iMessage

Adding a feature to iMessage’s search functionality

Project type: Add a feature

Role: UX & UI Designer and Researcher

Tools: Figma, Zoom

Duration: 4 weeks

Background

While iMessage's seamless Apple integration makes it the default U.S. messaging choice, its search capability has remained a notable limitation, especially compared to alternatives like WhatsApp or Telegram. This project set out to bridge that gap: transforming iMessage search from basic to powerful.

As a frequent iMessage user, I’ve often struggled with its limited search functionality. Scrolling endlessly to find old messages, while other apps like WhatsApp make retrieval effortless. This led me to a hypothesis:

Were other users equally frustrated, or was this just my personal pain point?

Double Diamond Framework

To validate this hypothesis and design a solution that truly addressed user needs, I followed the Double Diamond framework, structuring my process to move from broad research to targeted solutions while maintaining rigorous user focus at every stage.

3. Develop

  • Created low-fidelity wireframes to explore new functionalities.

  • Conducted usability testing on early concepts to gather feedback.

  • Iterated on designs based on user insights.

4. Deliver (Finalization & Handoff)

  • Developed high-fidelity wireframes with refined UI and interactions.

  • Ran additional usability testing to validate improvements.

  • Finalized designs based on feedback and prepared for development.

1. Discover

  • Conducted user interviews to validate my hypothesis and understand pain points

  • Performed competitive analysis to identify best practices in the market

  • Synthesized findings to uncover key user needs and challenges.

2. Define

  • Developed user personas based on research insights.

  • Prioritized features through impact vs. effort analysis

This structured approach led me to start with user interviews and inquire about their experience with iMessage and its search functionality

User Interviews

To understand user frustrations with iMessage's current search functionality and identify opportunities to make message retrieval significantly easier and more intuitive. This research aimed to:

  • Uncover pain points in users' existing search workflows

  • Discover how search limitations impact their messaging experience

  • Gather expectations based on users' experiences with other communication platforms

Method:

  • Conducted 5 moderated 1:1 interviews with active iMessage users

  • Focused discussion on:

    • Typical scenarios when searching for past messages

    • Emotional responses to successful/unsuccessful searches

    • Workarounds developed to compensate for search limitations

Key Insights:

  • Fragmented discovery: Most struggled to find messages without exact keywords, resorting to endless scrolling.

  • Feature envy: Non-US users missed WhatsApp-style search (in-chat, pinning), while US loyalists valued simplicity over power.

  • Universal ask: All wanted effortless retrieval that maintained iMessage's clean aesthetic.

Research Synthesis

User Behaviors & Pain Points

  • Search purposes

  • Keyword dependency:

    • Users struggle when exact keywords aren’t remembered

    • No partial-match or semantic search support

iMessage vs. Competitors

  • Non-US users (4/5):

    • Prefer WhatsApp’s advanced features (in-chat search, filters, pinning)

    • Perceive iMessage as "limiting" for complex searches

  • US user (1/5):

    • Satisfied with iMessage’s simplicity but acknowledged gaps (e.g., no date filters)

Critical Frustrations

  • Global search limitation:

    • Cannot search within individual chats

    • Forces excessive scrolling

  • Media retrieval issues:

    • Photos/files often "disappear" from results

  • Lack of visibility:

    • Search suggestions/categories are poorly surfaced

With my hypothesis confirmed that iMessage's search frustrations were widespread, I did a competitive analysis to understand the best practices for providing a good search solutions for users

Competitive Analysis

WhatsApp (Market Leader)

  • In-conversation search with message previews

  • Media-specific filters (photos, links, docs)

  • Date-based filtering (+ "shared media" view)

  • Pinned messages with quick access

The audit of the leading messaging platforms exposed iMessage as an outlier in search capabilities:

Core Limitations:

  • Only basic global keyword search

  • No context-aware results

  • Media/files often excluded from results

  • Zero organizational tools (pinning, labeling)

Competitor Advantages:

Telegram (Power User Favorite)

  • Semantic search (finds related terms/concepts)

  • Message categorization (personal vs. group)

  • Saved Messages "cloud" with folders

  • Advanced operators (from:, to:, before:)

Slack (Enterprise Benchmark)

  • Natural language processing

  • Cross-channel search

  • Custom reaction-based tagging

  • Search history/saved queries

Strategic Insights:

  1. The Basic-to-Power Spectrum
    iMessage sat at the far "basic" end while competitors offered tiered approaches - from simple searches to power tools.

  2. The Organization Deficit
    Every audited app except iMessage provided message preservation tools (pinning, starring, saving).

  3. Platform Paradox
    While Apple leads in device integration, it trails in message retrieval - creating friction in their "seamless ecosystem" promise.

Moving into the Define phase, I used the user interview findings to create a primary persona. This persona captured the main frustrations and behaviors around message search, providing a clear focus for design decisions.

Persona

These insights led me into “Busy Alex”

A persona who embodied the users' core struggles and motivations. Her story became our decision filter: every design decision had to prove it would simplify Alex’s chaotic day, not add complexity.

With our persona defining the core user needs, I turned to competitive analysis to identify potential solutions. The research revealed numerous possible features, too many to implement at once. To focus the efforts, I used a impact-effort matrix to prioritize enhancements that would deliver maximum impact with feasible development costs.

