The Past and Future of Mercari’s Discovery Experience, from the PM who Launched the Recommendation Team
Tomohiro Furusawa (@furufuru), a member of Mercari’s AI Team, has held a variety of positions in his three years at Mercari, including PM, engineer, and scrum master. Despite shifting among these roles, he has always been involved in improving the discovery experience at Mercari. We sat down with him to talk about how Mercari’s Recommendation Team has developed since its establishment, and some of the things he wants to work on in the future.
Tomohiro Furusawa (@furufuru) | Product Manager
Tomohiro discovered the joys of data science and software engineering, and before he knew it, he made the leap to the world of IT. After finishing graduate school, he used his background in library and information science to develop products for information retrieval and recommendations. He joined Mercari Inc. in 2019 to work on search evaluations as a member of the Data Science Team. He then established the Recommendation Team and is currently handling product improvement as a Product Manager.
“Since my days as a student, I’ve always been figuring out how to organize information and make it easier to use.”
──Can you tell us a little about what you’re working on right now?
Right now, I’m on a team focused on improving the core Mercari experience, which is the buying and selling experience. My own particular focus on that team is improving the buying experience.
When you think about customers purchasing items, and how exactly they find an item they want, the most common way is that they search for it. Searching is a process where a customer comes up with an idea of something they want, so they look for the item on Mercari, find it, and then they buy it.
Beyond searches, though, we’re also working to improve the “discovery experience.” This involves finding new purchasing needs or finding items based on purchasing needs without making any special effort to find it—in other words, without needing to search. You might open Mercari, and items you’re interested in or thinking about buying are right there. Or, while you’re searching for one item, you find another item you’re interested in, and you buy that one. That sort of thing is how we define the “discovery experience.”
Recommendations are a powerful tool in improving the discovery experience.
Mercari currently gives customers recommendations by controlling three units. One is “content,” which is about introducing items of interest. Then there are “topics,” AKA search criteria, which are item groupings that can be interpreted as such. Finally, there are “items,” which are the items customers actually purchase.
Tomohiro Furusawa (@furufuru)
Furusawa has consistently been involved in recommendations since joining Mercari in 2019
When I was in college, I worked on library and information science. Through academics I studied how to collect and store large amounts of knowledge and information and how to make it easily accessible to a wide range of people. Looking back at my career since my days as a student, I feel like I’ve always been involved in figuring out how to organize large amounts of information and make it easier to use.
When I was a student, I thought about a career as a librarian, but when I started interning as an engineer and data scientist, I found myself really interested in manufacturing and data usage. So, after I graduated, I decided to become a data scientist at Recruit. Recruit, which is where I worked before Mercari, has a lot of contact points with customers through all sorts of life events, so it was interesting just to look at the data. That said, I was also almost painfully aware that data is meaningless just by itself, and that its real significance lies in how it’s used. That’s when I started digging deeper into the technology of searches and recommendations.
What’s the difference between a “search” and a “recommendation?”
When we want a certain type of information, we use keywords and other things to ask questions through some sort of app, right? Then we look at all the results and get the information we want. That series of actions is what we call a “search,” and a search system is the mechanism that makes that experience possible.
However, if we utilize customer data we already have, we can provide information from the service side without customers needing to enter any kind of keywords or other information. A system that can provide information without a customer having to actively ask questions is a recommendation system. A “recommendation” is different from a “search” in that it refers not to an experience, but rather to a means used by the service provider.
Building good search and recommendation systems makes the service easier to use, and I think it ultimately bring us closer to the world I want to create, which is a world where people from all walks of life can access knowledge and information with ease.
Improving the recommendation system while shifting from position to position
──Why were you interested in a job at Mercari?
It started when a former colleague of mine was working on improving the search experience at Mercari and invited me to join. Mercari’s user base was growing fast, and the company had a lot of momentum, so I thought changing jobs would give me the chance to dig deeper into search technologies that use a wide range of data.
After joining Mercari, I started by evaluating the existing search system. All services—not just search functions—of course need to be built first, but they also then need to be improved through evaluation cycles.
With our service, a search is considered to have gone well when a series of experiences is born—for example, when you enter a keyword for something you want, you find the perfect item, and then make your purchase. On the flip side, a search doesn’t go well when, say, a customer is looking for an item but can’t find it using keywords, or a lot of items with little relevance show up in the search results.
Another issue unique to Mercari is that the search results sometimes show only items in good condition that can be purchased right away, which means the customer can’t compare and consider other items as well. The Search Team was using trial and error, repeatedly defining and evaluating what makes a search a success or a failure, to figure how to improve the search process.
We established Mercari’s Recommendation Team after that. I’d actually been quietly working on a prototype of the recommendation system before the team was formed, but the company also decided to focus on the recommendation system, so I ended up supporting the establishment of the team as a PM. After a while, Aki Saarinen (@akis) joined the team as PM and I shifted back to being an engineer.
Moving through the development process as an engineer, I found that a good development system hadn’t been created at the team level, so I acted as scrum master to fix the group’s development system while handling my engineer duties as well. After a while, things came together as an organization and the Recommendation Team started producing results, but there was also a shortage of PMs at the time, so I went back to being a PM again. So, although I’ve always been involved in the recommendation system, my job title has gone from PM, to engineer, to scrum master, and then back again.
Thoughts on getting recommendation features to contribute to GMV
──Have you hit any walls in your career so far?
Actually, it took some time before the Recommendation Team was able to successfully contribute to sales. Before the current system, items on the home screen were recommended to customers based on their past activity, but appearance wise, it was just a timeline listing one item after another. From there, we increased the types of components in the recommendation system to display recommended keywords and categories based on a history of customers’ recent activity. That was when we first saw a big boost in sales. Instead of just recommending items, we also display why an item is recommended and define functions for components that allow for deeper and easier exploration, and I think that has made things easier to understand for our customers.
The home screen went through big changes in terms of UI appearance during this period, but we also revamped background systems at the same time. To handle the labor-intensive process of developing home screen functions in a short period of time, we reworked most of the content displayed by the client into a form we can manipulate on the back end. We had always thought it would be great if the system could be modularized so that we could just change the back end to switch between home screens, but it took until around 2020 before we could actually develop and deploy it.
──Are there any other things you want to try or challenge yourself with at Mercari in the future?
The recommendation system is still using only relatively simple logic, so I’d like to try applying a broader range of technologies to solve advanced problems. We still need to address a lot of things, like developing data utilization infrastructure for the recommendation system. If you’re someone who loves approaching things from a variety of logical perspectives, definitely get in touch with us!
Mercari is a high-traffic service used by a huge number of customers, so someone with experience in operating a large-scale service will probably have an easier time grasping the essential points intuitively. That said, even if there are some things they don’t know, we love working with people who enjoy meeting challenges head-on!
In the second part of the interview, Tomohiro talks about the things he focused on while working at Mercari, and Mercari’s unique organizational culture, as someone with experience in a range of positions, including PM, engineer, and scrum master.