Taobao App
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During about 2 years working as a Interaction Designer at Taobao UED (User Experience Design), to make an analogy, I played as a role of a design consultant in a big company: taking requests from product owners and marketing department to help them deliver a better, user centric product, which means I have to understand their needs, identify product and business goals, and then find out ways to help them finish the tasks in every stage of the product cycle.
During the period, I took responsibilities of enhancing and ensuring the using experience of Taobao App on all mobile devices (mainly on iPhone and iPads); apart from that, I also participated in various kinds of projects, from internal organisational system design to a application of a brand new business model, by providing design solutions and helping in product strategic developments.
Above all, design with data analysis in rapid product cycles and at the same time taking into account of development resources are the most learned parts.
By applying both qualitative and quantitative research methods in design, there are some projects I would like to share with you:
1. Redesign MyFavorite
Background
MyFavorite, a function through which users can collect their liked items every time when their spent time shopping or surfing online, and provides references or direct links when they are searching for needed items.However, in the beginning this function on Taobao wasn't provided well. Some users even have never used this function; instead, they are using shopping cart to temporarily collect their considering-to-buy items. Though by doing this way users still have place to keep track on those items, this caused another issue that the capacity of shopping cart is always not enough, and then users start to ask for more capacity in shopping cart.
Therefore, we thought it is time to redesign MyFaverite.
By redesigning the function, we aimed to provide a more handy assistance to help user not only find their collected items, but also start their shopping journey from there. We also hoped that through redesigning the function and refining user journeys this way, the shopping experience on Taobao can be enhanced.
Design Process
To understand how users use this function and find out why they are or are not using, I conducted a series of online contextual inquiries.Firstly, I asked people around me who are existing users and have complete buying experiences on Taobao to show me their MyFavorite folders, and tell me their experiences of using it when they do the shoppings.
Despite of recording their sharing, I also asked questions according to what I’ve discovered from their folders. For example, as I found many users have similar items listed continuously in the folder, I asked them to recall and describe the situations while they added those items into the folder, and so on.
After interviewing five users respectively, I applied KJ method to summarize the results.
By analysis using interviews and KJ method, the definitions and contexts of using MyFavorite and Shopping cart are better defined.
As Show below, the flow chart presents the most-happened process in Taobao’s user journeys. Both the level of likeness and the buying tendency of items in MyFavirite and Shopping cart are quite varied.
Since all these are clarified and much better defined, the design of MyFavorite can therefore be conducted.
In the design proposal, an user may see grouped items on the top of the page (the orange-line area). All items in this area are selected from the user’s MyFavorite folder based on defined algorithm. The reason of doing so is to help users find a way to start their journey for shopping; especially when they have no idea where to start and are just lingering.
Besides, this area also provides opportunities for marketing teams to promote sales by seasons, holidays, events, or personalisation.
Design/ Wireframe
2. Design a new function: Item Comparison
Background
Comparison is always a unavoidable decision making process when buying something, and it is no exception shopping on Taobao. The reason why people need comparisons is to help them make better decisions. So it can be seen as an on-going process instead of the end of journey.Yet, what’s difficult in designing this is to know at which points in the whole journey on Taobao users may need comparisons to help them make buying decisions. Besides, people use different strategies and care about different factors while comparing among options; What's more, the whole process is mostly happening in secret that can hardly be understood by anyone, even users themselves.
Therefore, understand the processes and provide flexibilities by design are the key to success.
By literature reviews and some quick user interviews, I tried to disclose what happened in the process as much as possible.
Design Process
According to literature reviews, I found there is a factor called affect referral (Wright 1975) that influences users' decision-making process. Being influenced by the factor, users may tend to make choices based on recalled affect towards brands and experiences in their memories, bypassing reevaluation of the brands and comparison processes.
To gain more insights, I also chatted with people who are users and recently have bought items from Taobao in order to understand how they chose products when there is already a desire or need. I found that the intentions of buying items from familiar or trusted shops are surprisingly high. In other words, either the consumer loyalty on this platform affects to a certain degree their buying decisions, or the risk of trading with unfamiliar sellers is too high for them. That means, if people are told one of the items they are comparing to is from shops they've bought something from before, they are much more likely to choose to buy that one.
