How To Import Data Directly Into Google Analytics?

How To Import Data Directly Into Google Analytics?

Putting data into Google analytics can be done using numerous ways. Some of the commons one can be from a website with a JavaScript, using an SDK, or from any network connected device through measurement protocol. But one way that is gaining popularity in today’s times is- importing data through Dimension Widening.

Dimension widening allows you to import extra dimensions and metrics into Google Analytics via a CSV upload or import data using an API. Let’s get into detail and see how one can use Dimension Widening to import data-

Why Add Extra Data?

Firstly we need to know that why do we even need to add the extra data. We all know that analytics are most valuable when they are aligned closely with your business promotion strategies and tactics. Adding extra data like customer history, content publishing, etc. will provide a base to your data and make it easier to keep a tab on the performance and identify better opportunities. This is where Dimension Widening comes forwards.

Dimension widening is used to shift data to Google Analytics so that your data can be accessed on a single platform.

How does it Work?

Dimension widening allows you to upload two types of data-Dimensions and Metrics. Dimension is an attribute of a user or the sessions that user creates and metric helps the marketer in counting elements like time, money, clicks, etc. When you use dimension widening, you upload value for one or more dimension or metrics. Values can be uploaded for existing dimensions/metrics or new ones that do not exist in the analytics. When Google analytics will process the data, it will add your custom data to the existing data using a key. Google will then process your data and try to find a data with the similar key value in the Google Analytics. If the key gets matched, then the custom data will be added to Google Analytics.

Configuring Dimension Widening is a process that includes just 4 steps-

  • Identify the Data that You Want to Import

The first step involves identifying the data that you want to import to Google Analytics. While doing this you can ask yourself what data will help you in understanding the behaviour of your users? This step also requires you to define your key. Without defining your key, you cannot import data.

  • Create Schema in Google Analytics

After defining your key and deciding the dimensions/metrics that you want to import the next step is adding a schema to Google Analytics. Choose some property in the admin part, and then choose data and the Dimension Widening. The data asset that you import should have name. Make sure that the name is descriptive because you may be uploading multiple data sets. Now choose the view where you want your data to be applied.

  • Building CSV file

After you are done defining your schema, save it. At that moment you will be given the option to get more details of your CSV file or get an API key to upload your data programmatically. You can download the CSV template or get an API key.  Next click the Get Schema option.

  • Upload the CSV file or Send Data via API

You can add data in two ways- either by manually uploading the file or via an API.

Uploading Custom Data

Uploading custom data is what marketers actually look for so let’s see how it is done.

The process of uploading custom data is almost similar with just one difference i.e. you first need to define your dimensions in the Google Analytics admin section. To upload any dimension or metric, Google analytics first needs to define those custom dimensions and metrics.This is not a complex process. All you need you do is just give a dimension name and choose a scope. After the custom data has been defined you now need to create your CSV file with appropriate headers and upload the data.

Dimension Widening is a simple concept to be used to upload data to Google Analytics, but marketers need to keep in mind a number of things to avoid turning the simple process into a complex one. They need to ensure that data is not applied to historical data and need to remember that not all dimensions can be widened, etc. Dimension widening may seem quite limiting but we need to remember that it is still in the initial phase and will grow over time.


Share your thoughts