For the past two weeks, and as my final project for the Ironhack Bootcamp, I got the opportunity to work with the company OfferUp.
My process followed the Design Thinking Double Diamond, going through the following stages:
DISCOVER
Secondary Research
Business Analysis
Competition Analysis
Quantitative Research
Qualitative Research
DEFINE
Affinity Map
Value Prop. Canvas
User Persona
Empathy Map
User Journey Map
Problem Statements
DEVELOP
Brainstorming
Feature Prioritization
Value Prop Canvas
Jobs to be Done
MVP
User Flow
DELIVER
Prototyping
Usability Testing
Success & Failure Metrics
Takeaways
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.
The brief I was given for this project was:
How can we better empower high-volume business sellers that mostly buy and sell through local pickup?
Discover
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 Problems
- Fake accounts
- Item management
- Local pick-up scheduling
- Messaging system
Potential 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
Potential 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.
In addition, in order to gather quantitative data, I created and distributed a survey, which revealed that the top reasons for high-volume sellers to use OfferUp were to make money (87%,) but also to grow their business (37%.)
67% of them rather use local pick-up options, rather than shipping, and 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
With all this data gathered, I was able to start focusing on synthesizing my research by using visual sense-making tools.
First, by creating this 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 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.
Thanks for the information previously uncovered three problem statements emerged, which I then reformulated into how might we’s.
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
Keeping 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.
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
Consequently, here are the job stories, which also summarize users needs, uncovered previously, being avoiding fake buyers, getting rid of repetitive messages and having access to more efficient meetup scheduling tools.
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.
Product 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.
Finally, the research analysis and synthesis led to the formation of a minimum viable product with the following additional features.
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.
Deliver
In order to show the different features encompassed in the MVP, the prototypes followed 4 different user flows.
The Posting Flow
The Meetup Scheduling Flow
The Confirmation & Reminders Flow
The Closing Sale Flow
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…”
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 arose 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.
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
I chose to keep the posting flow very similar to the already existing one. As users are used to it, and already created mental models. I wanted to keep it simple and easy to go through.
On the price-setting page, sellers can now indicate if they are open to trade and decide what category of items they would be willing to trade theirs against.
They can also choose to only accept offers from verified users, and because there are different types of verified users they can choose which one or all of them.
When it comes to the delivery method they can also indicate their preferences between shipping, local delivery, or local pickup.
Going onto the offer-managing flow, when the buyer makes an offer, we can decide to accept it. When we do so, we get redirected onto a meetup scheduling page.
Here we have a novelty: pre-save locations, which are the sellers’ go-to meetup spots. They can add a new one easily, or just select their preferred one, which avoids re-typing the locations in every time.
They can also pick a date and time.
When confirmed, it will send an automatic message to the buyer with the location of the meetup, the date, and the time. When they accept it, both parties can now can the meetup confirmation and opt-in for reminders, cancel or reschedule the meetup, and even add it to a Google or Apple calendar if they wish.
When comes to the day of the meetup, both party will get a few reminders and will be encouraged to let the know they are on their way to the meetup easily, in order to keep the communication going, and avoid flaky buyers/sellers.
Finally, the seller can close the sale easily by accessing it directly from the messages. They can decide to either mark the item as sold or to delete the listing, as well as rating the buyer, and even add a comment if they wish.
I added an extra flow to show how the automatic messaging system would work.
Looking at the app from the buyer's point of view this time, when they decide to engage with the buyer, they are offered a choice of pre-made questions. These are questions I uncovered during my research.
As the seller already responded to that question in the item description, as soon as they pick one, it will just send out an automatic response, and inviting them to check the description again or to send another message to the seller if they need additional information.
In order to assess the outcomes of the new features, here are some success and failure metrics.
Success
- 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
- Negative reviews
- Low or non-increasing downloading rates
- Low NPS
- Low closed sales rate
- Low app ratings
- High churn rate
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.
Thank you so much for reading my case study!
I am now a proud graduate of Ironhack, with strong UX Design foundations, as well as an empathetic and analytical mind that I will keep building onto!
