Episode #35: How to Maximize a Mobile Game’s Business Potential with Ivan Trancik

In episode #35 of our podcast, we sat down with Ivan Trancik, SuperScale CEO and Founder. We talked about the mobile game industry, LTV growth and how to optimize a game's content for players.

In this episode of Mobile Growth & Pancakes, Jonathan Fishman (subbing in for Esther Shatz) is joined by Ivan Trancik, CEO and Founder of SuperScale. They discuss LTV growth via content personalization and the impact on mobile game scalability.

Check out all the other episodes of Mobile Growth & Pancakes here.

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“Look at the opportunities to learn because everything is about learning and growth margin. You need a marketable product. If you have a bad product, there’s nothing you can do. I’m an analyst, and I couldn’t fix a broken game. That is why you need a good product and team, along with the opportunity to learn and also fail.

Ivan Trancik

Key takeaways:

  • SuperScale is a technology solution company that helps games grow to their maximum potential. The team takes a holistic approach to find out a game’s weaknesses and comes up with solutions to increase performance. The company works with various game types, including hyper-casual, casual, and hardcore.
  • Translate the process of game growth into a business model. Identifying the best methodology of growing a game starts with analyzing things like UA and ASO. The next step is to understand how to optimize content for the players. 
  • There are several ways to acquire new quality players. Firstly, check the data to see if players play the game often. Secondly, identify the causes of the game’s negative results depending on its category. Thirdly, think about your game and how much you rely on it, especially when those spenders approach your user acquisition strategy.
  • Whether you monetize based on in-app purchases or through ads, focus on figuring out the true ROI on your budget spend and the performance of the channels you’re using. One challenge is the lack of effective optimization. Try diversifying the campaign mix and do some tests to evaluate potential results. 
  • Many things can go wrong in acquiring and integrating data and making the right decisions. One problem is data storage. Find out ways to integrate with a product to distinguish the devices from the same account. Take into account how data changes impact the performance of UA. Get a system to interact with the data and analyze simple business cases.
  • Within a purchase optimization, the change requires more expenses than optimization in the top end of the funnel or within ads. Focus more on the early stage in purchase optimization techniques to help the UA channels be more effective in finding you. 
  • For in-app purchase optimization techniques and strategies, you need to have a healthy economic situation. Start with less money and simple plans. Then figure out what the player wants and how much they can afford to pay to buy the content.

Maximize growth with iOS 15’s In-App Events



    Full Transcript:

    Jonathan Fishman: Hey, everybody. Thank you for joining another episode of Mobile Growth & Pancakes. I’m your host, Jonathan Fishman. I’m VP marketing here at Storemaven and today I’m really excited to have here with me Ivan from SuperScale. He’s the CEO and co-founder. How are you?

    Ivan: Very good. How about you?

    Jonathan: Pretty good. Pretty good today. Where are you based? Where are you calling from?

    Ivan: Well, there’s a story behind that, but currently we’re based in central Europe in Bratislava but the company was actually founded in Nordics. The first two years in Sweden and Finland and actually two years in London. Even my daughter was born there. I felt that I’ll be a Londoner for the time being but as I went to visit my parents, to show their granddaughter for the first time last March. We landed in Bratislava and 48 hours later lockdown, global pandemic. [laughs] All airports closed in London which was the worst place to be at the time. I’m like, “Okay, I guess I’m back in Slovakia again, stuck.” We have a big team there so over 60 people at this point. It was just nice to be in our main office. [unintelligible 00:02:00] as well. [laughs]

    Jonathan: Yes man, I’m sick and tired from these– There’s a new variant now everybody’s talking about. I hope it will end sometime but we’ll see. Do you want to tell us a bit about SuperScale and what you guys do?

    Ivan: Sure thing. Our mission at SuperScale, we scale the games to their maximum business potential. Our specialty, or our insights, if you find out that my whole career as a business analyst especially working with games is that the industries are so fast-growing and complex that there’s no such thing as a perfectly organized game. Any game from whether top 1,000 top, 100, top 10, we think can be more successful and better optimized for growth. This is where we step in. We holistically look at the whole game and its business model and find out what are the weakest ways, how to get more business performance out of schedule, or whether on the top end, middle, or the bottom end of the funnel. This is something we can discuss.

    Jonathan: Cool. Which type of games do you guys work with usually? Is it mid-core, hardcore, hyper-casual?

    Ivan: No. Actually, unlike nowadays, it’s anything from really hyper-casual, to casual, casual mid-core, or to hardcore. The platform, there is also a bit of evolution when I started working on games eight, seven years ago. I actually, with Facebook started with Facebook and worked these games then transitioned into a 50/50 mobile and PC console, also for bigger titles there. Then it became 95% mobile because of the pure growth.

