We are excited to introduce Aysu Yildiz, VP of Sales at AppNava. The company leverages machine learning to provide real-time predictions and segmentation tools for the gaming industry. In this interview, Aysu shares insights into how AppNava’s innovative solutions help developers maximize returns, prevent churn, and tailor marketing campaigns from the earliest stages of a player’s journey.
Congratulations on joining AppNava! Let us introduce you and, of course, AppNava to our audience.
Thank you! I am Aysu, VP of Sales at AppNava, and I am delighted to be here. AppNava is an ML-powered real-time prediction and segmentation tool that provides developers with the predicted future of their players’ behavior. It helps them take action on marketing campaigns and live operations from the beginning of the user’s journey to maximize returns per user and prevent churn.
Can you tell us more about the real-time predictions and how they work to benefit developers on marketing and live activities?
We have 12 ready prediction models for different objectives, mainly focusing on LTV, retention, and IAP propensity. Training them with past behavioral data for these models becomes customized to each studio. With the trained models, you can predict each incoming user’s future LTV/churn probability/IAP propensity as early as their third minute in the game, which helps you to take tailored actions accordingly. Developers can retain and monetize users much better by taking customized actions at the early stage per user.
Which challenges does AppNava solve?
With privacy changes, UA has become very challenging. Together with that, monetizing and retaining every user is even more important now for the success of developers and publishers. However, every user behaves differently and has a different wallet. That’s why developers use AppNava’s user-level prediction and ML solutions to optimize their UA, LiveOps, monetization, and game design and tailor the game experience for higher revenue and retention. Here are the use cases we help with:
- UA campaign optimization: Publishers constantly experiment with various channels and UA campaigns, but not all yield optimal results. With AppNava’s ML-driven LTV predictions, publishers can predict campaign success from day one, optimizing successful campaigns and swiftly discontinuing underperforming ones.
- IOS SKAN optimization: Publishers aim to attract high-value game users by transmitting conversion values to iOS within the first 48 hours of a player’s journey. However, as players may not make any in-app purchases (IAP) during this time, incorrect conversion values may be sent, disrupting user acquisition (UA) efforts. AppNava’s LTV predictions, utilizing game-specific models, enable publishers to accurately predict each player’s LTV from their first session, transmitting correct conversion values to iOS. This predictive LTV approach empowers publishers to acquire superior users for their games.
- Retaining users: User churn occurs for diverse reasons, and in the current landscape, losing players can be costly. AppNava’s retention-oriented models predict user churn probabilities as early as the first session, enabling the preparation of tailored scenarios and offer in real-time. This personalized approach enhances player engagement and game entertainment.
- Real-Time Segmentation: Many game companies traditionally employ rule-based segmentation methods, which often fail to effectively apply varied live scenarios to players due to their simplicity. AppNava offers segmentation utilizing all 12 real-time prediction models, enabling developers to achieve success with machine learning-driven tailored prediction models.
- Subscription and IAP uplift: Some games are focused on the subscription model and/or IAP revenue. AppNava has 2 different models for subscription propensity and IAP propensity. With the real-time prediction for each user, publishers can act on players while they are in the game to incentivize them on IAP and subscription.
- Personalized Liveops offers/ads: Each player has a different behavior, incentive to play or buy, churn reason, and wallet. With all models in AppNava, publishers can get real-time segmentation and predictions for players’ future behavior. With that information in hand, publishers can tailor scenarios such as varied bundle price points and ad frequencies for the players while they are still in the game. The personalized monetization efforts show a significant uplift in revenue.
- Product development: Developers often wonder why certain players behave differently in games. AppNava offers a unique solution to help identify variations in LTV, churn, subscription, and IAP behavior. AppNava highlights crucial points where behavior shifts occur by mapping game events into a funnel, enabling developers to enhance game experiences and increase retention and revenue.
Which use cases are the most popular right now?
UA optimization, SKAN optimization, dynamic pricing (personalized liveops/offer strategies), and real-time segmentation are the solutions AppNava is in demand for right now due to the high UA cost and the need to monetize each player effectively.
What is the competitive edge of AppNava?
We offer a comprehensive suite of models tailored to serve both product and marketing teams, including retention prediction, monetization optimization, LTV prediction, and UA optimization, all accessible on our platform. AppNava provides real-time, user-level predictions from the first session, empowering clients with actionable insights for informed decision-making.
Furthermore, our Smart LiveOps feature enables real-time segmentation; advanced with all the capabilities, AppNava emerges as the leader in the gaming industry, offering automation, agility, and advanced capabilities tailored specifically to meet the unique needs of gaming companies. Choosing AppNava as your partner will maximize success and innovation in the gaming sector.
One of the biggest reasons to work with AppNava is the unique strength to autonomously prepare the prediction models in a maximum of 1 day. As user bases expand, the platform’s predictive models provide valuable insights, ensuring that acquisition and engagement strategies remain effective at larger scales. By continuously updating and refining predictive models based on the latest data, Appnava ensures that game developers can adapt to changing market trends and user behaviors. This forward-looking approach helps companies stay ahead of the curve and maintain their competitive advantage over time.
What are some of the successful case studies or testimonials that showcase AppNava’s impact and value proposition?
At AppNava, we’re thrilled with the results we’re seeing, thanks to our top-notch ML algorithms and real-time user segmentation. Many game companies who’ve partnered with us have seen their user base and revenue skyrocket. We’re talking about a big impact in a short time. I am happy to share a couple:
- Kyoso was trying to increase player lifetime value (LTV), and we discovered a hidden weakness — nearly 80% of new users came from the low LTV segment! By quickly identifying the LTV segment of users in their first session, Kyoso started showing interstitial ads with different frequencies. It worked wonders — revenue increased by 22%!
- For Azerion. With the LTV Segmentation of AppNava, they started to show different IAP offers. The Segmentation algorithm assigned each player to a different IAP offer. For instance, players with a propensity of IAP in the 40% to 60% range find themselves in the “gray area,” indicating that they’re not entirely against making IAPs but are not entirely committed either. After Appnava’s prediction, they offered a “Starter Pack” with exclusive items & game currency at the lowest price for this group. This made it more likely for the player to make the first purchase, and they’ll feel like they got a great deal. As a result, the Ratio of completing the first purchase multiplied by two, and The IAP revenue of the game increased by 8% by accurately identifying the LTV Segment (predicted LTV).
What are your plans and goals for Appnava regarding product development and market expansion?
Since we started our product, we have focused on mobile gaming, but most recently, we have also been building R&D for PC and console games. Just like our current methodology, we will be building a data-oriented product. Currently, many PC and console games are being built and run with very uninformed decision-making regarding player behavior. We are aiming to open this up to developers.
Thank you for this lovely interview. AppNava sounds like it solves many challenges of developers, and we are very thrilled about it’s growth journey😊
Thank you – the pleasure was all mine!
