to read the blog post and it's kind of fun to kind of dig deeper into the things behind what ended up in the blog post. So let's start there. What are the things that led to that 60% increase in average revenue per user? There was actually like a bunch of

[1:56] experiments we did on mainly payable and pricing and there were particularly three experiments which kind of stood out and and brought that pretty pretty good lift in in Arpoo. One of them was uh sort of like a yearly plan as a as a default. So initially like on a pay wall we've showed the yearly and monthly plan next to each other on like the very first screen very first of payroll screen and then we've tried putting the monthly plan under sort of like a view all plans button so it wasn't really visible on the first glance and that really helped drive uh yearly plan adoption a lot like I think like a 15 or

[2:38] 20% percentage point so that helped a lot actually increasing the new revenue obviously or anything else that you that was part of that that you think really drove that success or you think it just like I mean a lot of people experiment with this but for some people it does seem to work where it's like the you know the yearly plan is listed with a monthly amount and then the monthly plan is listed at a much higher amount. Did you did y'all test that at all of like having the monthly plan maybe like way higher to where it makes the yearly plan look even better or or uh yeah what won

[3:07] in that? Yeah. Yeah, we experiment with that as well. Yeah. So what we've tried and succeeded was actually sort of like a monthly plan encoring. So we essentially added like a small line next to our sort of yearly plan which says like that that price is equivalent of I like 10 $10 $10 a month and that actually work very well in comparison into yeah into that sort of monthly plan which was obviously

[3:30] higher the price was like 25 let's say. So that kind of showed a yearly plan is such a such a good deal comparing in the in the cost. So that actually that worked very well and and kind of interesting. Wells that actually work very well particularly in Latin America uh in Brazil and Mexico. That's super. Why do you think it performed better in Latin American

[3:51] countries in the US? Yeah, because in the US it worked as well but it was the increase was kind of let's say a little bit less like it was like 10% lift in your revenue and in Latin America was way higher. was actually about something like definitely double digit was like 30 to 40 percentage and I think actually it worked better there because that's sort of just my hypothesis but I think because the sort of like purchasing power of those markets is lower than uh than in the US typically. So people tend to care more about like what's what's the price there and they tend to

[4:24] basically care more about yeah the cost of the the apps and the subscriptions they have and if they see that they this doesn't cost much when they actually look at it from let's say monthly perspective like what they pay by month it just kind of persuade them more and triggered more the the conversion behavior there. So yeah we were actually super happy to have their net there and online payroll. Had you already experimented with lower prices in those countries or was it a similar equivalent price to US prices? No, we we did actually was one just sort of another kind of key experiment which drove the

[4:58] 60% increase in ARPU that we did a lot of price testing as well and what some of those actually include also Latin America countries like typically yeah Brazil and Mexico which were kind of the top two geos from that region and so we've actually lowered the price so we we didn't have the equivalent of the US prices that's something what I think usually app store sort of suggests they basically just do the exchange trade right and then calculate. So no no we didn't well we originally had that I think in the past but then then we obviously tested a bit lower prices and

[5:31] it turned out it was actually better. What was the key metric you were tracking during this? Like what in setting up all these experiments, you're testing price, you're testing, you know, pay wall layout, you're testing placements, you know, do you have the monthly and annual, you're you're testing all these things. What was the unifying metric you were looking at to determine the success uh of those

[5:53] experiments? Yeah. So our sort of like a umbrella metric for all those monization experiments was the average revenue per user in the first seven days. So this kind of RPU RPU 7 7D 7D and yeah we've like intentionally used it because we well first of all we saw like more sort of potential in optimizing the new revenue rather than renewals and just because that we wanted to shorten the pay period. We wanted to optimize uh the new revenue because of also sort of supporting the user acquisition loop to allow like higher spend etc. So we want to drive new revenue as we saw that we can actually scale that more and we can

[6:34] actually compound that with the user acquisition and get like more revenue in in total and yeah so we that's why we sort of chose the early average revenue per user and specifically 7 day because at that time we had 3-day trial so we wanted to have the window to be long enough to cover you know those three four days and I think uh it was just 7 day I think mainly because actually revenue get showed like one of these arpoo like pre-default is actually that's actually maybe the first one is actually which was a available is is 7day so we choose like RP 7day just I

[7:13] think because of that so and it actually so it work pretty well yeah so basically we optimize for the new revenue and as you probably know and everyone else in the app business like lots of new revenue is coming from the very early days like very first few days so it was a good metric for just tracking making

[7:29] new revenue. Did you look back on some of those experiments and see the impact on retention? So, you know, did you knowingly sacrifice some long-term revenue for that quick return on ad spend? Typically, what where we look at retention or at least some like a proxy to retention like we use typically 7-day cancellation rate as a sort of proxy for retention rate or for renewal rate what it could look like. And we typically look at uh at this sort of proxy in when we did like price testing because I I've seen data that when you test in different prices particularly like higher prices usually you see sort of

