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My Full Design Thinking Process

Introduction

The Anyone App connects advice seekers who pay to have a short call with experienced advisors in virtually any subject or field, but users find that trusting and paying strangers to advise them is problematic. I researched and designed a new video feature to break down existing social barriers and increase trust between strangers.

Problem

The app has many social barriers that make it difficult to build trust between advice seekers and advisors.

User problem: users find it difficult to gauge and explore advisors who they do not already know or trust. Thus, users do not feel confident about paying for advice.

Business problem: The less confidence users experience, the less likely they are to pay for a call and generate revenue for the business.

Process

I followed the Design Thinking methodology: empathize, define, ideate, prototype, and test

Role

User Research

Product Strategy

UX Design

UI Design

Prototyping

Usability Testing

Tools

Miro

Typeform

Figma

Maze

Timeline

5 weeks

1. Empathy

πŸ‘€ Questions and Observations

To help me empathize and frame any problems with the product, I began by forming some questions and observations after a few days of using the Anyone app.

To easily document these I followed the structure [situation], [response], [problem to business or experience] to ensure I'm aware of user and business needs.

When Advisors cannot communicate their personality, users lack personal touch points and the confidence to choose the advisor that will give them the best experience and advice, which causes the user to essentially guess which advisor is the right fit for them.

When searching for advisors, users do not have the option to save interesting advisors to reference later, which causes users to spend time and energy re-finding advisors or to lose interest altogether.

When comparing advisors, users have no way to check their credentials, which causes the user to leave the app and seek confirmation elsewhere if they want to be confident in in their advisor choice.

πŸ” UX Research

To confirm or disprove my observations, I begin forming a hypothesis backed by data. I created a user survey to uncover the core user problems that I could prioritize for both user and business needs.

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Survey questions 1. What do you do? Work | Study | Both | Neither 2. Do you generally seek advice? Yes | No 3. Do you seek personal or professional advice? Personal | Professional | Both 4. Who do you normally turn to for advice? Friends/Family | Mentors | Colleagues | Published Articles 5. How willing are you to seek advice from someone you don't already know and trust? Unwilling - Willing (rating scale) 6. Which qualifications do you value the most in an advisor? Professional Status | Social Media Presence | Years of Experience | Knowledge of the Topic | Academic Accomplishments | Published Work | Other (Select all that apply) 7. If you find it difficult to find advice, can you tell us why? Open ended 8. Would you consider using an app to get personal advice? If not, why? Open ended (If not, why) 9. Would you pay for access to advice? If not, why? Open ended (If not, why)

2. Define

πŸ“Š Synthesis

Having shared my survey with users, the next stage of my case study was focused on synthesizing the data to define trends and formulate my hypothesis.

During the synthesis process, I segmented user responses and used an affinity map to prioritize the problem trends.

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Survey Results
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Affinity Map

My team and I mapped all open-ended questions with their respective responses in order to made user trends easier to digest.

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We then created affinity maps of different user segments based on themes, keywords, and sentiment in the user's response.

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Validated Observations

I after reviewing the synthesized data, I was able to validate my preliminary observation that users lacked the personal touch points needed to provide a strong buying confidence.

As a next step, I identified primary and secondary user frustrations to guide my "How Might We" statement and solution.

Primary Frustration

When trying to find an advisor, users have no personal connection to the Anyone advisors, which results in user reservations about paying for advice from people that the user does not already know or trust.

Secondary Frustrations

When comparing advisors, users are not confident about which Anyone advisor has the best advice and credibility for their particular situation, which results in hesitation and a lack of direction.

3. Ideation

How Might We Statement

With the problem defined, I jumped into the ideation phase with my team and worked through the solution design model, identifying users actual vs. optimal behavior.

This allowed me to form a how might we statement to begin forming a solution.

How might we build trust between the users and advisors in order to give users confidence when paying for advice?

Ideation Techniques

I utilized a series of ideation techniques. This allowed me to consider an array of solutions. I ideated using the Mindmapping technique, crazy ideas, and what could be improved and added.

I then prioritized my ideas based on user value, business value, effort, and time.

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What can I improve?

What can I improve?

SelectedNameUser ValueBiz ValueEffortTime
Advisor Card Personalization
High
High
Low
Low
Advisor Curation
High
High
Medium
Medium
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What can I add?

What can I add?

SelectedNameUser ValueBiz ValueEffortTime
Advisor Video Introductions
High
High
Low
Low
Survey tool to match users and advisors
High
High
Medium
Medium
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Crazy Ideas!

Crazy Ideas

SelectedNameUser ValueBiz ValueEffortTime
Mentor options
High
High
Medium
Medium
Group calls
Medium
High
Low
Medium

✍️ Hypothesis

Having prioritized ideas based on what can be improved, added, and crazy ideas, I wrote a hypothesis that helped me frame the problem for user and business goals.

🎯 The user goal is to reduce social barriers and increase trust and confidence in the Anyone advisors

🎯 The business goal is to generate more calls and increase revenue for the Anyone App

We believe that adding an advisor introduction video will make it easier for users to connect with advisors and build trust, resulting in more calls made and more revenue for the business.

4. Prototyping

✏️ Rapid Sketching

Following the creation of my hypothesis I rapidly sketched solutions in lo fidelity.

This helped me quickly map and understand the current product and consider options for how I could iterate directly in the product.

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Wireframes

I used a neutral color palette to avoid any decision bias and used this prototype to get internal feedback on user flow.

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Lo fidelity clickable prototype

Styles & Components

The low fidelity prototype helped me recognize frustrations with the experience that I improved at the high fidelity stage.

To create the high fidelity prototype I inspected the products style and followed the 8pt rule to effectively and easily create a prototype that was consistent with the product styling.

Before creating the prototype I defined styles and components to help me design consistently and quickly.

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High Fidelity Prototype

Below are the screens and final version of the prototype that I created. I included interactions and transitions in the prototype to match the product's flow.

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5. Testing

User Testing

With the high fidelity prototype created, I formed a testing script with scenarios and tasks for the user to complete in order to validate the prototype with real users.

I tested the prototype with users and gathered feedback following every task using Maze.

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After analyzing the results, the test achieved a 4.4/5 response for an intuitive experience and 100% of test users who answered the final question felt the Advisor into videos would be helpful when choosing an advisor.

Yet, while users value the video feature, many expressed their frustrations with the user flow from the video screen to actually making a call. Thus, the flow could use improved clarity, leaving a lot of space for the feature to be improved further.

I also identified issues with the unmoderated testing format and made notes on how to make future user tests more articulate. I learned that some of the friction during test was not the product, but rather the testing tool combined with user expectations for the testing environment.

Outcome

My team and I successfully delivered on user outcomes and business objectives by reducing social barriers, generating more calls, and increasing user confidence when paying for advice.

The Design Thinking methodology kept the focus on the user, beginning with user research and ending with user testing and validation of the hi fidelity prototype.