    Actually this year with the craziness around the NFTs and also the changes in the mobile ecosystem, we see web-based games are getting a bigger portion also in our portfolio and they have what we want. Actually, throughout the history, I would say most of our work was in mobile, something around. I’ll say right now web as a possible platform thanks to NFT increment.

    Jonathan: Awesome. Today we want to talk about two things. The first is a methodology to maximize a game’s business potential and its growth. The second topic is optimizing in-app purchases and monetization in general again to increase growth. Let’s start with the first topic. Can you describe in general terms your methodology or how you approach, getting and start to work with a new game and try to identify how to grow the game in the best way possible?

    Ivan: Yes, absolutely. Something maybe I will start describing what’s the usual way of how we see game companies approaching publishers approach of this topic. What you usually see is still a pretty big disconnect between the user acquisition and growth teams and their way, how they look at the game’s potential, growth opportunities, and properties. You can see, for example, if you talk about also how they manage their data, how they approach their priorities, and so on.

    This is where we also see a lot of ways of how it could be done better. It’s one thing if you look at games as a business model and look at it from the point of the player’s life cycle. First of all, obviously the top end of the funnel, you need to get the player some wage, probably through some paid user-acquisition campaign. We’re showing you some ads, some of them will click, some of them will go to the store page and install the game.

    Some of them will even find the icon nature and start it right, but we already have had multiple steps to look optimized even before the player has two solid games. Obviously, this is what I would call open-up the phone condition. This is usually the focus on a UA, ISO-related topic. Then you transition to the middle of the funnel, which obviously starts with your first session and then progressed from the early up to the game, probably towards the first version you purchased.

    After onboarding finishes, you’re able to track the game mechanics, you are able to progress somewhere. Obviously, as you consume more and more content, tons of players will end up as end game players. The bottom line will follow how to optimize the players, you’ll consume all the content that is just waiting for you to even something more, and so on. This is the way how we think about the game and this is also some parts of the funnel, how you should think about it.

    What we usually see is that there is not– Something that we see that we be liking is, usually there is not one personnel department that looks at the whole parts. You have your own department that looks at the first couples of the open funnel, you have the product team, which again, sometimes they focus maybe too much on the NBA players and producing more and more content. Maybe the new players suffer because the game gets more complicated and is more funnelled. Also, especially if you’re running the games for multiple years, what about those players in between, right? How to play the contents somewhere in the mid-game. This is something that you should be always mindful of, how does this whole game business fit you?

    Jonathan: Yes, I think something really interesting in terms of challenges, especially looking at the top of the funnel. Also, for these new players and basically personalizing the experience for them is the lack of user-level data which you get today, especially in the iOS ecosystem with the ATD framework and the depreciation of the IDFA. How do you approach these challenges? Let’s start from acquiring new players and quality players. In the past, game UA teams were extremely reliant on lookalike audiences and using user-level data and reporting it back to the ad networks to signal to these networks, “Hey, I want more of these players after they made any net purchase or did something valuable or a proxy of value within the game.” How would you approach this challenge today where you’re responsible for finding these quality players yourself?

    Ivan: That’s obvious, I wouldn’t say a billion-dollar question but even whatever, $50 billion questions.

    [laughter]

    Ivan: We look at some of the things you mentioned on that. Obviously, it’s worth looking into. This is what I also love about this industry that it’s absolutely about, you cannot really rely on something that worked whatever, even six months ago, a year ago to be working four or five years on [unintelligible 00:09:37].

    We see this way of how to look at these problems in a couple of ways. First of them is also, fortunately, we’re still not in let’s say for client advertising and evaluating, a good example is buying billboards. Obviously, I think generally because how the unusual data was easy to get and easy to evaluate. I think it showed in the way how the UA growth team and methodologies were structured and extremely heavy reliant on this fact. Another way is how to require players where to let’s say not prioritize. There were that many marketers that relied on something else, and then just a couple of networks that they could run this.

    These campaigns don’t exactly compare to us and didn’t really investigate some other channels, including TV offline, didn’t really investigate other platforms. The mobile was so dominant that’s interesting that the web was behind. By the way, how right now you have this data scarcity, and apparently, it will be just worse.

    You need to challenge it or cope with it in proper ways. First of all, fortunately, you still get some amount of usual amount of data based on the players who constant to you and to the managerial view you use. This is nice, obviously unfortunately it’s not like 90 or 80%, it’s it ranges from whatever 10 to 25, I guess depending on the game and the audience but still you have some, let’s say, baseline to work with. Then you need to look at what’s the game we are talking about. Is it like a game which you realize extremely heavy spenders, is it kind of about what’s this social casino or hardcore game, which absolutely was reliant on very efficient local audiences and ability of, I don’t know, Facebook, Google, and this kind of a network to find this 0.1% of payers that we’ll pay for the rest of it.

