Prompt: How can machine learning used in a retail store produe personalized, efficient, and userful consumer interactions?
How role might a team member working at the store play in supporting a positive experience?
Collaborators: Hannah Faub, Harrison Lyman, Matthew Norton

In this scenario video, Jenn walks into the retail store only to notice that it is busy. However, she sees the Fasttrack system welcoming her to check in, so she approaches it. The Fasttrack system uses facial recognition and it scans her face to retrieve her information. Jenn tells the system that she needs a new car battery and it adds her to the guestbook. The guestbook is used by team members to gather information and create a better experience. 

+ Machine Learning
+ Artificial Intelligence
+ Facial Recognition
+ Recommendations

+ Artficial Intelligence
+ Predicitive Maintenance
This project places an emphasis on introducing machine learning and artificial intelligence in a retail store – specifically an automotive store. 

The methods that were used during the research process include store observations, interviews, benchmarking, cultural probes, artificial intelligence hopes and fears matrix, and creating personas and scenarios. 

Through research and user testing we identified pain points for our persona Jenn Walker, who is still learning when it comes to cars. The identitfied pain points helped us create a personalized experience by making it easier for her to find the right part for her specific car, shortened employee interactions, knowledge surrounding car repair, and wanting to learn more and feel confident.