Triggers are the brain of your automation rules – they determine which reviews get automatic responses and which ones don’t. Think of them as smart filters that evaluate every incoming review against your criteria.
The secret to great automations: Start specific, then expand. It’s easier to broaden a narrow rule than to fix an overly broad one that’s responding to the wrong reviews.
Trigger conditions Every automation starts with a rating condition – this is your primary filter for targeting reviews:
Exact Targeting Range Targeting Exclusion Targeting “is exactly” – Perfect for specific scenarios
is exactly 5 stars → Thank your biggest fansis exactly 1 star → Handle crisis situations with careis exactly 3 stars → Target neutral reviews for improvement opportunities Be careful with “exactly 1 star” rules – these users are often extremely frustrated and may benefit from manual, personalized responses.
“is exactly” – Perfect for specific scenarios
is exactly 5 stars → Thank your biggest fansis exactly 1 star → Handle crisis situations with careis exactly 3 stars → Target neutral reviews for improvement opportunities Be careful with “exactly 1 star” rules – these users are often extremely frustrated and may benefit from manual, personalized responses.
“more than” / “less than” – Cast wider nets for related review types
is more than 3 stars → Catch all positive sentiment (4-5 stars)is less than 3 stars → Handle all negative feedback (1-2 stars)is more than 1 star → Avoid the most frustrated users while catching improvable feedback “More than 3 stars” includes both 4 and 5-star reviews, perfect for general positive response automations.
“is not” – Useful for excluding specific ratings
is not 5 stars → Target all reviews that aren’t perfectis not 1 star → Avoid extreme negative reviewsis not 3 stars → Focus on clearly positive or negative feedback Rating strategy examples
Condition: is exactly 5 stars
Use case: Thank users who love your app, potentially ask for referrals or feature requests.
Risk level: ⭐ Low – Happy users appreciate acknowledgment
The Improvement Opportunity
Condition: is exactly 3 stars
Use case: Users who like your app but have specific concerns worth addressing.
Risk level: ⭐⭐ Medium – These users can often be converted to higher ratings
Condition: is less than 3 stars
Use case: All negative feedback requiring immediate, empathetic response.
Risk level: ⭐⭐⭐ High – Frustrated users need careful, personalized handling
Text conditions Target reviews based on their content with powerful keyword matching:
Contains vs. Does not contain Contains Keywords Does Not Contain Perfect for identifying specific topics:
contains "bug" → Technical issues requiring dev team attention contains "love" → Positive sentiment worth celebrating contains "subscription" → Billing-related questions contains "crash" → Critical stability issues contains "update" → Feature requests or update feedback Pro tips:
Use multiple automations for different keywords rather than one complex rule
Include common misspellings: "recieve" and "receive"
Consider synonyms: "bug", "glitch", "broken", "error"
Perfect for identifying specific topics:
contains "bug" → Technical issues requiring dev team attention contains "love" → Positive sentiment worth celebrating contains "subscription" → Billing-related questions contains "crash" → Critical stability issues contains "update" → Feature requests or update feedback Pro tips:
Use multiple automations for different keywords rather than one complex rule
Include common misspellings: "recieve" and "receive"
Consider synonyms: "bug", "glitch", "broken", "error"
Great for excluding irrelevant reviews:
does not contain "free" → Avoid users only interested in free features does not contain "fake" → Skip suspected fake reviews does not contain "competitor" → Ignore comparative reviews does not contain "trial" → Focus on committed users Common exclusions:
Spam indicators: "fake", "bot", "paid"
Competitor mentions: Brand names, "better than", "switch to"
Price complaints: "expensive", "cost", "cheap"
Advanced text targeting
Topic Clustering Group related keywords:
Bug reports: "crash", "freeze", "error", "broken"Feature requests: "add", "wish", "would like", "feature"Positive sentiment: "love", "amazing", "perfect", "excellent"Billing issues: "charge", "subscription", "cancel", "refund"
Sentiment Detection Emotion-based targeting:
Frustrated: "frustrated", "annoying", "terrible"Excited: "awesome", "incredible", "blown away"Confused: "confusing", "don't understand", "how do"Grateful: "thank you", "appreciate", "helpful" Language conditions Serve your global user base by targeting specific languages:
Primary Market Focus International Users Global Catch-All “Language is English” – Target your primary market
Use your best templates and most detailed responses
Perfect for testing new automations before global rollout
Ideal when you have specific English-only offers or content
“Language is English” – Target your primary market
