Mintegral Articles

In 2025, AI agents will automate ad production and UA, reducing personnel needs. Privacy concerns persist despite Google's cookie reversal, driving contextual targeting and new identifiers. Advertisers will explore CTV and in-app inventory via cost-per-outcome deals. M&A activity ramps up, with deal volume up 118% YoY. AI-generated creative becomes crucial as targeting narrows, enabling scalable, varied ad creatives.

Early campaign metrics can mislead because they capture high-intent users first, while long-term performance depends on broader audiences and delayed monetization. Learning phases, monetization lag, and incomplete data make early ROAS unreliable. Ad ops teams should evaluate multiple completed cohorts and align optimization windows with conversion events to distinguish genuine trends from initial volatility. Sustainable scaling requires balancing early signals with patience for meaningful patterns to emerge.

Entertainment apps in 2026 are leveraging generative AI to accelerate content production, but high-quality video remains key. Growth strategies involve multi-channel acquisition across 10+ channels, with 50-70% spend on open networks. Emotional storytelling in ads and localization are critical for user engagement. Monetization is shifting to hybrid models, with 28.2% of apps mixing IAP and IAA, and 44.1% relying solely on ads. AI-driven platforms like Mintegral enable cost-efficient UA and revenue optimization.

The article explores the strategic use of CPI and ROAS campaigns on Mintegral, emphasizing that CPI is ideal for new apps to gather initial user data, while ROAS suits mature apps focused on high-value users. Running both in parallel can confuse algorithms and reduce efficiency. A key insight is the 'bidding challenge': bid high enough for impact but not overspend. Mintegral's Hybrid ROAS optimizes for both IAA and IAP, using oCPI bidding. Decision-makers should prioritize one model based on app stage and use tools like sub-source management to refine performance.

Non-gaming marketers like e-commerce, fintech, and subscription services are increasingly turning to mobile advertising, driven by rising costs on walled gardens. They are shifting from CPI to outcome-based models (e.g., ROAS, CPA), leveraging ML to find quality users beyond contextual placements. Key takeaways: ad platforms must enable direct revenue attribution, faster feedback loops, and product-first creative to serve these advertisers. The era of growth at any cost is giving way to quality-focused, intentional scaling.

Mintegral renewed SOC 2 Type 2 and SOC 3 certifications, confirming adherence to security, confidentiality, and privacy standards for the period Oct 2024-Sep 2025. This complements GDPR, CCPA, COPPA, ISO 27001, and ePrivacyseal. The article emphasizes data transparency and integrity, positioning Mintegral as an ad tech leader. It also lists three posts on early metrics, entertainment app growth, and CPI vs ROAS optimization.

Short-term ROAS and long-term retention often conflict because early conversions don't guarantee long-term value. To balance both, extend the optimization window to 7-14 days, use mid-funnel signals to bridge gaps, and align optimization with monetization model (IAP vs. IAA). Shift focus from early signals to retention as campaigns stabilize, and define clear payback windows upfront to avoid misleading optimization.

Digital health app growth shifts from acquisition to engagement, with AI health companions, femtech, and senior-friendly tools as key frontiers. Statista forecasts moderate 1.75% CAGR for fitness/wellness apps through 2030. Developers should prioritize hybrid monetization (IAA+IAP), smart UA with automated bidding, and interactive creative testing to maximize LTV and global scalability.

Mintegral's Target ROAS guide offers practical steps for ad ops decision-makers to optimize campaigns. Key insights include enabling data postbacks for accurate ML modeling, verifying event mapping to ensure correct revenue signals, reducing data discrepancies with MMPs by selecting proper report types and time windows, and incrementally tweaking budgets (e.g., adjusting ROAS goals by ≤10% weekly, or reducing by ≤5% for scaling). The guide emphasizes flexible adaptation based on regional and product differences to achieve better ROAS outcomes.

Target ROAS campaigns often fail to scale due to unrealistic targets, budget cuts during learning, short data windows, or frequent structural changes. To scale, focus on three pillars: sufficient budget for exploration, flexible ROAS targets during early learning, and adequate data windows to capture long-term value. Avoid micromanaging; instead, provide stable signals and exploration capacity for the algorithm.

