A fleet technology company that helps businesses manage vehicle visibility, driver safety, and day-to-day fleet operations across distributed teams, this company builds the software and hardware that gives small businesses and commercial operators real-time insight into their vehicles and drivers. With several thousand employees and a customer base spanning hundreds of thousands of fleet operators—from solo owner-operators to enterprise logistics teams managing thousands of vehicles—the company’s product portfolio covers GPS tracking devices, dash cameras, and compliance tools.

Customer feedback is central to that work. Fleet operators and small business owners have specific, strongly held views about the tools they depend on every day. For years, the company’s research team did the work of surfacing those views: conducting interviews, running usability studies, synthesizing findings. The problem was that those findings rarely traveled beyond the research function that generated them.

The research function was small, fewer than five people at its core, and had contracted from a larger team. As headcount fell, so did the team’s bandwidth to manually brief stakeholders on every finding. Product managers, UX theme leads, and engineers needed customer evidence to make decisions. Getting it meant scheduling time with a researcher who was already stretched across multiple concurrent studies.

The problem ran deeper than capacity. With no shared repository, insights from one study had no way of surfacing during a different team’s project planning. Tag structures had proliferated across dozens of individual projects, with the same concepts duplicated in each one rather than maintained at the workspace level. A researcher working on one product area had no reliable way to find findings from a study completed months earlier in a different part of the organization.

The research team tracked “customer listening hours” as a performance metric, targeting 400 or more annually, measuring time spent with customers against the audience reached in readouts. But those readouts rarely reached the people making decisions at scale. Customer intelligence was collected, synthesized, and filed. It just didn’t travel—until a single customer interview clip found its way to senior leadership. That’s where Dovetail came in.

The shift began with access. Previously, getting viewer access to the Dovetail workspace required a manual request through the company’s identity management system. The research team changed that: today, anyone in the product organization is automatically enrolled as a viewer. New users arrive in the workspace without being prompted, and the research operations lead monitors access, manages permissions, and keeps the workspace navigable for people who have never opened it.

The team invested in making the workspace worth arriving to. A Zapier connection with their project management system automatically creates Dovetail notes whenever a new research project is commissioned, giving the research operations lead advance visibility into everything being planned. Global tag structures were simplified, collapsing dozens of duplicated project-level tags into a small set of clear, workspace-wide concepts that the AI can match reliably.

Channels bring new signal in from outside the research function. Help center feedback—verbatim responses from users on support articles—is ingested monthly. Sales and customer success calls via Gong are an additional data source, pulling deal context and support intelligence into the same workspace where interview findings live.

The team’s priority was adoption over control—better for product teams to engage with research imperfectly than to wait for a researcher to hand-deliver it. Cross-functional teams use Chat to query research findings on demand, with researchers available to help teams sense-check what they’ve surfaced. Rather than controlling every handoff, the research team focuses on improving how accurately the workspace answers questions.

“What we’re doing now is a lot of referencing our previous work, and Dovetail has been really helpful—we’ve been focusing mostly on Search and Chat.”

— Research Operations Lead

The moment that accelerated everything was unplanned. The Associate Director of UX Research had been sharing highlight reels and interview clips informally with senior leaders, using Dovetail to surface relevant customer moments during round table conversations. At one such session, a clip landed in front of a senior leader. The response was direct: they wanted more.

“A clip landed in front of our leadership and they said: this information is great—we want to see more.”

— Research Operations Coordinator

What followed moved months faster than any roadmap had planned. Engineering was brought in to provision accounts through the company’s identity systems. The research operations lead prepared the workspace to be navigable for executives who had never opened it. Within days, the entire executive team had access to direct customer voice.

The payoff came at the company’s internal two-day conference for every manager with direct reports across the organization. The research team was given 1–2 hours of airtime across the event agenda: customer interview clips, highlight reels, and direct customer pain points woven into sessions that reached the company’s full management layer.

For a company whose customers are the fleet operators and small business owners who depend on their vehicles to earn a living, hearing that voice directly changed the tenor of leadership conversations. That’s what Dovetail makes possible: customer intelligence that was once locked inside a research team, now driving every decision in the building.