    Then we see that these games took the biggest hit so far because obviously you just got sold so it’s much less effective. On the other side, if you’re game, it’s getting more to the casual meet score, hyper-casual and something in between and you do not rely that heavily on these top spenders here. Yes, you have a healthy group. I wanted to show him the distributions of your players. You still can work quite well with this model, which obviously you need to enhance and extrapolate and get some assumptions. Your modelling is obviously got a bit worse and less reliant but still, you can work within and scale the game. Maybe you put some hit inefficiency but it’s not that developed because you could see in some other segments. Yes, definitely you need to think about your game and how much you are reliant, especially when it’s those spenders to approach your user position, strategy, and evaluation.

    Jonathan: Yes, I think that if I can imagine like a matrix and one scale is basically where you are in terms of hyper-casual and hardcore. Also, another scale is how do you monetize? Is it based on the purchase or even some type of game subscription or something really specific, or do you monetize through ads and your hyper-casual game, and most of the monetization happens in the first 48 hours or so after the user has opened the game.

    The more you are in that realm of hyper-casual monetized by ads the better you are because you’re still aiming for a really broad audience. Basically everybody almost, these days is a potential player as opposed to the other side of that matrix, which is what do these guys do these days? How do they still find these big spenders? What work would you recommend these folks do?

    Ivan: One way, how to look at it. I would say there are two problems with this. One is actual evaluation. Properly figuring out what is the true RY on your budget spend, because obviously, you cannot really pair that efficiency anymore. The second problem is the performance of the channels themselves, which is the information itself, obviously, it’s a bit of a problem. If you need to build a business case from your CFO or from your investor to pull more money into the channels, we should have really compelling model that this actually works. Again, what we are talking about, there are ways how to do it, how to have some a blended or a method which borrows the way how we would evaluate campaigns on TV, billboard, this stuff when you are looking at, “Okay, how much bigger is my revenue in this particular region, on an internal platform compared to that point where I wasn’t running the campaign, right?

    You can, even without attribution, you can assume some effect there, and if you enhance it with this water at 10, 20% of players that can attribute and there are opportunities for them. You can get some idea how it works and I think well, the bigger problem is actually the lack of effective optimization and would lack audiences. This is is the tougher problem to solve because we’re so dependent on the algorithm of Facebook UAC that they’re able to cope with this data loss to build the profiles after. What we usually see that you need to probably go a bit wider with your approach and join this campaign mix where you would add multi-points online, offline, do some test that is localized, and try to evaluate it based on the smart list.

    Again, this is something usually bigger games with some IPS with a bigger budget, even for testing are able to do because you need to invest quite a bit to see to get something significant. Actually, the small to mid-size games that relied heavily on local lives and stuff is really a real hit. It’s not an easy way out for these guys. The way how to go about it is really out of the box and they try to go the way how some of the big publishers go. Try to go back to the web where you can do some stuff or you can at least– It is validated or try to think about, again, some other parts of the funnel. Is it possible that I could get the signals or monetization signals earlier and help the algorithms optimize better? Am I able to tune the first 48 hours so that I have a higher chance to convert the players, some of the games, yes? Some of them, which it takes you easily a week to finish the onboarding are in a tough spot scanning-wise. Yes, I don’t think that every game is, unfortunately, the games were hit at different times and some games have much harder times to solve all of these issues and others.

    Jonathan: Yes. From things that I’m hearing there’s a lot of teams that are taking an approach, which is, I call it the sub-publisher optimization approach. It’s basically looking at the value in a bit we’ll talk about data and where this data is coming from but looking a bit about the value that users that come in from different sub-publishers, different source apps to your product page, through ad networks usually, how the value of users from each app is different. Then try to identify the contextual buckets of source apps that have a really good affinity with your game. For example, you might discover that these big spenders are usually coming in after they played games from a different category or a subcategory. If they played that, game, there’s a very high chance that they would be top spenders in your game.

    It’s basically like changing the responsibility as you said, everything is changing in our industry so fast. In the past few years, it was the role of the ad network, especially the self attributing networks, like Facebook to find you where these big spenders are. Now the responsibility has shifted and it’s now on these UA teams, these marketing and growth teams to find where are these users? Like, who are they?

    That’s a question that is crazy to think about but nobody thought about it in the past few years, who are these lookalike audiences? If you would put them in a room, would you be able to categorize them to understand who they really are, and now they have to ask these questions and find them where they spend their time? One of the ways to do it is to find other games from these contextual buckets like maybe there’s a high affinity between the hidden objects to match three specific IPs if somebody’s playing the game with a specific AP from other the Marvel Universe and you have a game that has some mentioning of the Marvel Universe, maybe there’s a high affinity there. Then to try to target your UA campaigns like that.

    That’s another approach that I’m hearing about. I want to talk a bit about data because you also mentioned to me before we started the methodology of management of basically the business data.

