Funding Signals a New Era for AI User Acquisition
AI user acquisition is moving from experimental to essential as investors back tools built specifically for mobile game marketing. Kohort has secured a USD 7 million (approx. RM32 million) Series A round led by The Raine Group, extending an existing partnership that began with Kohort’s seed investment. The funding is earmarked for building a full suite of player acquisition agents designed for mobile game studios, not generic app marketers. Raine’s involvement, as a long-time technology and media investor, signals growing confidence that AI-driven UA automation tools can solve one of the hardest problems in mobile games: acquiring profitable players at scale. By combining predictive modeling, automation and deep domain expertise, Kohort and similar platforms are positioning AI as a core part of the user acquisition stack rather than a bolt-on experiment.
Inside Kohort’s UA Agents: From Predictions to Campaign Control
Kohort’s approach centers on AI-powered player acquisition agents trained on USD 6 billion (approx. RM27.6 billion) in historical UA spend across hundreds of games. The platform delivers daily, campaign-level predictions with 95% accuracy, integrating directly with a studio’s data warehouse and training client-specific models in under 20 minutes. Its flagship Ktrl product generates network-specific bidding strategies and targeting for every campaign, supporting ROAS, CPI and CPE/CPA objectives while connecting directly to ad networks. Two additional agents round out the suite: on-demand deep research that benchmarks a studio’s performance against USD 1 billion (approx. RM4.6 billion) in annual spend flowing through the platform, and automated reporting that produces tailored decks and dashboards. Kohort argues that long-term LTV predictions and real signal understanding—not generic prompts—are the critical context for effective mobile game marketing decisions.
Why AI User Acquisition Agents Matter for UA Teams
For modern UA teams, mobile game marketing increasingly resembles quantitative trading. Budgets shift across networks and creatives in real time, and the difference between profit and loss depends on understanding long-term player value, ad network behavior and competing goals such as short-term ROAS versus whale-driven LTV. Kohort’s leadership openly compares top UA teams to high-frequency traders who need agents that act on real signals, not vague statistical noise. Their agents factor in how ad network algorithms react to data changes, how to balance short-term and long-term revenue and how to minimize wasted spend while scaling. By automating repetitive optimization and surfacing predictive insights, AI user acquisition agents free human marketers to focus on strategy, creative positioning and cross-functional collaboration with product and LiveOps, rather than manual bid changes and fragmented reporting.
Leveling the Playing Field for Smaller Mobile Game Studios
Historically, sophisticated UA strategies were the domain of large publishers with dedicated analysts, custom tools and substantial budgets. AI-driven player acquisition agents promise to narrow that gap. Because Kohort’s models plug into a studio’s existing data warehouse and spin up client-specific models quickly, smaller teams can access predictive LTV and optimization capabilities that once required in-house data science. On-demand research tools replicate the investigative work of senior UA managers, while automated reporting aligns stakeholders—from leadership to product—with a shared view of marketing performance. This combination of UA automation tools and domain-specific intelligence can help emerging studios compete for high-value players against better-funded rivals. As investors explore adjacent opportunities such as UA financing built on predictive LTV, the next wave of mobile game growth may be driven by studios that use AI not just to run campaigns, but to rethink how they scale.