Project
Company
Timeline
📋Problem Statement
While DoorDash offers a variety of food options, users with dietary restrictions often find it challenging to identify meals that meet their needs. This lack of personalization can lead to frustration and reduced customer retention.
🎯Goal
The goal was to design and implement a feature on DoorDash that would enable users to filter meals based on dietary restrictions, such as allergies or fitness goals. This feature would simplify meal selection for health-conscious users and those with dietary needs.
🔎User Research (Part 1)
The user research was conducted in two parts:
User Persona
Pain Points
Conducted one-on-one interviews with fitness enthusiasts, and users with medical dietary needs to gain qualitative insights. Here’s what I found out:
Lack of Personalization:-
Users with specific needs (e.g., gluten- and dairy-free) found manually sifting through menu items tedious.
45% of respondents reported frustration at being unable to save their dietary preferences for future orders.
Time-Consuming Experience:-
Users spent excessive time searching for suitable meals, often abandoning orders mid-process.
Trust Issues with Information Accuracy:-
50% of interviewed users were concerned about the reliability of allergen and ingredient information provided by restaurants.
Limited Filters:-
Existing filters (e.g., vegetarian) were too broad and didn’t cater to niche dietary requirements, such as keto or low-carb diets.
📑Wireframe
🎨Visuals
The research had two design versions for filtering dietary preferences:
Design 1:
Design 1 (Fig 1) featured two filter options on the home page to enhance accessibility and discoverability. One filter was prominently placed next to the search bar for quick access, improving proximity and reducing decision-making effort, aligning with Hick’s Law. The second option was integrated within predefined filters on DoorDash, adhering to Jakob’s Law, which leverages users’ familiarity with common design patterns for ease of use.
Design 2:
Design 2 (Fig 2) had a single, strategically placed filter option between the notifications and cart icons. This design choice was made based on Fitts’ Law, ensuring the filter was positioned within easy reach to minimize navigation effort and optimize user efficiency.
Both designs aimed to make the interface user-friendly, reducing cognitive load and guiding users toward efficient task completion.
🔎User Research (Part 2)
User Research Process
To understand the challenges faced by users with dietary restrictions and validate potential solutions, I conducted extensive user research using qualitative and quantitative methods. The research process involved three questionnaires: a pre-test and two post-tests.
After designing the dietary filter feature, I created interactive prototypes using Figma. Participants were tasked to use these prototypes to simulate real-world scenarios, such as finding a gluten-free meal.
A pre-test questionnaire was conducted to gather data on users’ behaviors, preferences, and challenges when navigating dietary options on food delivery platforms. After introducing the new design, post-test questionnaires were used to evaluate key metrics, including task completion time, ease of use, and user confidence in the system.
Quantitative Analysis:
Completion of time:-
As shown in Fig 3, the results show that the p-value is less than 0.005. Hence, the null hypothesis (H0A) is rejected, and the alternative hypothesis (H1A) is accepted. This indicates that there is a significant difference in completion time between Design 1 (Fig 1) and Design 2 (Fig 2).
Ease of use:-
The results, as shown in Fig 4, the p-value is less than 0.005. Hence, the null hypothesis (H0B) is rejected, and the alternative hypothesis (H1B) is accepted. This indicates that there is a significant difference in ease of use between Design 1 (Fig 1) and Design 2 (Fig 2).
Confidence:-
Since the p-value is more than 0.005 as mentioned in Fig. 5, the alternative hypothesis (H1C) is rejected, and the null hypothesis (H0C) is accepted. Hence, there is no significant difference in completion time between Design 1 (Fig 1) and Design 2 (Fig 2).
Qualitative Analysis:
Preference for filter option: Most participants preferred the filter option in Design 1 (Fig 1), finding it intuitive and helpful in completing their tasks efficiently.
Experiencing difficulties in completing the tasks: Most participants reported minimal to no difficulties completing the assigned tasks in Design 1 (Fig 1) especially, which means the design supports seamless interaction.
Intended design flow: In Design 1 (Fig 1), participants could navigate through the intended flow design without confusion, suggesting that the layout and structure align well with user expectations.
Outcomes
Quantitative Analysis
Task Completion Time: Participants using Design 1 completed their tasks faster than Design 2, with an average task completion time of 1 minute for Design 1.
Confidence Score: On a scale of 1 to 5, Design 1 received an average score of 4.
Ease of Use: On a scale of 1 to 5, Design 1 received an average score of 4. The key factors included clarity, ease of navigation, and reduced cognitive load.
Qualitative Analysis
Users mentioned the visual hierarchy in Design 1 made critical information more accessible.
Users displayed fewer errors and required less guidance while interacting with Design 1.
🎨Final Visuals
Thank you for reading my case study! I’d love to hear your thoughts!