    It’s a really big issue these days, with data being scattered, the data from MMPs being less reliable with the deprecation of the IDFA data even coming from SKAdNetwork from Apple, their own attribution solution. What’s your methodology to basically create a place or a BI system for teams to understand how their top-level KPIs change and how they might influence them by changing the marketing input as you said, like increasing budgets, changing creatives, doing any changes?

    Ivan: Yes, so this is my favorite topic because ultimately, it goes to the crux of the problem because obviously, ultimately your goal should be, “Yes, I’m going to improve the game or I’m going to do some business decision, I’m going to increase the budget or some campaign, or decrease VC and portfolio,” but the thing is that to make this decision so many things can go wrong, starting with the quality of the data you’re dealing with, continuing with the problems that you may have with it. The querying the data, interpreting the data and making the correct decision on top of that so there’s actually a long way [chuckles] that go wrong.

    Being a business analyst trade it makes you super paranoid, especially in any insight that you get. When it’s usually extraordinary claims require extraordinary do so sometimes, and especially in this industry, when teams of tens of people are able to make and manage a game which is played by tens and hundreds of millions of players around the world. Your intuition doesn’t necessarily work or scale that much.

    Sometimes you just cannot believe what you’re seeing and sometimes, yes there is data sometimes, yes, actually [chuckles] you do not truly understand why some players, well, they die. I love this example from hill climb racing it with our partners. I think we saw that for a long time there was this notion that interstitial ads or more interstitial ads will kill retention, and decrease the user experience, and so on and so on.

    They’re very cautious about implementing them but feel, at some levels, we run some tests to how to increase the frequency of ads, especially after this change, that made it more after you finish the race, you get this ad break and it continues further and it actually turned out to be a super weird result because we are seeing some crazy stuff. Like when we increase the frequency of ads, we’ve seen improved retention, in long term and fundraising is a franchise, which is downloaded by 1.6, or 8 billion times at this point.

    You have a ton of players at some changes and it was statistically a similar thing, everything, but nobody believed it. Even me it’s possible, but there’s some problem with a baseball legend, we run it again. I think we ran this for like a year and four iterations to find out things like that and ultimately find like, yes, for whatever reason, [laughs] we are able to increase the interstitials without any hidden reviews or retention and–

    Jonathan: How do you explain that? What happened there? Just interesting.

    Ivan: [chuckles] Okay, if I will be very honest, ultimately, you can never know. Our hypothesis at the end was that hey, actually, players after they finished a race or lap of the race, they actually liked having a bit of a time off before the next round to finally wind down after these events, that’s such an intense portion of the time so could be actually that, could be something different, hard to say that. Again, it’s how you handled the data and how we interpret it and how paranoid you are, I think this is how successful a growth strategy is.

    Coming back to your question. First of all, what we’ve seen, will be seen as a big problem is a disconnection within where you actually store data and where you have it. If you look at the growth themes, many times they optimize or generally how it works, usually by devices, not necessarily by players, by devices, which again has some advantages how to do that because if you’re running a big portfolio, I know I’m managing UA for 20 games, across whatever 50 platforms, mobile violence on, it gives you some framework, how to work with different kinds of games and how to do some bigger decisions.

    On the flip side, obviously, we have players who can play your game on multiple devices, obviously, you may misattribute some players that actually this is not a new player, this is an existing player that looks through a different device so how to duplicate and so on. It leads to some inefficiencies. Obviously, if you want to have more primary that counts that into you need to have an integration with a product, which understands that these players are actually from the same account. You have another layer of complexity to deal with it.

    Many teams choose not to do it and obviously, then suffer some consequences because of it.

    Also, on the product side, if you look at how the data is gathered, every team, every game, even with the same studio may choose a different data model or different data points, they track the different tools and do not really take into account how the changes for these days have impacted the performance of UA. This is where we’re getting into this peak conversation that actually was muted before IDFA and now it’s more pronounced, I’d say.

    The changes that you are doing on a product time, generally you can look at that, “Hey, I’m monetizing better my existing players so I’m doing some containers for those players who are with me years and for the mobile component do this stuff but do not necessarily have a big impact on the user acquisition and the new players which are coming in and you can attribute them and so on.

    How to balance this approach and how to make sure that you are not over-focusing on the end game players and do not really deliver the updates for the new ones. Thus the scalability on the UA suffers, and how to cope with it? Because again, more unorthodox as far as to the idea of a problem is that, “Hey, actually, you need to step up your game to monetize the new players to try buying” so your margin of error.

    The LTV increase in whatever they said on their 20th or what’s your roster that you optimize for is actually that big enough that you’ve offset the losses in precision and the losses in the performance that you’re seeing in the channel. I think this again puts a big emphasis for both UA teams and product teams, to have a more unified approach to the game as a whole and not having these collected data sources, data management, and ultimately optimizing for different things on different ends of the tunnel [unintelligible 00:28:23].

