Are you curious about how many companies use AI in advertising and marketing, especially in mobile gaming? In 2024, mobile games generated approximately $80 billion in in-app purchases, driving growth in the gaming industry. However, the rising cost of user acquisition (UA), increasing ad saturation, and growing player expectations pressure advertisers to innovate.
This is where AI steps in. From hyper-targeted ad delivery to dynamic creative optimization, AI transforms how mobile games acquire and retain players.
AI Use in Advertising and Marketing Statistics
Let's look into some compelling statistics that highlight AI's transformative role in mobile game advertising:
1. Key Statistics and Market Trends
AI-driven marketing is rapidly transforming mobile games, with data showing a surge in advertisers and creatives. In 2024, the number of mobile game advertisers increased by 60.4% year-over-year (YoY) to 259,700, and the number of ad assets rose by 15.4% to 46.2 million, signaling that advanced tools, many powered by AI, are enabling more campaigns and creative output.
Similarly, AI-powered ad targeting and optimization are evident in user engagement metrics: impressions per daily active user are doubling every year, and programmatic, AI-led auctions (in-app bidding) have overtaken traditional waterfall methods.
For you, these concrete numbers underscore how generative content and automated buying are boosting reach. By leveraging AI to generate and test ad creatives, personalize user acquisition, and run smart bidding, you can tap into the same momentum seen in these statistics, effectively scaling campaigns and doubling the impressions per player as shown by the latest data.
2. CPI & Performance Metrics
Expect CPI and ROAS benchmarks to look very different today. Thanks to SKAdNetwork optimization and creative targeting, iOS CPIs have eased recently – AppsFlyer reports iOS CPI down ~17% from Q1’23 to Q2’24, even as Android CPI surged ~48%. (Still, iOS remains about 3.5× more expensive than Android on average.) Liftoff’s data mirrors this: iOS casual-game CPI spiked, and iOS games now drive a slightly higher D7 ROAS (~5.9%) than Android.
ROI lift via ML optimization: AI-driven bidders and creative testing improve ROAS. UGC/realistic creatives (often A/B tested by AI) correlate with the highest retention and monetization.
In practice, this means utilizing AI tools to balance these dynamics – for example, machine-learning bidders that allocate spend to the platform that yields higher efficiency or return on ad spend (ROAS). UGC/realistic creatives (often A/B tested by AI) correlate with the highest retention and monetization.
3. Personalization & Predictive Analytics
AI and ML enable you to tailor acquisition campaigns by analyzing first-party signals and forecasting high-intent player segments. Adjust predicts 2025 will see “extensive use of AI” for personalized experiences, with “AI-driven automation of production processes” tailoring apps and ads to users.
Privacy-driven modeling: With 39% of game users opting into IDFA (the highest category rate), you have a decent database for personalization; however, privacy-utility gaps remain. To address privacy-driven data limitations, you must combine your first-party event data, such as in-game behaviors and transaction logs, with probabilistic models that infer install sources and user lifetimes. Probabilistic attribution techniques assign conversion probabilities to ad interactions using aggregated non-personal identifiers, making them resilient in cookieless and SKAN-driven environments..
Your UA stack should include ML-powered pipelines that craft signals from privacy-enforced noise, ingesting SKAdNetwork postbacks, aggregated event data, and deterministic pings to infer key metrics, such as install source and predicted spend.
4. Automation & Attribution
Programmatic bidding and AI analytics are now expected to be standard. AI-driven tools can reallocate budgets in real-time (dynamic bidding), spot fraud, and update campaigns autonomously. Below are some leading AI-driven programmatic Platforms:
Google Ads Performance Max: Harnesses Google’s machine-learning core to automate bidding and dynamically generate ad assets across Search, Display, YouTube, Discover, Gmail, and Maps, optimizing your budget allocation and creative mix in real time.
Meta Advantage+: Uses predictive algorithms to allocate spend across Facebook and Instagram placements, shifting your budget instantly based on live performance signals and user engagement trends.
You’ll combine deterministic MTA data from your mobile measurement partners with probabilistic AI-based attribution, then feed these signals into MMM to simulate how each channel investment drives metrics, such as lifetime value, closing the blind spots left by fragmented privacy signals.
By integrating deterministic MTA, probabilistic modeling, and MMM forecasts within a single AI-driven attribution stack, you optimize both day-to-day bidding and long-term budget allocation within a unified framework.
In the wake of ATT and Privacy Sandbox, where data sparsity is the new normal, you’ll augment SKAdNetwork postbacks with predictive analytics from AI-powered UA platforms that model spend, LTV, and ROAS to sustain your CPI targets and maximize return on ad spend.
By knowing that average CPIs and ROAS have shifted, plan for extended ROI windows, and invest in AI tools for targeting and attribution. The result is a more automated, data-driven UA strategy that maximizes the value of every ad dollar.
