人工智能的投资回报率

导言

人工智能在营销成果中的新作用

人工智能（AI）常常被誉为提高广告效率的终极解决方案，可以帮助品牌更好地规划、更聪明地工作并节省时间。它是每次行业活动的头条新闻，也是会议室里的热门话题。迄今为止，人工智能已被证明是简化策划流程的宝贵资产。但下一个大问题是：人工智能能否在规划阶段过后产生可衡量的影响？你怎么可能有足够的数据来衡量每一个接触点，并控制足够的外部因素，从而真正找出答案？

目标

量化人工智能的底线影响

谷歌试图通过对 YouTube 和搜索的最新人工智能解决方案进行终极测试，来揭示这些解决方案的底线影响：通过其人工智能工具对美国的 50,000 多个品牌营销活动和 100 多万个绩效营销活动进行测量。在截至 2024 年 6 月的两年时间里，这些活动涉及食品、家庭和个人护理、零售、服装、电信和汽车等多个类别。他们的人工智能解决方案包括

1. Video Reach Campaigns (VRC): Maximize reach with AI, combining multiple formats and optimizing towards a singular goal of efficient reach, non-skippable reach, or reach at a target frequency.

2. Video View Campaigns (VVC): Drive increased consideration by leveraging AI to optimize ad placement, delivering more views at a lower cost.

3. Demand Gen: Generate demand, drive performance, and deliver strong ROI with AI-driven, multi-format ads across YouTube and other visual surfaces, ideal for social advertisers.

4. Performance Max: Achieve more conversions and value across all Google channels, with AI optimizing performance in real-time. 

5. Broad Match: Expand reach beyond exact and phrase match, using AI to target additional, relevant queries and attract valuable customers, increasing conversions and improving ROI.

挑战

人工智能未经证实的跟踪记录

衡量人工智能解决方案的性能是营销生态系统中的一项新挑战。谷歌需要一种测量解决方案，能够将人工智能的独特影响从人工营销活动中分离出来，还能访问丰富的类别级数据，以有效捕捉其广泛的营销活动足迹。为了证明人工智能驱动的解决方案是否能够提高广告支出回报率（ROAS）和销售效率，他们需要一个能够有力回答其研究问题的营销组合建模（MMM）解决方案。

解决方案

连接绩效的点点滴滴

Google turned to Nielsen Marketing Mix Modeling’s new AI/ML-powered modeling platform to uncover ROAS and sales effectiveness. Nielsen took Google’s over 50,000 brand campaigns and over 1 million performance campaigns, controlled for external factors, and used advanced modeling techniques to aggregate digestible and accurate insights. Due to Nielsen’s extensive data categorization and data partnerships, Nielsen was able to provide the deep cuts of data needed for granular and speedy analysis.