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.
A/B testing platform choice determines whether high-volume UGC programs ship winning creative fast or drown in data lag and mismatched attribution.
Bot networks now mimic real engagement so well that legacy vetting fails; here’s how AI fraud-detection platforms fight back in real time.
A practical framework for auditing vendor claims of agentic AI media buying, separating real autonomy from repackaged automation.
Vertical AI agents win on speed-to-value; horizontal platforms win on scale. Here’s how mid-market marketing teams should actually choose.
AI-driven campaign launch tools are compressing influencer activation timelines from weeks to days without sacrificing compliance or creative quality.
Influencer discovery, payments, and analytics are merging into unified platforms in 2026 — here’s the vendor map brands need before renewing contracts.
Nebula’s GEO framework promises AI-search visibility for regulated industries, but pharma and industrial marketers need proof, not vendor claims.
AI creative refinement tools now flag underperforming creator assets and auto-suggest edits, turning campaign data into faster, cheaper creative fixes.
DemandScience’s Ionic platform applies buyer-intent data to B2B influencer selection—here’s whether ML scoring actually beats manual vetting.
AI-powered attribution finally connects influencer touchpoints to revenue, ending the guesswork that plagued multi-touch measurement for years.