Become a Customer Data Pro!
Each Data-led guide covers an important aspect of Customer Data, enabling you to learn rapidly and contribute to projects involving Customer Data Infrastructure (CDI).
Customer Data Platform is a hot category in SaaS right now. But is it just that or something more? Read this guide to learn what exactly it is and whether you need to adopt one or not.
Customer Data is a combination of user data (known as entity data) and interaction data (known as event data). This guide covers the ins and outs of customer data and the process of collecting and managing it.
Event data helps you understand exactly how your product is being used — whether or not users are performing critical actions and completing the tasks that will lead them to derive the product’s core value, or in other words, reach the aha moment.
Event data helps decipher what is going on inside a product while entity data tells you who is performing those events, enabling you to get a grip on your user personas and create user cohorts.
Customer Data comprises Event Data and Entity Data — you already know that if you have gone through the previous parts of this series. In this guide, you will learn about the components of event data, the preferred naming convention to define events, the categories of entity data, and the two main types of entities.
To decide which events to track and what data to gather, you need to list down questions you have about your users and their product usage. Once you start listing those questions down, you will realize that there is so much you want to know.
Introduction A data tracking plan (usually referred to as just tracking plan) is a document that acts as the source of truth for your customer
Data type is an attribute associated with a piece of data that tells a computer system how to interpret its value. Understanding data types ensures that data is collected in the preferred format and the value of each property is as expected.
Data democratization is the ongoing process of enabling everybody in an organization, irrespective of their technical know-how, to work with data, feel comfortable talking about data, and as a result, make data-led decisions.