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* Challenge Winner

I visualized the mobile checkout experience of eBay to

re-engage users.



This is an academic project for course Rapid Design for Slow Change. We worked with eBay UX Team. It aimed at redesigning the checkout experience for eBay. Our team were the winner of this challenge (1 out of 13).
I collaborated with a 4-designer group to conduct user research and brainstorming ideas. And I did my personal iteration after the project has been submitted for a while, just because I am really interested in this topic. So the interaction design and visual design iteration here are done by myself.




How can we redesign the checkout experience to reengage eBay user after a purchase?



The Dead End, The Same Confusion.


First, we tried to identify what exactly is the problem with the current experience. By looking at the current checkout experience, we found that the eBay mobile app supports a linear purchasing process. Once the user makes a purchase, their journey is over. There is no exploration or re-engagement.


- Do you close the app or go to the side navigation? Are any of the ‘related items’ interesting enough to actually click on? Should I search for something, even though I just got what I wanted? We just get a ‘Thanks!’ as if it is the end.


How do we extend the experience for our user?




Design for A Specific Segmentation: The Reveler.

Seekers are the same: intense, research-focused shoppers who must know anything and everything not just about a product, but about the entire category it’s in.



They set goals. They prefer a tried and trusted process. And when they buy the right thing, it’s a big relief that the task is over.


The Reveler is guided by ideas and feelings—a personal process. They love to shop.


For The Winner shopping is a game, like a scavenger hunt, where they hunt to get the best deal.

eBay has a really massive user base where we are talking about millions and billions of users. And each user has their own preferences and shopping goals. To learn more about our user group, as well as the constraints of this project, we conducted an interview with eBay UX Team. eBay provided four market segmentations to explore: the Reveler, the Seeker, the Winner, and the Mission Driven. We analyzed each segmentation, and sketching out early ideas. 


We decided to focus on the Reveler.


Why focusing on one segment? Knowing that different segments has different shopping goals and preferences, designing for all of them at once might lead us to not solving any of their problems very solid. And why the Reveler? We might have different strategy to engage different user segments during different stages of the whole shopping process. But we are talking about post-shopping experience. So who better to re-engage than a person who feels good when doing it - the Reveler?



Asking Questions, Making Assumptions, Sketching Ideas

We then started to ask ourselves a series of questions to provoke thinking. We also made our assumptions based on our knowledge about eBay and the reveler to further explore the problem space..

  • What does it mean to "re-engage" the reveler? How does that help eBay's business? How does that bring value to the reveler?

    • Encourage them to buy more items​, and/or add items to shopping cart/ watchlist

    • Encourage them to review items they bought previously

    • Encourage them to sell items they don't need anymore

  • What does it mean by "after a purchase"? It could be

    • Right on the checkout success page​

    • Next time when users open the eBay app

    • Before they receive the package

    • After they receive the package

  • Whom could be involved in the process?

    • Buyer​

    • Seller

    • Other buyers

  • Where could this re-engagement happen?

    • Online​

    • In-store



The Reveler: Shopping is An Exploratory Experience


To get more insights on reveler's shopping behaviors and goals, we interviewed several revelers. Questions we asked include but not limit to:

  • What are some of the reasons you go shopping?

  • How often do you shop online?

  • What do you want to know/ do after placing an order?

  • What stops you buy things?

  • What makes you frustrated when shopping on eBay?

  • Any other shopping experience that you really enjoy?

  • Can you give us a memorable shopping story of yours?

  • ...

We also let them take a look at the current checkout success page of eBay, and get their feedback on it. There are some usability issues: it does not provide any direct opportunity for users to edit/ cancel their order if they change mind.

I created this empathy map to provide a clearer understanding of who we are designing for.

We externalized and clustered the findings from previous research with affinity diagram. 

We asked ourselves what kind of design can provide an exploratory, immersive, and full-of-possibilities shopping experience right after a purchase? 

Key Takeaways



Their shopping goal is to have pleasurable experience, discover new and novel items.



It takes time for them to discover items match their ideas

Design Insights


Provide multiple purchase paths to allow for an exploratory shopping experience.



Highlight the experience of using a product, not just the functionality.



The Reveler looks forward to create combinations and enjoy what’s bought.


Provide possibilities of combination to spark imaginations.




Going Broad and Going Deep

Keeping our design goals in mind, we explored many different ideas, and many different paths for each idea. We thought about the holistic online shopping experience. We would try to let anything happen, digging into concepts (including far out futuristic ones). Those ideas, even “bad” ones, really spoke a lot about our underline assumptions and help us further refine our thinking.


With our design goals in mind, as well as take technical and business constraints in mind, we carefully evaluated each ideas. Also showed our early concepts to several users and cohorts to get their feedback. We landed on the most promising solution.






Help Users Discover Novel Items that Match with Their Purchases

Based on our research insights, we decided to focus on help users discover novel items that match with their purchases as the primary design goal. We also aimed at improving the usability of the checkout success page.


Learning from eBay's Competitors

I also took a look at other competitors to leverage their experience of not only designing the checkout success page, but also how they might help users to discover novel items . By researching their designs and analyzing their design context, I was able to form a better understanding of what's working and what's not working under the context of post-purchase.

Major Takeaways

  • Source of recommendations for discovery

    • eBay picks​

    • Buyer's purchase data

    • Purchase data from other buyers

    • Seller's promotion

Amazon Checkout Success Page

Etsy Recommended Items

Taobao Checkout Success



The New User Journey: No More Dead End

meet ebay Lookbooks  


Spark your imaginations, discover new possibilities.




Click Through The Prototype


Constantly Iterating and Validating

  • How might we display lookbooks on the checkout success page? 

  • How might user browse a lookbook? Do they care about items in the lookbook in the very beginning? 

  • How might user navigate between lookbooks?  Is it more important to navigate between lookbooks, or do they want to discover even more lookbooks?

  • How might user shop items in a lookbook? Do we direct them to the item page? 


Success Metrics and Constraints

Success Metrics 

In real world, to validate whether a concept meets user needs and create business value, it takes time and resource to do testing and data collection. In our case, we showed our early concepts to several users and cohorts to get their feedback. 


We also defined our metrics of what success of this design look like​​

  • Click rate on lookbooks

  • Purchase rate of items showing in the look

  • Watch/ Add to shopping cart rate of those items 

  • Time that users spend on those look-books



  • The recommendation algorithm 

    • Collection rate to further learn user preferences

  • Quality of images

    • Early stage: collaborate with sellers to boost their business

    • Later stage: user generated content, competition among sellers

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