    Jonathan: Yes, for sure, I think it’s a great point even if I take a step back is doesn’t matter if you’re in UA marketing or growth, you need to have one source of truth, like a place where, first of all, in my view, it’s also very important that business users will be able to interact with this data. It’s not enough to have one source of truth system, like a BI system where you have to rely on data engineers and analysts that write SQL and Python.

    They’re the gatekeepers of the data and you have to wait for this analysis and you can’t really explore data yourself. You have to have a system that even business users that don’t know SQL, don’t know how to code, can interact with the data and answer simple business questions. I completely agree with you on having the data from the product itself.

    I think it has to be connected with user acquisition data. Even now after this data, it doesn’t really make sense to rely only on the data from them and P because of the deprecation of the depreciation of the IDFA. I think that the Android ecosystem is going to go a very similar route which brings in the data sources side of the equation, which is looking at App Store connect data, something that a lot of teams have neglected in the past because when you wanted to see installs you looked at them in P and you saw, of course, there was a lot of distribution to organic and there’s a lot of challenges with the attribution logic and the window and everything but apps or connect has actually developed a lot in the past,

    I would say a year or so in terms of the data they provide to you. These days you can get first-time downloads, re-downloads, by-source, by the referring app, the source app drove the installed app. Now they’re really releasing cohort data. You can actually filter out sales data if you monetize through inner purchases by installing data. You can do really, really cool things with that data if you bring it into your system such as it’s an example more from the [unintelligible 00:30:43] but if you got featured for example, on a certain date or for two days, you can actually measure the uplift in sales from only from that cohort of users with apps or connect data is something you could never do before because you didn’t have this granular of data in app connect and not a lot of teams took that data into their internal BI systems.

    On the data source side, I think that the more we move away from user-level data and IDFA and device data on the UI side, the more you need to bring in aggregated data from the only source that makes sense which is apps or connects. I think that’s also something really important for folks to take from this.

    Ivan: Yes. Absolutely, I’m 100% with you. I’m looking forward actually as you say, we expect the Google Play, the Android ecosystem to again, unfortunately, do something very similar. We also need to see what data you’ll be able to query and to integrate and to take into account building the models on the other side and something that again, I cannot stress enough with what we’ve seen even in pre-IDFA or whatever, the single source of truths is very interest concept as working on more than 150 games at this point.

    Ultimately we as a business there are things that probably we need to start with. Getting all the business attention to one place and making absolutely sure that we trust the data and do some changes. What we were doing, we try to crosscheck all the sources of data.

    Even, let’s say some redundancy in the data collection is again, a topic which we were preaching for some time but now it’s even more pronounced. Let’s say the revenue which you track from firebase and in the purchase data and of the stores is aggregated from DVO and on top of that your NMB partners and the UA channels claim, do they match with what kind of precision.

    We’ve seen some crazy stuff like Google Play store which sends you the data or sends you the money ultimately and you should believe it. Also, it has some shortages. Sometimes there are errors in the data, sometimes we don’t know what’s happening so if you count that into this complexity, you’re getting more aggregated data from sources like connect is the great but fun fact even Apple or Google makes mistakes.

    They are sending you and will send you some data and if you have no way how to [unintelligible 00:33:26] this you can do whatever you do with your power to do the good decision and ultimately it may prove that it’s not working and not because you made a mistake about somebody from the dataset that you are unable to verify independently or crosscheck with something else. It’s giving you false negatives or positives where the players are coming from. They’re valuable to you.

    I think this is also something worth noting that what we see and this new ways how our players that rely on some very proprietary data sets will again have some limitations. It’s a good question. How much at the end you’ll be reliant on the platforms themselves that only them will have the full picture. They will own on the UA channel, the platform, the attribution, and very scarcely showed around and how much locked in you will be three years from now, five years for now. I could totally see that right now, it’s nothing that– It could be a couple of years out of the road, right?

    Jonathan: For sure. Yes, I think that’s the way, this app, that’s the way they’re moving here with SKAdNetwork as your attribution solution. I’m sure it will evolve and have a lot more data and will be way more reliable as a solution. I think even with custom product pages that are launching these days it’s supposed to launch every day now. They basically enable UA teams to do some attribution in an aggregated and user-private way because you can see sales on a custom product page level and a custom product page.

    If you configure it correctly, I can basically match a campaign or a network or a specific source that you choose to because it’s basically a unique or URL that you choose where you place it and then you can basically calculate the return of that spend once again in a reliable way. I think they’re going there on the UA site. I can also see search ads evolving and I don’t know exactly how there’s a lot of theories on how Apple will do it but I can see search ads evolving to be the biggest ad network there is.

