Liftoff(Vungle)Liftoff(Vungle)

Smarter Spending, Stronger Results: The Power of Dynamic Impression Pacing

By Machine Learning·2025年3月27日·4 分钟阅读

摘要

文章首先对比了静态与动态展示频次控制的区别。传统静态频次控制采用固定时间间隔(如1小时)来决定下一次展示,这种“一刀切”策略无法区分用户价值,导致高意向用户因规则限制而错失转化机会,同时低价值展示过多消耗预算。

为解决这一问题,Liftoff推出了基于机器学习的动态展示频次控制(Dynamic Impression Pacing)。该方案实时评估每次展示机会,综合考虑用户转化概率、上次展示的时效性及对收入的预期影响,动态调整出价策略:对高转化概率用户提高出价,对低价值用户延长投放间隔。

A/B实验验证了该方案的效果:相比静态控制,动态频次控制使展示量减少10%,但安装率提升且ROAS保持稳定,说明预算分配更高效。尤其是原本使用短频次间隔(<1小时)的广告活动受益最大。

对广告主而言,动态展示频次控制无需手动调整规则,ML模型自动优化,能够减少低价值展示的浪费,聚焦高意向用户,从而提升增量转化和整体ROAS。Liftoff表示将继续迭代ML模型,提升预测能力,以更好地平衡控制与自动化。

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