使用Moloco的AI解决方案增强小型流媒体的广告效果
当谈到交付有效 视频广告在美国，规模较小的公司往往面临重大挑战. 与亚马逊等主要平台不同, 谷歌, 和元, 这些公司缺乏广泛的观众数据 精准广告定位. Moloco aims to bridge this gap with its AI-based platform and streaming media service, providing 较小的飘带 用工具来提高他们的广告效果. 戴夫•西蒙, Moloco增长计划总经理, 最近讨论了公司的解决方案和最近的见解 YouGov的调查.
来自YouGov调查的见解
Moloco与YouGov合作，了解观众对广告的偏好. 调查显示，57%的消费者更喜欢 个性化的广告. 这一发现强调了广告相关性在留住观众方面的重要性. 例如, one of the common complaints about platforms like Hulu was the repetitive nature of ads, 是什么导致了观众流失. By 利用机器学习, Moloco aims to ensure ad diversity and accuracy, thereby enhancing the overall viewing experience.
了解Moloco的产品
Moloco, a machine learning company founded by former engineers from 谷歌 and Oracle, provides a platform that helps streaming companies optimize their ad delivery. The core idea is to make high-quality machine learning accessible to companies outside the 围墙花园 科技巨头. 最初专注于移动应用生态系统, Moloco已将其服务扩展到流媒体领域, 提供一个平台，预测哪些广告与观众最相关.
The platform leverages advanced machine learning algorithms to analyze vast amounts of data and predict the types of ads that individual viewers are most likely to respond to. This capability is particularly important in light of the YouGov的调查 findings, 这突出了观众更喜欢个性化的广告. 通过使用来自广告商的第一方数据, Moloco’s platform can tailor ad content to match the preferences and behaviors of specific viewer segments. 例如, a viewer who frequently watches cooking shows might receive ads for kitchen appliances or cooking classes, while a sports enthusiast might see ads for athletic gear or upcoming sports events.
利用第一方数据
A key feature of Moloco’s platform is its ability to integrate and utilize first-party data from advertisers. This integration allows for more 精准广告定位 based on actual user behavior rather than generic demographic data. 例如, 而不是仅仅依赖于Auto Traders的数据, Moloco’s platform can use specific purchase history from car manufacturers' CRM systems to predict which users are likely to be interested in new car models. By incorporating detailed 信息 about viewers' past interactions and preferences, Moloco确保广告具有高度相关性和吸引力.
这类基于结果的营销
传统的广告指标通常关注印象和覆盖面, 但莫洛科的方法强调结果. This means tracking and optimizing for specific actions that users take after viewing an ad, 比如应用安装或购买. 例如, 零售商可能会使用Moloco的平台来跟踪网站转化率, 优化广告投放以更有效地实现这些结果. 这种基于结果的方法使广告绩效与业务目标保持一致, 为广告主提供更多价值.
Moloco’s machine learning algorithms continuously learn and adapt based on the performance of ads, 改进他们的预测，以提高未来的目标. This dynamic optimization process helps ensure that ads are not only relevant to viewers but also effective in driving the desired actions.
Implementing Moloco’s system involves integrating it with the streaming service’s existing infrastructure. 这个过程, 这可能需要几个月的时间, 包括建立数据流和训练机器学习模型. 一旦实现, 流媒体服务可以期待看到广告性能的改善, 例如，从更少的印象和增加的广告相关性中获得更高的收益. 例如, Simon’s claimed that Moloco’s platform has been shown to be four to seven times more efficient at hitting key performance indicators (KPIs) for certain campaigns.
案例研究:jiocinine
Moloco的一个著名客户是 JioCinema是印度最大的流媒体平台. JioCinema used Moloco’s platform during the India Premier League (IPL), a major sporting event. The platform’s machine learning capabilities allowed JioCinema to manage thousands of ad campaigns across different languages and regions, ensuring that the right ads reached the right viewers in the right languages. This approach not only improved ad relevance but also maximized the advertising revenue during the event.
正如YouGov调查数据所显示的那样, the advertising industry must shift from mass-reach strategies to more personalized, 以成果为推动力的方法. Moloco的平台是朝着这个方向迈出的重要一步, providing smaller streaming companies with the tools they need to compete with major platforms. 通过利用先进的机器学习和第一方数据, 这些公司可以提高他们的广告效果, delivering more relevant ads to their viewers and achieving better results for their advertisers.
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