Learn how to implement real-time interventions in an ecommerce app using Signals

Welcome to the Snowplow Signals tutorial.

Snowplow Signals is a real-time personalization engine for customer intelligence, built on Snowplow's behavioral data pipeline. It allows you to compute, access, and act on in-session stream and historical user data, in real time.

This tutorial provides a hands-on introduction to Signals. You'll use Python to programmatically define attributes, services, and interventions, then test them with a demo ecommerce application.

To follow this tutorial, you'll need a Snowplow account with Signals enabled.

This tutorial should take approximately 20-30 minutes to complete.

What you'll learn

In this tutorial, you will:

• Set up your Signals connection through Snowplow Console
• Use the Signals Python SDK to define attributes, services, and interventions
• Calculate real-time user behavior metrics from ecommerce events
• Create intervention rules that trigger personalized experiences
• Test your Signals configuration with an interactive demo application

Deployment

If you have a Snowplow account, you can enable Signals through Snowplow Console. This provides:

• Integration with your existing Snowplow data pipeline
• Access to your production behavioral data
• Full production capabilities and support

Prerequisites

This tutorial requires:

• Python 3.7 or higher
• A Jupyter notebook environment (local or Google Colab)
• Basic familiarity with Python
• A modern web browser for testing the demo application
• A Snowplow account with Signals enabled