Use your best templates and most detailed responses
Perfect for testing new automations before global rollout
Ideal when you have specific English-only offers or content
“Language is [Specific Language]” – Serve key markets
Language is Spanish → Dedicated Spanish market supportLanguage is German → GDPR-compliant responses for EU usersLanguage is Japanese → Cultural context-aware responses “Language is not English” – Handle all international reviews
Pair with AI Translation for automatic localization
Use simpler templates that translate well
Great for ensuring no user is left without a response
Language Strategy Examples
Create detailed automations for English reviews first, then add simpler international versions:
5 stars + English + contains "love" → Detailed thank you with feature previews5 stars + not English → Simple thank you with AI translation
Customize responses for key markets:
German users: Include GDPR compliance mentions in data-related responsesJapanese users: Use more formal, respectful language structuresSpanish users: Include cultural context and community references Review length conditions Fine-tune based on how much detail users provide:
Short Reviews Detailed Reviews Exact Length “shorter than X characters” – Quick ratings with minimal text
shorter than 50 characters → Brief ratings like “Good app” or ”⭐⭐⭐⭐⭐”Perfect for simple thank you messages
Often just emoji or very brief sentiment
Best response strategy: Short, sweet acknowledgments
“shorter than X characters” – Quick ratings with minimal text
shorter than 50 characters → Brief ratings like “Good app” or ”⭐⭐⭐⭐⭐”Perfect for simple thank you messages
Often just emoji or very brief sentiment
Best response strategy: Short, sweet acknowledgments
“longer than X characters” – Engaged users with specific feedback
longer than 200 characters → Detailed feedback worth personalized responseslonger than 100 characters → Moderate detail, specific concernsThese users are more invested and expect thoughtful replies
Best response strategy: Address specific points mentioned
“exactly X characters” – Rarely used, mainly for testing
Useful for testing automation targeting
Can catch specific template lengths
Generally not recommended for production rules
Length-based strategy
Engagement Level Targeting Match response effort to user investment:
Short reviews (< 50 chars) → Brief, friendly acknowledgment
Medium reviews (50-200 chars) → Standard template responses
Long reviews (200+ chars) → Detailed, personalized replies
Quality Filtering Use length to identify valuable feedback:
Combine longer than 100 characters + contains "feature"
Target users who provide actionable feedback
Prioritize responses to detailed suggestions
Combining conditions (AND Logic) The real power comes from combining multiple conditions with AND logic:
Powerful combinations
Conditions:
5 starscontains "feature"Englishlonger than 80 characters Result: Happy users providing detailed feature feedback – perfect candidates for beta programs or surveys.
Conditions:
less than 3 starslanguage is not Englishcontains "bug" Result: Non-English users reporting bugs – needs immediate AI translation and escalation.
The Conversion Opportunity
Conditions:
exactly 3 starsdoes not contain "price"longer than 50 characters Result: Neutral users with specific (non-price) concerns that can be addressed.
Strategy framework
Start with Rating
Choose your primary rating target based on the business goal.
Add Topic Filter
Use text conditions to target specific types of feedback.
Consider Language
Decide if you want language-specific responses or global coverage.
Fine-tune with Length
Use length conditions to match response effort to user investment.
Testing your conditions Always test before going live! Create automations in disabled mode first, then check the Reviews Feed to see which reviews would have matched your conditions.
Debug checklist Too Few Matches Too Many Matches Wrong Matches Your conditions might be too specific:
Remove one condition at a time to see which is limiting matches
Check for typos in keywords
Consider synonyms and alternative phrasings
Verify your apps are properly connected
Your conditions might be too specific:
Remove one condition at a time to see which is limiting matches
Check for typos in keywords
Consider synonyms and alternative phrasings
Verify your apps are properly connected
Your conditions might be too broad:
Add exclusion keywords (does not contain)
Narrow the rating range
Add language or length restrictions
Split into multiple, more specific automations
Your conditions need refinement:
Review actual matched reviews in your feed
Add negative keywords to exclude unwanted matches
Use more specific keywords
Consider the context of keyword usage
Master these trigger conditions, and you’ll create automations that respond to exactly the right reviews at exactly the right time. Next, learn how to craft the perfect responses in the Actions guide.
