CRM, Lifecycle & AI Strategy
20 years in, I’m still asking: what if we tested this?
At Amazon, that meant helping put early propensity models into marketers’ hands. Now it means figuring out what AI-driven marketing actually does versus what it claims to do, and what customer behavior is actually telling us once you strip the dashboards away.
Everything else moved. Industries, customers, and the tools many times over. The one thing that didn't: follow the behavior, test the new idea, see if it moved anything.
AMAZONANCESTRYBED BATH & BEYONDSTANLEY BLACK & DECKER
BRAZE FORGE 2025 SPEAKERNORTHWESTERN MEDILL GUEST LECTURERBRAZE MARKETER OF THE MONTH

Why the fundamentals matter more now
The tools got cheap. That's the whole shift. Anyone can generate a thousand variants before lunch, so the advantage doesn't sit in production anymore, it sits in knowing which variant deserves to exist. That puts the value back on the unglamorous stuff. You actually read the data instead of skimming a dashboard. You build a test that's allowed to fail. You trust behavior over what people say they'll do. You write something someone's glad to get instead of one more email to ignore.
I learned that early in my career. The model was never the whole answer. The harder part was making the signal usable enough that people actually trusted it and built it into how they made decisions. That's stuck with me through every role since.
Across ecommerce, SaaS, global industrial tools, and big-box retail, I've watched the technology change and learned where it creates real advantage versus where it just creates more noise. This is where I put the tools and playbooks I've built along the way. I encourage you to test them in your own work and see what they can do.
The work
Run the playbook.Examine the argument.Test the model.
Applied AI
Built to be read by people and run by agents.
Machine-readable playbooks
Honest simulation
Evidence over adjectives
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