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.
Wunderkind, Tealium, and mParticle take different paths to resolving anonymous traffic — here’s which one actually protects revenue.
45% of AI marketing agents underdeliver because of identity fragmentation—here’s a root-cause framework to diagnose it before you scale.
Data contracts stop broken schemas from wrecking AI models—here’s why marketing teams need them before scaling automation.
A practical checklist for auditing identity resolution and CDP vendors before renewal, so you stop paying for match rates you never actually get.
B2B marketers need buying-group data models to unify multi-stakeholder identity and make AI attribution actually trustworthy.
GA4’s AI assistant surfaces top referrer insights automatically—here’s how to route those findings into revenue models instead of dashboards nobody checks.
Improvado, Segment, and mParticle solve the ad-to-warehouse integration problem differently — here’s how to pick the right one.
44% of marketers say their data isn’t AI-ready. Here’s a diagnostic framework to fix buyer-freshness gaps before scaling automation.
The 2026 AI marketing stack has a standard shape now: ad platforms feed warehouses, warehouses feed activation. Here’s how to map it.
Zig.ai’s forward-deployed engineer model exposes a hard truth: enterprise marketing data needs embedded technical talent, not another dashboard.