Revamping Craigslist Mobile for the 'Getting Things Done' Generation
During UC Berkeley's Information Architecture course, I spearheaded an IA overhaul to improve usability and support Craoglist's mission of community-driven growth and high-revenue task completion.
Team
Noynica Ahuja, Claudia Benitez, Margaret Seymour
Timeline
July 2022 - September 2022
Methodology
Gap Analysis, Task Prioritization, Card Sorting, Tree Testing, IA
(TLDR)
Craigslist’s mobile maze was losing users until we
restructured journeys from seeking to doing
The Problem
Craigslist’s trademark minimalism had become a liability on mobile. With hundreds of nested categories squeezed onto small screens, users spent more time hunting than completing tasks.
My Role
Led research and design activities including tree testing, card sorting, IA synthesis, and wireframing, collaborating with two peers.
Impact
Restructured Craigslist’s mobile IA from static categories to action-oriented flows, preserving its minimalist ethos while enabling faster task completion. This shift surfaced high-value actions earlier and improved direct success rates by 35–50% across key tasks.
(The Problem)
Craigslist’s simplicity was breaking
under its own weight
A maze for everyday tasks
Once the go-to marketplace for local communities, Craigslist built its reputation on simplicity and trust. But a closer look at its mobile information architecture revealed critical gaps:
Hundreds of nested categories on a small screen
High-friction task paths and unclear revenue-driving flows
Vague, overlapping labels
Users often began confidently but quickly stalled; our first round of testing showed only about one in three tasks ended in success.
Business at a tipping point
Each failed task erodes trust, slows down peer-to-peer exchanges, and nudges frustrated users toward faster-moving rivals like Facebook Marketplace and OfferUp.
Design Statment
How might we reduce category clutter and decision fatigue on Craigslist mobile while preserving its no-frills, community-driven ethos?
(Research Methods Applied)
Finding where users got lost
Before I mapped the cracks in Craigslist’s IA, I stepped back to define the larger-scale goals at play. For Craigslist, growth meant optimizing high-value mobile tasks while preserving its low-overhead, community-first model. For users, it meant being able to trust the platform to help them complete everyday tasks, without confusion or wasted effort.
Zeroing in high-impact tasks
Craigslist supports dozens of actions, but not all carry equal value and redesigning every edge case wasn’t realistic, or strategic.
From 25 possible tasks, I prioritized those most in demand and most tied to revenue. A task inventory and impact analysis surfaced the core of Craigslist’s peer-to-peer engine: jobs, housing, goods, services, events, and gigs. These high-value actions became the North Star for evaluating the IA.
Stress-testing existing IA
With high-impact tasks defined, I ran a gap analysis followed by tree testing to see how well the current IA supported them.
Gap analysis showed structural cracks
Using PowerMapper Cloud, I crawled the site (499 pages) and mapped them against existing categories. This exposed three major issues:
Missed opportunities — tasks with no clear path
Broken mental models — flows misaligned with user expectations
Redundant categories — overlapping labels causing friction
The analysis showed that high-value tasks were buried under clutter or forced into confusing paths.
Users lost their way after the first click
Next, I led the tree test with 12 participants across India, Canada, and the U.S.
Success rates were only 35%. Most users started in the right place, vague labels, weak filters, and long lists led them astray. Posting jobs, selling items, and navigating between Community and Forums were frequent failure points.
The problem wasn’t discovery, it was progression
Users don’t come to Craigslist to browse endlessly. They come to get something done - post, sell, apply, find. The current IA slows them down instead of helping them act.
Unclear Workflows
Creating a listing was split between For Sale and Create a Posting, leaving users unsure how to start.
Confusing Labels
Categories like Jobs, Services, Gigs, and Community overlap. Users hesitate, backtrack, or abandon tasks altogether.
Weak filters, overloaded lists
Even after reaching the right category, endless unfiltered lists cause fatigue and decision paralysis.
(From analysis to a clearer sitemap)
Understanding mental models through card sorts
Nouns orient, Verbs guide
I ran 18 card sorts (online and in-person) with participants grouping 40+ Craigslist tasks into categories that felt natural to them. To avoid bias, I normalized across three native-language speakers (English, Spanish, Hindi). This surfaced the most intuitive, consistent labels across linguistic and cultural differences.
Current IA felt familiar but trapped users in “finding” mode
when what they really wanted was to find → then do
Nouns orient users
People consistently grouped around broad nouns like Jobs, Housing, Services. These categories felt familiar and provided a mental “home” for different tasks.
Verbs crept back in
Actions like Sell, Post, Apply, and Find overlapped across categories, leaving users unsure where to go. Sell under For Sale or Post? Gigs under Jobs or Services? This led to backtracking, extra clicks, or abandonment.
Interestingly, non-native speakers and mobile-first users preferred verb–noun phrasing like Sell Item or Find Gig. For them, pairing the action with the object improved comprehension and reduced hesitation.
Two Navigation Models
The card sort made one thing clear: Craigslist’s IA could evolve in two very different directions. Nouns gave orientation, verbs gave momentum. The question was — which should lead?
Instead of assuming, I designed and tested two competing navigation models.
Model A: Category-first (noun-led IA)
Top-level nav: Jobs, Housing, Marketplace, Services, Community.
Second-level nav: Still nouns (e.g., Full-time, Part-time, Freelance under Jobs; Furniture, Electronics, Auto under Marketplace).
Verbs surfaced as CTAs on category landing pages (e.g., [Apply for a Job], [Post a Rental], [Sell Item]) but not as part of the IA tree.
Model B: Dual Navigation (verbs + nouns)
Quick Actions tab: Global verbs at the top (Buy, Sell, Rent, Apply, Hire, Connect, Donate).
Main Menu: Categories (nouns) listed in parallel (Jobs, Housing, Marketplace, Services, Community).
Category pages echo verbs as shortcuts, but they funnel into the same global Quick Actions flow (avoiding duplication).