OfferUp, empowering local high-volume sellers
Role
UX Designer
Team
Individual
Time
2 weeks
Tools
Figma
Adobe XD
Maze
Client
OfferUP is a marketplace on a mission to become the simplest, most trustworthy local buying and selling experience, reimagining the model for local, peer-to-peer commerce.
Brief
How can we better empower high-volume business sellers that mostly buy and sell through local pickup?
My process followed the Design Thinking Double Diamond, going through the following stages:
DISCOVER
Secondary Research
Business Analysis
Competition Analysis
User Research
DEFINE
Research Synthesis
Problem Statements
How Might We’s
DEVELOP
Ideation
Prioritization
Jobs to be Done
MVP
Information Architecture
DELIVER
Prototyping
Brand Building
User Testing
Metrics
Takeaways
DISCOVER
Business Analysis & Market Research
I dived into the process by first conducting some additional research to the one provided by the company, which led to the business analysis.
Filling out a Lean UX canvas, I was able to uncover the following information:
Business Problem
Fake accounts
Item management
Local pick-up scheduling
Messaging system
Outcomes
Higher retention rates
Better ratings & reviews
More pro sellers
Better use of promotion tools
Users
High-volume sellers
Local sellers
Pro Sellers
Verified Shops
Benefits
Higher sales
Brand building
Expansion of customer base
Time-saving
I then completed a competition feature analysis chart, by looking at both direct and indirect competitors, such as similar selling platforms like eBay or Craigslist, but also local garage sales and Saturday markets.
This tool allowed to uncover some features currently lacking or inconsistent on the market, as sellers access to analytics and community building, amongst others.
Going forward, I created two market positioning charts comparing seller brand building options to community reaching features, and product management options to communication quality between buyers and sellers.
These maps give a perceptual position of the product on the market, they also uncover the blue ocean areas, which are uncontested areas of the market, and help to realize how to differentiate ourselves from competition
Conducting some secondary research, I looked at reviews and comments in Google Play and Apple Store.
I discovered that the ratings of the company have been going down for a couple of years. Most of the 5 stars ratings date back to 2018/19, while most of the 1 star ratings are from 2020/21.
Users highlighted a few frustrations, but the main one, which also ties into my project, was the increasing number of fake accounts.
User Research
In addition, in order to gather quantitative data, I created and distributed a survey, which revealed the following information:
Top reasons high-volume sellers use OfferUp:
87%
to make money
37%
to grow their business
67%
rather use local pick-up options than shipping
80%
do not use promotion tools
My final bit of research consisted of interviews, in order to get a better grasp on the users, starting to look at their frustrations and expectations.
It was challenging to get access to local high volume sellers, but I managed to get the following qualitative data by reaching out to a couple of youtubers who have given tips to use the selling platform before (Netflips).
“ I enjoy haggling and bargaining with sellers and buyers, I think that’s the fun sometimes when you’re buying and selling within your community! “
“ I think the problem of “scammers” being able to continuously make fake accounts without having to do more than provide an email is what is pretty frustrating. “
DEFINE
Research Synthesis
With all this data gathered, I was able to start focusing on synthesizing my research by using visual sense-making tools.
First, by creating an affinity map, regrouping findings under common themes and topics.
It allowed to identify the recurring pains of users, such as fake or flaky buyers and repetitive messages as well as their potential gains, like opportunities for expanding their business and saving time.
I also used the customer side of the value proposition Canvas in order to recognize the functional, emotional, and social jobs our users are hiring OfferUp for.
It helped highlighting additional information such as the jobs to be done, pains, and gains of high-volume sellers.
JTBD:
- Make money
- Get rid of items
- Grow business
- Entertain
- Engage with the community
Pains:
- fake buyers
- limited item management
- inefficient meetups scheduling
- flaky buyers
- expensive promotion tools
- repetitive questions
- unattentive buyers
Gains:
- better communication
- higher satisfaction with app
- make more money efficiently
- grow business
- grow customer base/community
- avoid frustrations
Thanks to the previous research synthesis this user persona was born.
Sophie Sells A Lot is based in data and represents local high-volume sellers’ behavioral patterns.
She wants to expand her business on OfferUp and to close more sales faster more conveniently and efficiently, while still providing good customer service. I also added the most common questions sellers complain about getting on her card.
Here’s her journey map, paired with an as-is scenario.
The combination of these two tools focuses on what our primary user does, thinks, and feels at each stage of the selling process on OfferUp. It also allows to uncover design opportunity areas, specifically by looking at their emotions throughout the process. Indeed, they have a big impact on the actual behavior of users while using our product.
As the main concerns of high volume sellers are to avoid fake or flaky buyers and to save time, these opportunities for design laid here in the offer-management stage, when scheduling a meetup, and during the actual meetup.
These are also constraints our future solutions need to fit in for a better focus and impact on the users.
Problem Statements
Our high-volume sellers are frustrated when managing offers and messages because they constantly face fake accounts and lowball offers.
Our high-volume sellers are frustrated when deciding on and planning for meetups with buyers because they have trouble agreeing on locations and times.
Our high-volume sellers are sometimes deceived when meeting to close their sales because buyers don’t show up, or continue bargaining directly at the meeting point.
