Enhancing Ad Performance for Smaller Streamers with Moloco’s AI Solution
When it comes to delivering effective video advertising, smaller companies often face significant challenges. Unlike major platforms like Amazon, Google, and Meta, these companies lack the extensive viewer data that enables precise ad targeting. Moloco 旨在通过其基于人工智能的平台和流媒体服务来弥补这一差距 smaller streamers with tools to enhance their advertising performance. Dave Simon, General Manager of Growth Initiatives at Moloco, recently discussed the company's solutions and insights from a recent YouGov survey.
Insights from the YouGov Survey
Moloco partnered with YouGov to understand viewer preferences regarding ads. The survey revealed that 57% of consumers prefer personalized ads. This finding underscores the importance of ad relevance in retaining viewers. For example, 人们对Hulu等平台最常见的抱怨之一是广告的重复, which led to viewer churn. By leveraging machine learningMoloco的目标是确保广告的多样性和准确性，从而提升整体观看体验.
Understanding Moloco’s Offering
Moloco, a machine learning company founded by former engineers from Google and Oracle, provides a platform that helps streaming companies optimize their ad delivery. 其核心思想是让高质量的机器学习能够为世界以外的公司所用 walled gardens of tech giants. Initially focusing on the mobile app ecosystem, Moloco has expanded its services to include the streaming media sector, offering a platform that predicts which ads will be most relevant to viewers.
该平台利用先进的机器学习算法来分析大量数据，并预测个人观众最有可能回应的广告类型. This capability is particularly important in light of the YouGov survey findings, which highlighted that viewers prefer personalized ads. By using first-party data from advertisers, Moloco的平台可以定制广告内容，以匹配特定观众群体的偏好和行为. For instance, 经常观看烹饪节目的观众可能会收到厨房电器或烹饪课程的广告, 而运动爱好者可能会看到运动装备或即将举行的体育赛事的广告.
Leveraging First-Party Data
Moloco平台的一个关键特点是能够整合和利用来自广告商的第一方数据. 这种整合允许基于实际用户行为而不是一般的人口统计数据进行更精确的广告定位. For instance, instead of relying solely on data from Auto Traders, Moloco的平台可以使用汽车制造商CRM系统的特定购买历史来预测哪些用户可能对新车型感兴趣. 通过整合观众过去的互动和偏好的详细信息, Moloco ensures that ads are highly relevant and engaging.
Outcome-Based Marketing
Traditional advertising metrics often focus on impressions and reach, but Moloco’s approach emphasizes outcomes. 这意味着跟踪和优化用户在观看广告后采取的特定行动, such as app installs or purchases. For example, a retailer might use Moloco’s platform to track website conversions, optimizing ad delivery to achieve these outcomes more efficiently. This outcome-based approach aligns ad performance with business objectives, providing more value to advertisers.
Moloco的机器学习算法会根据广告的表现不断学习和调整, refining their predictions to improve future targeting. 这种动态优化过程有助于确保广告不仅与观众相关，而且有效地推动预期的行动.
实现Moloco的系统需要将其与流媒体服务的现有基础设施集成. This process, which can take a few months, includes setting up data flows and training the machine learning models. Once implemented, streaming services can expect to see improvements in ad performance, such as higher yield from fewer impressions and increased ad relevance. For example, Simon 's声称，Moloco的平台在达到某些活动的关键绩效指标(kpi)方面的效率提高了4到7倍.
Case Study: JioCinema
One of Moloco’s notable clients is JioCinema, the largest streaming platform in India. JioCinema在大型体育赛事印度超级联赛(IPL)期间使用了Moloco的平台. 该平台的机器学习功能使jiocincinema能够管理不同语言和地区的数千个广告活动, ensuring that the right ads reached the right viewers in the right languages. 这种方法不仅提高了广告的相关性，而且使活动期间的广告收入最大化.
As the YouGov survey data showed, 广告业必须从大规模覆盖策略转向更加个性化的策略, outcome-driven approaches. Moloco’s platform represents a significant step in this direction, 为小型流媒体公司提供与主流平台竞争所需的工具. By leveraging advanced machine learning and first-party data, these companies can enhance their ad performance, 为他们的观众提供更相关的广告，为他们的广告商取得更好的效果.
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