AI Strategy for Game Launch(1)

AI Guide for Engagement & Retention

As the game launch approaches, the concerns of business teams deepen.

“If we open this week and users flood in, what event will keep them engaged?”

“If unexpected issues arise, will we be able to respond immediately?”

“Our operations staff is limited, but user traffic is nearly impossible to predict…

In such situations, AI is more than just a technology. It becomes a strategic partner for professionals. In this article, we will cover four AI-driven automation practices that game teams can adopt right before launch

ℹ️ Delivering Personalized Content: AI Recommendation Systems

The era of showing every user the same event or reward is over. Some players thrive in PvP, others prefer collection content, while some focus on story-driven quests.

This often leaves business teams puzzled: “Why aren’t players trying this?” or “Why don’t they buy such a good reward?” The reason can be surprisingly simple: the content may be shown to the wrong audience.

ℹ️ If Every Player Is Different, What They See Should Be Different Too

AI recommendation systems analyze individual user behavior and surface the content they are most likely to care about in real time.

Epic Games’ 《Fortnite》 is a prime example of actively leveraging recommendation systems in both its in-game store and content exposure. The system analyzes data such as playtime, recent gameplay history, purchase records, and friends’ activities to dynamically present the items and events that each player is most likely to engage with at that moment. As a result, both early retention within the first three days and purchase conversion rates among new players increased, because players felt, “This game understands my preferences.”

Supercell’s 《Clash Royale》 also adopted personalized shop recommendations and achieved more than double the ROI. For instance, if a player is heavily focused on upgrading a particular card, the AI automatically composes and recommends a special package that combines the card with supplementary resources — such as upgrading materials or gems. This recommendation does not rely solely on frequently used cards. Instead, it incorporates a broader analysis of the player’s battle style, win rate, upgrade pace, and purchase history, applying probability-based predictions to determine “If we show this package now, the likelihood of purchase is high.” This level of precision in recommendation directly contributed to improved VIP conversion and significantly higher package ROI.

ℹ️How Do Recommendation Systems Actually Work?

AI collects behavioral data and identifies user interests and tendencies. Examples include:

Which mode is most frequently played (PvP vs. PvE)

When and how long does the player stay logged in

Which items were purchased, and which were ignored

Which content players spend more time on

What similar users with comparable play styles prefer

This goes beyond simply measuring “frequency.” AI predicts what a player is likely to want next and when to present it for maximum impact. For instance, if a player’s PvP activity has recently increased, the in-game store is reorganized to highlight PvP-related gear or cosmetics. Even signals such as whether their friends completed a particular quest may influence recommendations.

Core Technical Approaches - Recommendation AI in games is typically powered by machine learning and falls into three main categories:

■ Collaborative Filtering: Suggests content that players with similar preferences enjoyed
Example: If other collection-oriented players purchased a new skin, the system recommends it to me as well

■ Content-Based Filtering: Suggests items or content (genre, difficulty, items) with attributes like what the player has already liked.

Example: If I often play a specific character type, the system recommends new characters with similar traits.

■ Hybrid Models: Combine the above two methods for higher accuracy. This is widely adopted by large-scale game publishers

Recently, Deep Learning–based time-series prediction has also been applied to gaming. Borrowed from platforms like Netflix and Amazon, this approach analyzes behavioral sequences over time to predict what players will want to see or buy next. These models are refined with diverse signals such as play logs, spending patterns, content dwell time, peer behavior, and even engagement reactions such as likes, bookmarks, or shares.

ℹ️ Catching Players Before They Leave: AI-Driven Churn Prediction and Personalized Rewards

The most critical moment in game operations is the exact point when a player decides to leave. Playtime begins to shrink, mission participation becomes irregular, and rewards are no longer claimed. Quietly, that player is about to disappear.

AI detects these decisive churn signals by analyzing data flows.

It considers multiple factors such as login frequency, playtime, usage of specific content, changes in spending, and interactions with friends. Machine learning (ML)-based classification models study the behavioral patterns of past churned players, then assign a “churn risk score” in real time to players showing similar trajectories.

Rovio applied AI to reduce churn in its 《Angry Birds》 series. By identifying players who repeatedly failed missions and felt frustration at certain points, the system lowered difficulty or provided stronger hints in those segments, preventing disengagement. As a result, day-3 churn dropped significantly, and long-term retention improved.

In iGaming platforms such as online casinos and betting services, churn risk detection is coupled with personalized comeback rewards. For example, if a player who once enjoyed a particular slot game has not logged in recently — but previously responded positively to free tickets — the AI automatically sends them a return message with a free play opportunity.

Compared to broadcast-style rewards sent to all players, this personalized approach tripled reactivation rates and stabilized player lifetime value (LTV).

Churn prediction systems are particularly powerful in optimizing marketing spending. Instead of distributing identical rewards to everyone, resources are focused on players with high churn probability, improving both efficiency and ROI.

※ The content has become quite long, so I am posting it in two parts for easier reading. I kindly ask for your understanding

※ Disclaimer: This content reflects the author’s personal views and includes only publicly available examples. It does not represent the official position of any company mentioned