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. Since it is still an ongoing project, the insights and recommendations below are based on mock data created for illustrative purposes.

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.

Note: This is an ongoing project. Mock data and findings are presented below for illustrative purposes only. The app is anonymised.

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 as a treat or to avoid decision-making process when tired indicating emotional as well as functional motivations.
A few participants highlighted the social aspect, such as ordering with friends or partners as part of a shared experience.

Decision-Making & Comparison Behaviour

Around 70% of participants reported using two or more apps to compare prices, delivery fees, and available restaurants before ordering.
Users typically reported starting with their preferred app (based on habit or usual availability of their favorite restaurants) but switched when prices or promotions differed.
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) across apps.
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”).
Participants wished for a single app to compare everything in one place, echoing the projects’ core value proposition.

Context of Use

Most orders occurr evenings between 7–9 PM, often at home while multitasking (watching TV, resting, gaming, etc.).
During work-from-home days, users order lunch more often but with simpler, faster criteria (“just something nearby and quick”).
Decisions were often shared: “We scroll together and decide as a couple” or “I ask my flatmate which app has better restaurtants.”

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 active promotions.
The search bar was easily noticed, but the category filters were less visible, especially on mobile screens.
Some participants ignored banners entirely, assuming they were ads.

Task 2 – Search and Filter for Restaurants

Most users began typing specific food items (e.g., “sushi,” “pizza”) rather than browsing categories.
Filters were available but underused; participants mentioned too many filter options or unclear labels.
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.
The star ratings were useful but lacked clear context (number of reviews or recency).
Users often jumped back and forth between restaurant pages, suggesting a need for side-by-side comparison tools.

Task 4 – Place an Order

The checkout flow was generally smooth, but users were surprised by added delivery or service fees at the end.
One common issue: users wanted to customize items (add/remove ingredients) and found the process inconsistent across restaurants.
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.
Comparing options remained the biggest pain point — “I wish I could see all delivery times and total prices side by side.”
Several participants said they would welcome a meta-search tool to reduce effort and decision fatigue.

Actionable Recommendations

From Interviews

Design for quick, cross-platform comparisons — Enable users to view prices, delivery times, and restaurant availability across multiple apps.
Simplify data entry — Offer universal logins or autofill options for address and payment data.
Integrate context-driven features — Suggest options based on time of day or occasion (e.g., “Quick weekday lunch,” “Weekend treat”).

From Usability Tests

Highlight key information upfront — Ensure delivery fees, total price, and delivery times are visible early in the browsing flow.
Streamline navigation — Allow users to toggle between restaurants without losing their search context.
Revisit filter and sort design — Simplify filters and make sorting options (e.g., “fastest,” “cheapest”) more prominent.
Promote trust and clarity — Display transparent pricing and consistent item customization options.

Role & Skills Demonstrated

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