Search & Discovery

Search is often the primary way users find what they need. When search works well, users accomplish their goals quickly. When it fails, they leave frustrated—or worse, assume the content they’re looking for doesn’t exist. In 2025, search isn’t just a filter—it’s a discovery engine, a conversion booster, and a personalization tool.

Understanding how people actually search—their imperfect queries, their scanning behaviors, their frustration thresholds—helps you design search experiences that serve real human behavior rather than ideal scenarios.

How users actually search

Search behavior patterns

Users don’t search the way designers expect:

Query characteristics:

Queries are often short (1-3 words)
Over 30% of searches include typos
Users don’t know the “right” terminology
Queries are vague and exploratory
Users try multiple variations when results disappoint

Scanning behavior:

Users scan results quickly, not thoroughly
First few results receive most attention
Relevance judgment happens in milliseconds
Users often click the first reasonable-looking result

Search refinement:

Users refine searches when results don’t match expectations
High refinement rates indicate search quality problems
Some users give up rather than refine
Refinement patterns reveal user intent

Mental models of search

Users bring expectations from major search engines:

What users expect:

Typo tolerance (Google handles misspellings)
Synonyms and related terms
Instant suggestions as they type
Relevance-ranked results
Ability to filter/narrow results

What disappoints users:

Exact-match-only search
No results for minor typos
Generic results for specific queries
No way to narrow large result sets
Slow search performance

Autocomplete and suggestions

Why autocomplete matters

Autocomplete isn’t a luxury—it’s expected. 65% of users are more likely to use search that offers autocomplete (Google research).

Benefits of autocomplete:

Reduces typing effort
Corrects spelling before submission
Guides users toward findable content
Reveals system capabilities
Surfaces popular queries

Optimal suggestion count

5-8 suggestions is the ideal range:

Enough to be helpful
Not overwhelming to scan
Fits typical viewport without scrolling
Accommodates variety without decision paralysis

Avoid:

Too few suggestions (misses opportunity to help)
Too many suggestions (overwhelming, requires scrolling)
Inline scroll areas in autocomplete (causes interaction problems)

When to show suggestions

Timing matters for autocomplete effectiveness:

The problem: Some users always look at the keyboard while typing. If you display suggestions only after they finish typing, they may never notice the feature.

Best practice: Show suggestions after 1-2 characters, but design the interface so users who look up mid-typing will discover the feature.

Visual prominence: Make it obvious that autocomplete exists. Consider showing a prompt like “Start typing to see suggestions” before the first character.

A good autocomplete always supports keyboard navigation:

Up/Down arrows: Navigate through suggestions
Enter: Submit selected suggestion
Escape: Close suggestions without selecting
Tab: Can accept top suggestion (optional)

Visual requirements:

Selected suggestion must be clearly highlighted
Highlight must follow keyboard focus
Screen readers must announce selected suggestion (ARIA)

Enhanced autocomplete patterns

Go beyond simple keyword suggestions:

Tap-ahead suggestions (chips):

Display refinement chips below suggestions
Allow users to build queries incrementally
Similar to Google’s search suggestions
Reduces cognitive load

Category suggestions:

Show relevant categories alongside keywords
Help users narrow scope quickly
Example: “shoes” suggests “Women’s Shoes,” “Running Shoes”

Product/content suggestions:

Show actual items with thumbnails
Include availability, pricing, ratings
Reduces steps to conversion
Best for e-commerce

Query completion vs. query prediction:

Completion: Finish what user started typing
Prediction: Suggest what user might mean
Both are valuable; don’t conflate them

Avoiding autocomplete pitfalls

Don’t scroll: At IKEA, all query suggestions are visible without scrolling. Scrollbars in autocomplete cause interaction problems—let the dropdown expand naturally.

Don’t hide behind typing: Users who type while looking at keyboard may miss suggestions. Provide secondary discovery mechanisms.

Don’t show irrelevant suggestions: Suggestions should be contextually relevant, not just alphabetically matched.

Don’t forget mobile: Touch interactions differ from keyboard. Suggestions need larger tap targets on mobile.

