How we use aggregates for regression models on Hydrolix for user targeting

Context AdTech companies should be able to explore and test several aggregation strategies on the same dataset for debugging and model learning purposes. But let’s be honest, this really isn’t happening. DSP’s typically implement decision trees and logistic regression. Or as we like to call it, a spray and pray approach – where the bid […]

Price Predictions: How to Build Online Probabilistic Attribution Models for DSP Optimization

In the ever-evolving world of digital marketing, optimizing dynamic bidding to drive lower funnel metrics is crucial for Demand-Side Platforms (DSPs). A key component of this optimization is accurate attribution, determining which interactions and touchpoints contribute to conversions. Probabilistic attribution models provide a sophisticated approach to this, offering nuanced insights that can significantly enhance bidding […]

The Future of Ad Targeting: Leveraging Contextual and Intent Data with ChatGPT

In the ever-evolving landscape of digital advertising, marketers face the challenge of reaching the right audience effectively, especially as user targeting becomes increasingly restricted due to privacy regulations and changes in platform policies. As traditional user targeting methods face limitations, contextual and intent data emerge as powerful tools to drive ad targeting. This blog explores […]

Navigating Machine Learning in the Era of Apple’s SKAdNetwork: The Low Signal Quality Dilemma

In the realm of digital advertising, precision and data quality are paramount.  With advancements in machine learning, advertisers have been able to harness data to optimize ad targeting, measure effectiveness, and refine their strategies. However, Apple’s introduction of SKAdNetwork, a framework for privacy-preserving mobile app attribution, has brought significant changes to this landscape. While SKAdNetwork […]