Of course, there are also other factors, such as which one has the higher cost-value ratio, which one can be shipped and received first, or which one has better customer reviews, users on Taobao would probably take all these into account while comparing all items.
Hence, based on what I learned, the first version of comparison function was proposed.
Iteration:
After designing the first-version, we asked a few users to use it as a prototype on iPad.
From what their feedbacks, we evaluated the helpfulness of our first design. Although it can provide assistances while people are considering and choosing among items, most of users said it still lacks of the detailed information which might be even more essential when making hard buying decisions.
Therefore, we decided to iterate the concept a bit further, and redesigned the function by splitting views into two which allowed users to view two objects' details page at the same time.
By adjusting and re-organising information in the details page, the sizes and layout designs are changed from “page” to “card’. Which means, though users open two objects’ card to compare at one time, by simply closing one of them, the decision is made. And the comparison goes on, until final decision is made and something is bought in the end.
This continuous, iterative way of comparison works much better than the previous one by breaking up each complex comparing task into small pieces of steps. And it makes users feel much easier to select in this way, so they won’t be afraid of making buying decisions anymore.
Final Design
3. Redesign Recommendation System
Background
Recommendations system are always hard works; especially when there are billions pieces of contents.However, as a platform to connect buyers and sellers and to help sellers operate their daily business, Taobao spent great effort on figuring out and keeping refining their recommendation mechanism.
However, it is always difficult to make those recommendations fit right in users’ favour. So it needs to be customised accordingly, which relies heavily on the backend computing and algorithms. But what’s more important is what kind of data you are going to use, and how you use it.
Let's see how we improve the whole system by design.
Design Process
If you look into the old version, you will notice that these recommended shops were categorised in a very traditional way, by gender, by ages, or by functions; on top of that, the layout and interactions were very conservative as well (see Pic. 1).
Also, it failed to attract neither targeted or general users to visit, not even mention to make them stay on the pages.
Therefore, we decided to dig in deeper to figure out the whys.
By some analysis, we found that firstly, user can hardly feel the customisation of shop recommendations for them; secondly, though visiting those pages, users still focus too much on individual items instead of shops, therefore as long as users find no interests in the limited displayed items, they immediately leave.
Henceforth, we know clearly the two main objectives for this redesign:
1. Ensure users can reorganise information presented on the pages by shops, not items;
2. Enhance backend algorithm with Business Intelligence team to make those shop categories more sense to users in a more flexible and dynamic way, so that they can feel the content recommended more customised.
To achieve these two goals, it requires closely collaborations among marketing operators, designers, and business intelligence team.
During the process, we discussed with algorithm team to make categories more human and interesting, and closer to how people search for corresponding shops. As a result, we came out a solution using the concept of “keywords” and groups of people (人群關鍵字) to re-categorise all shops on Taobao. As you can see in the picture 2 below, the new shop categories are becoming more attractive and intuitive for users to choose by their interests. Besides, the “hot shop categories (row 2)” and “recommended shop categories (row 3)” are also provided to avoid the “Choice Overload” situations.
Also, in the new design, by revealing numbers of items contained by the shops (Pic.3 below ), we assumed users might have more awareness on shops instead of individual item. Last but not the least, we changed the layout of similar recommendations in order to reduce the distractions by opening among pages. According to the data tracked after release, we successfully attracted more visitors to go and stay in shops with deeper engagements.
Result Evaluation
As you can see in the Conversion Rates Analysis above, except for the IPV-UV (green line), other three factors for evaluating design are obviously increased, which indicates our success on leading users to visit shops instead of each item.
Furthermore, users start to collect more items and are more willing to buy directly whenever they found interesting items through the path. Result wisely, users are staying longer, buying more. This is beneficial for everyone: the user, sellers, and ourself, Taobao as a e-commerce platform.