> ## Documentation Index
> Fetch the complete documentation index at: https://docs.appreply.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Automation triggers

> Master precise targeting with conditions that catch exactly the reviews you want to handle

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.

<Tip>
  **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.
</Tip>

## Trigger conditions

<Frame>
  <img src="https://mintcdn.com/appreply/O2DeZuzCd2s24yTk/images/Screenshot2025-06-05at21.26.48.png?fit=max&auto=format&n=O2DeZuzCd2s24yTk&q=85&s=e8cb7c1bef7c977c0ab83fdff9ea6f76" alt="Screenshot2025 06 05at21 26 48 Pn" width="1468" height="1064" data-path="images/Screenshot2025-06-05at21.26.48.png" />
</Frame>

Every automation starts with a rating condition – this is your primary filter for targeting reviews:

<Tabs>
  <Tab title="Exact Targeting">
    **"is exactly"** – Perfect for specific scenarios

    * `is exactly 5 stars` → Thank your biggest fans
    * `is exactly 1 star` → Handle crisis situations with care
    * `is exactly 3 stars` → Target neutral reviews for improvement opportunities

    <Warning>
      Be careful with "exactly 1 star" rules – these users are often extremely frustrated and may benefit from manual, personalized responses.
    </Warning>
  </Tab>

  <Tab title="Range Targeting">
    **"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

    <Note>
      "More than 3 stars" includes both 4 and 5-star reviews, perfect for general positive response automations.
    </Note>
  </Tab>

  <Tab title="Exclusion Targeting">
    **"is not"** – Useful for excluding specific ratings

    * `is not 5 stars` → Target all reviews that aren't perfect
    * `is not 1 star` → Avoid extreme negative reviews
    * `is not 3 stars` → Focus on clearly positive or negative feedback
  </Tab>
</Tabs>

### Rating strategy examples

<AccordionGroup>
  <Accordion title="The Grateful Fan" icon="heart">
    **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
  </Accordion>

  <Accordion title="The Improvement Opportunity" icon="arrow-up">
    **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
  </Accordion>

  <Accordion title="The Crisis Manager" icon="warning">
    **Condition:** `is less than 3 stars`

    **Use case:** All negative feedback requiring immediate, empathetic response.

    **Risk level:** ⭐⭐⭐ High – Frustrated users need careful, personalized handling
  </Accordion>
</AccordionGroup>

## Text conditions

Target reviews based on their content with keyword matching:

### Contains vs. Does not contain

<Tabs>
  <Tab title="Contains Keywords">
    **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"`
  </Tab>

  <Tab title="Does Not Contain">
    **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"`
  </Tab>
</Tabs>

### Advanced text targeting

<CardGroup cols={2}>
  <Card title="Topic Clustering" icon="folder">
    **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"`
  </Card>

  <Card title="Sentiment Detection" icon="face-smile">
    **Emotion-based targeting:**

    * **Frustrated:** `"frustrated"`, `"annoying"`, `"terrible"`
    * **Excited:** `"awesome"`, `"incredible"`, `"blown away"`
    * **Confused:** `"confusing"`, `"don't understand"`, `"how do"`
    * **Grateful:** `"thank you"`, `"appreciate"`, `"helpful"`
  </Card>
</CardGroup>

## Language conditions

Serve your global user base by targeting specific languages:

<Tabs>
  <Tab title="Primary Market Focus">
    **"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
  </Tab>

  <Tab title="International Users">
    **"Language is \[Specific Language]"** – Serve key markets

    * `Language is Spanish` → Dedicated Spanish market support
    * `Language is German` → GDPR-compliant responses for EU users
    * `Language is Japanese` → Cultural context-aware responses
  </Tab>

  <Tab title="Global Catch-All">
    **"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
  </Tab>
</Tabs>

### Language strategy examples

<AccordionGroup>
  <Accordion title="English-First Approach" icon="flag">
    Create detailed automations for English reviews first, then add simpler international versions:

    1. `5 stars + English + contains "love"` → Detailed thank you with feature previews
    2. `5 stars + not English` → Simple thank you with AI translation
  </Accordion>

  <Accordion title="Market-Specific Rules" icon="globe">
    Customize responses for key markets:

