Author: Ava Patterson
Ava Patterson
Ava is a San Francisco-based marketing tech writer with a decade of hands-on experience covering the latest in martech, automation, and AI-powered strategies for global brands. She previously led content at a SaaS startup and holds a degree in Computer Science from UCLA. When she's not writing about the latest AI trends and platforms, she's obsessed about automating her own life. She collects vintage tech gadgets and starts every morning with cold brew and three browser windows open.
Attribution is no longer just reporting — it’s AI decision fuel. Here’s why brands must build unified identity resolution across every creator, paid, and owned touchpoint.
VideoAmp and Claritas are building unified identity stacks that threaten point-solution attribution vendors. Here’s what brand teams need to know before their next planning cycle.
A technical implementation guide for brand teams on structuring product data, creator metadata, and schema markup to maximize AI discoverability and monitor brand accuracy across major models.
Turn creator content into a reusable paid media asset with rights clearance, metadata tagging, and a proven ROI model for cross-channel repurposing.
Learn how to archive, rights-clear, and tag creator content as paid media assets — with contract language, metadata standards, and a clear ROI model for reuse.
White House pre-release AI review could stall OpenAI and Anthropic API updates brands depend on. Here’s how to audit vendor risk and protect your creative automation stack now.
Learn how to configure AI-powered fraud detection across 100-plus creator activations using synthetic creator detection, engagement scoring, and real-time bot alerts to protect campaign attribution da
Learn how to test InMobi’s agent-to-agent ad formats against paid social with proper control groups, attribution windows, and ROAS measurement.
Learn how to monitor social commerce creative fatigue, spot exhausted creator-product pairings, and rotate content systematically to protect add-to-cart rates.
A structured methodology for brand teams to independently verify generative AI ROAS claims using control groups, attribution windows, and red flag detection.