Wine Drop

An AI wine shopping assistant for wine beginners.

My Role

Timeline

Tool

Product Designer
Sept -Oct, 2021 (8weeks)
Figma, Protopie, Adobe After Effects
1. Overview
Situation

Feeling lost while shopping for wine?

While experimenting with different wines, I experienced hit and misses that came to searching, shopping, and tasting wines in order to find the best product. This sparked the question - why is it challenging for newcomers beginners to find the wines that best fit their preference?


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Objective

How might we help wine beginners search through options in order to find their match?

2. Research
User Research
πŸ“ Phase 1: User interview and survey

Identifying user's pain points.

I conducted a 28 user survey along side with 3 interviews. This survey included wine drinkers whose experiences range from intermediate to starters. Their age ranged from 21 to 28 and they were either college students or full-time workers. Based on the interview and survey, I found the following insights.

3. Problem
Why is it so challenging for beginners to find the wines that match their preference?
Problem Space #1

Too wide of variety of wines with unfamiliar names.

Wine beginners were confused by complicated wine terminologies. Users struggled to explain the types of wine that they were looking for.

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Problem Space #2

Hard to identify user’s wine preference

Wine beginners struggles with explaining their preference, due to difficulties in remembering the style and terms of the wines they enjoyed.

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Problem Space #3

Scan-based wine searching limits users' opportunities to find perfect-matching wines.

Scan-based wines limits options in offline stores. Because of this, it lowers the possibility of finding the best matching product.

How are users currently addressing these pain points?
πŸ“ Phase 2: Competitive Analysis

πŸ“· Scanning wine labels leaves the beginner with sparse wine options.

What are users using to combat their pain points? I analyzed the main competitor's pros and cons to specify design solutions. Based on e-commerce net sales or app store charts, the main competitors was Vivino.

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πŸ’‘ The Main Insight

Scan-based searching at offline store limits users' opportunities to find their best-matching products.

As competitor mainly focuses on scanning products in the visited offline store, this limits users from discovering the best-matching products.

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4. Solution

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Solution & Goal

Recommending wines based on user's unique preference is the key.

As there are over 10,000 different grape varieties worldwide, one can't try every different type of wine based on its region, grape, style, and taste. Instead, WineDrop guides users to find their ideal wines.

5. Design & Iterations
Main UI Design Rationale

‍Color System
The main color intuitively visualizes the context of wines. Furthermore, secondary pastel colors makes interface feel friendly.

Typography System
For legibility, Montserrat and Noto Sans are chosen for their contrast and expressiveness.

Personalized illustrations
The illustration of the user's wine preference uses organic lines to represent the personalized style of the user.

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Information Architecture

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Testing & Improvements

3 major improvements in my design

Based on various feedback from 4 other peers + mentor feedback, I continually iterated my design with 3 major improvements:

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6. Final Design

1/3

Personalized recommendation based on user’s preference

Personalized wine recommendations

Best-matching wines with users' preferences appear on recommended tab, and popular products among users appear on the best tab.

Search and filter products

User can customize their search with filters. The filter specifies users' preferences on grapes, taste balance, aroma, price, and alcohol percentage.

2/3

Scan wine labels for shopping

Scan wine labels and check the preference matches.

This AR scanner highlights the best-matching products along the aisle. This feature aims to decrease the burden of scanning multiple products.Β 

Store pickup option

The offline pickup feature suggests nearby offline wine shops with selected wine products. The map-based searching page highlighted wineshops that are nearby to the user's current location.Β 

3/3

User’s personal and data-driven wine preference

User's personalized wine preference card

A wine preference card visualizes the user's preference based on their previous wine reviews and orders. This personalized card aims to match wines that are similar to users' tastes.

Review your wines to make recommendation accurate

Wine Drop motivates users to add reviews through the wine history tab under the profile tab.

7. Result & Reflections

πŸ“Š Results

🌟 Impacts on both user and business

πŸ‡ Matching wines with user preference > 🍷 more consumers satisfied> πŸ›’ increase in sales > Β πŸ’° more revenue for business.

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🧐 Reflections
What I learned is ...

Designing with Empathy.

I learned that the approach to problem solving varies depending on the user's background level. I found out that the main purpose of the research stage is not to "simple search" function for wine beginners, but to "try" various wines and "establish" their tastes. Based on this user insight, we were able to discover the problems of products provided in the existing market, and we learned the lesson of designing products according to the user's perspective.

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If I had more time...

Explore data visualization UI

I believe that the wine preference card has a lot of room for improvement in terms of UI and illustration to help users understand their specific wine preferences.

Social feed for users.

I would like to design a community feed where users can see the types of wine their friends like and recommend. As people enjoy drinking wines with friends, family, or peers, having this feed would help people gather often.

Refine check out page for pickup or delivery.

I believe that the checkout page has a lot of room for improvement for UI to help users understand the different types of delivery users prefers. I am thinking of adding relevant filter options.

Thank you for reading!✨

For more work inquiries, or to grab a coffee email me at joh244@newschool.edu β˜•οΈ