Demystifying Product Analytics Tracking: A Non-Technical Product Manager’s Step-by-Step Guide

I’ve previously talked about how you can use data to build better products, even if you are non-technical. But how do you get started with collecting the data you need?

You want to understand your product usage, see what your users are doing, but how can you do this? Where do you start?

This can seem like a daunting task. Do you need to know coding? Do you need to implement everything yourself? How can I even define what I want to track in my product?

Don’t worry! With the right approach and understanding of best practices, you can successfully implement a robust tracking plan to gather valuable data about your product’s usage. I tried to boil this down into 6 short steps before, but thought I could do better.

Here I’ve compiled a more comprehensive guide, outlining the steps you need to take to set up product analytics tracking effectively. I’ve used some examples for each step, giving you practical insights into real-world scenarios I see all the time with the teams and products I work with.

We’ll cover a few essential steps, from defining your goals and identifying key metrics, to implementing the tracking code and making sure it’s working correctly. Obviously data governance is important, so we’ll touch on that too..

Let’s go.

Step 1: Goals and Objectives

Defining clear goals and objectives for your event tracking plan is crucial for aligning your tracking efforts with the desired outcomes. It’s important to start here so you know the “Why” behind your usage tracking, and why you’re building this plan. Some examples to get you started are;

Goal — Improve User Onboarding Experience

Objective 1: Increase user sign-up completion rate by 20% within three months.

Objective 2: Reduce user drop-off at key onboarding steps by 15%.

Objective 3: Identify the most common friction points in the onboarding flow and optimise them for better user engagement.

Goal — Enhance Feature Adoption and Usage

Objective 1: Increase the adoption rate of the new feature by 30% within one month of release.

Objective 2: Encourage regular usage of the core feature, aiming for at least 50% of users utilising it on a weekly basis.

Objective 3: Identify the feature areas where users face difficulties or experience low engagement and implement improvements to increase usage.

Goal — Boost User Engagement and Retention

Objective 1: Increase average session duration by 20% within three months.

Objective 2: Improve user engagement by increasing the number of interactions per session by 15%.

Objective 3: Identify high-value user segments and implement targeted engagement strategies to improve retention rates.

Remember, when defining goals and objectives ensure your goals are specific, measurable, attainable, relevant, and time-bound (SMART). This approach will help you establish clear expectations and enable effective tracking and analysis of events to drive meaningful improvements in your product.

Step 2: Key Metrics

Identifying key metrics is essential for measuring the success of your event tracking plan and assessing progress toward your goals. How can you know if you’ve achieved your goals if you don’t have metrics attached? Here are some examples:

Example Metric 1: User Engagement

• Daily active users (DAU): The number of unique users who interact with the product on a daily basis.
• Monthly active users (MAU): The number of unique users who engage with the product within a month.
• Time on site or app: The average duration users spend using the product per session.
• Number of sessions per user: The average number of times users access the product within a specific timeframe.
• Screen or page views: The total number of screens or pages viewed by users.

Example Metric 2: Conversion and Retention

• Conversion rate: The percentage of users who complete a desired action, such as making a purchase, subscribing, or upgrading their account.
• Churn rate: The percentage of customers who stop using or cancel their subscription within a given period.
• Retention rate: The percentage of customers who continue to use the product over a specific timeframe.
• Average revenue per user (ARPU): The average amount of revenue generated per user.

Example Metric 3: Revenue and ROI

• Total revenue: The overall revenue generated from product sales, subscriptions, or other monetisation methods.
• Average order value (AOV): The average monetary value of each purchase or transaction.
• Customer lifetime value (LTV): The predicted net profit attributed to the entire relationship with a customer over time.
• Return on Investment (ROI): The financial return gained from investment in the product, considering factors like marketing expenses and customer acquisition costs.

The key metrics you choose should align with your goals, be actionable, and provide insights into the performance of your product. Regularly monitor and analyse these metrics to track progress, identify trends, and make data-driven decisions to optimise your product and user experience.

