Hidden Messages

One of the actions categorized in the special section of Pingstreams Design Studio is the Hidden Message.

This action allows you to send messages to the system without being visible to the end customer.

There are several applications for this action, such as conditional logic, data storage, analytics tracking, and more.

Step-by-step Guide to Using Hidden Messages

To use the hidden action, first, navigate to the Pingstreams dashboard and then to the Design Studio.

For this example, I will evaluate the user’s email to determine if it is a company email. If it is, I will store it as the user’s company without sharing this information with the user.

To assess the user’s email, I need to use an action called “AI Task” and insert the following prompt in its prompt section:

Analyze the email address provided by the user and respond only and exclusively with the name of their company if it is a business email.

The user’s email is: {user_email}

Then, I will use the Hidden Message action and insert the AI-generated company information. In this scenario, we extract the user’s company from their email without sharing this information with the end user, as it is not necessary to share it with them.

In the next step, I will use another action called “Set attribute” to assign the user’s company name to a new attribute, as shown in the image below.

With this strategy, we can record the user’s company in a new attribute just by asking for their work email.

Common Use Cases for Hidden Messages

Hidden Messages are incredibly versatile and can be used in various scenarios:

1. Data Collection & Analytics

• Track user behavior patterns without showing the tracking to users
• Log conversation metrics for analysis
• Store conversion funnel data
• Record user preferences silently

2. Conditional Logic Support

• Set flags for complex conditional flows
• Store intermediate calculation results
• Maintain state information between conversation branches
• Create invisible decision trees

3. Integration Preparation

• Format data for external API calls
• Prepare webhook payloads
• Store processed information for later use
• Clean and validate data before transmission

4. User Profiling

• Build user profiles from conversation patterns
• Store inferred demographics or interests
• Track user engagement levels
• Create personalization data points

5. Debugging & Monitoring

• Log internal system states
• Store error information for troubleshooting
• Track conversation flow paths
• Monitor action execution results

Advanced Implementation Examples

Example 1: Sentiment Tracking

User Input → AI Task (Sentiment Analysis) → Hidden Message (Log Sentiment Score) → Continue Flow

Example 2: Lead Scoring

User Responses → Code Action (Calculate Score) → Hidden Message (Store Score) → Conditional Routing

Example 3: A/B Test Tracking

Random Assignment → Hidden Message (Log Test Variant) → Show Variant Content → Track Results

Best Practices

1. Keep It Organized: Use clear naming conventions for your hidden messages
2. Document Your Logic: Comment on what each hidden message stores and why
3. Avoid Overuse: Only use hidden messages when necessary to keep flows clean
4. Privacy Compliance: Ensure hidden data collection complies with privacy laws
5. Regular Cleanup: Periodically review and clean up unnecessary hidden data

Technical Considerations

• Hidden messages don’t count toward the user’s visible conversation
• They can store any text or formatted data
• Use them to pass data between different parts of your flow
• They’re processed in real-time like any other action
• Can be used with variables and dynamic content

I hope you found this helpful.

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