2.1

Research Strategy & Planning

I designed the research plan independently, aligning business exploration with user-centered discovery.

Initial activities included:

• - desk research to understand market growth and industry behavior
• - feature benchmarking across major delivery platforms
• - defining scope, timelines, and budget requirements

Industry analysis revealed:

• - global valuation growth from ~$107B (2019) to projected ~$182B (2024)
• - strong market leadership across China, the US, and India.

This groundwork allowed the research to move beyond isolated usability questions into strategic market exploration.

2.2

Method Selection — Why These Methods

A mixed-method approach was intentionally selected to combine behavioral depth with quantitative validation.

User Interviews + Experience Mapping

Chosen to uncover:

• - emotional drivers
• - perceived convenience
• - moments of friction during real purchase flows.

Execution included:

• - interview script creation
• - participant recruitment via Respondent.io
• - incentive coordination
• - live journey mapping during sessions.

Kano Analysis (Quantitative Prioritization)

To complement interviews, I designed an unmoderated Kano survey using Pollfish.

• 100 participants completed the survey.
• Users evaluated eight key features from positive and negative perspectives.

Features evaluated included:

• - Loyalty rewards programs
• - Real-time delivery communication
• - Scheduled orders
• - Reviews & ratings
• - Profile customization (diet preferences, etc.)
• - Chatbot customer service
• - Wishlists/favorites
• - Assisted ordering via AI systems (Alexa, Siri, Google).

This method allowed classification of features based on perceived value and expectation levels.

2.3

Insight Synthesis — Rainbow Analysis

Since no process existed, I designed a structured framework defining:

• - required sessions
• - stakeholder roles
• - documentation flow
• - design deliverables
• - PRD evolution

The framework transformed migration from reactive work into a repeatable system.

2.4

Strategic Mediation

To analyze qualitative data:

• - interviews were transcribed and processed in Dovetail
• - a Rainbow Analysis framework was used to identify patterns across themes and behaviors.

This process surfaced consistent visual patterns around:

• - emotional reactions
• - trust signals
• - purchase friction.
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• The project also introduced Dovetail App as a shared research repository for the team — later adopted as an internal learning resource.

2.5

Kano Matrix Results

Performance Features (Users actively expect and value these.)

• - Loyalty / rewards programs
• - Reviews and ratings
• - Chatbot customer service.

These features followed a linear expectation model — more refinement directly increased satisfaction.

Attractive Features (Not expected but highly valued.)

• - Real-time communication with delivery person
• - Scheduled orders
• - Wishlists and favorites
• - Profile customization
• - Assisted ordering via voice/AI systems.

These represented opportunity areas for differentiation.

2.6

Strategic Insights

Three major insights emerged:

1 — The importance of speed and convenience

Users highly valued quick flows and showed excitement around real-time progress visibility during preparation and delivery.

2 — Price is the biggest pain point

Unexpected fees at checkout generated strong frustration, revealing a transparency gap in pricing communication.

3 — Trust is built through communication

80% of participants reported higher satisfaction when:

• tracking their orders
• communicating directly with delivery personnel.

Trust and visibility proved to be critical emotional drivers.

2.7

Ideation Phase — ELITO Method

After synthesis, I facilitated collaborative ideation sessions using the ELITO framework.

The goal was not to design a final product but to:

• explore opportunity spaces
• generate potential feature directions aligned with insights.