Step 3: Critical Events

We have our goals, and the metrics we are going to use to measure them. Now we need the events and usage data to track our progress. You could just track everything (called Autotrack), but this isn’t the best idea. For the sake of time, getting value as soon as possible, and cost, you should focus on the most important or critical events you can measure. Such as;

• User Registration: Track when users create an account or register for your product. This event helps measure user acquisition and the effectiveness of your onboarding process.
• Account Activation: Monitor when users activate their accounts after registration. This event indicates successful user engagement and can be used to calculate activation rates.
• Feature Engagement: Track events related to specific feature interactions. For example, if you have a chat application, events like sending a message, joining a group conversation, or sharing media files can provide insights into feature adoption and usage patterns.
• Conversion Events: Identify events that capture important conversion milestones. For an e-commerce platform, this could include events like adding items to the cart, initiating the checkout process, completing a purchase, or subscribing to a premium plan.
• Onboarding Progress: Monitor events that signify user progress through the onboarding process. Events like completing a tutorial, filling out a profile, or setting up preferences can indicate user engagement and successful onboarding.
• Subscription Upgrades or Downgrades: If your product offers different subscription tiers, track events when users upgrade or downgrade their subscription level. These events help measure user value and can inform pricing or feature decisions.
• Error and Exception Events: Capture events related to errors or exceptions within your product. Examples include when users encounter a 404 page, experience server errors, or encounter validation issues. Tracking these events helps identify technical issues and areas for bug fixes or performance improvements.

Prioritise critical events based on their relevance to your goals and user journey.

The most important question to ask is, “is this event linked to a KPI and goal?” If the answer is no, then there’s no need to track it.

Regularly review and update your critical events based on the evolution of your goals, product updates, and any feedback you gather.

Step 4: Event Structure and Triggers

Now we have what we want to track, so we need to define the event structure and determine event triggers for our usage tracking. Think about the following:

Event Structure

Events are your data points, and are made up of the Event, and Properties. You need both, and want your data to be as usable and simple as possible. You’ll need to define:

Event Names: Choose a descriptive and meaningful name for each event. It should clearly represent the user action or milestone being tracked. For example:

• “AddToCart”
• “PurchaseCompleted”
• “FeatureUsage”

Some tools may let you change these after you’ve defined them, but you should really aim for a name that tells you what the event is, without being too generic that it could mean anything. It’s a tough thing to balance, which is why we also need…

Event Properties: Event properties provide additional context and details about the event. They help segment and filter the data for deeper analysis, and give you more detail about when and where the event was triggered. Make sure to include properties that are relevant to the event and can provide insights into user behaviour that is linked to our metrics. Examples of event properties include:

Event: “AddToCart”

• Properties: {“ProductID”: “12345”, “Category”: “Electronics”, “Price”: “£299”}

Event: “PurchaseCompleted”

• Properties: {“OrderID”: “ABC123”, “TotalAmount”: “£150”, “PaymentMethod”: “CreditCard”}

Event: “FeatureUsage”

• Properties: {“FeatureName”: “Collaboration”, “Duration”: “5 minutes”}

User Properties: User properties capture attributes or characteristics of the user who triggered your event. They provide valuable context for user segmentation and analysis, but are different to event properties because they are specific to a user, rather than the event. User properties can include demographic information, subscription level, location, or user type. For example:

Event: “PurchaseCompleted”

• User Properties: {“UserID”: “12345”, “SubscriptionLevel”: “Premium”, “Location”: “United States”}

Event: “FeatureUsage”

• User Properties: {“UserID”: “98765”, “UserType”: “Admin”, “Organisation”: “CompanyXYZ”}

Lots of tools used for usage tracking and product analytics include some properties as default, it’s always best to check these first as you will get some insight and value out of the box.

Event Triggers

Event triggers determine the conditions or actions that initiate the firing of your event. Most of these will be obvious, as the name of your event will correspond with the trigger and action your user is taking. Such as:

User Actions: Track events based on specific user actions within the product. Examples include button clicks, form submissions, or page visits.

Event Name: “AddToCart”

• Trigger: User clicks the “Add to Cart” button.

Event Name: “FormSubmission”

• Trigger: User submits a registration form.

System Events: Capture events triggered by system-level actions or milestones. These events can include page load, API calls, or data updates.

Event Name: “PageLoaded”

• Trigger: User visits a specific page.

Event Name: “DataUpdated”

• Trigger: The product data is updated in the database.

Time-Based Triggers: Track events based on specific time intervals or durations. This can be useful for measuring user engagement or inactive periods.

Event Name: “SessionEnded”

• Trigger: User is inactive for a specified period, such as 30 minutes.

Event Name: “WeeklyActivity”

• Trigger: User has engaged with the product at least once in a week.

Again some of these will be collected automatically by your analytics tool, especially session information. Also remember to select event triggers that align with your goals, capture meaningful user interactions, and provide valuable insights into user behaviour and product usage. Implementing a consistent event structure and triggers will ensure that your event tracking plan is well-organised, scalable, and provides actionable data for analysis.

Step 5: Implement and Test

Once you have defined your critical events and event structure, the next step is to implement the tracking code that will capture and send these events to your analytics platform.