Impact / Effort matrix

Through our Value/Effort matrix, we identified three implementation tiers:

1️⃣ Immediate Implementation (High Value/Low Effort)

  • In-chat search with message previews

  • Advanced filters (media type, date ranges)

  • Auto-suggestions (predictive search terms)

  • Message pinning/saving functionality

  • Improved media search

2️⃣ Future Roadmap (High Value/High Effort)

  • Voice search integration

  • Cross-app search integration

3️⃣ Lower Priority (Specialized Use Cases)

  • AI-generated search predictions

After prioritizing key features, I mapped iMessage’s core user flows to ensure my solutions felt native to existing interactions, identifying exactly where and how search enhancements would integrate without disrupting users’ mental models.

User Flow

With our persona established, key features prioritized, and user flows mapped, I transitioned into the Develop phase. Starting with low-fidelity wireframes, I began translating these research-backed requirements into tangible design concepts, focusing first on the core search functionality improvements identified as highest priority.

Low Fidelity Wireframes

I started sketching low-fi wireframes with the objective to test if the in-chat search, filters, and suggestions actually make message retrieval faster and less frustrating. These rough prototypes would help me validate the core concepts before polishing the design.

Starring / saving messages

Advanced search filters, improved media search and auto suggestions

In-chat search

Low Fi usability testing

Goal: to validate whether the new search functionalities (in-chat search, auto-suggestions, advanced filters, and starring messages) are intuitive, useful, and improve the user experience compared to the current iMessage search while still making sense for the Apple ecosystem.

Participants: 6 iPhone users

Format: remote & in person moderated sessions

✅ Successes

  • 100% adoption of starring (via long-press) and basic search

  • "In-chat search is way better than global search!" (5/6 users)

  • Auto-suggestions showed potential (67% usage) when predictions were accurate

Key Findings

⚠️ Pain Points

  • Filter invisibility: Only 1 user noticed them (17% usage)

  • Saved messages ambiguity: "Where do my starred messages go?"

  • Context gaps: Heavy iMessage users missed in-chat search

With low-fi prototypes validated, I refined the designs into high-fidelity wireframes, incorporating test feedback to polish interactions leading to the Deliver stage for final implementation.

Hi Fi Wireframes

In-Chat search

Advanced search filters, improved media search and auto suggestions

Saving messages

With high-fi prototypes now matching Apple's aesthetics, I tested to validate both functionality and seamless integration with iMessage's ecosystem

Hi Fi Wireframes Testing

Participants: 5 iPhone users

Format: remote & in person moderated sessions

Form this user tests, I found some interesting insights and opportunities for improvement

✅ Successes (High Intuitiveness & Adoption)

  • Basic Search – Fastest (18s) and most intuitive feature (9.8/10 ease).

  • Saving Messages – 100% completion rate (8.2/10), though retrieval needs refinement.

  • Recent Searches – Instant recognition (2s, 8.6/10), but users wanted expiration clarity and a "Clear All" option.

⚠️ Critical Fixes (Low Completion + Confusion)

  • Media Search – 40% completion (6.7/10); "Profile-first" UI clashed with user expectations.

  • Keyword Search – 40% completion (6.3/10); users misunderstood scope (e.g., expected cross-chat results).

  • Saved Messages Retrieval – Saving worked flawlessly, but locating starred messages felt unintuitive.

🔄 Enhancements (High Scores with Minor Gaps)

  • Filters – Highly intuitive (9.8/10); allow multiple filters at once to boost efficiency.

  • Recent Searches – Add duration labels and management controls (e.g., manual deletion).

Next Steps: Prioritize redesigning media/keyword search flows and clarifying saved-message storage, while preserving strengths like basic search and filters.

The feedback helped identify areas for improvement and guided the next round of iterations to further refine the user experience.

High fi wireframe Iterations

In chat search

User testing confirmed the intuitiveness of in-chat search, so I focused on refining minor UI details to better match Apple’s ecosystem standards.

Advanced search filters, improved media search and auto suggestions

Saved/starred messages

Testing revealed that housing saved messages in the user profile created discoverability issues. To align with iOS conventions while improving accessibility, I relocated starred messages to the '+' menu - mirroring Apple's placement of frequently used actions while maintaining visual consistency with the iMessage interface.

Key Filter Enhancements:

  1. Proactive Visibility - Filters now appear before typing begins, eliminating the need to start searching first

  2. Multi-Filter Flexibility - Users can now combine multiple filters simultaneously, with:

    • One-tap deselection

    • Enhanced color contrast for better visibility

  3. Recent Search Management - Added a clear option to remove recent searches, giving users full control over their history"

What’s Next

While our research validated core improvements, with more time and resources I would:

  1. Expand competitive benchmarking → Analyze indirect competitors handling complex search at scale

  2. Scale validation quantitatively → Conduct 100+ user surveys across key markets to statistically verify patterns in the findings

  3. Push innovation → Explore advanced features like AI-powered search suggestions

  4. Stress-test robustness → Simulate extreme edge cases (50+ participant groups, year-old message retrieval)

Reflection