Event analytics is a powerful tool to gather valuable insights into user behavior, track key metrics, and make data-driven decisions for digital products. Creating a well-designed event analytics schema is essential to ensure accurate data collection and meaningful analysis. In this guide, we will walk through the steps to create an effective event analytics schema for digital products.
Step 1: Define analytics goals
Before diving into creating an event analytics schema, it's crucial to clearly define analytics goals. Determine the specific product metrics and insights to capture through event analytics whether it be to track user engagement, conversion rates, or feature usage. Clearly defined goals will help structure the schema effectively and help monitor the business to make informed decisions.
Product metrics can be split into five main categories:
- Acquisition metrics: This encompasses all new signups and qualified leads to measure when users first interact with the product.
- Activation metrics: This includes activation rate / time to show how well user is moving through the a critical moment when user discovers product’s true value.
- Engagement metrics: This captures all metrics that measure a user’s engagement with the product and would therefore would notify the set of active users.
- Retention metrics: This informs how many users return to the product over time to understand retention, churn rate or free-to-paid conversions.
- Monetization metrics: This incorporates monetary revenue metrics such as monthly recurring revenue or average revenue per user.
Step 2: Identify key events
Identify the key events or actions that users can perform within the digital product that are relevant to your analytics goals. These events can be anything from clicks, form submissions, purchases, or feature interactions. Identify the user journey of the product and map out event triggers and begin to lay out the event schema. Brainstorm and make a list of all the events that you want to track.
Step 3: Determine event properties
For each key event, determine the relevant properties or attributes to capture. Properties provide additional context and details about the event. For example, if you're tracking a purchase event, relevant properties might include the product ID, price, quantity, and user information. Be mindful of the properties that are important for analysis and decision-making. These properties inform metadata that provides a deeper-understanding of user behavior and product engagement when analyzing key events.
Step 4: Establish event naming conventions
Develop a clear and consistent naming convention for events and properties. Use descriptive names that are easy to understand and maintain. Avoid ambiguous or generic names that can lead to confusion later on. Consistency in naming will ensure data integrity and simplify analysis.
Tips & Tricks:
- Use descriptive and meaningful names. Event names should accurately describe the action that is taking place.
- Be concise and keep event names relatively short and easy to understand.
- Use lowercase letters and underscores and don’t use spaces or special characters to ensure compatibility with programming and data tools.
- Avoid using ambiguous and generic names like “event1” or “click_event” to limit confusion on what the event is measuring.
Step 5: Define event hierarchy
Organize events into a logical hierarchy. Group related events together to provide a structured view of user interactions. For example, a parent event called "Page View," can have child events like "Button Click" or "Form Submission." This hierarchy helps the business understand the user journey and the flow of events.
Step 6: Document schema
Create comprehensive documentation for the event analytics schema. Include event names, properties, hierarchy, and their definitions. This documentation will serve as a reference for the team and future updates. Additionally, it will ensure consistency and clarity when sharing the schema with stakeholders or new team members.
At Studio we use this template to define and structure analytics schema across projects and ensure the product events, event triggers, and parameters are all centralized in one source-of-truth.
Step 7: Implement schema
Implementing the event analytics schema will depend on the analytics tool or platform leveraged. Most analytics platforms provide software development kits (SDKs) or APIs that allow you to send events and properties for tracking. Follow the documentation and guidelines provided by your analytics tool to integrate the schema into your digital product.
Studio recommends using Google Analytics to implement event analytics schema but there are many robust tools to choose from. For more information on how to get started with Google Analytics click here.
Step 8: Test and validate
Thoroughly test event analytics implementation to ensure that events and properties are being tracked accurately. Use test scenarios to verify that the data being collected aligns with analytics goals. Validate the schema by cross-referencing the captured data with your expectations. Make adjustments if necessary.
To verify and validate events and properties are capturing the right data, Studio uses the Debug View. First download the Google Analytics Debugger chrome extension. Once installed navigate to the Google Analytics dashboard to the Debug View to test the events in real time as the tester interacts with the product. This provides visibility into the metadata of the events and ensures the events are set up correctly.
Step 9: Iterate and improve
Event analytics is an iterative process. Continuously monitor and analyze the data collected. Gain insights, identify patterns, and refine analytics schema accordingly. Regularly review and update the schema to adapt to changes in the digital product or analytics goals.
Creating an effective event analytics schema is crucial for obtaining actionable insights from digital products. By following the steps outlined in this guide, the business can define clear goals, identify key events, establish event properties, and implement a well-structured schema. Additionally it’s important to continuously iterate and improve the schema to make the most of event analytics capabilities.