Target CPE campaigns optimize for in-app purchase costs using machine learning. Key success factors include consolidating regions into single campaigns with consistent pricing, enabling full-channel data for 50% more paying users, and choosing D0 vs D7 based on payback period. Early performance fluctuates during learning, but stable cost and volume indicate healthy campaigns.

The learning phase is critical for scaling ROAS campaigns, typically lasting 10-14 days. To shorten it without disruption, advertisers should keep targeting broad at launch, commit a sufficient learning budget, use mid-funnel signals like add-to-cart for more data points, and ensure data/creative readiness. Early volatility is normal; patience and proper inputs lead to sustainable scale.

Mintegral campaigns often underperform initially due to normal learning phase volatility, not platform issues. Advertisers should expect fluctuating ROAS as machine learning explores inventory. Stability requires sufficient data volume in each market, so consolidating budgets on priority geos and allowing time for optimization are key. Clean event mapping and consistent delivery support long-term success, while structural issues like missing events need setup corrections. Patience and realistic targets enable scalable performance.

The article argues that 2026 is an ideal time for advertisers to recommit to the open web due to rising costs in walled gardens. Key drivers include generative AI enabling scalable creative production, retail media expanding beyond owned properties, automation reducing operational friction, and cross-channel intelligence improving ROI. With consumers spending 59% of time on the open web versus 48% of ad spend, there's a significant opportunity to achieve cost-efficient performance. Advertisers should treat the open web as a central pillar, leveraging lower CPMs, flexible bidding, and improved attribution to stretch budgets.

The article provides campaign management tips for first-time Mintegral AppGrowth users. It recommends starting with broad targeting in CPI campaigns to collect data and enable algorithmic learning, then transitioning to smart bidding like Target ROAS as the app matures. It advises against running CPI and ROAS campaigns in parallel, favoring one optimization model at a time. For global campaigns, unified structures are preferred over fragmented ones to improve machine learning efficiency, provided budgets are sufficient per market. Key actions include using sub-source management to optimize performance and balancing centralized management with regional adaptation.

This article explains that poor ROAS campaign performance often stems from low-quality in-app event signals, not bidding strategy. Machine learning models rely on clean, well-mapped events to identify high-value users. Common pitfalls include inconsistent event mapping, improper timing, vague definitions, and technical fragmentation. To optimize on Mintegral AppGrowth, define a clear event hierarchy, double-check mapping in the dashboard, and treat event setup as an ongoing process. Clean signals enable faster model learning, smarter bidding, and sustainable ROAS growth.

AI is now central to mobile engagement, not an add-on. Leading brands like King (Candy Crush) use AI for real-time difficulty adjustment, boosting conversions 40%. Duolingo and Pinterest leverage AI for personalized experiences. For ad ops, AI improves targeting, creative delivery, and automation, enabling granular personalization without manual overhead. Marketers should let AI optimize targeting, bidding, and creative production to drive better user engagement and retention.

Transitioning from pure IAA to hybrid monetization requires a fundamental shift in UA strategy. Key tactics include lifecycle-based approaches: testing with sufficient budget for reliable data, growth phase focus on market share over immediate ROI, and stable phase flexible bid adjustments. A multi-objective framework using D0 (scale) and D7 (conversion) campaigns, plus dynamic IAA/IAP ratio monitoring, ensures balanced revenue. Avoid pitfalls like over-controlling testing budget, fixating on ROI during growth, and overreacting to stable-phase fluctuations.

The open internet offers app advertisers untapped growth beyond walled gardens like Meta and Google. With 59% of US consumer time spent on the open web but only 48% of ad spend, a significant opportunity exists. Machine learning now enables efficient targeting across diverse inventories. Rising costs and ATT-driven measurement issues in walled gardens push advertisers to diversify. Gaming and non-gaming apps benefit from relevant users, contextual ads, and flexible pricing. To succeed, marketers must learn the ecosystem, prioritize scale, and integrate MMPs. Mintegral provides a robust platform with SDK-powered traffic for effective open internet campaigns.

Gamified ads boost e-commerce performance by integrating game mechanics like progress bars, points systems, and interactive quizzes into the shopping journey. These ads drive engagement, conversion, and loyalty by leveraging behavioral psychology, making rewards feel earned and personalized. Key formats include progression-based rewards, quizzes for data collection, and tiered loyalty programs. Platforms like Mintegral's Playturbo enable scalable creation of such ad experiences, helping marketers attract and retain shoppers while improving long-term ROI.