Case Study 04 | Audience Strategy

Driving consistent persona adoption with AI.

Sinclair's teams needed a consistent customer lens they could use across product decisions and future research, not a persona deck that would sit on a shelf. After behavior-based personas were validated, I focused on adoption by building a Segmentation Hub with the OpenAI Codex agentic platform in two weeks. The tool helped teams tag past and future research consistently, retrieve persona evidence quickly, and embed the same customer definitions into product and research roadmaps.

Lead UX Researcher80+ marketsOpenAI Codex build | 2 weeks

AI powered persona adoption visual showing personas moving through an OpenAI Codex Segmentation Hub into roadmaps, research planning and evidence reuse. — The Segmentation Hub helped turn persona definitions into a reusable system for product and research decisions.

2 weeks

Independently built the Segmentation Hub with the OpenAI Codex agentic platform.

Shared model

Consistent persona tagging connected past and upcoming research to product and research roadmap decisions.

Overview

The real challenge was scaling persona adoption across teams

Creating personas was only the first step. The bigger opportunity was making sure product teams and researchers could reuse the same definitions in future work. The Segmentation Hub turned a one-time research deliverable into a repeatable system for customer-centered decision making.

Product adoption

Bring persona needs and behaviors into roadmap conversations so teams could discuss opportunities through a shared customer definition rather than geography or demographics alone.

Research adoption

Use the same persona tags across research briefs, stored evidence and future studies so findings could be reused and compared more consistently.

Foundation

A clear persona definition gave teams one customer language

The persona framework remained grounded in motivation and behavior. Instead of asking teams to memorize a large deliverable, I focused adoption on a few consistent elements: what the customer is trying to accomplish, how they behave, what creates friction and what could change their behavior.

The shared behavioral definition gave product and research teams a consistent customer lens.

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AI Enablement

I built the Segmentation Hub independently in two weeks

To move the personas from a static research artifact into an operating system, I independently developed a Segmentation Hub using the OpenAI Codex agentic platform. The tool organized participant evidence, persona definitions and research tags in one place so teams could use the same customer language after the original study was complete.

Consistent tagging

Past and upcoming research could be connected to the same persona definitions instead of creating new audience labels for every study.

Evidence retrieval

Researchers could locate representative evidence, participant moments and supporting context by persona when a new question emerged.

Reusable definitions

Product and research partners had one reference point for customer motivations, behaviors and needs across future work.

Actual Segmentation Hub interface with participant evidence, search corpus and segmentation data. — I built the Segmentation Hub independently with the OpenAI Codex agentic platform in two weeks.
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Adoption

The same persona definitions started showing up in future work

The adoption mechanism mattered as much as the original personas. I began asking which personas a new research request would affect, and I encouraged similar conversations with product partners so roadmap and research planning used the same customer language.

Research briefs became more consistent

New studies could identify the affected personas early, making recruiting, synthesis and evidence reuse easier to plan.

Past research became easier to reuse

Uniform tagging created a clearer way to connect earlier findings to current questions instead of treating every study as isolated evidence.

Product conversations became more customer centered

Persona motivations and behaviors gave roadmap discussions a stronger customer lens than geography or demographic targeting alone.

Result

The persona work became a reusable product and research system

The strongest outcome was not the persona document itself. It was a shared operating language that could travel into product planning, future research and evidence retrieval. AI helped me make that adoption practical by turning the framework into a tool teams could continue to use.

What changed

Personas moved from a research deliverable to a repeatable way to tag evidence, frame new studies and discuss customer needs in roadmap decisions.

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