AI User Acquisition Becomes a Core Game Studio Tool
User acquisition has quietly become one of the most critical—and expensive—functions in mobile game marketing. As hybrid casual titles and ad-driven business models proliferate, studios are under pressure to turn ever-growing marketing budgets into predictable, profitable growth. This is where AI user acquisition enters the picture. Instead of relying solely on human UA managers juggling dozens of channels and campaigns, startups are building AI agents that plug directly into ad networks and data warehouses, automating much of the decision-making. These tools promise player acquisition automation at a level of speed and granularity that manual teams cannot match, from real-time bid adjustments to cross-campaign learning. The implications for game studio tools are profound: the line between marketing stack and trading desk is blurring, and studios that master AI-driven UA are positioned to outspend—and out-optimise—competitors chasing the same players.
Kohort’s $7M Bet on Predictive UA Agents
Kohort has raised USD 7 million (approx. RM32.2 million) in a Series A round led by The Raine Group to build AI-powered user acquisition agents tailored to mobile game studios. The company’s platform is trained on USD 6 billion (approx. RM27.6 billion) in historical UA spend across hundreds of games, enabling daily, campaign-specific predictions that Kohort says reach 95% accuracy. Its flagship Ktrl product generates network-specific bidding strategies and targets for every campaign, across ROAS, CPI and CPE/CPA objectives, integrating directly with ad networks. Two additional agents provide on-demand deep research—benchmarked against USD 1 billion (approx. RM4.6 billion) in annual spend flowing through the platform—and fully automated reporting. Kohort positions this stack as more than a generic AI wrapper, arguing that accurate long-term LTV and ROAS predictions are the only context that matters for UA teams operating like high-frequency traders, where every mispriced user impression erodes margin.
From Manual Campaigns to Player Acquisition Automation
Traditional mobile game marketing relies on specialist teams manually tweaking budgets, bids and creatives, often with fragmented reporting and delayed feedback loops. Kohort’s UA agents are designed to collapse this complexity. By integrating directly into a studio’s data warehouse, the platform can train client-specific models in under 20 minutes and then continuously optimise campaigns toward 100% ROAS targets. Campaign optimisation, deep research and automated reporting work together as a single operating system: one agent adjusts bids and targets per ad network, another investigates performance anomalies or trend shifts, and a third produces tailored decks so executives, product and LiveOps share a unified view. For studios, this offers a path to “wasteless” UA spend, where every marketing dollar is evaluated against predicted lifetime value rather than short-term metrics alone. In practice, it turns marketing operations into a semi-autonomous, always-on trading engine for players.
Hybrid Casual Growth Raises the Stakes for Efficient UA
While Kohort builds the infrastructure for AI user acquisition, game makers like Grand Games show why this efficiency matters. Grand Games, focused on hybrid casual puzzle titles such as Magic Sort! and Car Match, has raised USD 70 million (approx. RM322 million) in a Series B round, bringing total funding to USD 103 million (approx. RM474.5 million). The company reports fivefold year-over-year revenue growth and millions of downloads, powered by a data-driven approach and five autonomous internal studios. A significant portion of its new capital is earmarked for expanding marketing efforts, scaling existing titles and supporting upcoming launches. As hybrid casual games rely on repeatable, scalable UA to fuel growth, the pressure to deploy sophisticated player acquisition automation rises. Studios with Grand’s ambitions increasingly need tools that can test, learn and optimise at the pace of their content pipelines.
An Industry Consolidating Around AI-Driven Efficiency
Taken together, Kohort’s fundraise and Grand Games’ marketing-heavy growth strategy highlight how the mobile gaming industry is consolidating around AI-driven efficiency. Investor confidence is flowing both into content—hybrid casual portfolios that can scale globally—and into infrastructure that makes UA more predictable and capital-efficient. For studios, the message is clear: relying solely on human intuition and manual dashboards risks falling behind competitors armed with specialised AI user acquisition tools. Over the next few years, AI agents that understand ad network dynamics, reconcile short- and long-term LTV, and automate reporting may become as essential as analytics SDKs or ad mediation. Studios that adopt these systems early can channel their marketing budgets into higher-quality experimentation and faster iteration. Those that do not may find their cost per player rising while AI-optimised rivals quietly bid away the most valuable audiences.