Data Tracking: What Is It and How To Create a Tracking Plan?

The most successful teams create implementation specs and tracking plans to plan their analytics implementation when launching new products.

This lesson will show you how to create a simple tracking plan that will reduce the amount of time and resources required to set up your analytics quickly and efficiently.

What is data tracking?

Data tracking refers to the collection, identification, and classification of individual data points throughout the data pipeline so they can be used in data analysis.

Data tracking refers to the tools that companies use to organize their data, as well as the ethical framework used to protect users’ privacy and security.

What is a data tracking plan?

Businesses create a data tracking plan to identify the events they want to track and the tracking methods that they will use. Data tracking plans will detail details such as where events should go in the code base, and why they are important from a business perspective.

Not only does tracking help everyone be on the same page when data collection and implementation takes place, but it also makes it easier for marketing, engineering, and analytics teams use the data collected and to understand it.

Your tracking plan should be available in an easily accessible format such as a Google Sheet or Wiki. This will allow multiple stakeholders to share and update it. Segment protocols can be used to manage and enforce your track plan from the app.

Good tracking plans do the following:

  • Summarizes the events and properties that need to be added
  • They are justifiable reasons why they should be tracked
  • You will need to add details where they are needed in the code base
  • Informs stakeholders about progress/completion

Also read: What Is Data Normalization And How To Work?

What are you looking for?

Analytics is crucial in the “Measurement” and “Learning” steps of the Build-Measure-Learn process that we discussed in the first lesson.

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The best way to determine analytics and to scope them out is to do so by figuring outWhat do you want to know about your product/business. Use a question, goal, or key metric. Asking questions encourages a structured and targeted approach to distinguishing meaningful events from noise. Tracking Red Button clicks probably won’t help you determine which marketing channels are driving the needle.

These are the questions you should ask if you’re new to adding analytics to your product.

  • Acquisition: Which channels are the best for acquiring new users?
  • Activation funnel: Where are people dropping off our product?
  • Retention: how do we reduce churn?

You can also ask more specific questions, which will allow you to track fewer events.

  • This pricing page will drive more sign-ups.
  • What is the lifetime value of our customers?
  • What can we do to improve our activation funnel?

Once you have posed the question, The next step is to compile a list of metrics to answer your question. These metrics will ultimately determine how data is tracked.

What metrics can help you answer your question?

Although the required metrics may vary from one question to another, these examples will help you understand how they all work together.

Acquisition

  • The growth rate of new users
  • User Attribution (Knowing the origin of the user)

Activation

  • Total users
  • User activation rate
  • Active users

Retention

  • Churn rate
  • user satisfaction (NPS)
  • engagement

What’s our customer LTV?

  • the average revenue per customer
  • Frequency of purchase per customer
  • Time to repay customer acquisition costs

Can this pricing page drive more sign-ups?

  • Pricing page view to signup conversion rate

And so on.

These metrics answer your question and provide a list of events you will need to track. Win, win!

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Let’s start by looking at the activation rate to find out how we can extract the events we want to send.

Example of data tracking: activation rate

Activation is the key step in the conversion funnel. It involves turning new users into engaged users. The activation rate is simply the percentage of users who are activated or become engaged after signing up.

Divide “active” users by the total users to get activation rates. The definition of “Active” will depend on the business you run, but it should indicate that the user has engaged and is on his/her way to becoming a paid customer.

By understanding key customer behavior indicators that show when your product is of great value to customers, we can help you define “active”.

For example, for community forums, some activation events can be “Voted”, or “Commented” to indicate that the user is participating in the forum, rather than “lurkerdom”. ”

Segment considers a customer active if they send data to more than one integration.

First, the customer needs to create an account. This event is called “Account Created”.

One of our key activation events, “Integration enabled” means that they have successfully set up Segment integrations. Data Sent is the other important activation event. It means that they have successfully used Segment to send data from another tool. This is the first step to getting value.

Now, it’s your turn to answer. What is the key event that shows your users are getting significant value from your product?

It’s a good time now to talk about naming conventions if you haven’t yet added any tracking events. A consistent naming convention, similar to programming style guides, will make it easy to jump in later to understand what the event you are tracking in an end-tool means.

While we like the object action framework, there are many other options.

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Also read: Data Discovery: What It Is, Uses and Tools

Which properties should you include?

Each .track() call may accept an optional dictionary properties. This dictionary can include any key-value pair that you choose. These properties are “dimensions” that allow analytics to group, filter, and group the events. These properties provide additional information on broad-based events.

Although you can throw anything you like in there, it’s best to stick with “less is more”. Wait until you have a question that requires filtering or grouping of customer information before adding this property.

Let’s take the example of “Account Created”, and let us say that we want to also analyze activation by marketing channel. We give the answer This parameter can be passed in the .track() request.

In the end tool, you can then group all users that came in from different URLs at the time Account Created occurred.

We might be interested in knowing which libraries are most popular, and how different integrations and libraries impact activation.

These two events can be tracked to measure activation and provide context on what effects activation has based on the properties.

Many customer data tools allow you to pass a dictionary of properties along with a track event. Segment code shows this, but many other tools, such as Amplitude, Drift and Customer.io, support similar schemas.

Ready to get started?

These are the rules to follow when you start creating your tracking plan. Keep it tidy, neat, and semantically helpful.

  • Do not create event names dynamically
  • Do not create events to track properties at lower levels
  • Every event should help you answer a question about the business
  • To build your funnel, start with your core customers.
  • Add events only if you feel they are missing