Next, we will explore some of the key AI technologies revolutionizing mobile game marketing.
Key AI Technologies in Mobile Game Marketing
Below are some key AI technologies:
1. Predictive Analytics:
You can leverage AI-driven predictive analytics to turn raw player data into powerful UA forecasts. These systems ingest first-party data, including session length, play frequency, and purchase history, and utilize machine learning models to predict user lifetime value (LTV) and churn risk. For example, AI-powered LTV models analyze individual player behavior to identify users who will yield the most significant returns.
In practice, you can bid more aggressively on lookalike profiles of high-LTV users and cut spend on low-value segments. Early adopters of predictive LTV tools (e.g., Liftoff’s LTV Optimize) have reported significant ROI gains, for instance, with notable improvements in D7 ROAS and overall profitability by focusing on predicted high-value users.
2. NLP & Chatbots:
AI-driven chatbots and NLP systems enable you to engage with players and handle support automatically. You can deploy conversational bots (in-app or on support channels) that utilize natural language processing to understand player queries and respond in real-time. These bots can answer FAQs, guide players through game mechanics, troubleshoot issues, or even facilitate in-game purchases, all via chat interfaces.
Because the bot taps into player profiles and history, it provides personalized assistance – for example, it might recommend a strategy based on a user’s past behavior. Over time, the chatbot learns from each conversation and adapts, thereby improving its accuracy and relevance. In effect, you receive 24/7 customer support and engagement without increasing headcount: routine questions are automatically deflected, freeing your team to focus on high-value issues.
3. Machine Learning & Deep Learning
Modern UA platforms utilize machine learning and deep neural networks to optimize ad campaigns based on live data dynamically. These systems continuously analyze campaign signals, including impressions, clicks, installs, and retention, to adjust targeting, creative selection, and bidding in real-time.
For instance, an ML-powered DSP might swap in different ad creatives mid-campaign if it learns that one video format performs better with a specific audience. ML algorithms also mine massive datasets to fine-tune audience segments: by clustering players on nuanced features, the AI ensures your ads reach the most likely to convert micro-segments.
Deep learning models excel at pattern recognition. For example, they can predict which device types or user interests correlate with high retention and automatically adjust bids accordingly.
The outcome is a hyper-targeted UA funnel: ads are shown to the right users at the right time, maximizing CTR and install volume while minimizing wasted spend.
Now that we have covered the technologies driving mobile game marketing, let’s discuss how these innovations reshape user acquisition strategies and their broader industry impact.
The Impact of AI on User Acquisition (UA) Strategies
Artificial Intelligence (AI) has emerged as a powerful tool to enhance user acquisition (UA) strategies, offering innovative solutions to streamline creative processes, optimize campaigns, and expand market reach:
1. Cross-Platform Campaign Optimization
AI synchronizes campaigns across multiple platforms (e.g., Apple Search Ads, CTV, TikTok) by adjusting bids, formats, and messaging in real time. For example, AI predicts which creatives perform best on specific channels (e.g., video ads for TikTok, playable for Google Ads) and allocates budgets accordingly.
2. Performance Monitoring
AI-powered analytics tools continuously track metrics such as click-through rate (CTR), cost per impression (CPI), and retention to identify underperforming ads. By sending alerts when an ad underperforms, you can make data-driven adjustments to improve campaign effectiveness and increase user acquisition. It also detects creative fatigue (e.g., declining engagement after 3–5 days) and auto-rotates assets to maintain user interest.
3. Creative Production
AI significantly enhances creative production by automating the generation of ad assets, including visuals, videos, and localized content, thereby streamlining the process. For example, you can generate 15 AI-powered UGC videos in under 1 hour at $7 per video, slashing CPI by 40%. This automation enables you to create high-quality ads efficiently, reducing costs and enhancing campaign scalability.
4. Creative Velocity
AI also boosts creative velocity by dynamically iterating on winning concepts (e.g., adjusting text overlays, stickers, or cultural references) and automating A/B testing to identify top performers faster. This rapid iteration enables marketers to respond to performance data in real-time, optimizing ads to attract high-value users and enhance overall user acquisition outcomes.
5. Moodboarding
Moodboarding is a crucial step in the creative process, involving the collection of visual ideas and themes to guide ad development. AI-powered tools like Mooed.ai streamline moodboarding by suggesting relevant images, color palettes, and design concepts based on input parameters. These tools utilize generative AI, such as DALL-E, to create high-quality visuals, allowing you to quickly visualize and refine the aesthetic direction of your ad campaigns. While moodboarding is a general creative practice, its application in mobile gaming ensures that ads are visually compelling and aligned with the game's branding. For instance, "ugly ads" with lo-fi aesthetics have gained traction due to perceived authenticity.