    If you think about it, they don’t need to do a lot. They just need to enable, they don’t even need an SDK, they just need to release something like Apple ads and an in-app advertising product for app developers and then they can be like a one-stop-shop for developers on the IOS ecosystem. Come develop on our ecosystem, it’s the largest and more lucrative ecosystem in the world.

    You’re going to get a really easy and privacy first way to monitor your app with in-app ads if you want, you have enough purchases of course and we’ll give you all the marketing tools that you need. You won’t need an attribution provider because we have SKAdNetwork and you’ll have a lot of data to perform analysis through apps or connect. They’re probably going to enable you to take that data in a more easy way to whatever system used to analyze their performance. I can see how they will grow in that direction. I want us to talk a bit about inner purchases because we want to cover that as well. Do you want us to start talking about why and when should a game team basically approach in-app purchase monetization and what it is in your view?

    Ivan: Yes, so I think we touched a bit in the context of the whole maximizing the potential. First topic, how you can look at in-app purchases and how to optimize them in a different way to achieve different goals. Again, we see that more game studios and publishers are looking at is again focusing a bit more how to get the losses or the perceived slower growth in the [unintelligible 00:37:35] of the world to something that they have in control or the product of the game which again is something that just you are able to directly to change and get some uplift again if you focus more on this part but it has also some caveats how to go about it.

    First of all, before we talk about, okay, in-app purchases, what a game you have a pure driven hyper-casual game so how to implement in a purchase economy, essentially, that makes sense. This is even possible in your title to have some metagame in mind or to develop a new title with these events. I would view that ultimately again, which is resilient to a change and whatever Apple, Google or platforms will throw at us or major direct channels will throw at us, you’ll be nimble and you all could work around the changes that you have. In attribution changes that you have in add monetization because again, we’ve seen some expected changes that how to put ads. We were able to show on certain platforms at certain times and what frequency will it be?

    Will it be let’s say free or let’s say up to the developer like now or not? I think the conversation is also that this will be something that will change in the future. How do your hyper-casual data which realize just on this will be able to scale if you get suddenly for whatever reason they need it for that? Again, in purchases, I think it’s a hedge or healthy economy correctly implemented. It’s a hedge against different problems or different changes in the ecosystem.

    If you already have purchased these or in-app purchase based economy in your game. In my mind is something that amounts to anywhere between 30 to 100% of your revenue which probably also should think about a bit diversifying on the other side. There is a lot that can be done but the problem in my opinion, with inner purchase optimization, is that usually, the changes that require it are much more expensive than let’s say optimization in the top end of the funnel or within the ads in the game. This is where you need to be very careful how you choose your battles, because anything that you do with in-apps, whether it’s the special offers or economic journey use, or introducing your content or repackaging the stuff that you have already in-game each will take quite a bit of development time probably.

    It’ll take quite a bit of testing time and run time and ultimately it can actually have even negative impact because sometimes it’s just, again, non-trivial and unintuitive effect on that. Maybe some special offer of the [unintelligible 00:40:46] The rules that you imagine will actually cannibalize the core economy and so forth. This is why I think in-app purchases are generally harder to optimize or many developers do this way and more [unintelligible 00:41:01] it doesn’t develop but I think it’s the right time to talk about it as this is an avenue of growth that you have still under your control.

    When we roll back to our conversation with the UA and IDFA, and the way how this works and how Google Play will work. In-app purchases this early in the game, our first 24, 48 hours, are we able to pull it off? Are we able to design the game, which is able to send signals early on and frequently enough so that it actually will help your [unintelligible 00:41:36] would get two more relevant players or again, an easier game that’s I did hardcore or monetize this very after 14, 20, 30 days you’ll see the first conversions for your most valuable players? How do you cope with that?

    The big topic that I would describe is this early game in-app purchase optimization. They are specifically catered to attributed players or somebody that you would buy through the research journals. They are made in a way to convert early and cover more often than before. This is a completely different strategy than you would use if you would just optimize for any existing player to give you a bigger approach.

    You need to also think about what is the stage of your title. If you have some level of profitable UA spend and you think you can improve it, then probably you should focus more on this early stage in-app purchase optimization techniques to help the UA channels to be just more effective in finding you new players. If already in the game is, let’s say at the end of your life cycle and you are just hoping to monetize more out of your existing players to improve their P&L probably that session need to focus on the early stage of the final but actually cater to existing top players and top spenders. This is the whole game how to think about it.

    Jonathan: Awesome. It’s really cool how you view the connection between the life cycle of the game and were like the early game, mid-game, or end game. You focus on in-app purchase monetization. I think, yes the connection to SKAdNetwork basically, if you can get players to make an in-app purchase early, really early in the game like in the first 24, 48 hours if you manage to put it off, it’s amazing.