Search results design

Result presentation

Essential result elements:

Title: Clear, scannable headline
Snippet/description: Context showing why result matches
Match highlighting: Bolded query terms in results
Metadata: Category, date, rating, price as relevant
Thumbnail: Visual preview where appropriate

Relevance signals:

Order results by relevance, not alphabetically
Explain ranking when possible
Show match quality indicators
Surface related content

Match highlighting

Highlight query terms in results:

Helps users confirm relevance quickly
Shows where matches occur in content
Guides eye to relevant portions
Reduces need to read full snippets

Implementation:

Bold or color-highlight matched terms
Show snippet context around matches
Handle partial matches appropriately
Consider stemming (search → searched, searching)

Result counts and pagination

Show result counts:

“127 results for ‘wireless headphones’”
Helps users gauge scope
Informs whether to refine or browse

Pagination approaches:

Traditional pagination: Clear, accessible, works everywhere
Load more: Good for browsing, less cognitive load
Infinite scroll: Use cautiously—accessibility concerns, footer issues

Sorting options

Common sort options users expect:

Relevance: Default for keyword searches
Date: Newest/oldest first
Price: Low-to-high, high-to-low
Rating/popularity: Best-rated, most popular
Alphabetical: For directory-style browsing

Best practice: Remember user’s sort preference per session. Don’t reset to default after every action.

Facets let users narrow large result sets without knowing exact query terms.

User benefits:

Navigate without memorizing terminology
Discover what’s available
Progressively narrow scope
Understand product/content landscape

Show counts:

Display number of results per facet value
Example: “Running (47)” not just “Running”
Helps users predict result impact
Hide or disable facets with zero results

Order thoughtfully:

Most common values first (by count)
Or logical order (size: S, M, L, XL)
Or alphabetical for long lists
Consider user expectations per facet type

Multi-select vs. single-select:

Multi-select: User wants any of these (OR)
Single-select: User wants exactly this (filter)
Be clear about behavior
Consider both within same interface

Progressive disclosure:

Show most-used facets by default
Collapse or truncate less common ones
“Show more” for long facet lists
Don’t overwhelm with all options at once

Applied filters visibility

Show active filters clearly:

List all applied filters
Make each filter removable individually
Provide “Clear all” option
Update result count as filters change

Prevent dead ends:

Hide or disable combinations that yield zero results
Warn before applying filter that narrows too much
Offer to relax filters when results are few

Mobile filter considerations

Drawer pattern:

Filters in slide-out drawer
Keeps search results visible
Apply/clear buttons at bottom
Works well for many filters

Chips pattern:

Horizontal scrolling filter chips
Quick access to common filters
Good for few, simple filters
Combines with drawer for complex filtering

Zero-results handling

The problem with dead ends

“No results” shouldn’t mean no options, yet 50% of sites offer few paths forward from zero-result pages.

User impact:

Users often encounter zero results when content exists but doesn’t match their query
Dead-end pages cause frustration and abandonment
Users may assume content doesn’t exist when it does
Lost sales and engagement

Five strategies for zero-results pages

1. Explain clearly and politely:

“We couldn’t find anything matching ‘wireles headphnes’”
Don’t blame the user
Explain why (no matches vs. typo)
Suggest corrective actions

2. Offer spelling corrections:

“Did you mean ‘wireless headphones’?”
Maintain bank of common misspellings
Auto-correct obvious typos
Show original query for transparency

3. Relax filters automatically:

“No exact matches. Here are related results…”
Search individual keywords when phrase fails
Broaden category scope
Show partial matches with explanation

4. Suggest alternatives:

Related categories: “Try browsing ‘Audio’ or ‘Electronics’”
Popular searches: “Others searched for…”
Similar products/content
Different search terms

5. Personalized recommendations:

Based on browsing history
Based on similar users
“You might also like…”
Keep users engaged even when search fails

Zero-results page elements

Essential elements:

Clear message that no results were found
The original query displayed
Visible search box for retry
Spelling suggestions if applicable
Alternative paths forward

Optional enhancements:

Friendly/empathetic tone
Help text for better searching
Contact support option
Popular or featured content
Humor (when appropriate for brand)

Humanized messaging

Don’t make users feel stupid:

❌ “Your search returned 0 results”
✅ “We couldn’t find anything for ‘xyz’ – let’s try something else”

Show you care:

Empathetic language reduces frustration
Acknowledge the situation
Offer concrete help
Disney uses humor to ease dead-end disappointment

Search accessibility

ARIA patterns for search

Combobox pattern (for autocomplete):

<div role="combobox" aria-expanded="true" aria-haspopup="listbox">
  <input type="text" aria-autocomplete="list" aria-controls="suggestions">
</div>
<ul id="suggestions" role="listbox">
  <li role="option" aria-selected="false">Suggestion 1</li>
  <li role="option" aria-selected="true">Suggestion 2</li>
</ul>

Key requirements:

role="combobox" on container
aria-expanded indicates dropdown state
aria-autocomplete signals suggestion behavior
role="listbox" and role="option" for suggestions
aria-selected for current selection
Live region announcements for dynamic updates

Full keyboard operability:

Focus visible on all interactive elements
Tab order logical and complete
Arrow keys work in suggestion lists
Enter/Escape have predictable behaviors
No keyboard traps

Announce dynamically:

Number of suggestions available
Currently selected suggestion
Search results count
Filter application results

Example announcements:

“5 suggestions available”
“Wireless headphones, 1 of 5”
“127 results found”
“Filtered to Electronics: 34 results”

Voice search integration

The rise of voice search

Voice search is becoming standard: by 2024, over 50% of online searches were predicted to be voice-based.

