AAP Store of the Future
In-Store POS System, Scenario Video
2019
In-Store POS System, Scenario Video
2019
PROJECT OVERVIEW
What We Did
AAP aims to bring ML and AI to the automotive aftermarket to help our customers better understand their automotive needs and get them back on the road. This project emphasizes introducing machine learning and artificial intelligence in a retail store – specifically an automotive store.
The methods 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 about cars. The identified 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.
Results
We shared all our conducted UX research on the current in-store experience for the customer, in addition to a scenario video and usable prototypes for all the in-store interfaces.
AAP aims to bring ML and AI to the automotive aftermarket to help our customers better understand their automotive needs and get them back on the road. This project emphasizes introducing machine learning and artificial intelligence in a retail store – specifically an automotive store.
The methods 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 about cars. The identified 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.
Results
We shared all our conducted UX research on the current in-store experience for the customer, in addition to a scenario video and usable prototypes for all the in-store interfaces.
STORE AS HELPER
Objective
To introduce machine learning into a retail space. The research question was: How might a retail space leverage machine learning to produce personalized, efficient, helpful consumer interactions that allay user concerns? What role would an employee play in supporting such positive experiences?
To introduce machine learning into a retail space. The research question was: How might a retail space leverage machine learning to produce personalized, efficient, helpful consumer interactions that allay user concerns? What role would an employee play in supporting such positive experiences?
Collaborators: Randa Hadi, Harrison Lyman, Matthew Norton
Process
The process included different kinds of research, including store observations and interviews, creating personas and scenarios, benchmarking other retail spaces, and creating an AI hopes and fears matrix. We also made "as is" and "to be" User Journey Maps. From there, we began sketching and creating storyboards of initial concepts. Then, with the feedback from our sponsors, we made rough ideas and conducted user testing and hi-fi prototypes that could be used in our scenario video.
The process included different kinds of research, including store observations and interviews, creating personas and scenarios, benchmarking other retail spaces, and creating an AI hopes and fears matrix. We also made "as is" and "to be" User Journey Maps. From there, we began sketching and creating storyboards of initial concepts. Then, with the feedback from our sponsors, we made rough ideas and conducted user testing and hi-fi prototypes that could be used in our scenario video.
The store ecosystem includes: AUTO fasttrack, a smart guestbook, and a vehicle classifier
In this scenario video, Jenn walks into the retail store only to notice it is busy. However, she sees the Fasttrack system welcoming her to check in, so she approaches it. The Fasttrack system uses facial recognition to scan her face to retrieve her information. Jenn tells the system that she needs a new car battery, and it adds her to the guestbook. Team members use the guestbook to gather information and create a better experience.
Fasttrack:
+ Machine Learning
+ Artificial Intelligence
+ Facial Recognition
+ Recommendations
Guestbook:
+ Artficial Intelligence
+ Predicitive Maintenance
AUTO adapts to the time of day and customers, which dictates the tone and content.
Using the asile screens, customers can converse with AUTO through thier browsing experience.
Customers can access AUTO accross the AAP ecosystem: website, mobile app, in-store, in-car
Cuustomers can commnicate with AUTO via voice interface or switch to text interface on their phone.
AUTO suggests purchases to the customer based on likely maintenance needs.
Tablet-based guestbook communicates custoemr needs and vehicle info quickly to sales team, including smart predcictions.
The guestbook draws from big data to forsee potential automotive issues for each customer.
This video is an example of one of the ways a customer would interact with AUTO.
In this video, this is what the screen looks like when it is not assisting a customer.
Implications
Customers associate the store with expedited service in urgent situations.
Create a more meaningful, informed interaction with any team member.
Customers associate the store with expedited service in urgent situations.
Create a more meaningful, informed interaction with any team member.
HANNAH FAUB PORTFOLIO LAST UPDATED 2023