How Might We’s
HMW avoid high-volume sellers from getting fake buyers to reach out and lowball offer?
HMW facilitate high-volume sellers’ meetup scheduling experience?
HMW make sure that high-volume sellers are able to close their sale as planned?
DEVELOP
With these in mind, I was able to jump into the developing stage, starting with a brainstorming session, which then led to this mind map.
It regroups ideas under different main categories, such as more efficient messaging systems or reminders and updates, which I attempted to build onto using the “yes, and?” method.
Ideas Prioritization
In order to prioritize features and solutions, I created this MoSCoW and value vs. effort matrix, which classified the ideas into four categories.
Going forward in the process I mainly focused on the ones that landed into the “must-have,” and “should-have,” as they bring the most value to both our users and accompany.
Additionally, completing the product side of the value proposition Canvas allowed for analyzing the pain relievers and gain creators the features would bring to high volume sellers, still in an optic of solution prioritization.
Features:
- Automatic message response system
- Save favorite meetup locations
- Opt-in for reminders
- Choose what potential buyer to receive offers from
- Better item management
Pain Relievers:
- Save time
- Avoid uncertainties
- Avoid forgetful mistakes
- De-clutter inbox
- Avoid repetitive questions
- Avoid frustrations
Gain Creators:
- Processing more sales more quickly
- Increasing feeling of trust
- Efficiency to schedule meetups
- Increase safety
Main Job Story
When selling items on the OfferUp app, high-volume local sellers want to be able to conduct sales quickly, so that they can make money efficiently, which makes them feel accomplished and satisfied.
Features Job Story
When managing their offers and planning to meet up locally with buyers, high-volume sellers want to increase their certainty and assurance, so that they can avoid fake buyers and frustrations, which makes them feel more secure.
MVP (additional features)
An automatic messaging system to avoid repetitive questions.
An option to only accept offers from verified users.
A more convenient way to schedule meetups with buyers, saving preferred locations and times.
A reminders and updates system to avoid flaky buyers when comes the meetup day.
An easy way to close sales.
Information Architecture
In order to show the different features encompassed in the MVP, the prototypes followed 4 different user flows.
The posting flow
The offer-management & meetup scheduling flow
The confirmation & reminders flow
The closing-sale flow
DELIVER
Low Fidelity Prototype
I first created low-fidelity wireframes and presented the prototype to 7 people.
Using sketches and rapid prototyping allows for fast and cheap testing, reducing risks and refining the MVP values.
I tested it through Maze with 4 different missions, following the user flows I mentioned earlier. I was able to communicate directly with the testers, thus getting their first impressions and frustrations in real-time along the assessment.
7/7
testers said the flow was easy to navigate
3/7
said some call to actions were not contrasted enough
7/7
believed the new features added value
I found two screens with the highest rates of misclicks and problems, the all to actions were not clear enough and hard to find
I also collected the following reactions from testers.
“What I’m looking for is hidden!”
“It would make more sense for me to choose an actual date rather than just a weekday…”
Mid-Fidelity Prototype
Building the mid-fidelity wireframes, I started fixing the issues uncovered during that low fight testing.
Testing the mid-fis I added a fifth mission.
Here again, some problems arised on a couple of screens, mainly because instructions might have been too vague, and buttons not actionable, but not necessarily the same ones than for the lo-fi.
I also gathered both quantitative and qualitative data from this testing session.
5/5
testers said the flow was easy to navigate
1/5
wished it had more interactivity on some parts
5/5
finished all 5 missions using the right path
“I wish I could both mark my item as sold and delete the listing at the same time”
“It would be good if I choose what I want to trade my item with”
Going forward, I use this design system provided by the company, as I also created some of my own components.
High Fidelity Prototype
Finally, here is the high fidelity prototype, keeping in mind the features focus being:
- avoiding fake buyers
- getting rid of repetitive messages
- having access to meetup scheduling tools
In order to assess the outcomes of the new features, here are some success and failure metrics.
Success Metrics
Positive reviews
Increase in downloading rates
Increase in number of high-volume seller
High NPS (Net Promotion Score)
Increase in number of closed sales
High app ratings
Low churn rate
Failure Metrics
Negative reviews
Low or non-increasing downloading rates
Low NPS
Low closed sales rate
Low app ratings
High churn rate
Key Takeaways
To conclude, I wanted to highlight some of my key takeaways from this project.
First of all, it’s okay to make assumptions, as long as you recognize your knowledge gaps. That was a hard thing to learn for me, especially towards the beginning of my process. Indeed, I was somewhat confused by the vagueness of my brief, and not having direct access to stakeholders right away discouraged me a little, until I decided to go off of my assumptions of what my mission was, which made sense regarding my research’s findings.
When I finally got the chance to ask questions to the OfferUp team, I was comforted in the route I took.
I also found it was more challenging to make changes to an already live app, as users are already used to that app. You don’t want to create new frustrations while fixing pre-existing pain points. Consequently, it is important to acknowledge the subconscious mental models they have already formed and to listen to what they enjoy about the app, not just to the issues.
Finally, I wish I could have tested the new features with actual high volume sellers because I believe their output would have been more beneficial to me, as they are the direct users to these additional features consider, and for the reasons mentioned above.