Asks - Getting help from your friends on Likewise

Asks are a long-running Likewise feature that helps users quickly get suggestions from helpful members of the community.

Problem

In the early days of the app, individual network sizes were small and users were having a hard time discovering content on their own. Users needed a way to reach out to the broader community for help.

Outcome

We created a way for users to quickly poll the broader community for helpful suggestions, so they could more easily find their next favorite book, podcast, and more. This resulted in:

  • Strong influencer engagement

  • Millions of Asks created

  • Tens of millions of suggested books, movies, podcasts, and shows.

My Role

I was the sole designer at Likewise in the early days of this feature and the app as a whole, but over time I worked on Asks with:

  • Multiple designers

  • 2-3 engineers

  • Product managers

Exploration

Problem

In the early days of the app users were having fun sharing their favorite movies, books, etc on their profiles and following friends to see their favorites. However, a few opportunities quickly became apparent:

  • Users lacked a way to directly ask for suggestions for new movies, books, etc. Conversations were happening in the comments section, but this was awkward.

  • With social interactions limited strictly to your friend network initially, and friend networks being small at the time, users were very limited in their ability to discover new content from the community. We needed to open up a bit from our initial concept of ‘ideas from people you trust’.

Competitor research

For initial research we looked into various ‘ask the community’ and assisted search features across the market. Major inspirations included:

  • Polls and questions on Instagram and Twitter. We liked the casual, lightweight nature of these posts. Quick to create, quick to answer.

  • Major Q&A apps like Quora, Yahoo Answers. We liked the ways these apps drew in subject matter experts and encouraged helping for helpfulness’s sake.

  • Looking at specialty search engines for how they treat things like genre/cuisine filtering, etc. We wanted to keep the process of finding recommendations as effortless as possible.

  • Time-sensitive posts were trendy at the time, and we were drawn to their potential to emphasize the ‘need it now’ quality of asks for tips while traveling, impending events, etc.

Use cases

Through interviews with early testers prominently featuring friends and family (we were a tiny startup) we focused in on two simple use cases that we assigned the ‘seeker’ and ‘helper’ personas.

  • The seeker is the creator of an ask, looking for help from the community. We aimed to make the act of creating a poll easy, and make sure they quickly got a good volume of high quality responses.

  • The helper is a benevolent subject matter expert, eager to share their expertise to friends and strangers alike. We needed to make the process of responding to an Ask lightweight and rewarding.

In the alpha days, all users were in Seattle, including our personas!

Core features

Top support these use cases we created a list of MVP features.

  • Users can create an Ask for suggestions within a single category (Book, Movie, etc). Limiting to one category would simplify the process of responding as well as simplify development.

  • Creator describes their request in a text field. We experimented with various methods of building the Ask over time, but ultimately returned to text; users were willing to put in the effort if it meant better results.

  • Other users can submit structured suggestions from our database of items. Before, users would respond to text posts with text suggestions, so for Asks we let them add items from our graphed database.

  • Individual social network sizes were small, so we opened up the audience for Asks to the entire app.

  • We wanted to create a sense of urgency, but needed to balance a time limit against the fact that the app still had a small user base. We eventually settled on a 1-week time limit for Asks.

  • Users involved in the post receive notifications when responses arrive, as well as when the Ask expires.

We created user flows for the two experience categories we’d identified; seeker/creator and helper/responder.

Iteration

Version 1 - Exploring in pre-alpha

Early wireframing allowed us to test out a wide range of concepts and flows, from the more customizable and complex to the simple.

  • Too much customization was overwhelming, especially too much at the same time. In future versions we aimed to make more settings automatic or removed them entirely.

  • Users saw utility in the core concept, but the existing look at feel was cold and technical. Later iterations brought a greater sense of warmth and fun.

  • The audience customization tools were originally part of a value of ‘recommendations from people you trust’ but wound up being irrelevant to most users, and were eventually removed.

Too many options being thrown at the user at once during creation, but the core of the feature would remain fairly consistent and successful for years.

Version 2 - Building a distinct experience

Working with Tectonic, a design agency, we crafted a new look and feel as well of a distinctive style of input. Tectonic recommended what we termed a ‘mad-lib’ style form for composing the initial post, using a sentence with swappable sections to compose a prompt.

  • Users responded well to the bold typography and cheery, bright color fills of the new UI.

  • We’d made things ‘too simple’, and the ‘mad lib’ form was producing very generic prompts. Users wanted to be able to just write out what they wanted, so we added in a text field.

  • The mad-lib form also had practical issues when it came to layout, and didn’t play nice with long strings of text. Usage of this form style was gradually removed, improving the experience.

  • Suggestions were listed in simple chronological order, with no way to discern relative quality. In response we added voting, which both solved the initial problem and increased overall engagement.

  • There was no way to pose clarifying questions to the Asker without submitting a suggestion first, so we added a comment section separate from individual responses.

  • The ‘trusted sources’ feature fell flat for new users who lacked a pre-existing network of friends on Likewise. We ultimately removed this setting, and made all Asks visible to all users, with friends getting notifications.

  • The first version lacked comments sections, which limited engagement. We quickly added comments.

Version 3.x - Refining usability

Once the product had been live for a while

  • Added voting and sort-by-votes by default, with prominent social proof imagery in the form of user profile images. Askers now had a greater sense of the quality of individual suggestions, and there were a lot more opportunities for engagement.

  • Users were engaging well with our commenting system, so we extended it with threading within comments.

  • A long standing confusion resulting from some overlapping action functionality was resolved by giving Asks a unique Upvote action. We stopped getting complaints of unexpected recommendations showing up on personal profiles.

  • The center-aligned look negatively impacted readability, and overall information hierachy was flat. We revamped the overall layout to make things more compact and give us room for more helpful metadata.

  • Users would lose track of the Suggest action when scrolling down a well-populated ask, so as part of a broader effort to normalize content views across the app we added a floating Suggest button.

An older version on the left, a later version on the right.

Version 4 - Hierarchy and cleanup

The most recent iteration I worked on focused on improving the relationship of key actions with content and generally improving readability.

  • More clearly delineate one suggestion from the next to avoid the ‘huge continuous wall of text’ problem. The view as a whole became far less overwhelming as a result

  • Separate and elevate the ‘Upvote’ button to be more clearly acting upon the card as whole as that elements primary action. We saw improvements in total votes as a result.

  • Group social elements outside of the item card. This reduced overall noise.

  • The creator of the Ask needed a clear action to take, since they’re not adding suggestions of their own. So, the Save action was elevated.

Since these changes were implemented, the average number of responses to each Ask nearly doubled over the course of a few months:

Results and learnings

Tens of millions of votes cast

Millions of Asks created

  • Asks are a long-running and popular feature of the app, going strong after 5+ years.

  • Asks wound up forming a core element of how influencers interact with their audience on the app, and Asks by celebrities like Bill Gates and Paris Hilton garnered hundreds of unique suggestions thousands of votes and comments.

  • Innovation with Asks has continued after my departure in 2022: in 2023 the team has started integrating modern AI enhancements.

More projects

Whitepages

Likewise