Overview
This guide shows you how to use webhook data from Chameleon to create powerful automations without writing code. Whether you want smart tour monitoring, user engagement alerts, or automated reporting, you can build these workflows using popular automation platforms.
What you'll learn:
5 proven use cases with step-by-step implementations
Platform-specific setup instructions
Best practices for scaling your automations
Bonus: How to choose the right automation platform for your needs
Examples use common automation platforms (e.g. Zapier, Make, Pipedream) but you can adapt these for your own platform.
We encourage you to download this article and feed into your LLM of choice to help you craft examples for your own use case.
Use Case #1: Smart Tour Monitoring with Digest Alerts
The Problem: Getting overwhelmed by individual tour notifications when you need actionable insights about tours that require attention.
The Solution: Collect tour events throughout the day, then send intelligent daily/weekly summaries with AI-powered analysis and prioritized action items.
What You'll Build
Smart filtering for tours needing attention (API-triggered, missing URL rules, low completion rates)
AI-powered analysis that prioritizes issues and provides recommendations
Digest-style notifications that prevent alert fatigue
Direct links to review tours in Chameleon dashboard
Universal Implementation Steps
Step 1: Set Up Webhook Collection
Create a webhook endpoint in your chosen platform:
Zapier: Add "Webhooks by Zapier" trigger → "Catch Hook" Make.com: Add "Webhooks" → "Custom webhook" Pipedream: Add "HTTP / Webhook" trigger Power Automate: Add "When a HTTP request is received" n8n: Add "Webhook" node
Step 2: Configure Chameleon Webhook
In Chameleon Dashboard: Go to Integrations → Webhooks
Add your platform's webhook URL
Select topic:
tour.startedSet webhook secret and save
Step 3: Add Smart Filtering Logic
Platform-Agnostic Filter Conditions:
Condition 1: webhook.kind = "tour.started" AND Condition 2: ( tour.group_kind = "delivery" OR tour.group_kind = "api_js" OR step.quantifier_urls.length = 0 )
Platform-Specific Examples:
Zapier Implementation:
Filter by Zapier: - (Required) Kind: Exactly matches "tour.started" - Tour Group Kind: Contains "delivery" OR "api_js" - OR Step URL Rules Length: Less than 1
Make.com Implementation:
Filter Module: "Tour Alert Criteria" - Condition 1: {{1.kind}} equals "tour.started" - Condition 2: {{1.data.tour.group_kind}} equals "delivery" - OR Condition 3: {{1.data.tour.group_kind}} equals "api_js" - OR Condition 4: {{length(1.data.step.quantifier_urls)}} equals 0Pipedream Implementation:
// Node.js Code Step export default defineComponent({ async run({ steps, $ }) { const webhook = steps.trigger.event.body; if (webhook.kind !== 'tour.started') { $.flow.exit('Not a tour.started event'); } const tour = webhook.data.tour; const step = webhook.data.step; const isApiTriggered = ['delivery', 'api_js'].includes(tour.group_kind); const hasNoUrlRules = !step.quantifier_urls || step.quantifier_urls.length === 0; if (!isApiTriggered && !hasNoUrlRules) { $.flow.exit('Tour does not meet alert criteria'); } return webhook.data; } });Step 4: Store Events for Digest Processing
Data Storage Approach by Platform:
Zapier: Use "Storage by Zapier" to save tour data with key format: alert_YYYY-MM-DD-HH-mm-ss_tourId
Make.com: Use "Data Store" modules:
"Add/replace a record" with key:
alert_{{formatDate(now; "YYYY-MM-DD-HH-mm-ss")}}_{{tour.id}}Store tour data as JSON
Pipedream: Use built-in data stores:
await $.service.db.set(`alert_${new Date().toISOString()}_${tour.id}`, tourData);Step 5: Create Scheduled Digest Sender
Set up a second automation that runs daily/weekly:
Zapier: "Schedule by Zapier" → "Every Day" at chosen time Make.com: "Schedule" → "Every day" trigger
Pipedream: "Schedule" trigger with cron expression Power Automate: "Recurrence" trigger set to daily n8n: "Cron" node with daily schedule
Step 6: Retrieve and Analyze Stored Data
Data Retrieval Examples:
Make.com:
Data Store → Search records: - Key pattern: alert_* - Filter results by date range
Pipedream:
// Get all alerts from last 24 hours const yesterday = new Date(Date.now() - 24 * 60 * 60 * 1000); const alerts = await $.service.db.list(); const recentAlerts = alerts.filter(item => new Date(item.key.split('_')[1]) > yesterday );Step 7: Add AI Analysis
AI Service Options:
OpenAI (Most Popular):
Model:
gpt-4orgpt-3.5-turboAll platforms have OpenAI integrations
Claude (Anthropic):
Available via API in Pipedream, Make.com
Model:
claude-3-sonnet-20240229
Platform AI Services:
Microsoft: Use Azure OpenAI in Power Automate
Google: Vertex AI for advanced users
Universal AI Prompt Template:
Analyze this collection of Chameleon tours that need review: SUMMARY: - Total alerts: [COUNT] - API-triggered tours: [API_COUNT] - Tours without URL rules: [NO_RULES_COUNT] TOURS REQUIRING REVIEW: [LIST_OF_TOURS] Provide: 1. Overall Priority Assessment (High/Medium/Low) 2. Top 3 Most Critical Issues (1-2 sentences each) 3. Recommended Actions (3-4 bullet points) 4. Weekly Trend Assessment Keep response under 200 words, focus on actionable team insights.