6. Dubbing & Localization
AI-powered dubbing tools streamline multilingual ad campaigns by auto-generating voiceovers and subtitles. This ensures ads resonate culturally without manual translation efforts. This capability is essential for expanding market reach and acquiring users in international markets, where language and cultural nuances significantly impact the effectiveness of ads.
7. Upscaling of Creatives
High-quality visuals are crucial for engaging potential players, particularly given the mobile gaming market's diverse range of devices and screen sizes. AI technologies are used to upscale low-resolution assets (e.g., static images, old videos) and enhance creative elements, such as textures and effects brushes, ensuring ads look sharp and professional across platforms.
Integrating AI into UA strategies enhances cost efficiency, streamlines workflows, and improves campaign performance. By utilizing first-party data to inform ad creatives and targeting strategies, AI can increase conversion rates by 10–15%. While some argue that AI risks diluting human creativity in conceptual work and illustration, most view it as a tool that frees marketers to focus on strategy and innovation.
Retargeting and re-engagement are crucial to maintaining user interest. Let’s now explore how AI can help you retarget players effectively and ensure your campaigns keep them engaged long-term.
Using AI to Retarget & Re-engage Players
This section explores how AI can identify at-risk players, personalize re-engagement efforts, and boost retention rates:
1. Leverage AI for Smart Player Segmentation
Utilize machine learning to group players by behavior and value automatically. Instead of one-size-fits-all ads, feed your game’s analytics (session data, spend, level progress, etc.) into an AI-powered audience builder or clustering tool. The model will identify cohorts (e.g., “lapsed high spenders” or “social casuals”) that you can target with tailored campaigns. For example, the Bagelcode case study showed that AI-driven segmentation identified high-LTV dormant users and boosted ARPU by 33%.
Tools: Look for AI audience builders or machine learning (ML) segmentation solutions. These are often integrated into mobile analytics and retargeting platforms. They ingest player data and output target cohorts.
Outcome: Precise cohorts enable you to tailor offers (such as “welcome back” rewards) to each group’s specific needs. Brands report a 20–35% higher return on investment (ROI) when ads target the right segment.
Use predictive analytics to identify players likely to churn (based on declining play frequency or spend) and place them in a special re-engagement group.
2. Predict and Target Likely Churners
Train a churn-prediction model to catch drop-offs before they entirely quit. An AI model can use historical data to score each player’s churn risk. You can hit them early with incentives once you know which players are drifting away.
How-to: Build or use a supervised ML model (e.g., decision tree or neural network) trained on past players’ activity until churn. Score your active user base regularly.
Execution: Create a “rescue campaign” for high-risk players – personalized push or email offers, free lives, or special content – and suppress ads for already-active players to avoid fatigue (use a “cool-down” setting).
In practice, that might mean sending a “we miss you” bonus to players who haven’t logged in a week, or offering a discount on in-game currency to mid-tier spenders who suddenly stop playing. The goal is to engage them just in time before they churn.
3. Personalize Creatives and Offers with AI
Use AI-driven creative optimization to make ads and messages feel custom. Dynamic Creative Optimization (DCO) tools, often powered by artificial intelligence (AI), automatically tailor ads to each segment. For example, swap out characters, game scenes, or text based on user history. One proven approach is to show in ads exactly what a player missed, such as a level map or character they left off with.
Creative testing: Run frequent A/B tests with AI guidance. For instance, an AI algorithm can be set up to test ad variations and pause those that underperform automatically.
Video and interactive ads: Consider dynamic video or playable ads that evolve based on user actions. AI can even generate seasonal or themed variants – e.g., automatically creating a Halloween version of your best ad with just one prompt.
Personalize the pitch, mention their name in a push notification, reference their last game action, or recommend an in-game item they've viewed.
4. Optimize Timing and Channels with AI
Let AI find the right moment and channel to reach each player. Advanced ad platforms utilize machine learning (ML) to determine when a player will most likely respond to an ad. For example, they analyze past login patterns and adjust bid timing so that ads appear just before a player typically plays. Use AI tools to schedule push notifications or emails at times of day when each player is most active. You should also vary the frequency cap intelligently: avoid bombarding players, but ensure that you retarget them quickly after a lull.
Multi-touch campaigns: AI can orchestrate cross-channel retargeting. For instance, send a push notification to lure someone back, followed by a Facebook ad or in-app ad that reinforces the message. Deep linking (below) is key on all channels.
Data-driven decisions: Many demand-side platforms (DSPs) now incorporate real-time bidding AI. These systems automatically shift the budget to where it works best and can pause bids if performance drops.
Tools may suggest a “cool-down” period after a purchase to prevent oversaturating loyal players.
5. Use Deep Linking to Reduce Friction
Always send players exactly to what grabbed their interest. Embed deep links in your ads and messages so users land in the relevant game section, not just the app homepage. Deep-linked campaigns can triple conversion rates versus generic links.
