AutoVision

Transforming Automotive Retail with Generative AI
High fidelity mockup of AutoVision

The Challenge

Searching for right automotive parts takes a lot of time and energy. Additionally, understanding what the right part is for your vehicle requires extensive research and knowledge. How might we help the AutoZone customer find what they are looking for faster?

Solution

Utilizing the power of generative AI, AutoZone will enhance the product discovery process for users and offer trustworthy advice at scale.
Role
UX Designer
Industry
Automotive
Timeline
June 2024 - August 2024
Team
Solo Project
Tools
Photoshop
Figma
Powerpoint
I spent my summer at AutoZone working as a UX designer. For my final project, I proposed a new search functionality utilizing generative AI.
AutoVision
Two prototypes, a signed in and signed out version. Shows how a user can use AI in the search and discovery process.
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1a

Design Challenge

How might I help the AutoZone mobile app customer find the right product faster?

1b

Who I am Helping

Utilizing research conducted by the DXR team, AutoVision targets a specific power user. He is represented by the persona Ambitious Alonso. He is a 20 year old automotive enthusiast who is constantly fixing and enhancing his ride. In an emergency, his first stop is always AutoZone.

Persona displaying the needs, wants and thoughts of Alonso
Ambitious Alonso User Persona
2a

My Design Process

I talked to many different teams to gain an understanding of the problem space.

Headshot of a woman

UX Research Team

I initially scheduled meetings with our in-house UXR team to understand mobile app users and consumer behavior on e-commerce platforms, and how this differs from in-store behavior.

Headshot of a man in a suit

Marketing Team

I wanted to get a clear understanding of the most important and top selling categories. This was important to understand because it would help me determine what product to focus on for my designs.  

Store AutoZoner

Store AutoZoner

Supporting the stores is an important part of the aftermarket automotive industry. I interviewed Taylor, who has been working at the Germantown AutoZone store for 2 years. I wanted AutoVision to mimics the advice a store AutoZoner would give to a customer.

Headshot of a man

Generative AI Team

I needed to get a clear understanding of the capabilities of implementing a large language model into our mobile app.

number 5
Headshot of a woman

Mobile App Team

Once I designed a mid-fidelity version of my idea, I talked to the mobile app team to gather feedback. A Senior UX designer was able to provide valuable feedback on how I can create a full end to end experience by thinking about the signed in user and the signed out user.

number 6
Headshot of a man in a suit

Analytics Team

An important part of releasing a new feature is understanding the impact it could potentially have with numbers. The analytics team helped me find data on the business benefit and also how much this would cost.

2b

Designing a Prototype

After getting a clear picture of the needs and benefits, I began designing a high fidelity version of AutoVision. I started by creating a mind map to highlight all the key features I wanted to include in the prototype.

A woman writing on a whiteboard
White boarding session
3a

Competitive Analysis

I looked at three different companies in the retail space who have successfully launched a Generative AI search feature.

Etsy Gift Mode
Users take a quiz that asks them questions about the occasion, the gift recipient's relationship to them etc. Then, utilizing Open AI’s GPT- 4, Etsy Gift Mode generates tailored gift recommendations. Thereby addressing common gift buying challenge of not knowing what to buy.   

an iPhone with Pinterest gift mode open

AMZ Rufus
Amazon is one of AutoZone's biggest competitors in the e-commerce space. They launched Rufus which is an AI chat bot.

An iPhone with Amazon rufus opened

Carrefour Hopla
Carrefour is a multinational chain of supermarkets founded in France. Users can ask Hopla any questions related to food. For example, I asked Hopla how to make ratatouille and it recommended me all of the products necessary to make this dish.

A laptop with carrefour hopla open
4a

Utilizing AutoZone's Global Style Guide

AutoZone has an extensive library of components and a robust style guide. I utilized both to help design AutoVision. Since I was proposing a net new experience, I also designed  new components to that would fit with the product.

All design components for AutoVision
Component library for AutoVision
4b

Product Features For Signed In Users  

Iconography
Search User Flow
High fidelity he search page for
Tailored Prompts
Clear UX Writing
One of the UX designers suggested I clearly label the search as being an AI search function
High fidelity model of the home page for AutoVision
Trustworthy Advice
High fidelity model of a sample response from AutoVision
One Click CTA
Helpful Badges
Tailored Products
High Fidelity model of a product recommendation from AutoVision
Easy Access
Exclusive Feature
High fidelity model of the past searches page on AutoVision
4c

Product Features For Signed Out Users  

General Suggestions
Add Vehicle CTA
High fidelity model of AutoVision
Second Access Point
Different Header
Past searches is a signed in only features
A high fidelity model AutoVision add vehicle notification
Bottom Sheet
A high fidelity model of how a user can add in their vehicle
Sign In CTA
High fidelity prototype of AutoVision's product recommendation page
5a

Thinking Through Technical Feasibility

When choosing a third party vendor, I had to consider three key factors, price, quality, and speed. In the end, I believe working with Google's AI studio will be the best choice.

Price refers how many dollars it costs per 1M tokens. 1 token does not necessarily represent 1 search query.

Bar chart showing the price of various LLM providers

Speed is measured by output tokens per second. In other words, it is how quickly the model can generate text.

A bar chart showing the speed of various LLM providers

A model's quality is measured by many things. Some of the most important are: perplexity (how well a probability model predicts a sample), human evaluation, how well it translates other languages.

Bar chart showing the quality of various LLM providers
6a

Presenting My Project

During the last week of my internship I presented my idea to three directors in the e-commerce department at AutoZone.

Me, presenting my idea to an audience
Pitching my idea to other designers
7a

Next Steps

Providing customers with trustworthy advice has always been a core tenant of AutoZone. Before launching the product to customers, I would recommend having an internal team validate proof of concept and monitor the results to ensure accuracy. Generative AI can be extremely beneficial to retailers. The technology is only getting cheaper and cheaper and more and more accurate. I can't wait to see what the future will hold.

7b

Retrospective

My time at AutoZone was an incredible learning experience. I had the opportunity to join a talented team and build upon established design foundations. The final project I worked on enabled me to demonstrate my ability to think critically about the company's needs and envision an effective solution. I am especially grateful to all the people and teams who helped make my summer unforgettable!