Target CPE 是 Mintegral 推出的智能出价模型,专注于优化特定应用内事件(如购买)的成本。与 Target ROAS 不同,它更适用于以直接 IAP 收入为主的广告主,要求 IAP 收入占比超过 70%。投放初期系统会进入学习期,表现出成本波动,但随着算法识别高价值用户,安装量和成本会趋于稳定。
数据基础是 Target CPE 高效运行的核心。Mintegral 建议广告主共享全渠道数据(包括安装、收入、付费率)和丰富的事件数据,以便模型更快理解早期行为与最终转化之间的关联。实测显示,开启全渠道数据回传可为 Target CPE 模型带来至少 50% 的付费用户增量。
在优化目标选择上,D0 和 D7 的选择取决于产品的回本周期和付费指标密度。如果用户通常在安装后立即转化,D0 更合适;如果价值在安装后几天才体现,D7 效果更佳。同时,要求 D7 独立付费设备数达到 D0 的两倍,以确保模型有足够的样本进行稳定学习。
多地区投放时,应优先保证每个目标市场有足够的独立付费用户量级,否则建议延迟上线。最佳实践是将所有符合条件的国家合并到一个 Target CPE 广告系列中,并为类似市场设置统一出价,从而让算法共享所有付费样本,提升学习效率和投放稳定性。
总体而言,Target CPE 从启动阶段就帮助广告主规模化获取高价值用户。建议广告主结合自身数据能力和产品特性,与 Mintegral 团队沟通确认投放结构,并积极接入全渠道数据,以实现稳定的起量和增量提效。
Cross-channel marketing analytics isn't about putting Meta, Google, and TikTok numbers side by side—they often double-count the same customer journey. Fragmented identity is the real culprit; without a first-party Customer User ID, attribution measures platform credit, not customer value. The article explains that deduplicating conversions across mobile, web, and CTV can lift attributed revenue by 30–60% and improve ROAS by 20%. It walks through attribution models, warns against platform-native analytics, and advises using an independent MMP for true cross-channel measurement. Ad ops takeaway: fix identity resolution first, because AI-driven optimization and budget allocation depend on trustworthy, deduplicated data.
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.
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.
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.
The open internet presents unique challenges for performance advertising: fragmented identity, closed first-price auctions, and non-stationary supply. Moloco's CARA compound architecture tackles this with six integrated technical domains—Campaign Automation, Supply, Ad Recommendations, Bidding, Creative, and Signals—running on a unified ML infrastructure. Key insights for ad ops: the system continuously learns from every interaction, uses knowledge distillation to serve real-time predictions under 10ms latency, and validates improvements through rigorous live experiments. In 2025, 65 validated model updates reduced CPA by 17% and improved ROAS by 27%. The key takeaway: compound AI architectures that connect prediction, bidding, creative, and data can unlock measurable performance gains beyond walled gardens.
Smart+ is TikTok's automation suite that lets advertisers control which modules—such as targeting, budget, and placements—are automated. Key features include modular control, Smart+ Catalog Ads (29% CPA improvement in tests), and Symphony Automation for AI-generated creative. The article highlights expansions into the Traffic objective and new tools like Asset Manager and Summary. For ad ops, the value is balancing automation with manual oversight, optimizing for mid- and lower-funnel goals, and leveraging product catalogs for personalized ads.
In 2025, non-game apps surpassed games in revenue, with total in-app spending hitting $167B. APAC publishers drove a $2.58B increase in gaming revenue. Short Drama and AI Assistant categories saw explosive growth, while Blinkit, Shopee, and DeepSeek led their sectors. For ad ops, this signals shifting user attention toward lifestyle, commerce, and AI tools, creating new inventory opportunities beyond gaming.
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.
广告平台正经历类似LLM向LMM的演进,多模态数据融合决定了平台的智能上限。Fox收购Roku、Publicis收购LiveRamp等交易的本质是获取数据模态,构建从创意到归因的闭环信号链。广告主应优先选择能打通全链路数据、持续复合优化的平...
印度游戏市场已赢得安装量竞赛,下一步关键在于通过用户留存、价值衡量和精细化投放实现可持续增长。数据显示印度Q1 2026游戏下载量达8.57亿,其中中重度游戏以3.27亿次领先,但97.5%来自Android带来了高流失挑战,且营销渠道平均...
短剧应用成为非游戏应用市场最大黑马,全球下载量同比激增95.5%至14.5亿次,新兴市场贡献83%的份额。混合变现模式占据主导地位(57.6%),激励视频eCPM高达Android基线的11.4倍,插屏视频达7.8倍。报告强调,开发者需综合...
本文分析了可玩广告与视频广告在印度市场的表现,关键数据显示视频广告CTR(72%)略高于可玩广告(63%),且均远超全球平均水平。文章建议开发者重视可玩广告的预安装体验价值,优化视频广告的初始吸引力,并借助Playturbo工具高效制作创意...
代理型AI(Agentic AI)正在重塑媒体购买流程,将手动投放策略转向基于目标的自动化系统,让营销人员专注于更高层次的策略决策。关键数据显示,Google PMax和Meta Advantage+在成熟营销者中的采用率分别达到91%和8...
本文指出,随着Cookie受限和确定性身份可靠性下降,广告业正从基于上下文的精准定向转向基于概率的预测系统。关键优势在于通过SDK直接获取供应、降低延迟,并利用机器学习实现实时优化。实践意义是,具备预测能力的平台能突破传统内容场景,以更低成...