Design considerations:

Natural language query handling
Longer, conversational queries
Intent recognition over keyword matching
Error tolerance for speech recognition mistakes

Supporting voice queries

Microphone input:

Visible microphone icon in search box
Clear activation state
Real-time transcription display
Easy cancellation

Query processing:

Handle natural language (“show me red shoes under fifty dollars”)
Extract intent and parameters
Confirm interpretation with user
Graceful fallback to text results

Search analytics and improvement

Key metrics to track

Query metrics:

Search volume: How often users search
Unique queries: Variety of search terms
Query length: Average words per query
Null result rate: Searches with zero results

Behavior metrics:

Refinement rate: Users who retry/modify queries
Click-through rate: Clicks on results
Time to first click: Speed of finding relevant result
Search exit rate: Users who leave after searching

Conversion metrics:

Searches leading to desired actions
Revenue/value per search
Search vs. browse conversion comparison

Using analytics for improvement

High refinement rate:

Indicates poor initial results
Suggests need for better matching/ranking
May indicate autocomplete gaps

High null result rate:

Content gaps or vocabulary mismatch
Need better synonyms/typo handling
Consider content additions

Popular queries:

Ensure top queries have excellent results
Prioritize content/product additions
Use for autocomplete suggestions

Recent Research (2024-2025)

Search UX Evolution

According to Design Monks’ 2025 guide, search boxes have evolved from simple filters to product discovery engines and personalization goldmines. The 2023-2025 period saw generative AI integration where intent matters more than exact input—search now guesses what users want before they finish typing.

Autocomplete Best Practices

Baymard Institute research found that only 19% of sites implement all 9 essential autocomplete UX design patterns correctly. Key findings include avoiding inline scroll areas, showing 5-8 suggestions, and supporting full keyboard navigation.

AI-Enhanced Search

Gartner reports that by 2025, 75% of organizations will use AI-based solutions to enhance customer experiences, including search. Reinforcement learning helps adapt search to changing trends and user behaviors.

Zero-Results Page Impact

Baymard’s research on no-results pages found that 50% of sites offer few paths forward from zero-result pages, creating unnecessary dead ends. Their 5 proven strategies include offering alternatives, relaxing filters, and providing personalized recommendations.

Error Tolerance Requirements

Research indicates that over 30% of searches include typos, demonstrating the critical need for error-tolerant search systems. Sites without typo handling lose users who could otherwise find what they need.

Voice Search Growth

Multiple studies confirm that voice search exceeded 50% of online searches by 2024, making voice input support increasingly essential for comprehensive search UX.

Medium research on search box anatomy emphasizes that search is a high-intent interaction—users who search are actively looking to accomplish something. Optimizing search directly impacts conversion rates.

Implementation checklist

Search feature audit

Autocomplete: 5-8 suggestions, keyboard navigable, screen reader accessible
Typo tolerance: Common misspellings handled, “did you mean” suggestions
Match highlighting: Query terms highlighted in results
Result counts: Shows total matches, updates with filters
Facets/filters: Available for large result sets, shows counts per value
Zero results: Helpful message, alternatives offered, search box visible
Mobile optimization: Touch-friendly, appropriate keyboard, voice input
Performance: Fast initial results, suggestions appear quickly
Analytics: Tracking queries, refinements, null rates, conversions

References

Foundational Work:

Baymard Institute — E-commerce search UX research
Nielsen Norman Group — Search usability studies
ARIA Authoring Practices — Combobox pattern

Recent Research:

Master Search UX in 2025 — Design Monks
9 UX Best Practices for Autocomplete Suggestions — Baymard Institute
5 Proven UX Strategies For “No Results” Pages — Baymard Institute
The Anatomy of a Perfect Search Box — Medium (2025)

Practical Resources:

Search UX Best Practices — Pencil & Paper
Five Simple Steps For Better Autocomplete UX — Smart Interface Design Patterns
Search Results Page Design: UI/UX Best Practices — Halo Lab
Search bar UI: Best practices — LogRocket
Empty State UX Examples — Pencil & Paper
3 Guidelines for Search Engine “No Results” Pages — NN/g

Standards:

ARIA Combobox Pattern — W3C