The Anyone App is a networking and advice tool where people pay for a short call with experienced advisors in virtually any field.
I was given a directive to design for increased user calls which is how the company produces revenue. I used the Design Thinking Methodology to research and design a human-centered solution.
Table of Contents
Hypothesis and ideation
Design and prototype
To help empathize with users and frame any problems with the existing product, we began by forming some preliminary observations and hypotheses about the app experience after using it for a few days.
To weigh our observations, we created a user survey to uncover the core user problems that we could prioritize for both user and business needs.
We shared our survey with Anyone users and then synthesized the data to define trends and formulate our hypothesis.
After filtering the quantitative data, we transferred all open-ended questions into Miro to made user trends easier to group and digest.
Identified user and business problems
We defined trending social barriers around user confidence, trust, and advisor credibility. This validated our preliminary observation that users lacked the personal touch points needed to feel a connection with the advisor and provide a strong buying confidence.
Users find it difficult to gauge and explore advisors who they do not already know or trust. As result, users do not feel confident about paying for advice.
The less confidence users experience, the less likely they are to pay for a call and generate revenue for the business.
Hypothesis and ideation
We utilized a series of ideation techniques which allowed us to consider an array of possible solutions. We ideated using the Mind-mapping technique, crazy ideas, and what could be improved and added before prioritizing our ideas based on user value, business value, effort, and time.
Crazy ideas exercise
After evaluating our design options, I settled on designing a new video feature to introduce new advisors and build personal connection and credibility through vocalics and body language. The video feature allows users to hear the advisors voice, gauge how they communicate, and build a personal connection before placing a call.
I rapidly sketched experiences in low fidelity to help me quickly consider options for how I could iterate directly in the existing product.
Low fidelity user flows
Then, I wireframed my best design idea in low fidelity to get a rough idea of how the feature would fit into existing user flows and impact interaction designs.
Styles and components in Figma
Finally, I went ahead and built out the design with new and existing design component that fit seamlessly into the product design system.
High fidelity design
I built out the high fidelity experience using components and autolayout.
With the high fidelity prototype created, I formed a testing script with scenarios and tasks for test users to complete in order to validate the prototype designs. I tested the prototype with users and gathered feedback following every task using Maze.
While a few users expressed frustrations with the flow from the video screen to actually making a call, the majority of testers signaled that they would find the feature valuable in their decision to choose and call an advisor.
The video feature allows users to hear the advisors voice, gauge how they communicate, and build a connection before placing a call. The connection can build trust that reduces social barriers which means users feel comfortable making more paid calls and increasing revenue for the business.
Copyright Josh Meyers 2023