Summer 2024 Internship

OVERVIEW

Optimized customer product search experiences through an AI-driven, personalized digital product.

As the only intern for this project in the gen AI team, I aimed to help users discover relevant items more quickly and effortlessly through generative AI. Initially, the product lacked understanding of user context before, during, and after searches. I addressed this by designing empathetic personalization that anticipated user journeys and empowered decision-making with click-through rate data and gen AI.

Role

UXUI Design Intern

Company

adMarketplace

Location

New York, NY

Timeline

June - August 2024

Design

⁃ Journey map, Interaction Diagram, Defined the value propositions
⁃ Sketch, Wireframing, Low-fidelity prototyping, Clickable prototyping in Figma
⁃ Final Deliverables
⁃ Built conversational chat experiences for consumers.

Research

Collaboration

⁃ Key stakeholders in weekly cross-team meetings including AI/ML engineers, Front/Back-end developers, Product team.
⁃ Presented AI-driven UX implementation opportunities.
⁃ I worked closely with the design manager and the product manager.

Role

UXUI Design Intern

Company

adMarketplace

Location

New York, NY

Duration

June - Agust 2024

Design

⁃ Journey map, Interaction Diagram, Defined the value propositions
⁃ Sketch, Wireframing, Low-fidelity prototyping, Clickable prototyping in Figma, Final Deliverables
⁃ Built conversational chat experiences for consumers.

Research

⁃ Analysis and synthesis alpha group user test feedback after the demo.
⁃ User Interview and User Testing.
⁃ Defined a relationship between AI and customers to leverage Gen AI.

Collaboration

OVERVIEW

Optimizing customer product search experiences through an AI-driven personalized digital product.

Aimed to help users discover relevant items more quickly and effortlessly through generative AI. Initially, the product missed the specific context and needs of users before, during, and after searches. I addressed it by designing empathetic personalization to anticipate user journeys and empower their decision-making with click-through rate data and gen AI.

Role

UXUI Design Intern

Company

adMarketplace

Location

New York, NY

Duration

June 2024 - August 2024

Responsibilities

• Optimized customer product search experience by defining the relationship between AI and customers and leveraging Gen AI.

• Designed AI-powered personal shopping assistant digital product which included researching and building conversational chat experiences for users.

Design

• Journey map, interaction diagram

• Defined the value propositions

• Sketch, Wireframing, Low-fidelity prototyping, Clickable prototyping in Figma

• Final Deliverables

Research

• Analysis and synthesis alpha group user test feedback after the demo

• User Interview and User Testing

Collaboration

• Key stakeholders in weekly cross-team meetings (AI/ML engineers, Front/Back-end developers, Product team)

• Presented AI-driven UX implementation opportunities

I was the only intern responsible for this project in the gen AI team, and I worked closely with the design manager and the product manager.

Role

UXUI Design Intern

Research

Defined a relationship btn AI and humans

Implemented conversational experiences

Analyzed and synthesized alpha group user test feedback after the demo

Design

Built frameworks, including ecosystem map, journey map, and interaction diagram for the shopping journey

Defined the value propositions

Designed Interactions

Created Sketch, Wireframing, and low fi prototyping

Crafted clickable prototypes in Figma

Conducted onboarding process and customer profiles

Collaborated with other stakeholders

Attended Weekly meeting with AI, ML, frontend, and backend developers

Participated in weekly product team meetings

Presented AI-driven UX implementation opportunities for the product to the entire product team

Company name

adMarketplace

Employment type

Advertising Technology

Location

New York City Metropolitan Area, NY

Duration

June 2024 - August 2024 (12 weeks)

FINAL DESIGN

Scenario 1. A non-linear shopping journey where the AI understands user clicks and suggests products.

Personalized, context-based suggestions

Jobs To Be Done

Discover perfect shoe that matches my style in a large number of options.

Problem

The limited mobile screen only displays four options at a time, making it frustrating and time-consuming for customers.

How To Solve (use case)

Help users narrow down their choices and make easier purchase decisions by using AI to personalize suggested options and questions based on their browsing behavior.

