AI and DSPs: science, astrology, & doing your homework

TL;DR

Demand side platforms (DSPs) use machine learning to optimize bidding in mobile advertising by leveraging various signals and context to make accurate predictions and improve ad performance.

Transcript

so what will effectively trying to do is remember yesterday very accurately at extremely high scale what actually happens when a demand side platform engages machine learning to boost your bids hello and welcome to growth masterminds my name is John katzir using machine learning of course in Mobile advertising and to drive bidding is super interest... Read More

Key Insights

• 🚗 DSPs leverage various signals and context to make accurate bidding predictions in mobile advertising.
• 💁 Device profiles and publisher information are used to better understand user behavior and target relevant audiences.
• 😀 Contextual data, including app descriptions and app categories, play a crucial role in optimizing campaign structures and creative selection.
• 🤕 Machine learning models enable real-time A/B testing and creative optimization to improve ad performance.
• 🤩 Accuracy and adaptability are key factors in successful machine learning-based bidding strategies.
• 🎰 Machine learning models need to consider dramatic events and changes in trends through incremental training and adaptable pipelines.
• 🙃 Privacy changes, such as restricted access to device IDs, require DSPs to rely on alternative signals and context for accurate bidding predictions.

Questions & Answers

Q: What signals are used in machine learning for bidding in mobile advertising?

DSPs leverage signals like user connection, device engagement, contextual information, and session data to make accurate bidding predictions.

Q: How do machine learning models create device profiles?

Machine learning models analyze device data like device UA string, pixel density, and number of cores to segment devices into different categories based on value and context.

Q: How is contextual data used in campaign structuring?

Contextual data, such as app descriptions and categories, are used to measure the distance between different apps' context, helping optimize campaign structures and creative selection.

Q: What role does machine learning play in creative optimization?

Machine learning models enable real-time A/B testing of creatives, allowing for data-driven decision-making and the identification of high-performing ad variations.

Key Insights:

• DSPs leverage various signals and context to make accurate bidding predictions in mobile advertising.
• Device profiles and publisher information are used to better understand user behavior and target relevant audiences.
• Contextual data, including app descriptions and app categories, play a crucial role in optimizing campaign structures and creative selection.
• Machine learning models enable real-time A/B testing and creative optimization to improve ad performance.
• Accuracy and adaptability are key factors in successful machine learning-based bidding strategies.
• Machine learning models need to consider dramatic events and changes in trends through incremental training and adaptable pipelines.
• Privacy changes, such as restricted access to device IDs, require DSPs to rely on alternative signals and context for accurate bidding predictions.
• Machine learning is a powerful tool but requires careful data management and clean inputs to ensure reliable and effective outputs.

Summary & Key Takeaways

• DSPs utilize machine learning to enhance bidding in mobile advertising by leveraging signals such as user connection, device engagement, contextual signals, and session data.

• Machine learning models can create profiles of devices and publishers, allowing for better targeting and understanding of user behavior.

• Contextual data, including app descriptions and app categories, are used to measure the distance between different apps' context, helping in campaign structuring and creative optimization.