Driver Exoneration Journey Mapping
UX Research
This piece showcases my research work to develop as-is journey maps that document the typical process surrounding driver exoneration in the fleet industry. This was a high priority research roadmap initiative in Q3 2025 that aimed to support future Product and Engineering initiatives for Verizon Connect’s product Reveal. These journey maps and supporting research have had a lasting impact on Verizon Connect’s understanding of driver exoneration, driver recognition opportunities, and the user needs of fleet managers and drivers.
Overview
Problem
Past research indicates fleet managers and drivers want to use Reveal as a mechanism for positive reinforcement, but most existing features have punitive applications. The business wants to invest more heavily in these positive experiences, such as driver exoneration functionality. To do so, the Product and Engineering teams need a better understanding of as-is journeies so as to best focus their solutions to user needs.
Project Goal
Understand the as-is driver exoneration experience from the fleet manager and driver perspective.
Identify customer needs and opportunities for potential future roadmap initiatives surrounding driver exoneration.
My Contribution
I was the lead UX Researcher on this initiative.
Team
Stephanie Baione, Mimi Lu
Timeline
June 2025 - August 2025
Tools
Figjam, userinterviews.com, Pendo, Dovetail
Background
In the business of driving, fleet managers have a strong incentive to keep a watchful eye on every aspect of the work. This includes but is not limited to areas like vehicle maintenance, cargo securement, compliance management, driver training, and the all-important topic of driver safety. Depending on company policy, fleet management can become a very thorough task that incorporates dashcam technology, AI hazard detection, and driver coaching to ensure drivers do not slip up on critical safety procedures (e.g. attentive defensive driving). Businesses like Verizon Connect have invested a lot of time and money into developing optimized tools to identify driver mistakes so they can be curbed and improved upon.
But what about when a driver does an excellent job?
Not all cases of harsh braking, tailgating, or even speeding are always the result of bad driving. Sometimes a good driver will swerve out of their lane to avoid a rear-end collision, saving their company thousands in repairs, and of course, triggering a harsh driving alert for their fleet manager to review.
We know these scenarios happen—and that some can be quite common—but fleet management tools as a whole are light on opportunities to proactively track and reward these scenarios that are worthy of driver exoneration. And for the experts at Verizon Connect to best build high value solutions, we need to know more about what fleet managers are already doing, how drivers feel, and how we can help.
Project Goal
Understand the as-is driver exoneration experience from the fleet manager and driver perspective.
Examine existing Reveal functionality and its relationship to driver exoneration
Identify customer needs and opportunities for potential future roadmap initiatives surrounding driver exoneration.
Project Timeline
This project took place from June to August 2025 and consisted of fleet manager interviews, driver interviews, and a customer survey.
1. Exploratory Research
Research Methods
1. Fleet Manager Interviews
2. Driver Interviews
3. Reveal Customer Survey
Interviews
Method Selection: With our research focus on the as-is experience, testimonials and open conversation were our most valuable resource for defining user journeys.
Target Cohort: Driver exoneration is relevant to all fleet managers and drivers in the fleet management industry, not just Reveal customers. For an unbiased and diverse perspective on the topic, we elected to interview fleet managers and drivers irrespective of the fleet management tool their company uses. This also opened the doors to learn about if and how competitor products have addressed driver exoneration.
Key Research Questions:
What does driver exoneration mean for you and your company?
Tell me about a time you exonerated a driver?
How was [your fleet management tool] involved in this process?
What do you wish you could change about your driver exoneration process?
Does driver exoneration ever involve driver recognition?
Reveal Customer Survey
Method Selection: We had a hypothesis that existing Reveal features might indirectly support driver exoneration. Proving or disproving this hypothesis would identify quick wins to better support this group and would provide direct customer feedback in addition to the non-customer interviews.
Target Cohort: We constructed a Pendo survey that would appear on a Reveal driver video in response to user interaction with certain features. This guaranteed that all respondents were exhibiting driver exoneration behaviors, and the survey would not impact other users’ experience.
2. Research Analysis & Synthesis
Research Synthesis & Communication
Research gets read out too. Here’s how.
Major Learning 1
xyz
Major Learning 2
xyz
Major Learning 3
xyz
Evaluation 1
This was an evaluation test.
Insights
These are impacts
Recommendations
Impact of the research.
3. Readout and Impact
Beta & Release
The beta and immediate release.
Future Thinking
Recommendations based on post-launch