[8:06] higher cancellation rates and lower granular rates with higher prices it's kind of quite reasonable. So, and I remember a couple of tests where we actually like tested a different price and the price actually mostly like a higher price actually turned out to be the winner on the new revenue. But when we actually modeled having that new price for a year long sort of calculating a bit long long-term revenue and we saw that we would actually sacrifice in a long term mainly because the renewal rates just dropped because of the proxy 7day cancellation rate just was way higher than for the baseline price. So then we decided not to do that

[8:43] and kept the original price and sacrifice a bit of yeah less new revenue but sort of having more new revenue in the long term. One of the things I talked about in another one of these state subscription apps podcast was how a lot of times experiment results don't stack. So you get a 10% win here and a 20% win there and a 15% win here. And then then you look at it at the end and you actually haven't raised average revenue per user by the the sum total of all those experiments. You're kind of like getting 10% here but losing 5% there and getting 20% here but losing 10% there. What do

[9:23] you feel like was the key to actually stacking those successes? Were you looking back as you tested each of those to see if it had a negative impact on some of your previous tests to be able to generate that such a large increase in average revenue per user essentially like executed a bunch of yeah pay one and price test and when whenever we seen there is a sort of like positive results whenever we've seen yeah there is a there's a statistical significant winner usually we've sort of retested also in other markets so usually let's say we've tested in the or in the Europe and if it works there then

[9:59] we've retested in other our key markets to make sure that actually it will work there and it happened a couple of times that something we've only rolled out in some of the markets and not in let's say all over the world just because it we haven't really seen a positive impact sometimes also negative so we make sure that particular change is actually bringing additional incremental revenue you know in all those key segments are according the geo typically and if we haven't seen that then we didn't roll it out and then sort of in a high level we've monitored the the RP 7day typically actually in the revenue get

[10:35] charge to essentially make sure that for the new cohorts of users we actually seen that lift uh in the AR of course it's sometimes sort of hard to isolate it from UA changes from other externalities but and we tried to our best to see if actually after rolling out we're actually seeing that and and yeah and then then yeah luckily we were yeah we actually saw that yeah we implemented changes and we seen that it's actually going up the RPU and we were sort of happy to happy to see that

[11:07] and and kept that process keep going. Uh were there any surprising results like something that worked incredibly well in Brazil but was terrible in the US or something amazing in Europe that failed in Latin America? Maybe one surprising thing was uh something what we tested particularly in Asia. It was actually in Japan. It was like a long scrolling pay wall with a lot of information like there was lots of social proof reviews. There was like a good comparison between the free tier and and the pro tier. And that design actually worked incredibly well in Japan driving I think 20% lift in revenue. But actually the same design kind of failed

[11:48] in the in the US. actually in the US more sort of kind of easy to read or more kind of cleaner design with like a slider and the videos in the background and just very punchy messages work much better actually than the sort of very in-depth and very descriptive design for Asia. Yeah, that's fascinating. I did want to talk now about placements. So, you know, of course, as with most subscription apps these days, Mojo does show a payw wall on onboarding, but I was surprised to see the stats that that only 50%, and I say only 50% because in the revenue cat data, we see more like 80% of payers

[12:25] across, you know, the entire subscription industry happen in that first day. So, what else have you done to drive those conversions after the that initial uh onboarding? Yeah, that was actually that was actually the number one thing which surprised me the most when I actually joined Mojo. It was my first sort of mobile first kind of business job I've did that actually there's so much actually revenue coming from the very first day. But then I learned actually it's very normal and it's even like lower than just yeah most apps do like 80%. And I think actually the that the number like 50% is actually it's actually a good signal that if the

[13:03] app actually does that I think it's it's a very good signal for the app that it can actually drive a good revenue also from existing users. It's it's likely have a good retention and essentially a lot of existing users and it's kind of sounds to me like a really very healthy sort of behavior and and and and signal. There's a I think a couple of things I think why Mojo has 50%. One is that the free tier is, you know, is actually I think it's pretty good also comparing also to some other competitors. Actually in the free tier you can actually can do quite a lot. So it's not that restricted

[13:38] like maybe some other ads. Uh there's a lots of features which is actually available in the free tier lots of content. So that's one thing. So like the let's say generosity of the free tier which was intentional. The other aspect is that we've run also like the payw wall campaigns for existing users which I think not a lot of maybe not most of the apps actually do and I think it's actually one of the kind of underrated things which I think most of the apps should do. So essentially is running a payw wall campaign. So, so is she triggering the pay wall in certain behavior for existing users either as an

[14:13] app open like we did in Mojo or after some key behavior event like when you yeah do something key in your app like I don't know sharing something or whatever the key kind of engagement event is. So that payroll campaign actually drove I think about 15% of new revenue from the existing user base which is yeah just pretty a lot and it's super simple to set up and it wasn't really kind of having any negative reviews on that from the users. So it's kind of a no-brainer

