Project involvement

Role

Lead Product Designer

Team

Growth

Overview

This was a fast-paced experiment to explore how we could improve conversion and retention by making it easier for habitual customers to reorder from their favourite restaurants. It was carried out as part of the Growth team, and I led the design work from problem framing through to MVP launch and iterations.

Problem

Frequent customers were ordering from the same restaurants multiple times a week, often ordering the same or similar items. However, placing those same orders still required going through the full menu each time. We suspected this was a missed opportunity to create a quicker, smoother path to checkout.

Goal

Validate whether an easier reordering mechanism could drive higher order frequency, conversion speed and retention.

Success metrics:

• Increase in order frequency
• Higher retention
• Growth in weekly active customers (WACs)

Design Process

Research

We had data that showed frequent users (ordering 4+ times per week) rarely ordered from more than 2–3 restaurants. I complemented this with informal qualitative research, speaking to colleagues and friends about their ordering habits. This uncovered two distinct patterns:

• Some people re-ordered the same meal, exactly as before.
• Others re-ordered a similar base meal, often adjusting for the number of people.

From this, we identified two jobs to be done:

1. When I want to reorder the exact same thing, I want it to be fast and seamless.
2. When I want to reorder something similar, I want to tweak my last order quickly.

We also identified known limitations around location, restaurant opening times, and dynamic menus. These were considered upfront so we could design a scoped MVP around them.

Ideation & Design

The initial MVP focussed on speed to test. I reused existing components to reduce engineering lift and get us to a testable prototype quickly. The UI lived within the open restaurant menu view and allowed customers to select items from their previous order, with “select all” as the default. We later inverted this logic to improve flow, defaulting to all items selected and allowing deselection instead.

Following strong early results, we iterated on the entry point. The table row pattern we initially used felt cluttered and inflexible. I redesigned it as a card-style carousel that sat neatly above the menu, providing more room for future reuse (like set menus).

A third planned iteration was scoped with the Core team. Their work on set menus built on this reorder mechanism but aimed for a smoother experience that wouldn’t rely on the user navigating through a full menu.

Collaboration

This project involved close collaboration with engineers and PMs in the Growth team, alongside occasional input from Data and Core teams. We worked in short, test-driven cycles and shared feedback asynchronously using Loom walkthroughs and Slack check-ins.

Solution

The Design

The final MVP introduced reorder suggestions directly on the restaurant menu, showing a user their previous order with an option to select all or edit. This gave users a shortcut to checkout while staying within familiar patterns. The follow-up carousel design made the feature more scalable and cleaner in the interface.

Key Features

• Quick access to previous orders directly in the menu view
• Default “select all” behaviour to minimise effort
• Space-efficient carousel layout to allow for multiple saved reorders

Design Rationale

By designing within current UI paradigms, we were able to move fast and test with confidence. Starting with the open menu view removed friction around availability and location logic. Iterating the entry point gave us a more future-proof design. We consistently anchored our decisions around the two primary user jobs.

Outcome & Impact

Results

• Nearly 60% of the target cohort used the feature
• Conversion rate exceeded 80% among those users
• Overall cohort conversion: ~32.3%
• Noticeable uptick in order frequency

Metrics

• Increased weekly order frequency for high-frequency customers
• Growth in feature engagement and completion
• Improved retention within the test cohort

Qualitative Feedback

The feedback was overwhelmingly positive. Users appreciated the convenience, and the data backed up the improved experience. Internal teams also saw potential for this UI pattern to support other use cases (like set menus).

Reflection

This project highlighted the power of fast, lean experimentation. By designing within constraints and using real behaviour data, we unlocked a high-impact feature with minimal engineering effort. If done again, I’d explore how to make the feature more discoverable across other surfaces, and better account for dynamic menus to reduce invalid order edge cases.