Step 8: Send Smart Notifications
Slack Integration (All Platforms):
{ "text": "📊 Chameleon Tour Review Digest - [DATE]", "blocks": [ { "type": "section", "text": { "type": "mrkdwn", "text": "*Daily Tour Review Summary*\n[AI_ANALYSIS]" } }, { "type": "section", "text": { "type": "mrkdwn", "text": "*Tours to Review:*\n[TOUR_LIST_WITH_LINKS]" } } ] }Email Template (Universal):
Subject: Chameleon Tour Review Digest - [DATE] 📊 DAILY TOUR SUMMARY [AI_ANALYSIS] 🔍 TOURS TO REVIEW ([COUNT] total): [DETAILED_TOUR_LIST] 📈 QUICK STATS: • API-triggered tours: [COUNT] • Tours without URL rules: [COUNT] • Total requiring review: [COUNT]
Expected Results
Input: 100-500 webhook events per day
Processed: 5-15 tours flagged per day
Output: 1 focused digest with 5-10 actionable items
Time Saved: 2-3 hours of manual tour review per week
Use Case #2: Survey Response Analysis & Follow-up
The Problem: Need to act quickly on survey feedback and identify trends across responses.
The Solution: Automatically analyze survey responses with AI sentiment analysis and trigger appropriate follow-up actions.
What You'll Build
Instant alerts for negative feedback or low NPS scores
Automatic sentiment analysis of open-text responses
CRM updates based on satisfaction levels
Support ticket creation for users reporting issues
Key Webhook Topics
survey.completed- Full survey submissionsresponse.finished- Individual question responses
Implementation Framework
Smart Response Routing Logic
IF nps_score ≤ 6: → Create support ticket → Add to "At Risk" CRM segment → Send to customer success team IF nps_score = 7-8: → Add to nurture email sequence → Tag as "Neutral" in CRM IF nps_score ≥ 9: → Add to "Champions" segment → Request case study/testimonial → Notify sales team for expansion opportunity
AI Sentiment Analysis Prompt
Analyze this survey response for sentiment and key themes: Response: "[USER_RESPONSE]" NPS Score: [SCORE] User Context: [ROLE] at [COMPANY_SIZE]-person company Provide: 1. Sentiment: Positive/Neutral/Negative 2. Key Issues: List 1-3 main concerns mentioned 3. Urgency Level: Low/Medium/High 4. Recommended Action: One specific next step 5. Keywords: 3 tags for categorization Keep analysis concise and actionable.
Platform-Specific Examples
Zapier Workflow:
Webhook trigger on
survey.completedFilter for responses with text content
OpenAI analysis of response text
Formatter to extract sentiment score
Paths based on sentiment:
Negative → Create Zendesk ticket
Positive → Add to HubSpot "Champions" list
Neutral → Add to Mailchimp nurture sequence
Make.com Workflow:
Custom webhook receives survey data
Router splits based on NPS score ranges
OpenAI module analyzes text responses
HTTP modules update CRM with tags
Slack notification for high-priority issues
Use Case #3: High-Value User Engagement Alerts
The Problem: Missing opportunities to personally follow up with important prospects or customers when they show interest.
