Verizon Connect works with IOT products to solve the way people, vehicles, and things move through the world.
Over the past years, Verizon Connect focused on emerging technologies to analyze the road and driver behavior in real-time.
Surveillance cameras are a well-known way to protect and record video in case something happens.
In the telematics industry, that's not distinctive. Different camera models can be installed inside and outside vehicles. The primary user for those cameras is the person responsible for managing fleets.
In addition to many other problems, the fleet managers have to deal with 3 different issues:
Fleet managers face rising insurance costs every year, between 10% and 100% increases. Most insurance companies offer lower premiums if fleets have cameras installed.
Determining that a driver is not at fault in an incident can help fleet managers avoid expensive compensation payments.
Identifying patterns of unsafe driving would enable coaching and review, thereby increasing overall fleet safety and reliability.
After analyzing the current technology, industry competitors, and user needs, we identify and set some project goals. The main goal was to create a solution for fleet managers, using software and hardware to achieve those 3 key points:
Help fleet managers to protect their business and lowering insurance costs.
Saving time by only showing relevant video clips within minutes of an unsafe event.
Help managers to coach their drivers on better behavior.
We also translated those goals into some quantitative success metrics to make sure we had a way to measure the solution's effectiveness.
I worked as a product designer with an end-to-end process from problem framing thought final visual design.
Were I was collaborating with cross-functional teams in an agile and lean environment.
1 Design Lead
2 Product Designers
1 UX Researcher
1 Content Strategist
2 Cross-functional Squads
With the support of the localization and data science team
This project involved different stakeholder management and idea exploration before getting to the final solution, including a few user research sessions to validate our assumptions and ideas.
My main focus was to define a direction and vision for an MVP and start building with it.
We based our initial assumption on a dashboard for the fleet manager. It was possible to view some drivers' insights and safety scores, including a detailed overview of an incident.
Even though the concept performed well in the user test, we decided to switch the direction and rescope the idea. The solution provided a lot of value for the fleet manager and a great future vision, but it was less scalable to build.
Our selected approach included an IA-based tool that uses computer vision to classify videos of unsafe events, where the fleet manager could be updated for incidents moments after it happens. And also, search and filter for a specific video, getting more detail about it.
As a complex project that involved integrating software and hardware, we divided the flows to be built by different teams.
And because of that complexity, it was crucial to spend more time in the ideation phase to test and refine new approaches.
After launching the initial version, we were able to upsell this new feature and increased MRR by 15% in the first week after launch.
Today, the product has already evolved a lot with new add-ons, like a camera recording inside the vehicle, proving the concept’s effectiveness and market acceptance.