PORTFOLIO

Case study 1

SafeMode Fleet
Management App

Car services, movers, and freight transport depend on large numbers of vehicles traveling in every direction. Drivers, who are notoriously self-sufficient, report to fleet managers who have the difficult task of retaining them in a competitive industry.
SafeMode’s current mock-up is a design intended to focus on data analysis

Background

AI Takes Over, Managers Can Relax
Aspiring to develop an app that will ease a fleet manager’s tasks, SafeMode will use networks of existing sensors built into each vehicle and process the massive flow of data using cloud-based services (or Artificial Intelligence). The AI processes analyze the data to maintain profiles of driver behavior and automate driver incentives. For example if the data indicates a driver has made safer turns, used less harsh braking, and observed speed limits they can receive a cash reward automatically. A fleet manager has the option to call or text drivers but for the most part is freer to focus on other things.
Expected Outcomes for the Fleet Manager App
1. Better driver-manager relationship

2. Safer driving behavior

3. Drivers are not tracked, and happier for it

4. Drivers are consistently well incentivized
Springboard IDP


The Springboard Industry Design Project pairs students in the UX Design Career Track program with internships, providing them job experience before completion of their certification.

Project Goals

The goal of this project was to make comparisons to SafeMode's competitors and review their current design, but most importantly to ideate some new functions and create new mock-ups
that express their product vision.

Challenges and Hypotheses

Operating a vehicle as an occupation transforms a person’s driving. Time and distance become obstacles to pursuing a cash goal. But does safety intentionally take a back seat? SafeMode assumes cash incentives dominate as the only method for rewarding and retaining drivers. Can SafeMode really decrease manager-driver direct engagement, and at the same time compel drivers to drive more safely?
The challenge to success for the fleet manager app will rely heavily on the behavioral profiling from AI, which SafeMode claims is possible today - and they are counting on proving it works.
Ideation on driver profile enhancement.
SafeMode Assumptions Validated in Interviews with Fleet Managers
Drivers don’t care [about safety]
Drivers and managers don't get along
Drivers don't want to be monitored
Drivers only care about money
Fleet managers are overworked
Fleet managers spend too much time trying to retain drivers.
Notes on SafeMode’s mock-up video.
Assumptions Not Validated
AI solutions can ultimately improve driver engagement (it's untested so far)

Drivers can’t be motivated in other ways besides cash (a great topic for more interviews)
Drivers love the solitude and autonomy of their work - and while they welcome more feedback from managers, they prefer positive feedback - information that should be adapted for the individual.
Mapping the hypothetical red routes
The design trend of SafeMode’s FM app is highly specialized. Charting the red routes based on their demo video helped me to understand their approach, but it illuminated only a few critical tasks. Flows like these make it easier to pinpoint where new features will fit in.
Ideation on the use of big data (including pandemic.)
An affinity diagram comprised of the validated assumptions and interview verbatim helped me to understand how the tensions between driver behavior and the aspirations of the fleet managers co-exist.

Core Recommendations

1. A Map of all active drivers by location.
2. Graphs comparing live driver profile data.
3. Safety/Environmental Projections based on AI.
4. Action Notifiers for strategic engagement.
1. A Map of all active drivers was a function fleet managers rely on daily. They should be given the option to use familiar tools.
2. A graph comparing driver profile statistics. Managers can opt to develop gamification in their fleet. They might need tools to contrast profiles and specific performance data.
3. This screen compares actual and historical data in specific time periods for gaining insights on what is happening on the routes that affect driver planning and safety.
4. “Action Notifiers.” Managers can engage strategically with drivers. This is a feature that will not only automate some of the communication, but it will more importantly enable fleet managers to transform the character of driver-management relationships from negative to positive.
Re-cap
SafeMode’s vision of AI automation and safety analyses puts them well on their way toward having the most cutting edge fleet management system and the most engaged drivers.
Portfolio/Case study 2
Capstone project

Gansu Mandarin Learning App

A vocabulary-building game
Today’s most popular language learning app, DuoLingo, has 70 million users.  The CEO had this to say about his customers:  
They aren’t actually that interested in learning... they just need something other than Candy Crush to spend some downtime.  
For those who do need to learn Mandarin seriously, two things are vitally important:
It needs to keep your attention...

...and has to fit your individual needs
The Style Guide for Gansu was inspired by photos of Gansu province, western China.
Gansu Style Guide
Mood board hosted on Milanote
Ideas on what an app could do: since people already take lots of photos why not extend that behavior to their language learning?
Home screen evolution from Sharpie to Sketch hifi mock-up.
How Gansu works
Rote memorization has its limits. And it's boring. A memory matching game is slightly more fun, but Gansu would go a step further.

The user can take a photo of an object and record its pronunciation in Chinese with their own voice. That memory becomes a "slab" which will appear on the playing surface.

The memory matching game  works in much the same way as other games, by finding matching photos. Each time a photo "slab" is tapped it flips to reveal the photo image of an object on the other side, and plays the pronunciation (or it can display a speech bubble with sound muted.) Successfully finding its twin slab on the playing surface removes the two slabs, and so on until all the slabs are gone.

Pinching would allow zooming in and out of the playing surface so that you can move around and find the slab you are looking for on a small screen.

Level up! In preferences you'd be able to set how many slabs are on each level, background image/color, volume level, speech bubbles, etc.

Other possibilities would include importing images (instead of using the camera) and audio pronunciations from translation sites.
Personas reflecting respondents interviewed about their Mandarin learning experiences at home and abroad. Done in Sketch.
OCR Translation and spoken Pinyin could be built into the input process, as well as spoken feedback.
Guerrilla Usability Testing: Drawings of the matching game and screens of the photo/audio recording functions. There were used for guerrilla usability tests at a coffee shop on five random respondents.
Lo-Fi renderings in a user flow.
High fidelity mock-ups done with  Sketch used in remote moderated and unmoderated usability tests.
A first rendition of what a playing surface might look like with slabs turned over for play mode.
Learnings
- Usability testing for a game has to be tightly controlled and customized to the limitations of the prototyping tool used.

- If you are inspired by your project and the research has convinced you that it will work, presenting the project persuasively to stakeholders will be a lot clearer and easier.