The Solution: Get real-time notifications when VIP users engage with key content, with automatic account enrichment.
What You'll Build
Real-time alerts for enterprise prospects viewing pricing content
Notifications when existing customers explore new features
Sales team alerts for high-engagement trial users
Customer success notifications for at-risk accounts showing renewed interest
VIP User Identification Logic
VIP Criteria: - plan_type = "enterprise" OR "professional" - team_size > 50 - deal_value > $10,000 (from CRM lookup) - account_status = "trial" AND days_remaining < 7
Smart Notification Rules
IF VIP user starts pricing/enterprise tour: → Immediate Slack to account owner → CRM activity log with tour details → Calendar invite suggestion for follow-up IF existing customer explores new features: → Notify customer success manager → Update expansion opportunity score → Add to feature adoption tracking IF trial user shows high engagement: → Alert sales team with engagement summary → Prioritize in CRM pipeline → Trigger personalized demo email sequence
Implementation Template
Account Enrichment Steps
User Lookup: Query CRM for account details using email/company
Scoring: Calculate engagement score based on tour progression
Context Building: Combine user data + tour data + account history
Smart Routing: Send to appropriate team member based on account ownership
Notification Format
🎯 VIP ENGAGEMENT ALERT User: [NAME] ([ROLE]) Company: [COMPANY] ([PLAN_TYPE]) Action: Started "[TOUR_NAME]" Engagement Score: [SCORE]/100 Context: • Account Value: $[VALUE] • Last Activity: [DATE] • Tours Completed: [COUNT] Recommended Action: [AI_SUGGESTION] [View in Chameleon] [CRM Profile] [Schedule Follow-up]
Use Case #4: Onboarding Completion Tracking
The Problem: Need visibility into which users complete onboarding and where they drop off in the process.
The Solution: Track tour progression across multi-step flows and automatically update user records with completion status.
What You'll Build
Multi-tour completion tracking for complex onboarding flows
Automatic user segmentation based on progress
Alerts for users exiting before critical steps
Weekly funnel performance reports
Onboarding Flow Tracking
Tour Sequence Definition
const onboardingFlow = { "welcome_tour": { order: 1, critical: true }, "account_setup": { order: 2, critical: true }, "first_project": { order: 3, critical: false }, "team_invite": { order: 4, critical: false }, "advanced_features": { order: 5, critical: false } };Progress Calculation Logic
User Progress = (Completed Critical Tours / Total Critical Tours) * 100 Completion Status: - 0-25%: "Getting Started" - 26-75%: "In Progress" - 76-99%: "Almost Done" - 100%: "Completed"
Drop-off Analysis
Exit Point Tracking
FOR each tour.exited event: - Record exit step and timestamp - Calculate time spent before exit - Categorize exit reason (if available) - Update user's progress status - Trigger intervention if critical tour
Intervention Triggers
IF user exits critical tour: → Send personalized email with help resources → Schedule automated follow-up in 24 hours → Notify customer success team if enterprise user IF user stalls for 3+ days: → Send progress reminder email → Offer 1:1 onboarding session → Update CRM with "stalled onboarding" tag
Use Case #5: Product Adoption Insights Dashboard
The Problem: Need to understand which features users discover through tours and how that impacts long-term adoption.
The Solution: Analyze tour completion patterns to identify successful feature introductions and optimize tour content.
