Research Plan: Grubble Project

This case study was completed as part of my User Research Certificate Program. I designed the full research plan, including the research questions, interview guide, and usability test script. The findings, insights and recommendations are presented in a final report.

Background

With an evolving food delivery landscape, users are more inclined towards platforms that allow them to compare and select from many options. Our company wants to develop a platform that aspires to become the "Kayak/Skyscanner for online food ordering" — the ultimate all-in-one search platform where users can seamlessly search across different food delivery services to compare prices, delivery times, etc., to find the best deals. It will aggregate multiple food delivery services such as Deliveroo, Uber Eats, etc.

We are in the discovery phase of this project. Before designing the platform, we first want to understand users of current food delivery apps - their goals, behaviours, context and experiences. We will conduct user research, and after completing this research, we will explore potential design solutions for the platform.

Research objective and questions

The objective of this research is to understand the goals, behaviours and context of people who currently use food delivery apps. It also aims to understand their experience of using these apps.

To explore and understand the goals, behaviours, usage context, and overall experience of people who currently use food delivery apps, in order to identify pain points, unmet needs, and opportunities for improving cross-platform food delivery search and comparison, we aim to answer these specific research questions (separated into different subgroups):

User Goals & Motivations: What motivates people to use food delivery apps? In what situations or contexts do users typically choose food delivery over other options (for example, cooking, dining out, etc.)?
Behaviours & Decision-Making: How do users typically choose what to order and from which platform to order? Do users compare multiple apps before they order? If yes, how? What drives the need to compare across platforms? What aspects do they typically compare (e.g. price, delivery time, restaurant availability, fees, ratings)?
Context of Use: When and where do people usually place food delivery orders (such as time of day, day of week, location)? What devices / environments are they typically using (mobile at home, desktop at work)? Are they alone or together with others while making food delivery decisions?
Experience & Pain Points: What type of usability issues do users experience in food delivery apps? What common problems arise while comparing restaurants, prices, or delivery options across different apps? Are there any repetitive or time-consuming tasks they would like to be simplified? Have users encountered problems or inconsistencies when comparing options across different apps?

Methodology

Five moderated research sessions combining short interviews and usability tests (remote).
Tasks focused on exploring, comparing, and ordering food via the App A.
Data gathered through note-taking, screen recordings, and think-aloud commentary.

Recruitment

Participant Profile

Aged 18–34.
Any gender.
Uses food delivery apps at least twice a month.
Already using mobile food delivery services and familiar with more than one app / platforms.
Living in an urban area where multiple food delivery platforms operate.

Target audience

Our target audience is 55.82% male and 44.18% female. The age distribution is shown in the table below:

Age	Distribution (%)
18-24	22.44
25-34	31.93
35-44	19.34
45-54	13.47
55-64	7.88
65+	4.94

Justifications

User Goals & Motivations: Research questions under this point will help understand why users turn to food delivery and when they prefer it over alternatives. They will provide foundational insight into user needs and drivers for food ordering. These questions will help identify emotional/situational triggers behind food ordering, which can be crucial for designing relevant features in our app, such as messaging or entry points to our comparison app.
Behaviours & Decision-Making: Since our app aims to make cross-platform comparison simple and effective, these questions explore whether users already attempt to compare options, how they do it, and what variables matter most. By understanding current behaviours, we can identify gaps and inefficiencies and explore which of these our app could solve. These also help validate the core concept of our product: there is a real and unmet need for comparison in this area.
Context of Use: Our design decisions should reflect real-world contexts in which users engage with food delivery in their lives. These questions will reveal when / where and in which social contexts these food delivery decisions are made. Understanding these factors will help us optimise user flow and features of our platform.
Experience & Pain Points: To design a better experience, we need to understand what is not working well now. These questions will reveal current user issues and flaws, especially related to app comparison and decision-making. This insight will help us find out key opportunity areas for differentiation, such as simplifying repetitive tasks.

Justification for the Participant Profile

Age: This age group (18–34 y.o.) makes up the majority of the target audience (54%+) and they are the most active demographic group in using mobile apps and digital services, including food delivery.
Gender: A similar gender distribution of our target audience allows us to include all genders equally.
Using food delivery apps at least twice a month: Using food delivery apps with this frequency means that they can recall recent experiences and provide detailed feedback. This group is more likely to engage in platform comparisons and make conscious decisions.
Familiar with more than one app / platform: Our product will rely on users who are familiar with multiple food delivery services so they can compare them. Users who use only one single platform will not be able to provide the comparison behaviour.
Living in urban areas with multiple delivery platforms: Urban areas have higher competition among delivery services, giving users access to various platforms. Therefore, participants in urban areas are more likely to have experience switching between apps.

