Definition
Voice Search ASO refers to App Store Optimization strategies targeting voice-activated app discovery through virtual assistants: Apple's Siri Suggestions, Google Assistant, Amazon Alexa, and emerging voice search on app stores. Voice search is qualitatively different from text search: users speak longer, more conversational queries ("Show me a productivity app that helps with team collaboration" vs. typed "productivity app team"). Currently a small percentage of app discovery (~2-5% across platforms), voice search is growing rapidly (projected 15-20% by 2028) and requires different keyword and metadata strategies.
How It Works
Voice Search Mechanisms by Platform
Apple Siri Suggestions:
Siri Suggestions are AI-generated app recommendations displayed on iOS/iPadOS Spotlight search and Lock Screen:
- How it works: Siri analyzes your device usage patterns, app launch history, and time of day to suggest apps you're likely to open
- Voice activation: Users can say "Hey Siri, open [app name]" or "Hey Siri, show me a [category] app"
- Search exposure: Your app is eligible for Siri Suggestions based on whether users' behavior suggests they'd want your app (not directly optimizable, but metadata and recent engagement matter)
- Metadata role: Siri reads app title, subtitle, keyword field to understand app category; relevant metadata increases likelihood of suggestions
Optimization:
- Ensure app title/subtitle clearly describe the app's primary purpose (Siri uses this to categorize)
- Keep keywords focused (Siri filters apps with keyword-stuffed metadata)
- Drive engagement (users who keep your app on home screen are more likely to get Siri suggestions for it)
Google Assistant App Discovery:
Google Assistant can launch apps and execute queries via voice:
Explicit launch:
User: "Hey Google, open Spotify"
→ Google Assistant launches SpotifyQuery routing:
User: "Hey Google, find me a podcast about tech"
→ Google Assistant routes to podcast apps (based on category + usage data)Implicit suggestion:
User: "Hey Google, what's a good productivity app?"
→ Google Assistant suggests top-ranked productivity apps from Google PlayOptimization:
- Ensure app category is correct in Google Play (Google Assistant uses category to route voice queries)
- Keyword field should include category descriptors (e.g., "podcast", "productivity")
- High star rating and reviews influence recommendations
- App engagement signals matter (retention, DAU)
Amazon Alexa Voice Discovery:
Alexa is the most voice-search-friendly platform:
User: "Alexa, open Task Manager"
→ Alexa directly launches Task Manager app
User: "Alexa, find me a fitness app"
→ Alexa routes to Amazon Appstore fitness category, suggests appsOptimization:
- Register clear voice invocation name (e.g., "My Task Manager", "Quick Planner")
- Implement Alexa SDK for deeper integration (allowing Alexa intents within your app)
- Include conversational app description (people ask Alexa differently than typing)
- Leverage Feature Bullets with voice-friendly descriptors
Voice Query Characteristics vs. Text Search
Text Query (typed):
"task manager"
→ 2-3 words, functional keywordsVoice Query (spoken):
"I need an app to organize my tasks and share them with my team"
→ 15+ words, conversational, intent-focusedDifferences:
- Length: Voice queries 3-5x longer
- Phrasing: Natural language ("help me organize") vs. keywords ("task management")
- Questions: Voice queries often phrased as questions ("What's the best fitness app?" vs. typed "fitness app")
- Specificity: Voice queries often more specific ("Find me a meditation app for sleep" vs. "meditation")
- Hesitation: Voice includes filler words ("Um, I need, like, a productivity app" → recognized as "productivity app")
Natural Language Processing & Semantic Intent
Voice assistants use NLP (natural language processing) to extract intent from spoken queries:
Query: "I need an app to help me meditate before bed"
→ NLP extracts: intent=meditation, context=sleep/relaxation, use-case=nighttime
Assistant then matches against apps with:
- Category: meditation or health/wellness
- Keywords including: meditation, sleep, relaxation, mindfulness
- Reviews mentioning sleep/bedtime
- High engagement from users with sleep-tracking habits
Optimization implication: Include intent-related keywords in description and subtitle, not just functional descriptors.
Siri Shortcuts & Google Actions
Siri Shortcuts (Apple):
Advanced voice integration where apps create custom voice commands:
User: "Hey Siri, start my morning routine"
→ Triggers a custom shortcut that opens 3-5 apps in sequenceApp developers create shortcuts in the Shortcuts app:
- Users enable the shortcut, name it, assign it to Siri
- Voice activation then runs the shortcut
ASO Implication:
- Apps that support Shortcuts gain voice discovery through user-created shortcuts
- Promote Shortcuts capability in description and website
- Include "Shortcuts compatible" in keyword field (small but relevant audience)
Google Actions (Google):
Similarly, Google Actions are voice-activated automations:
User: "Hey Google, start my work routine"
→ Triggers Actions that open Gmail, Calendar, Todoist, etc.Apps can support Google Actions via Assistant SDK.