Note: you might need development or engineering support at this step, depending on whether you have access to your product codebase or not.

This code is typically embedded in your product’s codebase, implemented through a third party tool like a CDP, or integrated through a tag manager. Your analytics tool will have comprehensive guides to how you tag and track based on the implementation method you choose, so it’s important to follow these to make sure tagging is correct. I can’t list all examples here, but if you’ve planned and built your events in the right way this will hopefully be pretty simple to understand and do.

After implementing the tracking code, it’s crucial to thoroughly test and validate that events are being accurately captured and sent to your analytics platform. This helps ensure the integrity and accuracy of the data you collect. You might not have your own QA team, so a lot of this you can do yourself, start with:

• Perform user actions that trigger events and verify if they are captured correctly.
• Test various scenarios, such as error conditions, to ensure that corresponding events are recorded.
• Verify that event properties are accurately populated and reflect the expected values.
• Monitor real-time event tracking to ensure events are being received by your analytics platform.
• Cross-reference event data with expected outcomes or known user actions to verify accuracy.
• Use third party debugging tools or log analysis to identify and troubleshoot any tracking issues.

This stage can be done in batches. Try implementing a single feature, or single user journey, run through the journey yourself and see if the events are coming through. If they are, copy and replicate the implementation across your other journeys, events, and metrics.

By implementing and testing your tracking code effectively, you can have confidence in the accuracy and reliability of the data collected. This, in turn, enables meaningful analysis and decision-making based on the insights gathered from your event tracking plan.

Step 6: Documentation and Governance

Governance is a big word, and can seem overwhelming, but documenting your tracking plan is crucial for ensuring consistency, collaboration, and clarity across your team. It serves as a reference point for understanding the events being tracked, their properties, and their purpose.

Build your plan and documentation in the place that works best for you (Google Sheets, Confluence, Notion, Figma, Excel) it doesn’t matter, just make sure it includes:

• Event Inventory: A comprehensive list of all the events being tracked, along with their names, descriptions, and event properties.
• Event and User Properties: The properties associated with each event, including their names, types, and descriptions.
• Event Triggers: The triggers or conditions that initiate the firing of each event. This helps ensure that events are captured accurately.
• Data Dictionary: This will define and explain the meaning of event properties, user properties, and other data points used in your tracking plan.
• Data Retention Policy: The retention period for the event data and specify any regulations or compliance requirements that apply.
• Versioning and Change History: Track changes made to the tracking plan over time, including updates to events, properties, and triggers. This helps maintain a historical record and facilitates collaboration.

Once you have your plan, you’ll also want to think about wider governance themes and practices. This will ensure that your tracked data is accurate, reliable, and protected. It involves defining rules, processes, and responsibilities for handling and managing data. Consider the following aspects:

• Data Privacy and Compliance: Ensure compliance with relevant data protection regulations, such as GDPR or CCPA. Implement measures to protect user privacy and secure sensitive data.
• Access Controls: Define roles and permissions for accessing and modifying the tracking code, analytics platform, and data. Limit access to authorised individuals to maintain data integrity.
• Data Quality Assurance: Establish procedures for ongoing monitoring and validation of tracked data. Regularly review data accuracy, identify anomalies, and address any issues promptly.
• Data Documentation and Metadata: Maintain a data catalog that documents the meaning, source, and usage of tracked data. Include metadata, such as data types, units, and dependencies, to provide context for analysis.
• Data Retention and Deletion: Define policies for retaining and deleting data based on legal requirements and business needs. Ensure that expired or outdated data is properly managed.

If you’ve purchased an analytics tool, a lot of this will already be included as standard. So even though this is something to think about and be aware of, it’s more important to have your documentation in place. Think of it like this;

If someone new joined your team today, would they be able to start using your data just using the tools and documentation you’ve provided?

If not, it probably needs to be improved.

Summary

Collecting and analysing product data is incredibly exciting, and although it may seem like a monumental task to get started, it doesn’t have to be that complicated.

Simply start by defining your goals, identifying key metrics, and determining critical events. Then you’ll be gaining a deeper understanding of your product’s performance. Implementing tracking code and testing the implementation will give you confidence in the accuracy of the data you’re collecting, and you’ll start to see the events come through for you to analyse.

Don’t forget to document your tracking plan, as it will be your trusty reference. Establishing data governance ensures the privacy and integrity of your data, keeping things in order.

By following these steps you’ll hopefully now have the tools and examples at your disposal, so it’s time to dive in! Embrace the learning process and remember that practice makes perfect. Experiment, analyse, and use the insights gained to make data-driven decisions, and build a better product.