Research Operations

The bottleneck was research throughput, not research craft.

Demand for customer research was growing faster than the team could support. Recruitment could take months, insights lived in scattered decks, and product teams repeated questions because previous evidence was difficult to find.

I reframed the work as an operating-system problem: build reusable infrastructure around recruitment, knowledge, enablement, analytics, and administration so specialist research time could be spent where judgment mattered most.

Turn one-off service work into reusable capability.

I rebuilt recruitment as a standing pipeline with reusable screeners, consent, incentives, and scheduling. I established Dovetail as a shared customer-insights repository and drove adoption so teams could find and reuse evidence instead of starting over.

The goal was not central control. It was a system product teams would choose because it made their work faster and clearer.

Scale judgment without turning democratization into quality drift.

The “Teach to Fish” program let cross-functional stakeholders run appropriately scoped research themselves. It shipped with intake rules, playbooks, training, and documentation standards so self-service did not mean “anything goes.”

The trade-off was deliberate: give away routine work to protect specialist capacity for studies that require deeper research expertise.

Run research operations like a product.

Power BI dashboards validated historical trends and supported roadmap prioritization. Smartsheet, Airtable, and Zapier automations handled repetitive operational work, including payment triggers on process completion.

The combination of workflow design, analytics, and automation reduced administrative friction while giving leadership better visibility into demand and evidence.

Research became infrastructure the organization could reuse.

The system increased research capacity without requiring every request to pass through the same small group of specialists. Recruitment moved inside product-decision windows, customer evidence became reusable, and teams had clearer guardrails for self-service.