AI cross-platform location hub scraper
Key Feature 1
AI location extractor
This feature makes process of switching between apps to bookmark places much faster.
Key Feature 2
Proximity alerts
Proximity alerts allow users to be reminded about locations they saved in Postcard — which leads to increased likelihood to visit.
Key Feature 3
Memory Saver
Users can log locations they've been to and sort them by category for later visits.
Initial Observation
People are compulsively saving content across social media platforms and don't use what they've collected, creating digital clutter without purpose
Oftentimes, users of social media save thousands of posts — bookmarking recipes never cooked, screenshotting outfits never worn, saving workout videos never tried. Over time, these posts are never revisited and forever lost. leading to lost opportunities.
Narrowing Down
While digital hoarding affects many content categories, I found that travel-related content showed unique patterns
Compared to other types of content (fashion, cooking, lifestyle), travel-related content carried higher stakes for users which is due to the fact that users tend to have higher emotional investment, greater intent to act, and more frustration when saving/using travel content.
User Interviews
Multi-platform research and planning leads to disorganized social media saves
After refining my problem space, I chose to conduct user interviews with fellow peers to gain deeper insight into which aspects of saving and using places to visit posed the greatest challenges.
Problem Statement
How might we make it easier for travelers to go from inspiration to planning, so their saved content turns into real travel decisions?
Goals
Experience Flow Analysis
Users felt irritated by the repetitive back-and-forth workflow, while trying to organize saves
With users saving content across various platforms, the manual process of consolidating this information into external tools like Google Docs or Apple Maps requires significant time investment.
Final Solution Concept
I created an app that combines travel inspiration from multiple platforms by using AI to extract location data through a simple share button
Refinement
Throughout my designing the final solution, I user tested and used feedback to drive design decisions
I got the opportunity to test my low-fidelity prototype with 3 people: Zach (22), Ethan (20), and Serena (19)
User Flow
I reorganized the information architecture to align with the finalized functionality and layout
Design System
My Reflections
Combining data with qualitative user research enables me to paint a comprehensive picture of user experience and identify what users actually need
AI cross-platform location hub scraper
ROLE
Product Designer
TIMELINE
6 Weeks
TOOLS
UX Research
Visual Design
Prototyping
OVERVIEW
Tastes in One Place.
Postcard uses AI to save place recommendations from social media onto a map, so you can easily organize and plan where to go.
The Challenge
In today's world, social media has become a place to discover recommendations through search. However, users collect this content but rarely act on it—they're not actually visiting these places they've saved.
Key Feature 1
AI location extractor
This feature makes process of switching between apps to bookmark places much faster.
Key Feature 2
Proximity alerts
Proximity alerts allow users to be reminded about locations they saved in Postcard — which leads to increased likelihood to visit.
Key Feature 3
Memory Saver
Users can log locations they've been to and sort them by category for later visits.
Initial Observation
People are compulsively saving content across social media platforms and don't use what they've collected, creating digital clutter without purpose
People are compulsively saving content across social media platforms and don't use what they've collected, creating digital clutter without purpose
Oftentimes, users of social media save thousands of posts — bookmarking recipes never cooked, screenshotting outfits never worn, saving workout videos never tried. Over time, these posts are never revisited and forever lost. leading to lost opportunities.
Narrowing Down
While digital hoarding affects many content categories, I found that travel-related content showed unique patterns
While digital hoarding affects many content categories, I found that travel-related content showed unique patterns
Compared to other types of content (fashion, cooking, lifestyle), travel-related content carried higher stakes for users which is due to the fact that users tend to have higher emotional investment, greater intent to act, and more frustration when saving/using travel content.
User Interviews
Multi-platform research and planning leads to disorganized social media saves
Multi-platform research and planning leads to disorganized social media saves
After refining my problem space, I chose to conduct user interviews with fellow peers to gain deeper insight into which aspects of saving and using places to visit posed the greatest challenges.
Problem Statement
How might we make it easier for travelers to go from inspiration to planning, so their saved content turns into real travel decisions?
Goals
Experience Flow Analysis
Users felt irritated by the repetitive back-and-forth workflow, while trying to organize saves
Users felt irritated by the repetitive back-and-forth workflow, while trying to organize saves
With users saving content across various platforms, the manual process of consolidating this information into external tools like Google Docs or Apple Maps requires significant time investment.
Final Solution Concept
I created an app that combines travel inspiration from multiple platforms by using AI to extract location data through a simple share button
Refinement
Throughout my designing the final solution, I user tested and used feedback to drive design decisions
Throughout my designing the final solution, I user tested and used feedback to drive design decisions
I got the opportunity to test my low-fidelity prototype with 3 people: Zach (22), Ethan (20), and Serena (19)
Design System
My Reflections
Combining data with qualitative user research enables me to paint a comprehensive picture of user experience and identify what users actually need
Combining data with qualitative user research enables me to paint a comprehensive picture of user experience and identify what users actually need