JD Case Study · Target
Director, Quantitative UX Research & AI Enablement
A Director owning quantitative UX research, experience measurement, and Voice-of-Customer — translated into product decisions — plus responsible AI-enablement of the research function. The VoC + measurement + experimentation muscle here is exactly the one I built for a CX Research & Market Intelligence brief; and the velocity of this portfolio is itself proof of AI-scaled research workflows.
What they’re actually buying
Make Target learn faster about its guests. Own UX metrics, benchmarking, survey research, longitudinal VoC, and experimentation — and turn them into decisions product acts on. Then make that research AI-ready: structured, reusable, discoverable, with responsible guardrails for agents and synthetic users. The job is rigor and reuse, at scale — pairing what guests do with what they say.
How I’d own it — first 90 days
Days 1–30
Map the measurement landscape
Audit the UX metrics, VoC programs, survey and benchmarking work. Baseline the experience KPIs, meet UX / Product / Analytics, and find the highest-value measurement gaps and the decisions they’d unblock.
Days 31–60
Standardize & make reusable
Establish survey-design / sampling / analysis standards and a reusable insights repository, and ship an experience scorecard tied to product roadmaps. Pilot AI-assisted synthesis (open-end coding) behind a quality bar.
Days 61–90
Prove AI-enabled research
Ship one responsible AI workflow — AI-assisted coding or a guard-railed synthetic-user pilot — with a governance and quality framework, plus a benchmark study that lands a real roadmap decision.
Signature analysis: behavior vs. voice
The quant-research core: pair the behavioral funnel with the VoC signal and look for the gap — the step where guests succeed behaviorally but say it hurt (or quit but rate it fine). That divergence is the fundable research question. I’ve done exactly this: on my Upbound brief, a 1.2-star Apple-vs-Google-Play gap on lease-to-own apps flagged an Android friction no behavioral metric surfaced.
Illustrative figures to demonstrate the lens — behavioral funnel against VoC, with AI (synthetic users, AI-assisted open-end coding) as the way to scale the measurement responsibly.
Requirement → proof
| What the JD asks for | What I’ve already shipped |
|---|---|
| Quantitative research & experience measurement | A real VoC quant finding — the Apple-vs-Google-Play rating divergence as a fundable research signal. |
| Voice-of-Customer / longitudinal listening → action | VoC synthesized into a theme → opportunity → action read for executives. |
| Experimentation & behavioral analysis | An A/B + incrementality readout with confidence intervals and a sample-size calculator. |
| Research standards, governance & reusable repository | Every artifact here follows one sourcing standard — cited, dated, reusable. That’s research-ops discipline, shown. |
| AI-enabled, scalable research workflows | The velocity of this portfolio — a dozen sourced, cited analyses — reflects AI-accelerated research and synthesis workflows I designed and direct. |
The same engine
This role is the CX-research and experience-measurement muscle I already put on display in my Upbound market-intelligence brief — VoC signals, experimentation, insight-to-decision — now scaled with AI. Same engine, research side. I don’t treat "quant UX research" and "market intelligence" as different jobs; they’re the same rigor pointed at a different question.
See the CX-research brief →Quantitative research that pairs behavior with voice, translates to decisions, and scales with AI — I’ve already shipped it, in public. Let’s talk about how Target learns. — Paul Brown