You can find the usability test script here.

Findings

Click for Affinity diagram

Click to download Final report.

1. Interview Insights — Understanding Goals, Behaviours, and Context

Motivations & Goals

Most participants ordered food delivery for convenience and time-saving, especially after work or on weekends.
Several mentioned ordering when they don’t want to cook or to be practical, indicating emotional as well as functional motivations.
A participant highlighted the social aspect, such as ordering with friends/ partners as part of a shared experience.

Decision-Making & Comparison Behaviour

2-in-3 of participants reported using two or more apps to compare prices, delivery fees, and available restaurants before ordering. In-app comparisons were also reported when selecting dishes from multiple restaurants, and then comparing them in the basket.
Users typically reported starting with their preferred app (based on habit or usual availability of their favorite restaurants).
2-in-3 reported that they usually switch when prices or promotions differ.
They stated that the comparison is usually manual and time-consuming, requiring switching between apps and re-entering similar search terms.
Key factors influencing final decisions: delivery time, total cost, restaurant familiarity, and promotions.

Pain Points in Current Experience

Hidden fees or unclear delivery costs frustrated users late in the checkout process.
Users disliked duplicate data entry (address, card info), reporting sometimes the apps don’t save their personal information.
Lack of cross-platform consistency in restaurant availability caused confusion (“Some places are only on one app”, “Some places have discounts in one app but not in another”).
A participant reported a wish to be able to chat with a real person when encounter a problem instead of bots.
A participant reported that a single app comparing everything in one place would be convenient, echoing the projects’ core value proposition.

Context of Use

Most orders occur evenings in dinner time, often at home.
During work-from-home days, users order lunch more often but with simpler, faster criteria (“just something nearby and quick”), and order familiar dishes from the restaurants they already know.

2. Usability Test Insights — Observing Real Interactions (App A)

Task 1 – Explore the Homepage

Users appreciated App A’s clean visual layout but some struggled to locate personalized recommendations or they reported that the main page contains too much overloading information.
The search bar was easily noticed and used without issues, but the category filters were inefficient, there was not a “vegan” filter, and “vegetarian” filter also did not work properly.
Some participants ignored banners entirely, assuming they were ads or spams.

Task 2 – Search and Filter for Restaurants

Most users began browsing categories directly. Only one had to type specific food requirement (e.g. “vegan”) because they were not able to find it in filters.
Several participants said they expected sorting options (e.g., “fastest delivery,” “lowest price”) to be more prominent.

Task 3 – Explore Options

Participants found the restaurant cards visually appealing, but some complained that delivery times and fees weren’t immediately visible.
A participant recommended the “previous orders” page to be simplified with smaller pictures but including more useful information like availability and delivery time.
The price tags were useful but lacked clear context (what they mean relatively).
One participant mentioned an automatic translation would be quite useful.
Users often jumped back and forth between restaurant pages, suggesting a need for a side-by-side comparison tool. The basket comparison tool was quite efficient, but required further development.

Task 4 – Place an Order

The checkout flow was generally smooth, but users were surprised by added delivery or service fees at the end.
Navigation back to the basket sometimes led users to lose progress by emptying the cart, causing mild frustration.

Task 5 – Reflection on Experience

Participants found the overall process familiar but fragmented across apps.
Missing filters and comparing options remained the biggest pain points — “I wish I could see all delivery times and total prices side by side.”

Actionable Recommendations

Opportunity-1: Improve food discovery and comparison features to reduce friction, support decision-making, and increase user satisfaction.

Add clear, consistent icons/labels for dietary options (vegan, halal, lactose-free, gluten-free).
Allow users to filter directly for dietary preferences at the homepage and search level.
Provide optional automatic language/localization support for menu items.
Ensure repeat orders and current basket items are clearly visible across navigation.

Opportunity-2: Users want to compare multiple restaurants or items in the cart, but the app doesn’t clearly display pricing, delivery fees, or basket totals across selections.

Show full cost breakdown (item price + delivery fee + service fee) directly in the cart for all items.
Allow users to see and compare items from multiple restaurants before checkout.
Highlight differences between options (price, delivery time, rating) for easy decision-making.

Suggested Action Plan

Short-term: Fix labels, improve vegan and other filters, enhance basket visibility.
Medium-term: Introduce multi-restaurant cart comparison and explicit cost breakdown. Include automatic translation option.
Long-term: Consider personalization (preferences saved, recommended restaurants), localization and improved homepage organization to reduce cognitive load.

Validation: Conduct usability testing after changes to ensure the improvements actually reduce friction and support comparisons.

Role & Skills Demonstrated

Interview design and moderation · Usability testing · Thematic analysis · Insight synthesis · Translating findings into actionable design opportunities