We created this in early 2019, when Integromat was experiencing rapid growth and adding almost a thousand new users every day. Naturally, a lot of data was being generated but we didn’t have any data infrastructure in place to collect behavioral data and run experiments.

We obviously needed tools and while I went shopping, I also initiated the process of deciding what data to collect and for what purpose. I had listed down my burning questions, figured out the first set of campaigns and experiments I wanted to run, and created this tracking plan where I listed the events, associate properties, and all the other details that would help ease the instrumentation process.

Traditionally, this document allowed product managers and engineers to collaborate on the instrumentation needs of product teams as they shipped new features and wanted to track feature usage.

As the use cases for behavioral data have expanded, growth teams got into the process of deciding what data to track in order to run data-powered experiments and campaigns. Moreover, at organizations without dedicated Product teams (or where Product is rolled into Product Marketing as Airbnb did recently), the tracking plan is created and maintained by a Growth or Marketing leader, and in some cases, by a member of an Ops team.

This tracking plan acted as the source of truth for the behavioral data we collected and sent to our product analytics tool as well as two other tools that powered lifecycle emails and in-app experiences. Together, these tools acted as our growth stack and enabled us to run experiments and campaigns powered by event data, Needless to say, those efforts helped identify points of friction, optimize the onboarding, and increase the activation rate.

Prior to setting up our customer data infrastructure and growth stack, all our efforts were focused on organic acquisition, and there wasn’t much for us to do once the user was in the product.

This Artifact was how we started our data-powered growth efforts, which led to the company’s acquisition for over $100M – a pretty good outcome for a bootstrapped, product-led organization.

Before building our tracking plan, I spent countless hours going through every tracking plan I could lay my hands on to come up with a format that was comprehensive and enabled me to be as specific as possible, while also keeping the tracking plan simple and less overwhelming for other team members. When I finally shared the first version with the two engineers who were working with me on our tracking project, they seemed pretty happy and found it easy to follow so we didn’t really run into any challenges.

However, a lot of questions did come up during the implementation process – from the engineers but also from me – and to keep things organized, I decided to add a sheet to the tracking plan to keep track of the questions and the answers.

All of these little design choices made it really easy for new team members to quickly understand what data we were collecting and what was in the pipeline.

Arpit Choudhury

This data tracking plan acts as a source of truth for databeats' event data. The primary purpose is to act as a living document that contains all the information related to the behavioral data teams gather about users and customers.

The Ultimate Product Data Tracking Plan by databeats

The Ultimate Product Tracking Plan at databeats

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