    * **German users:** Include GDPR compliance mentions in data-related responses
    * **Japanese users:** Use more formal, respectful language structures
    * **Spanish users:** Include cultural context and community references
  </Accordion>
</AccordionGroup>

## Review length conditions

Fine-tune based on how much detail users provide:

<Tabs>
  <Tab title="Short Reviews">
    **"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
  </Tab>

  <Tab title="Detailed Reviews">
    **"longer than X characters"** – Engaged users with specific feedback

    * `longer than 200 characters` → Detailed feedback worth personalized responses
    * `longer than 100 characters` → Moderate detail, specific concerns
    * These users are more invested and expect thoughtful replies

    **Best response strategy:** Address specific points mentioned
  </Tab>

  <Tab title="Exact Length">
    **"exactly X characters"** – Rarely used, mainly for testing

    * Useful for testing automation targeting
    * Can catch specific template lengths
    * Generally not recommended for production rules
  </Tab>
</Tabs>

### Length-based strategy

<CardGroup cols={2}>
  <Card title="Engagement Level Targeting" icon="chart-line">
    **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
  </Card>

  <Card title="Quality Filtering" icon="filter">
    **Use length to identify valuable feedback:**

    * Combine `longer than 100 characters` + `contains "feature"`
    * Target users who provide actionable feedback
    * Prioritize responses to detailed suggestions
  </Card>
</CardGroup>

## Featured review condition

Target reviews that are highlighted or featured in app store listings.

<Tabs>
  <Tab title="Is Featured">
    **"Featured review is"** targets reviews currently shown as featured in the store listing.

    These reviews are the most visible to potential users browsing your app page. Create specific, well-crafted responses for them since they have an outsized impact on download decisions.

    **Use case:** Pair with AI-generated responses and detailed instructions to make sure your reply to a featured review is polished and addresses the reviewer's points directly.
  </Tab>

  <Tab title="Is Not Featured">
    **"Featured review is not"** targets all non-featured reviews.

    Use this to apply different response strategies to regular reviews versus those highlighted by the store.
  </Tab>
</Tabs>

<Tip>
  Featured reviews change over time as the app store rotates them. If a review loses its featured status after your automation already responded, the response remains in place.
</Tip>

## Combining conditions (AND logic)

The real power comes from combining multiple conditions with AND logic:

### Example combinations

<AccordionGroup>
  <Accordion title="The Engaged Power User" icon="star">
    **Conditions:**

    * `5 stars`
    * `contains "feature"`
    * `English`
    * `longer than 80 characters`

    **Result:** Happy users providing detailed feature feedback – perfect candidates for beta programs or surveys.
  </Accordion>

  <Accordion title="The International Crisis" icon="globe">
    **Conditions:**

    * `less than 3 stars`
    * `language is not English`
    * `contains "bug"`

    **Result:** Non-English users reporting bugs – needs immediate AI translation and escalation.
  </Accordion>

  <Accordion title="The Conversion Opportunity" icon="arrow-up">
    **Conditions:**

    * `exactly 3 stars`
    * `does not contain "price"`
    * `longer than 50 characters`

    **Result:** Neutral users with specific (non-price) concerns that can be addressed.
  </Accordion>
</AccordionGroup>

### Strategy framework

<Steps>
  <Step title="Start with Rating">
    Choose your primary rating target based on the business goal.
  </Step>

  <Step title="Add Topic Filter">
    Use text conditions to target specific types of feedback.
  </Step>

  <Step title="Consider Language">
    Decide if you want language-specific responses or global coverage.
  </Step>

  <Step title="Fine-tune with Length">
    Use length conditions to match response effort to user investment.
  </Step>
</Steps>

## Testing your conditions

<Warning>
  **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.
</Warning>

### Debug checklist

<Tabs>
  <Tab title="Too Few 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
  </Tab>

  <Tab title="Too Many Matches">
    **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
  </Tab>

  <Tab title="Wrong Matches">
    **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
  </Tab>
</Tabs>

***

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](/auto-replies/actions) guide.

<CardGroup cols={2}>
  <Card title="Actions" icon="bolt" href="/auto-replies/actions">
    Master template-based and AI-generated responses
  </Card>

  <Card title="Templates" icon="folder" href="/essentials/templates">
    Create response templates that power your automations
  </Card>
</CardGroup>