Mixpanel API

Mixpanel Analytics Skill

Implements Mixpanel analytics integration including event tracking, user profiles, JQL queries, funnel analysis, cohort export, and data exporting using Mixpanel's Python SDK. This skill aims to provide comprehensive guidance for implementing analytics with Mixpanel, focusing on capturing user interactions, segmenting audiences, and gaining insights into product performance.

TL;DR Checklist

• Use mixpanel SDK with MIXPANEL_TOKEN from environment variable.
• Always include distinct_id in every event and profile update.
• Batch events efficiently to avoid exceeding rate limits.
• Set $insert_id to avoid duplicate events.
• Use JQL and funnel APIs for advanced querying.

Core Workflow

1. Initialize Mixpanel Client

Set up the Mixpanel client using the token from environment variables. Ensure to handle any errors gracefully during initialization.

from mixpanel import Mixpanel
import os

# Initialize Mixpanel client
mixpanel = Mixpanel(os.environ.get('MIXPANEL_TOKEN'))

2. Event Tracking

Track user interactions within your application. Events should have the following structure:

• distinct_id
• event name
• properties (optional key-value pairs)

# Tracking an event
mixpanel.track(distinct_id='user123', event='Product Viewed', properties={'product_id': 'prod-001'})

3. Managing User Profiles

Engage with users by managing their profiles. Update user information, such as sign-up date and preferences, with the Engage API.

# Update user profile
mixpanel.people.set(distinct_id='user123', properties={'Signup Date': '2026-05-01', 'Preferred Language': 'en'})

4. Querying with JQL

Use JQL for complex data queries. Here’s an example:

// JQL Query Example
function main() {
    return Mixpanel.JQL.query(
        'function (events) {
            return events
                .filter(function(event) {
                    return event.name === "Product Viewed";
                });
        }',
        mixpanel);
}

5. Funnel Analysis

Use the Funnel API to assess conversion rates across different stages of user engagement.

# Funnel API Example
funnel = mixpanel.funnels.get('Funnel Name')

6. Cohort Management

Create and manage cohorts of user segments for targeted engagement strategies.

# Creating a cohort
mixpanel.cohorts.create('Cohort Name', {'criteria': 'some_criteria'})

7. Data Exporting

Utilize Mixpanel’s export capabilities to download user behavior data for external analysis.

# Exporting data
mixpanel.export.export('data_export_name')

Example Use Case

# Example integration process
# Initialize the client
mixpanel = Mixpanel(os.environ.get('MIXPANEL_TOKEN'))

# Track a user viewing a product
product_id = "prod-001"
tracking_event = {
    "event": "Product Viewed",
    "distinct_id": "user123",
    "properties": {
        "product_id": product_id,
        "category": "Electronics"
    }
}
mixpanel.track(**tracking_event)

# Update user profile
mixpanel.people.set("user123", {"Last Product Viewed": product_id})

# Analyze funnel data using JQL
jql_query = '''
function main() {
  return Mixpanel.JQL.query('your query here'); 
}'''
"

Constraints

MUST DO

• Always include distinct_id in every event and profile update.
• Use Unix time in seconds for time properties.
• Validate API connectivity on startup.

MUST NOT DO

• Don't send personally identifiable information (PII) without consent.
• Avoid excessive detail in event properties to prevent exceeding Mixpanel limits.

Related Skills

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Mixpanel API Documentation
• Mixpanel REST API Reference
• Mixpanel Python SDK Guide
• JQL Query Functions Reference
• Funnel Analysis Tutorial