Wavyn

Overview

Wavyn start-up was building a driving assistant for commuting drivers. I worked on a team of engineers, product manager and a graphic designer to develop an alert system to prevent accidents in cars.

Our goal was to design a mobile application that easily allowed drivers to react quickly in potentially dangerous situations and minimize mobile phone usage.

UX Process

We discovered that drivers were struggling to concentrate on driving. During contextual inquiries and interviews, we have identified several types of distractions.We found out that participants have a need or dependence on interacting while driving and they do so while heavy traffic, stop lights, moments of boredom and monotony, situations that they seemed to view as very low risk. This research allowed making personas and an empathy map that helped concentrate better on the users and context of the problem we tried to solve.

We used a lean approach, I created the paper and clickable prototypes based on the findings. I carried out usability testing and A/B testing as we had 2 versions of the design (aiming to have the less distracting interface). We made an analytics dashboard where we could track KPIs while testing the product. One of the biggest deficiencies we found in researching that drivers were using several auto mob apps, each one for a different purpose.

After building the beta version of the product, beta testing was carried out using snowball sampling as well as attracting testers via social media and beta family tool. Drivers enjoyed timely alerts and feedback about their driving habits. They found useful having a score that showed how much time they were driving safely, the amount of time being distracted or speeding.Participants also commented that they had difficulties mounting the phone correctly, on the sensitivity of alerts and additional bugs that were ranked by severity, frequency, and urgency to be fixed.

Result

We designed technology for cars that allowed drivers to receive sound and visuals alerts to prevent forward collision and speeding, reinforce good driving habits while commuting.

Next Steps

Use analytics to measure which features are used most often.

Screenshots of working process
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