I'm a researcher and a builder. The work I'm best at sits at the seam between evidence and decision.

The thread running through my work is the same question I've been chasing since graduate school: how do you measure what people actually do, distinct from what they say they do, and translate that into something a business can act on?

I started in academic research. After a Ph.D. in Communication Studies at the University of Iowa and a year as a visiting researcher in the Discourse and Rhetoric Group at Loughborough, I joined Northeastern University as a tenure-track faculty member in 2002. There I developed G2X, the first independently validated framework for measuring the spread and ROI of word-of-mouth marketing using longitudinal test/control with validated purchase. The methodology drew enough commercial interest that I negotiated a formal technology-transfer agreement with Northeastern in 2007. I ran the company and my faculty position in parallel for two years before leaving academia in 2009 to focus on the business full-time.

The company that became Purchased grew from that origin into a broader consumer insights firm working with the world's largest advertisers and retailers. Over eighteen years I led methodology, product, and the major client relationships. We built Shopalong™, a custom mobile platform that powered a top U.S. retailer's holiday-season omnichannel war room across consecutive Thanksgiving-through-Cyber-Monday periods, with results validated against the retailer's internal POS data. With a leading consumer-technology company's internal audience-measurement team we developed a proprietary Full-Funnel Impact of Digital Advertising methodology, adopted broadly across that company's account teams and backed by a benchmark database that now exceeds a million data points. For another major consumer-technology firm we ran an operating-system launch tracking program, a year-long search-experience study, and a multi-country enterprise trial app localized for thirty countries and twenty languages. For a global apparel holding company we won a multi-year, multi-million-dollar global customer experience program covering their flagship brands — beating established research firms on agility and bespoke fit.

If there is a single thing eighteen years of this work has taught me, it is that insights only earn their keep at the decision layer. Sound methodology and defensible data are table stakes. The engagements that mattered — the ones that actually changed pricing, product, or media spend — weren't the ones with the most rigorous design. They were the ones where someone took the time to listen carefully, understand what the client actually needed (which is often different from what they say they need), translate cleanly between the technical and non-technical people in the room, and carry the work through to a decision someone made differently. That has always been the part of the job I'm best at, and the current AI moment is making it more valuable, not less. As the friction of basic execution — access, cleaning, analysis, even initial synthesis — collapses, the premium shifts decisively toward judgment, translation, and last-mile activation. Insights is being reclassified into decision intelligence, and the people who can credibly sit at that layer are the ones who can do both halves: understand the methodology well enough to know what the data can and cannot say, and understand the business well enough to know what to do about it.

That conviction is part of why I've spent the past year building and shipping production agentic AI systems myself, end-to-end with Claude Code. The detail lives on the projects page. The builds matter to me as evidence: the senior research and insights world is full of opinions about AI; far fewer people have actually shipped systems that work in production against real users and real money.

Outside work I run a short-term rental business in the Finger Lakes of upstate New York and tinker with the card game my family has played for thirty years.