Scenario 2. AI engages the user in a conversation by asking contextually relevant questions, helping them discover the right product through informed decision-making.

Research-based, conversational support

Jobs To Be Done

Find cosmetic products that make skin glow, suited to my skin type.

Problem

Exhausted from compiling information on required product features and confused about what is necessary and which details should be prioritized.

How To Solve (use case)

AI provides research-based questions and options for answers to help users consider the required product features before making purchase decisions, allowing the search journey easier and smarter.

Ask AI

Jobs To Be Done

Obtain immediate clarity from specific, self-focused questions.

Problem

Additional follow-up questions require users to leave the app to search on Google.

How To Solve (use case)

The Ask AI interface is consistently placed to allow access and utilization at any time.

Scenario 3. AI helps users find outfits by using event details and user preferences, creating a joyful, relaxed shopping process for important events like weddings or dates.

Joyful, mix-and-match search

Jobs To Be Done

Prepare a confience-boosting outfit for a special event.

Problem

Traditional, time-consuming searches add stress to the shopping experience.

How To Solve (use case)

AI generates various appealing combinations for consumers to engage in a mix-and-match activity. AI-driven searches de-stress the process and allow for an enjoyable search experience.

ITERATION

Optimized mobile screen interface to better accommodate AI Chat with product results.

User testing

"It's not easy to distinguish between chatting and product results when asking and answering questions to AI."
- User feedback

Wireframing

Before

After

What I did

Modified the design principle of 'consistency' to fit the context of AI chat, encouraging users to respond more easily.

Add background color to improve distinction between two different interactions: chatting and product results.

Divide the content into manageable chunks to make it less overwhelming and more intuitive.

For consistency, implement a fixed chat space rather than one that changes based on the amount of conversation.

Transition to full screen and hide the product results section to help users focus on their question interaction.

Optimize the accessibility and usability of interactions with Ask AI.

1
Improved clarity and reduced confusion by consolidating scattered AI buttons into a unified floating button.

A major usability feature is the inclusion of follow-up questions.
‍

2
Conversation is translating into an overlay interface to allow AI interaction while flexibly viewing product results.

A major usability feature is that users manually answer the AI's questions.
‍

3
Enabled users to resize the AI chat screen by dragging, depending on their contextual needs.

When users want to ask follow-up questions, the chat interface expands to full screen.

CONSUMER SHOPPING JOURNEY MAP FOR PRODUCT SEARCH & BROWSING

Product-oriented search and general browsing may start differently, but follow the same four steps—each laborious and overwhelming to users.

An analysis of users shopping behavior patterns demonstrated that there are five necessary steps which all shoppers must complete to find matched items such as general research, information gathering, product comparison, product evaluation, and narrowing down search results. Whether they are doing general browsing or a product-oriented search, users still must to through this process. As a result, users demand flexible context- and situation-based assistance during their journey.

INTERACTION DIAGRAM

Consumer shopping journeys are nonlinear.

A user’s shopping goal can change mid-journey and across multiple touchpoints. For example, the user starts the journey on Instagram where they are intrigued by an ad about a brown purse. The user clicks on the ad and scrolls down the page, where a pink wallet catches their eye. The consumer then clicks on the pink wallet to learn more. It’s important to know that the goal may shift throughout the course of the shopping journey, like here when the user starts by shopping for a bag and instead ends up shopping for a wallet.

IMPLEMENT CONVERSATIONAL DESIGN

AI personalized-communication supports consumer shopping journeys flexibly across multiple contexts.

By questioning “How do we design a relationship?”, I wanted to describe AI as a personal assistant which communicates with suggestions by sharing history and preferences while maintaining the initial search goal, which can shift depending on the user’s context. The focus is on understanding this context in addition to accuracy. Ask AI's aim is to collaborate and learn from interaction between AI and users to help them to discover ideal items. Gen AI provides context-based queries and generates product results based on the user's answers. AI researches product information that a user needs to consider before making a purchase decision.

