In this episode of App Growth Talks, we talked to Günay Aliyeva, Co-Founder of Gamelight. Watch the interview and read the transcript below.
Hi everyone! My name is Lina, and this is the latest episode of App Growth Talks, a series of interviews with ASO, user acquisition and mobile growth experts. We are starting an interview with a seasoned mobile growth expert in the gaming category – Günay Aliyeva, Co-Founder of Gamelight. Hi Günay, thank you so much for joining me!
Hi, Lina. Great to be here! Thank you for the invitation.
Lina: Awesome! I know that we have pretty much to discuss today, so let me start right away. Could you please share your thoughts on the importance of user retention in conjunction with mobile user acquisition? How do you at Gamelight strike a balance between these two crucial aspects?
Günay: Sure. For UA managers, user retention is super important, because what’s the point of acquiring users if you are not going to retain them? I think what is missing sometimes by UA managers is that some professionals focus too much on lower bids, lower CPIs, or new user count, because that’s what they have the most impact on.
User retention might be difficult sometimes for UA managers, because it heavily relies on the effectiveness of the product. It relies on the game, its overall performance, and killer features that keep the users engaged. But there are ways that UA managers can improve user retention metrics.
For example, the first thing that comes to my mind is to avoid using misleading or deceptive advertisements. While it’s true that such ad creatives have high conversion rates, low cost per install, and ability to attract a large volume of new users, they often lead to disappointment. Users expecting one thing and getting another tend to uninstall the app, which is the best-case scenario. In the worst case, this practice can result in negative reviews and fraud complaints.
Also, what can help with long-term user retention is rewarded marketing platforms. Take, for instance, platforms similar to Gamelight, where users are incentivized with virtual currency or in-game items for viewing ads within the game. These platforms not only track user behavior and preferences for optimizing future advertising campaigns but also create a mutually beneficial ecosystem. Users are rewarded for their time and attention, advertisers effectively target their audience, and game developers find a new revenue stream to monetize their games.
So, if a UA manager aims to prioritize user retention, exploring rewarded marketing platforms could be a worthwhile strategy to consider.
Lina: Sounds reasonable, thank you! In a competitive market, how does Gamelight differentiate itself from other mobile marketing platforms offering user acquisition solutions for game publishers?
Günay: Indeed, the market is saturated with mobile marketing companies and rewarded marketing platform providers, making them a common find. However, what sets Gamelight apart is our AI algorithm, developed in-house. It is tailored to optimize towards highest ROAS, ARPU, and retention for game publishers who partner with us. Our approach goes beyond just displaying ads to the highest bidder that often happens in most other platforms; it’s about strategically aligning ads with the right users to maximize effectiveness and value.
Rewarded marketing platforms are about much more than just offering rewards for certain events and playtime. The key lies in the sophisticated algorithms that operate behind the scenes. These algorithms analyze the match between a user and an ad creative even before install: if the user is the right fit, when ads should be shown, to whom it should be shown, which users should see which games and so on. The whole process is not anymore just bare bones of showing some games to some users and giving them rewards. Instead, it is the algorithm that analyzes and optimizes the campaigns. That would be the biggest difference between Gamelight and other networks.
Lina: Thank you! AI is one of the hottest topics in mobile marketing today. I’d say it’s 100% a competitive advantage if you’re already working with AI and in the near future, this will become a must. Speaking of AI, could you please share any notable case studies or success stories where Gamelight’s AI algorithms significantly outperformed traditional user acquisition methods for mobile game publishers?
Günay: We have a lot of case studies published in our blog, but I’d like to highlight one that earned us the Platinum Award and the Best Mobile Marketing Platform Award from dotCOMM. This particular success story involves a collaboration with a top-grossing game publisher aiming to promote and scale their games globally.
The publisher set a substantial daily budget of $100K, totaling $3M monthly. Our objectives were clear: to achieve high retention, engagement, and ROAS. Leveraging our advanced AI algorithm, we efficiently reached a proper target audience right from the campaign’s start, effectively bypassing the costly learning periods that can sometimes cost as much as $50K.
This strategic approach paid off. From the launch, we accurately identified users who were the right fit for the campaign and app. The results were impressive: we exceeded their long-term ROAS goals by 30-40%. Additionally, we achieved exceptionally high ARPU numbers. While they typically saw a $4-5 ARPU, our campaign boosted it to nearly 4.2-4.3 times higher, approaching $20. This approach is particularly noteworthy for popular games where most of the potential audience have already installed it. By targeting users with the highest ARPU, we significantly increased the company’s overall revenue.
Lina: Sounds impressive! Thank you, Günay, for sharing. What role does data analytics play in optimizing the UA campaigns through your platform? Could you provide an example of a data-driven improvement you’ve made recently?
