ANYONE

Helping users locate the right advice from experts.

Anyone app allows its users to easily schedule a 5 minute call with an expert to answer any burning questions they may have about a given topic of interest.

As part of an immersive UX/UI bootcamp, I was tasked with exploring how to enhance the app. My solutions were based on the brief provided my Sam Ducker, the lead designer at Anyone, where he highlighted areas within the app that he felt may benefit from further improvement.

Role

User research • UX design • UI design • Prototyping • Usability testing

Tools

Miro • Typeform • Figma • Maze

Challenge

Anyone app allows its users to easily schedule a 5 minute call with an expert to answer any burning questions they may have about a given topic of interest.

As part of an immersive UX/UI bootcamp, I was tasked with exploring how to enhance the app. My solutions were based on the brief provided my Sam Ducker, the lead designer at Anyone, where he highlighted areas within the app that he felt may benefit from further improvement.

Solution

The app had a very basic search functionality and limited categorisation options.

My solution focussed on introducing a more comprehensive filtering and sorting functionality, as well as refined categorisation and taxonomy of information.

Outcome

Unmoderated usability tests showed my iterations were largely successful with a high completion rate via the expected path, however, the prototype limitations were a possible cause for a higher than expected mis-click rate.

Discovery

Usability review

The starting point of my research was conducting a usability review using wireframe flows for both the sign-up process and call flow. This allowed me to assess the current user experience and gain a better understanding of any challenges users may have had while using the app.

Sign-up process wireframe flow

Call wireframe flow

Questions & Observations

Next I formed some initial observations to help frame any problems within the app identified during my usability review. I documented these using the structure [situation], [response], [problem to business or user experience].

When [browsing content], users [can't filter by interest] which [causes the user to struggle digesting the content].

When [signing up to the app], users [very quickly appear on the central feed], which [causes a lack of understanding about the product].

When [on a call], users [do not get given sufficient feedback], which [causes confusion for the user while chatting to a contact].

User survey

A user survey was then created to confirm what the core problems were with the app. This allowed me to validate my initial observations, and ensured when forming a final hypothesis, that it would be based on data from users.

Survey questions

We are testing Anyone, an app that helps users get better advice from experts.

Which industry do you work in? Design | Tech | Finance | Health | Retail | Other (please specify: ____________)

Do you ever seek to learn from experts in your career? Yes | No

How often do you look to improve your knowledge on a given topic? Daily | Weekly | Monthly | Yearly | Never

Do you ever struggle to find the most relevant information on a given topic? Yes | No

Where do you usually find expert knowledge to help with your career? Apps | Social networks | Books | Podcasts | Events | Other (Please specify: ____________)

Depending on your answer, why do you prefer to find your information that way?

What did you find most frustrating when you last sought knowledge from an expert?

Why is it important to you to gain knowledge from an expert?

Why is it important to you to build your knowledge base?

Thanks so much for taking the time to complete this survey!

Definition

Once I had gathered my survey results I created an affinity map, synthesising the data to identify any clear patterns and trends. Information was clustered into groups, helping me to define two user segments, as well as to prioritise the main problems that would inform a hypothesis.

Career learner

Professionals looking to enhance performance in their career

Skills learner

Designers and creatives looking to imrpvoe their skills and find inspiration.

Validated observations

Patterns and trends identified in my affinity map helped to validate my early observations on users' frustrations. An iterative, agile approach to resolving any identified frustrations then gave me a clear focus on what was achievable. I identified the primary frustration based on what I felt would provide the greatest gains for both user and business.

Primary Frustration

When [looking for specific knowledge] users are [faced with conflicting or inconsistent information] which results in [confusion and a lack of trust].

Secondary Frustration

When [looking for experts] users are [struggling to verify the experience and credibility of the advisors] which results in [questioning the validity of the advice]

When [wanting to absorb advice] users are [limited by the lack of formats] which results in [ineffective learning and comprehension]

How might we statement

A clearer picture of the problem allowed me to define user’s actual and optimal behaviour that became a foundation for a how might we statement which would guide ideating towards my solution.

How might we... help users find the most relevant information to help them more easily find a trusted and appropriate advisor.

Development

I conducted a series of ideation techniques ensuring I considered a range of solutions. These ideas were then categorised as a ‘crazy idea’, an ‘improvement’ or an ‘addition’, and prioritised against user value, business value, effort and time.

Hypothesis

Once I had prioritised my ideas, I wrote a hypothesis to help frame the problem around user and business goals.

We believe that giving users a better means of searching for topics of interest on the explore page when using the app, [ would help them to find the most relevant advisors for their interests ] and [ thus result in increased engagement, retention, and overall adoption of the app. ]

[ ] User goal [ ] Business goal

Sketching

Sketching out solutions helped me to quickly understand and consider how I could iterate directly in the current product.

Delivery

Style guide to high fidelity prototype

In order to turn my concept sketches into a high fidelity design, I inspected the current product to define a range of basic styles and components following the 8pt rule. This allowed me to then create a prototype that was both consistent and accurate.

Colours

Action colours

Text colour

Surface colour

Alert colours

Typography

Inter

Grumpy wizards make a toxic brew for the jovial queen.

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Heading text

Heading one
40px/48px
Heading two
32px/40px
Heading three
24px/32px
Heading four
16px/24px

Body text

Large text
20px/28px
Medium text
16px/24px
Small text
12px/20px
Tiny text
12px/16px
Overline
12px/16px

Buttons

Large button
20px/32px
Medium button
16px/24px
Small button
12px/16px

Grid

Spacing system

Spacing distance
Role
2px
Space created Mobile navigation icons and text
4px
Space created betwen icons and text
8px
between buttons; icon and text inside menu items; between text and checkbox, radio button and switch

12px
Horizontal spacing within text fields and padding between vertical lists
16px
Horizontal spacing within buttons and margins between text fields

My final prototype included various interactions and transitions matching the product current flow, helping me to facilitate testing the effectiveness of my solution with users.

Testing

I validated my design and hypothesis with users by way of an unmoderated usability test via Maze.co, forming a test script that included a range of scenarios and tasks for the user to complete.

Outcome

Results from my unmoderated usability test were positive with a high completion rate via the expected path. However, testing highlighted that there were areas of the app that would benefit from further refinement. Based on my results, I would concentrate further development on the filtering options within the app, as this seemed to offer the greatest benefit to users.

Results

Task No. 1

Completion

80
%

Direct: 20%

Indirect: 60%

Bounce rate: 20%

Usability score

32
/100

Takeaways

Let down by a higher than expected mis-click rate and bounce rate as well as low completion via the expected path. Further analysis of heat maps and comments suggested to me that this was due to limitations within the prototype and a preference for a search option that was not functional within the prototype

Task No. 2

Completion

100
%

Direct: 20%

Indirect: 60%

Bounce rate: 20%

Usability score

70
/100

Takeaways

A high completion rate and reasonable percentage of users completing the task via the expected path, but somewhat let down by a high mis-click rate. After reviewing the heatmaps and comments, refining the layout and positioning of certain content may have improved this result.

Task No. 3

Completion

100
%

Direct: 20%

Indirect: 60%

Bounce rate: 20%

Usability score

75
/100

Takeaways

A high completion rate and a good percentage of users completing the task via the expected path. A high mis-click rate was once again a concern. After reviewing the heatmaps and comments, refining the layout and positioning of certain content again may improve this result.