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 aims to bridge this gap with its AI-based platform and streaming media service, providing 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 personalised ads. This finding underscores the importance of ad relevance in retaining viewers. For example, one of the common complaints about platforms like Hulu was the repetitive nature of ads, which led to viewer churn. By leveraging machine learning, Moloco aims to ensure ad diversity and accuracy, thereby enhancing the overall viewing experience.
Understanding Moloco’s Offering
Moloco, a machine learning company founded by former engineers from Google and Oracle, provides a platform that helps streaming companies optimise their ad delivery. The core idea is to make high-quality machine learning accessible to companies outside the 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.
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 survey findings, which highlighted that viewers prefer personalised ads. By using first-party data from advertisers, Moloco’s platform can tailor ad content to match the preferences and behaviors of specific viewer segments. For instance, 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.
Leveraging First-Party Data
A key feature of Moloco’s platform is its ability to integrate and utilise first-party data from advertisers. This integration allows for more precise ad targeting based on actual user behavior rather than generic demographic data. For instance, instead of relying solely on data from Auto Traders, 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 information about viewers' past interactions and preferences, 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 emphasises outcomes. This means tracking and optimizing for specific actions that users take after viewing an ad, 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’s machine learning algorithms continuously learn and adapt based on the performance of ads, refining their predictions to improve future targeting. 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. 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 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.
Case Study: JioCinema
One of Moloco’s notable clients is JioCinema, the largest streaming platform in India. 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 maximised the advertising revenue during the event.
As the YouGov survey data showed, the advertising industry must shift from mass-reach strategies to more personalised, outcome-driven approaches. Moloco’s platform represents a significant step in this direction, providing smaller streaming companies with the tools they need to compete with major platforms. By leveraging advanced machine learning and first-party data, these companies can enhance their ad performance, delivering more relevant ads to their viewers and achieving better results for their advertisers.
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