What You'll Build
Feature discovery tracking based on tour completions
User segmentation by features explored
Product team insights on tour effectiveness
Automated feature adoption reports
Feature Mapping Strategy
Tour-to-Feature Mapping
const featureMapping = { "analytics_dashboard_tour": ["custom_reports", "data_export", "realtime_metrics"], "collaboration_tour": ["team_sharing", "comments", "version_control"], "automation_tour": ["workflows", "triggers", "integrations"], "advanced_search_tour": ["filters", "saved_searches", "bulk_actions"] };Adoption Correlation Analysis
Adoption Score = (Features Used / Features Shown in Tours) * Usage Frequency Success Metrics: - Tour → Feature Usage within 7 days - Feature Retention after 30 days - Expansion to related features - Overall product engagement increase
Analytics Dashboard Creation
Key Metrics to Track
Discovery Rate: % of users who complete feature tours
Activation Rate: % who use features within 7 days of tour
Retention Rate: % still using features after 30 days
Expansion Rate: % who adopt related features
Report Generation Logic
Weekly Report Includes: 1. Top Performing Tours (highest activation rates) 2. Feature Adoption Trends (week-over-week changes) 3. User Segment Analysis (adoption by role/plan) 4. Tour Optimization Recommendations (based on drop-off data)
Implementation Framework
Phase 1: Foundation Setup
Choose Your Stack
Select Primary Platform: Based on team technical skills and existing tools
Set Up Webhook Connection: Configure Chameleon webhook with your platform
Create Basic Flow: Start with simple tour completion notifications
Test with Sample Data: Verify webhook delivery and data structure
Essential Integrations
Communication: Slack, Microsoft Teams, or email
Data Storage: Platform's built-in storage or external database
AI Service: OpenAI, Claude, or platform's AI features
CRM/Analytics: HubSpot, Salesforce, Mixpanel, etc.
Phase 2: Core Automations
Build Primary Use Case
Choose Starting Point: Pick one use case from the examples above
Implement Basic Version: Start with essential features only
Add Filtering Logic: Prevent notification overload
Test with Real Data: Use actual webhook events to validate
Optimization Steps
Volume Management: Implement batching/digest features
Error Handling: Add retry logic and failure notifications
Data Quality: Validate webhook payload structure
Performance: Monitor execution times and API limits
Phase 3: Advanced Features
Scale and Enhance
Add AI Analysis: Implement intelligent insights and recommendations
Multi-Channel Notifications: Expand beyond single communication method
Advanced Filtering: Add user segmentation and conditional logic
Integration Expansion: Connect additional tools and services
Monitoring and Iteration
Usage Analytics: Track which automations provide most value
User Feedback: Gather input from notification recipients
Performance Metrics: Monitor execution success rates
Continuous Improvement: Refine logic based on real-world usage
Best Practices
🎯 Start Simple, Scale Smart
Begin with One Use Case: Master a single workflow before expanding
MVP First: Build basic functionality, then add advanced features
User Feedback Loop: Regularly check if notifications are valuable
Gradual Complexity: Add filters and AI analysis once basics work
📊 Design for Volume
Batch Processing: Group similar events to reduce noise
Smart Filtering: Not every webhook event needs an action
Rate Limiting: Respect API limits of connected services
Data Cleanup: Archive or delete old events to prevent bloat
🤖 Leverage AI Effectively
Summarization: Use AI to digest batches of events, not individual ones
Prioritization: Let AI rank issues by urgency and impact
Actionable Insights: Focus on recommendations, not just analysis
Cost Management: Monitor AI usage to control expenses
🔧 Technical Considerations
Error Handling: Plan for failed API calls and missing data
Data Security: Use secure connections and proper authentication
Scalability: Design workflows that work as your user base grows
Documentation: Keep clear records of your automation logic
📈 Measure Success
Automation Metrics: Track execution success rates and error frequency
Business Impact: Measure time saved and actions taken based on notifications
User Satisfaction: Survey teams receiving notifications for usefulness
ROI Calculation: Compare automation costs to manual process time
Common Webhook Data Reference
Please review our full webhook documentation here to get an understanding of the payloads and data available for you to leverage.
