My First Week with Digital Analytics

 The first week starting off with the CXL Mini-Degree course in Data Analytics has been a new and jovial experience in itself. Starting off with the very basics of what Analytics as a domain is, and how it can be used to not only leverage the potential of a company but also hedge its position to make it stand strong through all types of terrain. What analytics basically aims to do is to replace the mysterious black box in our brains that clouds our vision, and helps us combat this issue which prevents us from seeing exactly how and from where we can generate revenue and/or leads to further grow the business.

The whole concept of understanding just how important data collection, processing, and representation is hit me like a ton of bricks as soon as I saw how Google uses the data collected from devices and helps companies market that information to reach higher potentials. The simple click of a button could mean the difference between monitoring real-time data across devices and foolishly charting future plans based off of speculation.

The core aim of every business is to make profits and do right by the investors due to which it sets various objectives; planning a booming IPO (or maintaining a high stock price), achieving/maintaining a decent market capitalization, and providing a product or service of intrinsic value to the consumer. However, the core of all the above still remains the successful analysis of data and perfect execution of leads gathered by said data. The instructor raised an interesting point, by pointing out that the goals we set should be DUMB; Doable, understandable, manageable, beneficial. Some key benchmarks, called the Key Performance indicators are a must to be kept in mind to ensure the maintained growth and stabilized performance of the company in a digital sphere; such as Revenue per customer, Conversion Rate, and Average items per cart completed.

Moving onto the limelight topic of interest; digital analytics to analyze data off of websites, apps, and all kinds of digital interfaces, I was shown the wonders of GA4 and the power it harnesses over its predecessors such as Universal Analytics. GA has been shown to have historically had a strong backend, which has gone through constant upgrades from its inception over 15 years ago. The recent integration with Firebase has led GA to forego its previous hit-based data model to move towards an event-driven data model, which provides a clearer picture with respect to the information being provided, since we are able to extract a larger volume of individual data with a high degree of accuracy. The introduction of Automatic Measurement to supplement the eventual upgrades in the backend and UI has exponentiated the capabilities of GA4, since now adding onto the cross-platform integration of Google Tag Manager and Google Data Studio will provide the average company a chance to harness the power of data to their advantage.

Moving onto the nitty-gritty of it all, the three tools used above are so crucial in the digital sphere that their integration has created a behemoth in the marketing sphere which is a sight to behold. Linking the flawless data collection off of various devices and sources across platforms provided by Tag Manager, to the impeccable reporting capabilities of Data Studio, GA has benchmarked efficient data storage onto the market. Furthermore, the addition of Google Signals, Realtime Reports, DebugView, and the available tools for Segmentation and Audience Building provide further assistance to analyze and utilize the data available to see the segments that lead to revenue generation. 

Funneling was one avenue that took me by surprise and reminded me somewhat of what I had seen while querying data over MySQL since in both cases we had raw data fed into the program and we would receive output on the basis of the conditions that we would impose onto the program. However, this was intricately different as well ! Here, we could classify funnels as open and closed funnels, wherein one could allow the users to enter at any step, and the other requires the user entry at the very beginning. 

Additionally, I saw BigQuery linking as a major win for the GA4 software, since now we had the ability to export our data to be used into any platform that we want, which allows us a multi-platform utility that we can use to integrate the strong points of various softwares to our advantage.

Then, I actually started the technical implementation of what I had learned. Starting off by creating a dummy website on Weebly, I found out how to plant my trackers and Google Tag Manager into the mainframe so I could monitor and comprehend how and where I could direct my traffic to achieve maximum output. I started off with a basic template and added a few minor details to make it more personalized. The link for the same will be provided below. (P.S., I name my site DataLytics101. Neat, right?)

I plan on using the same techniques on my own site to improve user traffic and user experience while monitoring how and where I could improve. What struck me the most while fiddling around with the settings, was how integrated all of it was on the GA4; I could enable advertisements and monetization and parallelly see where the majority of my audience was headed to cross the paths between the two and create ample avenues for profit. Added with Real-time tracking along with the storing of historical tracking, I could gauge the retention rate of users who visit my site, and that provides me with a deeper insight into the individual analytics.

However, the most fundamental change in my thought process occurred when I realized that aggregated analytics or ‘averaged’ analytics hold no influence over the final decision making since the outflow of ideas we receive on its basis would definitely not work on an individual level, which basically boils down to either stagnation or low-level growth.

Here is the link to the site I am learning everything from!

https://cxl.com/

Here’s my site’s sub-domain, check it out! 

https://datalytics101.weebly.com/