Günay: Without data, there would be no Gamelight, let’s put it this way; it’s the cornerstone of our operation. In contrast to traditional algorithms, which operate on a “if-else” rule base – like “if a user meets A criteria” or “if creatives are of B type” then “expect C outcome” – our AI-driven methodology is far more sophisticated.
AI requires extensive data inputs. We gather a lot of data points, including user playtime, behavior, demographics, font details, device manufacturer, and more. This rich data set feeds into our algorithm, which is designed with specific goals in mind: to maximize ROAS, ARPU, and retention for our advertisers.
The algorithm doesn’t rely on predefined rules. Instead, it independently determines the most suitable user for each game, the optimal time to recommend a game, and the most effective way to present it. The recommendations also vary based on how long the user has been with us. For example, consider a user who has just downloaded a game. Probably, you don’t want to recommend a new game to them: you want them to explore and enjoy the first one.
There are a lot of criteria that we analyze and leverage, and it’s very difficult for app professionals to manually spot them and interpret their interrelationships. Manual targeting might focus on basic parameters like gender, iOS version, and other standard demographics. But with AI algorithms, you can analyze way more data, something beyond human capability. We handle the data in a much more granular way that leads to more effective outcomes.
Lina: Yep, AI could be a great helper. In your case, it sounds really interesting. You mentioned that you’d prefer users to play and enjoy games for some time before they see another game ad. This is, I think, closely related to LTV and retention. Speaking of that, how do you at Gamelight approach UA for mobile games, and what strategies have proven to be the most effective in driving user engagement and retention?
Günay: First of all, Gamelight is a mobile marketing platform, but we also operate as an app developer and publisher. The traffic that we deliver to our partners originates from our own self-published game recommendation platforms. Basically, it means we develop, publish, and advertise our own games.
We understand the UI challenges game publishers face, because we use the same platforms they do to acquire users. With Gamelight, things are simpler. Our algorithm makes campaign optimization effortless. Setting up a campaign takes just 2-3 minutes – it’s quick and straightforward, with no need to spend half an hour or more on complicated setups and targeting. In just a few minutes, you can launch your campaign, set your ROAS goals, and let the campaign optimize itself as it runs. This makes the UA strategy with Gamelight easier compared to others. UA managers just need to set their goals and can then focus on more strategic aspects.
For example, let’s say UA managers want to launch a seasonal campaign for Christmas, Black Friday, Easter, or any other holiday. Typically, they experiment with various platforms, strategies, and creatives to find what works best. From our experience at Gamelight, UA managers often approach us after having tried almost everything without success. The game might even fail because they tried so many platforms, wasted so much budget and it didn’t work when what they really needed was rewarded play time or rewarded events. The same goes with creatives. Sticking to one type of creative, like gameplay, might cause them to overlook another approach that could be more successful. The key for UA managers is to diversify: split the budget across different methods, identify the most effective ones, and then focus on optimizing those specific channels and formats.
Lina: Thanks, Günay. Makes sense. Don’t put all eggs in one basket, they say. It’s all about continuous tests and trying new things, so it sounds reasonable to me. Speaking of games, as you shared that you are a mobile game publisher yourself, you know that there are a lot of genres. How do approaches, tactics, and strategies differ for casual games that are popular today, and, for example, for hardcore games? Also, which metrics do you use to measure the success of those games? Although you already mentioned some of the metrics, could you please elaborate on that?
Günay: Yes, let me give you a sneak peek at how the algorithm works for different verticals. We have access to usage play time. We monitor usage play time, meaning we can track when and how long our users play any game. This data comes directly from the users themselves, not from third parties or game publishers. Users share their game playing habits and genre preferences with us in exchange for rewards. Then we can analyze their gaming patterns and behaviors. For instance, if a user has spent several hours playing games in the last 24 hours, and 80% of their playtime was on strategy games with the remaining 20% on casual games, we take note of this. We look at which games they play, how long, and when – every aspect of their gaming behavior. But it’s not just about the quantity of time spent on a particular genre. Sometimes, a user’s combination of casual games and other app usage might suggest they’d enjoy strategy games. It’s all about understanding and predicting user interests based on their habits.
The same goes with other game genres too, whether it’s casual, word puzzle, or any other category you can think of. We segment users based on what games they play and compare them with other users who have similar gaming habits. Then based on this user behavior, we recommend the new games. That’s how basically it works for each game category. Whenever you launch a new game, we identify games that are somewhat similar to yours. From there, we determine which users are the most likely to enjoy this new game. It’s all about matching the right games with the right users.