Below are some examples:
User Profile Fields
{ "profile": { "email": "user@company.com", "role": "product_manager", "plan": "professional", "team_size": 25, "department": "Product", "last_seen_session_count": 47, "feature_flags": ["advanced_analytics"], "custom_attributes": { "onboarding_stage": "completed", "trial_end_date": "2024-08-15" } } }Tour/Experience Fields
{ "tour": { "id": "tour_abc123", "name": "Dashboard Onboarding", "group_kind": "api_js", "published_at": "2024-07-15T09:00:00Z", "urls": { "dashboard": "https://app.chameleon.io/tours/abc123" }, "stats": { "started_count": 1247, "completed_count": 892, "exited_count": 355, "timestamp": "2024-07-29T10:30:00Z" } } }Step/Interaction Fields
{ "step": { "body": "Welcome to your dashboard!", "preset": "modal", "quantifier_urls": [ "https://app.example.com/dashboard*" ], "buttons": [ { "text": "Get Started", "action_url": "https://app.example.com/setup" } ] } }Survey Response Fields
{ "response": { "question_text": "How satisfied are you?", "answer_text": "Very satisfied, great features!", "nps_score": 9, "rating": 5, "choice_selected": "Extremely Satisfied" } }Troubleshooting Guide
Webhook Issues
Problem: Webhook not firing
✅ Verify webhook URL is correct in Chameleon
✅ Check selected webhook topics match your triggers
✅ Test webhook delivery with manual tour interaction
✅ Review automation platform logs for incoming requests
Problem: Missing data in payload
✅ Different webhook topics provide different data fields
✅ Some fields may be null based on user type or tour configuration
✅ Use conditional logic to handle optional fields
✅ Check Chameleon webhook documentation for field availability
Automation Platform Issues
Problem: Workflow timing out
✅ Simplify complex logic or break into multiple steps
✅ Add error handling for external API calls
✅ Consider using data storage for multi-step workflows
✅ Check platform execution time limits
Problem: Too many operations/notifications
✅ Add more specific filters to reduce processed events
✅ Switch from individual alerts to digest processing
✅ Use user segmentation to focus on relevant audiences
✅ Implement deduplication logic for repeated events
AI Integration Issues
Problem: AI responses inconsistent
✅ Provide more specific prompts with examples
✅ Include relevant context in the prompt
✅ Set temperature/creativity parameters appropriately
✅ Add validation logic for AI response format
Problem: High AI costs
✅ Batch multiple events into single AI analysis
✅ Use shorter prompts and limit response length
✅ Consider less expensive models for simple tasks
✅ Cache AI responses for similar events
Advanced Techniques
Multi-Platform Workflows
Combine multiple automation platforms for complex workflows:
Make.com for data processing and logic
Zapier for final integrations with business tools
Pipedream for custom API calls and advanced transformations
Webhook Chaining
Use one automation to trigger another for complex multi-step processes:
Chameleon → Platform A (filtering) → Platform B (AI analysis) → Platform C (notifications)
Custom Scoring Systems
Build sophisticated user scoring based on tour engagement:
const engagementScore = { tour_started: 1, tour_completed: 5, button_clicked: 2, survey_completed: 3, high_nps_response: 10 };Dynamic Content Personalization
Use webhook data to customize future tour content:
Track which features users explore most
Adjust tour content based on user role/plan
Skip tours for features already mastered
Choose Your Platform
You should first check whether your organization already uses one of these existing platforms, and if so, choose that.
Please bear in mind that if you're sending your user data to a third party, that likely qualifies it as a subprocessor, and accordingly will need to be listed in your security documentation and covered as part of any security audits.
Zapier - Best for Beginners
Pros: Easy setup, 6,000+ pre-built integrations, visual workflow builder
Cons: Limited data transformation, higher cost per operation
Best for: Simple workflows, connecting popular tools
Cost: Free plan (100 tasks/month), paid plans from $20/month
Make.com - Most Powerful
Pros: Advanced logic, visual flow designer, affordable pricing
Cons: Steeper learning curve, fewer pre-built integrations
Best for: Complex workflows, data transformation, cost efficiency
Cost: Free plan (1,000 operations/month), paid plans from $9/month
Pipedream - Developer-Friendly
Pros: Code flexibility, powerful data processing, generous free tier
Cons: Requires some technical knowledge, less visual
Best for: Custom logic, API integrations, technical teams
Cost: Free plan (10,000 invocations/month), paid plans from $19/month
Microsoft Power Automate - Enterprise
Pros: Office 365 integration, enterprise security, SharePoint connectivity
Cons: Complex pricing, Microsoft ecosystem focused
Best for: Large organizations using Microsoft tools
Cost: Included with Office 365, standalone from $15/user/month
n8n - Open Source
Pros: Self-hosted option, full control, no vendor lock-in
Cons: Requires technical setup, limited support
Best for: Privacy-focused organizations, custom deployments
Cost: Free (self-hosted), cloud plans from $20/month
Additional Resources
Chameleon Webhook Documentation available in our Developer Docs
Platform-Specific Tutorials: Available in each automation tool's help center
AI Prompting Best Practices: Anthropic Prompt Engineering Guide
Webhook Testing Tools: Use tools like ngrok for local testing and development
Need help with implementation? Contact our support team