When and how users meet Gen AI and what and how Gen AI helps the users. I created crucial questions by answering the curiosities regarding what users need. How does our solution meet the users’ needs? How does Gen AI meet user needs? How does Gen AI support decision making? And what kind of datasets are needed to improve Gen AI? AI generates questions to help users navigate and to check the current status to make wise purchase decisions. The user selects an answer from options generated by AI, based on research users need to consider before making a purchase decision.

ANALYSIS & SYNTHESIS OF THE ALPHA GROUP USER TEST FEEDBACK AFTER THE FIRST DEMO

Complementary relationships are built between users and AI.

“The more prompts I answered, the more specific product results I got”

"Help me to recognize the important product features"

There was mutual learning relationship—users are learning how the AI is working and the AI is adapting to the users input. Feedback from the product's Alpha Launch surveys show that users naturally learn the product features by recognizing how their queries influence product results, and the product improves future search results based on users' feedback on satisfaction and relevancy.

WHAT I LEARNED

Designed engagement-rich AI-driven interface to empower users’ product search experiences.

While designing gen AI-driven UX and UI, I aimed to support users in their contextualized journeys by offering more personalized solutions, which required real-time updates. However, this functionality is typically only feasible at large tech companies, so I had to find alternatives, such as anticipating user needs by analyzing click-through data history. To collect more data, I worked to encourage user engagement with AI by enhancing accessibility in the UI. If this limitation is addressed, I believe contextual AI support can be developed to flexibly accommodate users’ unexpected and evolving needs throughout their journeys.

FINAL DESIGN

Scenario 1. A non-linear shopping journey where the AI understands user clicks and suggests products.

Personalized, context-based suggestions

Jobs To Be Done

Discover perfect shoe that matches my style in a large number of options.

Problem

The limited mobile screen only displays four options at a time, making it frustrating and time-consuming for customers.

How To Solve (use case)

Help users narrow down their choices and make easier purchase decisions by using AI to personalize suggested options and questions based on their browsing behavior.

Scenario 2. AI engages the user in a conversation by asking contextually relevant questions, helping them discover the right product through informed decision-making.

Research-based, conversational support

Jobs To Be Done

Find cosmetic products that make skin glow, suited to my skin type.

Problem

Exhausted from compiling information on required product features and confused about what is necessary and which details should be prioritized.

How To Solve (use case)

AI provides research-based questions and options for answers to help users consider the required product features before making purchase decisions, making the search journey easier and smarter.

Ask AI

Jobs To Be Done

Obtain immediate clarity from specific, self-focused questions.

Problem

Additional follow-up questions require users to leave the app to search on Google.

How To Solve (use case)

The Ask AI interface is consistently placed to allow access and utilization at any time.

Scenario 3. AI helps users find outfits by using event details and user preferences, creating a joyful, relaxed shopping process for important events like weddings or dates.

Joyful, mix-and-match search

Jobs To Be Done

Prepare a confience-boosting outfit for a special event.

Problem

Traditional, time-consuming searches add stress to the shopping experience.

How To Solve (use case)

AI generates various appealing combinations for consumers to engage in a mix-and-match activity. AI-driven searches de-stress the process and allow for an enjoyable search experience.

3 KEY TAKEAWAYS (FEATURES/INSIGHT)

Context-based suggestions: Real-time observation by tracking user clicks.

Shopping journeys are not always linear, as revealed by analyzing the user's shopping behavior patterns while creating journey maps and interaction diagrams. Although general browsing and product-oriented searches may begin at different points, they share several common steps, such as general research, information gathering, product comparison, product evaluation, and narrowing down search results. Therefore, users need flexible assistance based on their situation.

Conversation support: Gen AI creates personalized and research-based queries.

Ask AI is designed aim to collaborate and learn each other AI and the users to help them to discover ideal items. Gen ai provides context based queries (questions) and product results based on the user's answer. AI researches a product information that a user need to consider before making purchase decision and narrowing down their selection.

Search with joy: Playing with their prefered items to find matched items.

The consumers feel pressure to prepare the big event. The entertainment help a user find the ideal items with joy and reduce cumbersome or pressure. For example, I need cute and lovely outfits for my first dating or I need a dark blue dress for my friend’s wedding in Chicago.