Lina: This is awesome. You have a lot of insights analyzing so much data. Sounds cool! We talked a lot about AI today, and for sure this is a huge topic. Another hot topic today, and of course for the near future, is personalization. My next question is how your platform can be used to provide personalized suggestions for players in the games? How does it work and what are the key factors that influence those recommendations?
Günay: Well, at any given moment, we don’t recommend more than 3-4 games. Our goal is for users to genuinely engage with these games, rather than overwhelming them with a barrage of 20 games at once, which lacks personalization and feels like random advertising. We carefully curate the game ads we show, ensuring they align with the users’ preferences as predicted by our data analysis. This targeted approach tends to be more effective. Users receive game suggestions tailored to their preferences, and game publishers reach players likely to enjoy their games.
For us, the whole process is very dynamic and it’s always unique for users and game publishers. No two users will get the exact same game suggestions unless their app usage data, demographics, and preferences are identical. It will always be different which games we recommend, when we recommend them and what ads users will see. This benefits users as they get to play games they truly enjoy. It’s also good for game advertisers, as it results in fewer uninstalls during D0-D7. We consistently see high tutorial completion rates, typically between 96-98%. This indicates strong user engagement, because the games align well with the users’ interests from the start.
Lina: You have proved that personalization actually works. That’s really cool. I also wanted to discuss another kind of controversial, but important aspect. As far as I understand, you utilize data of players to provide personal recommendations via AI algorithms. But do you face any challenges when working with AI and utilizing data for that? Because I believe that some ethical challenges could pop up. During App Growth Week, many of our experts shared that without AI, personalization won’t be possible soon, or mobile marketing. So, everyone should be working in that direction. But some ethical changes do exist. The question is, have you already faced them and how do you deal with them?
Users have the option to withdraw their consent at any time, but since they benefit from the process, there is no ethical problem, so to say. In traditional models, advertising revenues are typically shared with publishers. Users end up just seeing ads, with no direct benefit. In contrast, Gamelight shares these revenues with our users. While we do earn from game advertisers, we also distribute rewards to users. This means users not only see ads but also gain from them. It’s not merely about using their data; it’s a collaborative approach where everyone benefits. By sharing data, users play a part in a system where they can earn rewards, making it a win-win situation for all involved.
Lina: It’s a situation where everyone involved is interested, engaged, and satisfied.
Günay: It is, absolutely. The key is that people should enjoy the games without being bothered by the ads. Annoying ads indicate something’s not right – maybe the recommendation is off, the ad itself isn’t appealing, it’s shown at the wrong time, or it’s just not the right ad for that user. When we match the right ad with the right person at the perfect moment, everyone wins. The user discovers games they’ll love, game publishers gain a loyal user, and our platform succeeds in bringing them together.
Lina: Thank you, Günay. I can’t argue that because ads can be annoying for sure, and that’s really cool that you do everything to avoid that and to provide some personalized recommendations. Let’s move on to the last but not least question. You mentioned it already and we discussed that Gamelight is a game publisher. I wanted to ask, as the gaming industry evolves in this highly competitive environment, how do you manage to balance between maintaining an already popular, already loved by players game titles and introducing new games? How to maintain that balance? Could you please reveal some secrets? Because to me it looks pretty hard. Research shows that a big percentage of new games get closed very soon after launch, they don’t succeed. So how to maintain those titles that are already popular and bring new titles to the market at the same time?
Günay: Yeah, also a very good question and very tricky one because there’s no straightforward answer. There is no white or black here. It’s a gray zone. How do we do it with our game publishers? First of all, we avoid bombarding them with too many recommendations, as this could lead them to abandon older games, which isn’t our goal. We aim for them to enjoy games over months and even years. But at the same time, if we don’t recommend more games, then it’s basically one game recommendation platform. Users always want to try something new too. We always make sure to balance maintaining popular titles and introducing new games, even though we would win more from introducing new games. If we show more games, users download more games, and we make higher margins eventually. At the same time, it’s a short-term win: users will download more games, but they will not stick around for all of them. No one plays 10 games at the same time.
You should always make sure that there’s always something new for users, because if you don’t introduce a new game, someone else will introduce it. You should always make sure that there is something fresh coming up, completely new concepts or refined old ones. The secret is not to overdo it, because that can result in lower retention, lower engagement, and higher churn rates. Users can maximum focus on like 2-3 games at a time. Finding a balance is the most difficult and important thing here.
Lina: Totally agree. Thank you so much, Günay, very interesting! You shared so many cool tips, insights on your in-house strategy and how your platform works. Also, I think we got really cool advice for young game developers. I’ve had a pleasure talking to you today!
Günay: It’s great being here, Lina. Thank you so much for your interesting questions. Thank you!