Bringing A.I. Into Automotive Retail

"We turned the store into the helper and the employee into a superhero!"


Approached by Advance Auto Parts needed to weave new vehicle and facial recognition A.I. into a streamlined, personalized, efficient, useful consumer interactions.

Collaborators: Randa Hadi, Hannah Faub, Matthew Norton

Predictive maintenance

The Research & Process 

Observation

Precedents

Questionnaires & Interviews

Hopes and Fears Matrix/ Affinity Map

Persona

Scenarios

Journey Mapping: Before /After

Sketches

Storyboarding

Prototyping

User Testing

Revision

12+ Hrs of In-store Observation

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    Findings for "learner" customers:  

    • Before Work/ Lunch Break/ After Work
    • Batteries/ Fluids/ Other 
    • Alone
    • Most Required Employee Assistance 
    • 10+ Mins in Store
    • M/F = 2/1
    • Age Range 35-54
    • Neutral/Negative Emotions

    Findings for employees:  

    • Hard to get through the rush hours
    • Lots of customers
    • Each customer takes about 15 mins
    • Customer's don't know much about their cars.
    • 50% of guests require car visits

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A.I. Is No Stranger to Retail

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    Current AI recognition technologies in retail perform much more complex tasks than just identification, but reference multiple databases, and produce in depth useful information to consumers instantly. 



    Uses:

    • Wayfinding
    • Product Recognition/Finding
    • Product Recommendation
    • Situational Assessment
    • Circumvent Need for Employee 

benchmarking

Photo By: John Doe

Using Cultural Probes to Fill Hopes & Fears Matrix

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    Twenty volunteers filled out a cultural probe on A.I. and privacy. Using a matrix we were able to extract quantifiable insight from their abstract responses. 


    Findings:

    Uncertainty about A.I.

    Fear of dehumanization by A.I.

    Fear loss of control of A.I. 

    Confidence in potential capabilities of A.I.

    Hope for reliability of A.I.

The "Learner" Persona Before & After Journey Map

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    We used "as is" and "to be"  user journey maps based on in-store observations and store data.


    Pain Points Identified:

    • Dead Battery
    • Late Call to Boss
    • Long Line in Store
    • Feeling Ignored
    • Process is Slow 
    • Must Rely on Employee's Word

Sketching and Ideating How to Maximize the A.I. 

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    Of the many concepts three stood out by solving multiple pain points and producing a magical experience.


    In Store A.I. Concepts:

    • Auto - The Virtual A.I. Agent, can greet customers by name, assesses their situation and checks them in.
    • In-Isle Product Compatibility and guest locator using  facial recognition. (my idea)
    • Guestbook Employee Interface with Predictive Maintenance allows employees  to keep track of guests, find compatible vehicle parts, and suggestions of what may need fixing soon. (my idea & design)

Storyboarding the Experience

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    Sketches of how the user experience would unfold in sequence informed the final filming of the proposed UX and UI design.

Prototyping

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    A common fear of A.I we found was dehumanization. My goal was to maintaining a human connection by making a tool that organized and predicted pertinent information, 

    turning employees into superheros.


    Unfortunately an NDA does not allow me to disclose much of my research on the AAP employees, only customers.


    Guestbook app functions


    • Guestbook with Arrival / Departure
    • Who is Where in Store 
    • Reason for Visit
    • Customer History
    • Compatible Parts and Products
    • Purchase Preferences and Reviews
    • Speed Perks Member Y/N
    • Predictive Maintenance- Based on maintenance data from other similar vehicles what may need fixing soon.

Guest Book
Empowering Employees to Be The Superhero

This part was all me.
Although a team project, I was the owner of the Guest Book POS System featuring Predictive Maintenance A.I. - featured in BIG DATA. BIG DESIGN. by Helen Armstrong.

Guest Book
Empowering Employees to Be The Superhero

An Intelligent Ecosystem Providing a Smooth Journey

The result as a whole was a well orchestrated and intricate behind the scenes implementation and organization of the data gathered by the AI.


AAP employees in actuality are part of the UI for the store. Turning the recognition AI into a useable tool that empowered employees to provide fast, informed and personalized service to customers had the most seamless improvement to user experience.

Wider Implications

  • Customers associate AAP with expedited service in urgent situations.
  • Guests create a more meaningful, informed interaction with team members.
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