FightPandemics (now is as known as DoinGud) is a platform that ensures those who need help could more easily connect with those who have help to offer. The Direct Message feature allows individuals and organizations to connect within the platform itself instead of using other platforms or methods to exchange private information. This would bring values because users would come back to check their messages, creating repeat use (traffic), engagement (interest and traffic), and connection for the user (satisfaction).
We aimed to quickly roll out the feature with essential capabilities along with the platform MVP launch within 3 weeks.
I was the main product designer working on this feature with 2 product managers and a team of engineers for the web app and iOS/Android mobile apps.
The direct message feature with core capabilities is now live on the web app. 🚀 After the feature's release, 67 messages were created within the first month. The messaging feature of iOS/Android apps is still in development, which will be launched in Q3, 2021.
Individuals and organizations can search for posts of offers or requests on the platform. During a meeting with 2 PMs, I learned that the current platform only allowed them to communicate via publicly visible comments under posts, which did not promote trust or safety during the information exchange. Therefore, we decided to build the Direct Message feature that allows people to communicate in a more private manner. Also, it should let users recognize the posts which they're referring to in the same way as they comment under posts.
I took a mobile first approach, which helped me focus on core content and make it concise and easy to understand. To help message senders and recipients be able to identify what offers or requests are being discussed, I designed a solution that displays associated posts in chat conversations.
My team and I screenshotted some apps that we use to learn about the current trend of messaging. I summarized some key insights that could be applied to the design—
We defined different use cases and listed out feature specific behaviors, including message request/ acceptance, notifications, block/unblock users, edit/delete message, and archive conversation. Then, I translated these requirements into task flows that clarify high-level steps of users to use the messaging feature. The task flows not only helped answer how users would be able to access and interact with the feature but also helped communicate effectively with PMs and engineers about how it works. Â
I first started thinking of navigation about where users can access Direct Message and other features in the mobile app. I explored 3 options based on different assumptions. After a consult with another designer from the notification team, we decided to do a light testing, asking people which they prefer and how frequently they use those features. However, the feedback I got surprised me because these 3 options seemed all workable for people.
After the testing, I realized that it really depended on what we'd like to shape the user behavior. Also, I got the inspiration that people are likely to use Direct Message more often to discuss offers/requests with others. Moreover, Direct Message and Notification are important features to keep people engaged and retained on the platform for the business. Thus, I created Option 4, which we all agreed with and met the current demands.
People message others via posts, meaning they'd like to contact the post authors to discuss more details. Therefore, in order to let both the message senders and recipients know which posts (offers or requests) they're going to discuss, the associated posts would always be kept in the conversation. Accordingly, I designed the UI on which the message sender could see the post snippet when messaging via a post.
To let the message senders and recipients know and remind what offers or requests they're discussing, I applied what I've learned from the competitive analysis and explored 3 options. However, none of them were ideal for the team. They concerned about the post title won't be seen because of the limited space in Option 1. The Option 2 and 3 were similar, but the Option 3 looked less text heavy. Also, they concerned about how users could know there are some unread messages in the message requests inbox.
As a result, based on Option 3, I designed Option 4 with the indication of the number of unread conversations in messages and message requests inboxes and the blue dot indicate unread messages. We eventually went with Option 5 because it makes the read and unread messages more distinguishable.
To keep the associated post in a conversation, I designed Option 1, in which the post snippet sticks on the top and each conversation would only have 1 post content. However, there was a potential problem after discussing the design with my team. There may be multiple communication between the same recipients and senders without a unified chat conversation for multiple posts.
Therefore, I designed Option 2, which solved the above problems. The solution allowed to have multiple post content in a conversation. It could save multiple conversations between the same senders and recipients. Although users might feel confused about which post is targeted one in discussing by seeing multiple post content in one chat, this solution could help build a long-term connection between users and help to track the previous records of exchanged details easier.
After the mobile experience was finalized, it was straightforward to translate it into web experience.
I should be more aware of how to leverage quantitative and qualitative research to inform decisions. When working on deciding message's entry point, I did a round of preliminary user testing with some participants to validate my assumptions. I'd like to know which options users prefer and how often they use those features, but no clear conclusions were reached after the testing. It's apparent that doing qualitative research here was not effective. Thus, I learned that if I'd like to know users' use patterns, I should collect sufficient quantitative usage data to perform analysis in a more meticulous manner.