    I think it’s extremely challenging for most games. It’s truly dependent on the type of the game. I saw actually rare cases where some game companies managed to pull it off with incentive. One example is a game where basically, it allows you to play. I can’t really share the name of the game but basically, you can play a mini-game and then you win a prize and they ask you, do you want us to ship this prize to you? Then you make an in-app purchase because you need to pay for that shipment. I don’t know, a really low number just for that shipment.

    You want something we’re going to ship it to your home, just let’s make this in-app purchase really early on and I think that’s a really creative way to do it but extremely tough and extremely challenging for most games to get that part done for the purposes of producing a signal to the ad networks and through the conversion values of SKAdNetwork. I want to ask you a bit about techniques and strategies for in-app purchase optimizations. There is manual optimization, rule-based, segmentation, personalization. Can you talk a bit about that?

    Ivan: Yes and by the way, thank you for the example it’s a great one. I would also put it in a bit different perspective this early game optimization because in my mind, in this new world, it’s also about the new games and the new products that you’re about to develop. I think you should bear in mind that the decision making, which product would have a bigger potential or some higher fail potential right now will also depend, are we able to monetize early enough?

    You have genres that are able to pull it off. I would definitely highlight, for example, simulation or some of the strategy of genres, or even merge or this [unintelligible 00:45:41]. Genres were able to give you a lot of value early on with a simple enough core that you can understand [unintelligible 00:45:48] and they’ll give you this boost in 24, 48 hours.

    Again, it gives you a leg up among any other titles, which are unable to pull it off in this optimization game. Also, there’s an interesting, let’s say question or challenge for product teams that are designing a new game. How to count into account that the way you would do, how early are you able to monetize on the in-apps also will help you or limit scalability and the way how you would go about the in-app.

    First of all, you don’t need any optimization. We talk about, you need to have a healthy economy or healthy portion or non-trivial portion, not in-apps in your game. Until you have that nothing that you would do on the optimization side would really matter because well if the game is making whatever 10,000 on in-apps per month, even if you double to 20K, it’s probably not more than that.

    First of all, we usually have a healthy portion and a healthy scale of in-apps to do anything about. Until you are there’s not really– You shouldn’t really focus on any of other stuff. Once we came there that hey, actually I have a healthy economy, which goes well within my core than with my metagame. I see that the players are able to regularly repeatedly spend money for some amount of time that I see it in might roster in my [unintelligible 00:47:25] That I had some healthy growth and I have enough content so it’s lost more than whatever wee amount or so. Then you are in a position of, okay, I am I able to get some extra value out of this?

    The way how I usually would go about it is that first of all on top of your core economic and [unintelligible 00:47:50] You will probably implement some offers. Probably you may start with simple ads or whatever all starter picks for all players for five bucks. Okay, fine. If you make it, you can actually mess up even at this stage, if you give out too big of a discount and give too much of a premium or short receipt a player, you may cannibalize your economy and after an initial spike, you can actually use more than you gain.

    I would like to quote Google’s own research into this from hangover analysis, even in top 250 grossing, I think there were like 20 or 30% gains but you are able to do these net negative sales on their content when they’re discounted too much. Although the spike on the day looks very great. Even Google on this aggregated data, I could see that the value after was bigger than the spike. You essentially lost the money.

    This is where you need to start being smart about it. I would say there are a bunch of general rules. I would quote, first of all, is don’t discount too much. A discount should be as little as possible. I’m not really a big fan of massive discounts especially when it messes with the game economies for massive discounts of premium or currency is usually a bad idea. What you would probably focus on at least while you’re going through this sophistication or added value for the player from this manual. First to rule by segmented or to personalize, is that how good are you in figuring out what the player actually wants and how much is he able to afford to get the content?

    This is where I think it goes extremely well with the whole user experience because your game is a digital product. We are able to actually track quite a player’s behavior, you know your game, you are able to probably figure what drive the purchases generally in the game, or what is the intent, what is the stage of the game where the player is, what is relevant content for him, especially if you are in a content-heavy game or whatever RPG strategy and so on. Then help the player not to fool himself and buy the so-called boot traps that many games just offer something which is not relevant.

    A player will buy them. I know that they just spend whatever, $5, $10, $20 on something that is literally no value and doesn’t help them progress and actually may even stop paying or playing. Something that actually helps them actually gives them value, actually doesn’t cannibalize the economy, and improves the conversion for the next purchase. I think this is where we’re going in this interesting territory. That’s where the in-app optimization, you need to take into account the cannibalization. You need to take into account the longer-term impact than just the day you show the offer.

    You also need to show the payment retention, so what’s the conversion for the next purchase compared to the previous one. You start getting this model value where you have many different metrics to track about each stuff. Obviously, it gets quite a bit complicated when you think about how to go about this size, how many players to try out. Every test can be actually worse [unintelligible 00:51:30] worse. How to limit the damage to a minimal amount of players before you roll it out to everyone.

