Marketing Mix Modeling (MMM) provides a more accurate view of incrementality than last-touch attribution (LTA), as evidenced by a case study where MMM attributed 43% more Day 7 revenue events to TikTok at a $5,000 daily spend.
See it on page 15Traditional LTA is increasingly inadequate for mobile gaming due to privacy regulations like Apple’s AppTrackingTransparency and a systemic bias that undervalues top-of-funnel media.
See it on page 7User acquisition costs in the gaming industry are projected to exceed $130 billion by 2025, necessitating more sophisticated measurement tools to manage rising expenditures.
See it on page 6The global gaming industry is expected to reach three billion players by 2029, with North American and Asia-Pacific markets generating $50 billion and $84 billion in revenue respectively as of 2023.
See it on page 5Studios spending over $160,000 monthly per region across at least five media partners are the primary candidates to benefit from adopting an MMM-based attribution framework.
See it on page 10A dual-wielding strategy is recommended, utilizing LTA for real-time tactical creative optimization while employing next-gen MMM for long-term strategic budget allocation and forecasting.
See it on page 24This analysis explores the transition from traditional last-touch attribution (LTA) to next-generation marketing mix modeling (MMM) within the mobile gaming industry. It posits that while LTA has long been the standard for measuring return on ad spend (ROAS), it is increasingly inadequate due to systemic signal loss from privacy regulations (such as Apple’s AppTrackingTransparency), the rise of multi-platform gaming, and a heavy bias toward bottom-of-funnel channels that ignores the incremental value of top-of-funnel platforms like TikTok.
The findings highlight a significant shift in the global gaming landscape, noting that the industry is projected to reach three billion players by 2029. Despite this growth, marketers face rising user acquisition costs, which are forecast to exceed $130 billion by 2025. Data from Kochava and TikTok indicates that LTA frequently under-attributes early-stage revenue events. For example, a case study shows that at a $5,000 daily spend, an MMM model attributed 43% more Day 7 revenue events to TikTok than a traditional LTA model, revealing that LTA often fails to capture the full impact of video-forward media.
The scope of this research is global, with specific emphasis on the North American and Asia-Pacific markets, which accounted for $50 billion and $84 billion in 2023 revenue, respectively. The methodology involves comparing aggregated market-level data against granular user-level data to demonstrate how MMM identifies channel saturation and incrementality without relying on depreciating user identifiers.
The conclusion advocates for a dual-wielding strategy where studios utilize both LTA for tactical, real-time creative optimization and next-gen MMM for strategic budget allocation and forecasting. Organizations spending over $160,000 monthly per region with a diverse mix of at least five media partners are identified as the primary beneficiaries of this advanced attribution framework.