[14:43] to have that. Yeah, that's surprising. So so free users they open the app and immediately see a pay wall. Yeah, they hit a pay wall. Yeah. And actually it worked. Yeah. I was and quite a lot of users actually Yeah. It actually drove a lot of revenue. Yeah. Did you check churn and other things? Like I I mean I guess that's the thing

[15:00] is like you do always kind of trade off. So maybe there was a little bit of churn but then the additional revenue kind of made up for that but any other things you track like you said there were no negative reviews which that that's shocking to me. People seem to go out of their way to like you complain about things like that in the app store reviews but support retention like any any downsides to this payw wall on app open. It was on the app open, but it was I think the frequency is set to one pay wall per week. So you don't actually even if you open the app every day and a

[15:33] couple of day couple multiple times a day, you just essentially have that payroll only being triggered once a week essentially the frequency was pretty low. So which also I think played some role why users weren't complaining. But I I specifically actually asked a colleague from support and he didn't really mention that user complaining. Uh I did once like a quite thorough analysis of all the reviews and try to find like what users you know complain about not really from that particular reason but I was more interested like what user like what I didn't like and I actually haven't really seen any specific negative about having a pay

[16:06] wall a lot of times displayed etc. So yeah, so actually it was a good thing here for us. It's surprising sometimes when people are getting something for free, they tolerate more than we assume sometimes, you know, like uh I know a lot of games and even some regular apps have full screen interstitial ads. I mean, Dualingo is actually a great example of this. You know, they they do it very tastefully, but they have a full screen

[16:31] unskippable takeover ad after a lesson. So, I think people do kind of intuitively get the exchange of value there of like, oh, I'm using this completely for free. And I think you can get away with it more if you're premium tier is very generous, like you were saying. And maybe that's kind of the key. It's like if if your premium tier sucks, one, you're probably not going to get much retention anyway, so not many people are going to see the app open pay walls. And then two, like they feel like they're, you know, pulling one over on you or or, you know, getting something getting, you know, they feel like

[17:03] they're getting a lot of value. And so when they see that that uh payw wall, it's probably less offensive in those kind of situations. So, but fascinating how how well that worked. 100%. Yeah, I really encourage everyone to essentially Yeah, just try that. Just say, "Yeah, test it, AB test it, just measure it." And I think yeah, most of all you will be surprised that the users will not react the way how you kind of offer fear they react. So it's uh I definitely would recommend just trying things out. The last thing I wanted to touch on was experiment velocity. You all did a lot of tests. H how do you

[17:38] keep up with that? How do you isolate variables? Yeah. How do you think about testing velocity? Oh yeah, that was it was I think super important. And I actually think that it's the kind of number one growth asset as a yeah as everyone who actually wants to optimize pay walls and pricing. If you have good research, good prioritization, if the velocity is I think even kind of it's even maybe more important because there's more shots you take, there's essentially higher chance you win. There's a shorter sort of feedback loop, better learning cycle. So it's it was a key and yeah I did a lot of experiments and I think maybe the

[18:14] number one thing which allowed me to do that was having kind of a third party payable platform allowed me to being autonomous and and iterate fast and and really kind of shorten the the cycle of actually like coming up with experiment developing it launching it from yeah months or weeks to to to essentially days. So that it was so kind of important that I think without it I would definitely wouldn't be able to get that amount of experiments live in yeah in that short time. That makes a tense sense. And you know Mojo is a pretty big app and you were doing a ton of user

[18:49] acquisition. So you did have a lot of users coming in to be able to do the test on. But did you have a particular testing velocity? like would you be launching it on a weekly cadence or even like 3 days and then wait for the data to make a final decision but while you're already kicked off another test? It was more weekly or bi-weekly cadence but what was also important to say that we mostly run experiments. We typically kind of broke down the audience the the new users to sort of three buckets according to GIO. It was like a US or kind of US, Australia, Canada kind of

[19:25] English, US English packet. Then there was the Europeans and then there was Latin America. And we typically had like this streams of tests for each this specific kind of geo bucket and we've run tests in all of them three in parallel and each test kind of lasted usually one or two weeks because in those three GE those are kind of this three key geo segments we had and we were able to get the statistically significant results usually in those one

[19:55] or two weeks for each of those buckets. So we were able to to conduct experiments there. I think a great way to summarize this whole episode is you should be testing a lot. If you're not, you're leaving revenue on the table. Again, it's just such a clear example that you were able to increase average

[20:11] revenue per user by 60% in 5 months. That is such a huge win for the company ability to acquire new users and then you know over the long haul building that subscriber base over time. So such a fascinating lesson and thanks for for sharing your insights today. Thanks thanks again for having me David. It was really yeah a pleasure to talk

[20:30] with you. Anything else you wanted to share as we wrapped up? I encourage folks if you want to learn more about your payroll testing and monetization just you follow me on LinkedIn. I tend to share some of the advices and experience there. So yeah hopefully you can learn something more. Awesome. We'll put a link to that in the show notes. Thank you so much Michael

[20:47] for joining me today. It was really fun. Thanks a lot David. 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.

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