    This is where the methodology [unintelligible 00:51:41], that if you have a playbook, how to go about it, and how to maximize this sections you go for, you’re absolutely able to just grow faster and in a more safe way than if you would just do some ad hoc changes here and there and hope and look at this. Be happy with spikes, and so forth.

    Jonathan: Yes, it’s really interesting. I think that to me, there are two really good takeaways here. One of them is around– It actually connects back to the early game, in-app purchase optimization. If you try to force it, you can get a lot of players’ best. Yes, you’ve got an in-app purchase early on but as you said, the content wasn’t val– Maybe it wasn’t valuable even yet because they didn’t get to know the core loop of the game and they didn’t form a habit of playing the game just yet so they don’t really feel the value of that in-app purchase.

    Either way, you made a heavy discount, you somehow created an incentive for them to make the in-app purchase and you got it but you ruined their monetization down the road. The conversion rate for the next purchase tanked because they didn’t appreciate that and they stopped paying or playing, as you said. Understanding that there is a trade-off in everything that you do, basically, everything you do in marketing has a trade-off, but the trade-off here is extremely important to take note of. That was my first takeaway.

    The second takeaway is that, and going back to the beginning of the conversation, when you look at the entire funnel of the game, you don’t only have to solve the early part of the top of the funnel, the UA or the first-time user experience, and how do you improve that part of the game, or just the in-app purchase parts. You can basically work on all of them, and then if you let’s say, these days, if you suffer the hit from the IDFA deprecation and all of that, you can solve it not only with one of these pillars by improving all of the steps of the funnel, and monetizing better, getting more players onboarded with a great first-time user experience, and then improving the UA funnel as we talked in the beginning. That’s another great takeaway to look at the entire funnel. We’re running a bit out of time. I just want to ask you a few questions to ask all of our guests. The first one is, if you could give us one tip to an aspiring mobile growth marketer to want to join a team today, what would it be?

    Ivan: I would definitely join it. To become a growth marketer, you have a bunch of problems you need to solve. First of all, you need to have a good product with a reasonable amount of players and budgets to play with. Probably it’s not something that you can get on your own probably. Unless you have, whatever, top 500 wrestling title in your drawer you can play with. Definitely look at the team. Look at the opportunities to learn. Everything is about learning.

    Growth marketer, you specifically need a product that is marketable if you have. If you have a bad product, there’s nothing you can do. I’m an analyst, and I couldn’t fix a broken game. This is why we need a good product, a good team, an opportunity to learn, and the opportunity to also fail, in that sense. It’s hard to do it on your own or learn just from blogs or podcasts. You need to try it with your hands. I would optimize definitely about anything else.

    Jonathan: Great advice. For folks that are already in the industry today, who do you learn from? What’s your favorite mobile growth or mobile marketing resource? What do you read?

    Ivan: This is a great one. My greatest resource is LinkedIn, literally because if you start connecting with leaders and the industry influencers, they started sharing some of the content from, again, various podcast, blogs, their own stuff, or the snippets, or there’s a great conversation [unintelligible 00:56:18]. Actually, I wouldn’t even pick one specific podcast, although, obviously, [unintelligible 00:56:25] It’s great. We have a bunch of other examples that are right that is worth following. Actually, I’m fun of a well-curated LinkedIn feed.

    I actually have a really good time when I go to LinkedIn and try to interact. This is also what I love. W when you can interact with the content, not just read it. LinkedIn, you share a blog, you share a podcast you liked. You write some comments and ask a question. In your comment, some other very knowledgeable people can give you some of their point of view. LinkedIn, 100%.

    Jonathan: Awesome. Because we’re all about pancakes here at Mobile Growth & Pancakes, what’s your favorite flavor of pancake?

    Ivan: Strawberry cream.

    Jonathan: Strawberry cream. That’s good. Lastly, work, if people want to chat with you, reach out to you, work with you, talk to in any way, where can they find you? On LinkedIn? [chuckles]

    Ivan: LinkedIn, easy. Even at superscale.com, it’s pretty easy to reach me. I think I love to interact also within the framework of LinkedIn. it’s a bit underrated, but a great source maybe for some. For me, it’s number one.

    Jonathan: If anyone listening to this one to work on maximizing the games business potential and working with Ivan and the team, or optimizing in-app purchases, or anything that relates to growing your games, feel free to reach out to him. Ivan, thank you very much for doing this. This was a pleasure. I learned a lot and I’ll talk to you soon.

    Ivan: Likewise, I learned a lot as well. I love how you put it in perspective from your side. Thank you very much, and looking forward to getting this live.

    Jonathan: Talk to you soon.

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      About Ron Gordon
      Ron is Storemaven's Head of Marketing, the one person you would have guessed will know what this mobile growth talk is all about. A misguided law student and journalist, Ron brings to the table some lack of seriousness the Hitech realm is desperately in need of. In his spare time, he's mainly trolling Whatsapp groups.

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