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Data and Analytics -Real Saviour of Marketers Part-1

Data & Analytics
Last couple of weeks we were seeing deeper on experimentation, running A/B tests best practices. This week let’s dive deep into the data analytics and analysis skills you’ll need to map out and optimize for your KPI’s and goals.
 
The core idea behind this unit is to learn more about the data you have at your disposal, and how to understand consumer behavior. To enhance your growth marketing strategy and drive value for your organization.

Data and analytics module consists of the following topics in detail,

  • Google Analytics for beginners analytics reports, event tracking, and behavioral data analysis.
  • Intermediate Google Analytics attribution, custom reports, and data audits.
  • Google Tag Manager for beginners tracking, tagging, and the data layer.
  • Attribution consumer behavior, analytics, and tactics for various channels.
  • Excel and Sheets for marketers Most critical tool to any marketers to crunch numbers, store data in an effective way.
In this article, I am not going to deep dive into the bits and pieces of Google Analytics as it is itself an extensive topic. I will give a broad overview, which will give one a very good start to begin and further learning materials one can follow.

History of Google Analytics

Urchin from Google (urchin.js)

In March 2005, Google acquired the web statistics analysis program Urchin, in November. Then it introduced the first version of Google Analytics. Urchin was initially developed in 1998 and gradually revolutionized the way customers came to engage with their new online medium.
 
Have you ever noticed those utm parameters on some URLs? Quantified Systems and Urchin were the creators. UTM stands for Urchin Tracking Module (UTM) parameters and are widely used to track traffic campaigns.
 
History-of-GA

Classic Google Analytics

The Classic Google Analytics was formed by two Google Analytics versions: Synchronous and Asynchronous.
 
Google Analytics Synchronous Code (ga.js)
In 2007, Google introduced the ga.js page tag. The urchin.js page tags continued to work, but managers were encouraged to update their existing sites with the new page tag or use it on all new installations.
 
Benefits were new tracking functionality, such as the ability to track e-commerce transactions in a more readable way and offering more control over tracking services.
Google Analytics Asynchronous Code (ga.js)
In 2009 Google announced a new Asynchronous Tracking Code snippet that made use of the same ga.js code. The asynchronous implementation allowed webpages to load faster, data collection and accuracy were improved, and errors caused by dependencies were a thing of the past.
 
The old JavaScript snippet continued to work as usual and the use of the new snippet was entirely optional. However, using the new snippet was recommended for improved performance.

Universal Analytics

Analytics Tag (analytics.js)
Released in October 2012 as a beta for “large enterprises, such as Premium customers” and later opened to the public in 2013,
Universal Analytics offered new tracking codes for websites and tools that gave more in-depth information about user behavior in addition to various back-end improvements. In this version, Google changed the ga.js to the analytics.js tagging framework.
 
Global Site Tag (gtag.js)
In 2017, Google released the global site tag (gtag.js) code. This new library worked across Google’s site and conversion measurement products, allowing users to use only the gtag.js code to manage different products rather than managing multiple tags.
The gtag.js framework sends data to Google Analytics, Google Ads (formerly Google Adwords), and other Google services in a unified and standardized way. Before the global site tag, Google Analytics and Google Ads used different tagging frameworks.
 
While Google recommended upgrading to the gtag.js, the previous analytics.js could still be used.
 

Google Analytics 4 (GA4)

October 2020 Google introduced the next generation of Google Analytics, Google Analytics 4 (GA4).
 
While clients previously needed two platforms (GA and Firebase) to analyze both app and web, GA4 aims to unify the reporting between app and web instances.
 
GA4 also emphasizes an event-based tracking approach. That too with the advent of machine learning and Artificial Intelligence to predict the user behavior across the site and app.
 
 

Let us see the features improvement by the major 4 versions,

 
Urchin 2005 included:
  • Unique visitor tracking
  • Visitor segment reporting
  • Marketing campaign result reporting
Classic Google Analytics included additional tracking capabilities including:
  • Event tracking
  • Multi-channel funnels
  • Realtime reporting
Universal Analytics 2014 brought with it:
  • Custom dimensions and metrics
  • Attribution reporting
  • Measurement protocol
Google Analytics 4 2020, bringing with it the following features:
  • Event-based.
  • Machine learning-driven.
  • Privacy-centric tracking.
  • Automatic and Web + App tracking.
 
This gives an overall picture of GA evolution. I suggest interested people
 

Google Tag Manager,

Google Tag Manager is a free tool that allows you to manage and deploy marketing tags (snippets of code or tracking pixels) on your website (or mobile app) without having to modify the code directly.
 
How GTM works.
 
Information from one data source (your website) is shared with another data source (Analytics) through Google Tag Manager.
GTM becomes very handy when you have lots of tags to manage because all of the code is stored in one place.
GTM
 
As same as for GA I recommend getting a glimpse of GTM at Analytics Academy from Google.
 
For detailed learning get into CXL academy.
 
Personally, I suggest another source called Analytics Mania.
 
 
Finally, here are some key takeaways: 
  • Never go into Google Analytics without a specific question in mind.
  • A proper analytics setup will provide you with the information you need to find a specific answer more easily. GTM is the key in achieving this.
  • Don’t track everything. Focus on a few important metrics or behaviors, and then iterate from there.
  • Finally test, test, test.
 
Thanks for reading, in the next article, let’s take a deep link into attribution models and their importance.

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