Current Voice Search Market Share
Apple Siri: ~10-15% of app discovery on iOS (includes Spotlight, Lock Screen, voice)
Google Assistant: ~5-8% of app discovery on Android
Amazon Alexa: ~15-20% of discovery on Fire devices (higher on smart home + Fire TV)
Overall: Voice is growing but still <5% of global app discovery (2025 estimate)
Growth trajectory: Projected to reach 15-20% by 2028 as voice becomes more natural and accurate.
Formulas & Metrics
Voice Search Optimization Priority Score:
Score = (Projected Voice Market Share × 0.40) +
(Ease of Voice Command × 0.30) +
(User Intent Match × 0.30)High score = prioritize voice search optimization.
Example: Fitness app
- Projected voice share for fitness: 20% by 2028 = 0.20 × 0.40 = 0.08
- Ease of voice: High (users say "Open Fitbit" easily) = 0.90 × 0.30 = 0.27
- Intent match: High (meditation/fitness app names match voice commands) = 0.85 × 0.30 = 0.26
- Total Score: 0.61 (prioritize voice optimization)
Voice Query Search Volume (emerging metric):
Current tools (Sensor Tower, App Annie) don't yet provide voice search volume separately from text. Emerging tool: voice-specific keyword research tools from companies like Verbit.
Best Practices
- Optimize for intent, not just keywords — voice queries express intent more clearly than text. Include intent-related terms in description (e.g., "helps you focus", "track your mood", "organize your team").
- Include question-format keywords — add phrases like "How do I", "What's the best", "Find me a" in your description to match question-phrased voice queries.
- Create voice invocation names (Alexa) — if targeting Amazon Alexa, register a clear, memorable voice name.
- Implement Shortcuts/Actions — if you have development resources, create Siri Shortcuts or Google Actions. It's a small voice discovery win today but growing.
- Test voice discovery — manually test with Siri, Google Assistant, Alexa to see if your app appears for natural language queries.
- Monitor growth — voice search is growing but still nascent. Don't over-invest now, but track growth trajectory.
- Avoid voice-unfriendly names — app names that are hard to pronounce or sound awkward when spoken (e.g., "2U2Me" vs. "To You To Me") perform worse in voice discovery.
Examples
App Title & Voice-Friendly Naming:
❌ Voice-unfriendly:
- "TK-93" (hard to say)
- "2Do" (users say "To Do" but app is "Two Do", confusing)
✅ Voice-friendly:
- "Todoist" (easy to pronounce, matches typed search)
- "Calm" (single word, memorable)
- "Slack" (single word, clear pronunciation)
Subtitle for Voice Intent:
Task management app:
❌ Text-optimized (generic):
Subtitle: "Organize Your Tasks"
Description: "Task management for teams and individuals..."✅ Voice-optimized (intent-focused):
Subtitle: "Organize Your Tasks & Collaborate with Teams"
Description: "Help your team stay on track. Get reminders, priorities, and real-time updates...help you collaborate without meetings...focus on what matters."When user says "Alexa, find me an app to help my team collaborate," the voice app (which indexes description text) now matches on "collaborate" + "team."
Dependencies
Influences (this term affects)
- Keyword Research — voice keyword research requires different methodology
- Subtitle — voice intent often captured here
- Full Description — voice queries match against full description text
Depends On (affected by)
- Voice assistant market penetration (grows with each platform update)
- App Store category accuracy
- Retention Rate — engagement signals influence Siri Suggestions
Platform Comparison
| Aspect | Apple Siri | Google Assistant | Amazon Alexa |
|---|---|---|---|
| Voice discovery share | 10-15% on iOS | 5-8% on Android | 15-20% on Fire devices |
| Primary mechanism | Siri Suggestions + typed voice | App routing + voice queries | Direct voice launch + routing |
| Metadata role | Title, subtitle, category | Title, category, reviews | Feature bullets, description |
| Custom integration | Siri Shortcuts | Google Actions | Alexa Skills Kit |
| Query type | Mostly implicit (suggestions) | Explicit + implicit | Mostly explicit |
| Optimization ease | Medium (algorithm-driven) | Medium (category-driven) | Easy (voice names optimizable) |
| Growth rate | Moderate (3% CAGR) | Fast (8% CAGR) | Fast (12% CAGR on Fire) |
Related Terms
Sources & Further Reading
- VoiceFirst.FM: Voice App Discovery Trends
- Voicebot.ai: Voice Commerce & App Discovery Report
- Apple: Siri Suggestions Best Practices
- Google: Assistant App Integration Guide
- Amazon: Alexa Skills Kit Documentation