Maggie Chen
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HoneyComb.ai

App Usability Testing on Honeycomb.ai

HoneyComb.ai is a mobile application that helps people with food allergies or dietary restrictions to discover foods and restaurants nearby. The study aims to help the company locate usability issues to improve the user experience.

HoneyComb.ai has released an update on 2020/05/07 based on the result of usability testing.

Context: One month design evaluation report
Project Type: Academic
Time: Winter 2019
Role: Project Management, UX Research, Data Collect, Data Analysis
Tools: Heuristic Evaluation, Interviews, Observations, Questionnaires, System Usability Scale
Team: Maggie Chan, Keefe Liew, Cindy Kuo

Process

The study was done with 15 participants with various food allergies and dietary restrictions between the age group of 18-24 and 25-34. The selected participants have experienced searching restaurants or foods on apps like Google Maps, Yelp, Zomato, DoorDash Uber Eats, and SkipTheDishes.

The study began with our participants filling up the consent form and pre-questionnaire. Then we assigned the participants to complete some tasks by using the app and recording the interaction. We encouraged participants to speak out their thoughts while navigating through the app. Last, we played back the record and asked participants to walk us through the challenge they had while completing the tasks and filled up the post-questionnaire for feedback.

Findings

Our team gathered these findings from the user interviews and questionaries.

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Recommendations

The average System Usability Scale(SUS) score from our 15 participants was 65.16. A raw SUS score of 65.16 converts to a percentile rank of 40%, which can be interpreted as a grade of a D, which we would recommend the software fixing some usability issues on the further update.

We would suggest:

  • Provide an onboarding process since the navigation can confuse users who have not used similar apps before.

  • Label and remove repeated buttons to simplify the user flow.

  • Create interaction when receiving users' actions to avoid confusion on wheater the system has interpreted the order.

  • Resize or highlight the interactive elements to lead users to click on the items.

  • Provide immediate feedback on restaurants or dishes that are unavailable at the moment.

Reflection

HoneyComb.ai is my first User Research project, and I discover my interest in UX research through the study. The biggest challenge was finding enough participants within a short time frame and setting up appointments with each participant to gather the results. With our research, HoneyComb.ai gains more understanding of their audience then considers revising the design.