Tip: In your ad URL or notification payload, specify the exact game context (level ID, reward offer, etc.). Test on devices to ensure the link opens the correct screen.
Outcome: Players love it – they skip repetitive menu navigation and feel instantly rewarded. You’ll see more quick re-installs or app opens from lapsed users when you “meet them where they are.”
For example, retarget players who abandoned a level or in-game store by directly linking them to that screen. Implement deep links across all channels (ads, push, email, social) so players resume right where they left off.
Finally, continue to improve using data and AI. Don’t “set and forget” your retargeting creatives or audiences. Utilize AI-enabled analytics to accurately measure incrementality (actual campaign lift) and return on investment (ROI). For example, run holdout tests where an AI model predicts what a user would have done without the ad, to gauge its impact accurately. Also, label your campaigns well and let ML algorithms analyze which variants drive the most conversions.
While re-engagement ensures player retention, protecting your ad spend from fraud is just as critical. Let’s take a closer look at how AI can detect and prevent fraudulent activities in your ad campaigns.
Ad fraud is a stealthy threat in mobile user acquisition (UA), bots and fraudulent SDKs can inflate clicks, installs, or even bidding prices. AI techniques counteract by learning to identify anomalies in your traffic. Your fraud-prevention system utilizes advanced machine learning to monitor campaign metrics in real-time and flag suspicious patterns – for example, an abnormal spike in clicks from a single IP or a surge in installs with no corresponding engagement.
When the AI detects such anomalies, it automatically blocks or filters out the offending traffic before it ever counts towards your metrics. Major game studios, such as Supercell, already utilize ML-driven monitoring to prevent fraud before it impacts the budget.
By continuously learning, the AI adapts to emerging fraud tactics: every new scam it encounters is used to retrain the models. In practice, this means that fraudulent clicks and fake installs are identified and excluded, ensuring that your CPI and ROI calculations accurately reflect genuine user activity.
On the tech side, AI fraud detection combines anomaly detection, behavioral analysis, and even graph models. Machine learning excels at detecting the subtle signs of fraud that humans might miss – for instance, it can recognize that a sudden burst of installs all share the same device fingerprint, a classic clue of fake installs. Once the AI flags a fraud pattern, real-time protections, such as device fingerprinting and delayed attribution, prevent those installs or clicks from being credited.
The net effect is that your UA budget is shielded from invalid traffic. In other words, AI ensures that your ad spend reaches genuine gamers, preserving the integrity of your campaigns and maximizing genuine ROI.
Conclusion
As cost-per-install benchmarks continue to rise, especially on iOS, and audience saturation tightens, relying solely on manual monitoring and rule-based optimizations will no longer suffice. Predictive analytics and dynamic creative testing have emerged as critical levers, enabling the identification of high-LTV cohorts, forecasting churn risks, and tailoring ad experiences in real-time. All while preserving user privacy through probabilistic and deterministic attribution techniques.
From automatically detecting underperforming creatives to shifting budgets toward top-performing channels, AI has proven its ability to boost ROAS, extend payback windows, and defend against ad fraud, transforming UA from a series of reactive tasks into a cohesive, data-driven strategy.
To put these insights into practice, Segwise AI steps in as a seamless extension of your UA toolkit through two specialized AI Agents. The Campaign Monitoring Agent continuously ingests data from your MMPs and ad networks, flags anomalies in ROAS or CAC trends, and delivers proactive recommendations for budget reallocation, creative rotations, or targeting refinements. This ensures you catch creative fatigue and performance dips before they erode your return on ad spend (ROAS) or customer acquisition cost (CAC) trends.
Meanwhile, the Creative Agent applies multimodal AI to automatically tag every visual, text snippet, and audio cue across your ad assets, surfacing the elements that drive installs and engagement.
AI in mobile gaming ads enables hyper-targeted ad delivery, dynamic creative optimization, and predictive analytics, improving user acquisition and retention by tailoring campaigns to specific player segments.
2. What are the current trends in mobile game ad budgets?
In 2024, global mobile game ad budgets declined, with certain genres, such as mid-core and casino games, cutting back while others, such as hyper-casual games, gained share.
3. How can AI help improve Return on Ad Spend (ROAS)?
AI-driven tools like machine-learning bidders and creative testing optimize campaigns in real time, improving ROAS by dynamically reallocating budgets and testing ad creatives for better performance.
4. What role does AI play in user acquisition for mobile games?
AI uses predictive analyti cs to segment players by behavior. This allows game marketers to target high-value players and adjust campaigns based on real-time data, resulting in more efficient user acquisition.
5. How does AI prevent ad fraud in mobile gaming?
AI monitors campaign metrics in real-time, identifying anomalies like bot activity and fraudulent installs. It then filters out suspicious traffic, ensuring ad budgets are spent only on genuine users.
