Transforming Data Into Knowledge
The platform principle course on Google Analytics is similar to their fundamentals course, but explains more on how data is handled and later presented. If businesses do not understand the components that go into their data, their data is essentially meaningless. This is why it is important to truly come to terms with the underlying principles of data, and what data is relevant because insights based off data later lead to business decisions.
“Not everything that can be counted counts, and not everything that counts can be counted.” -Albert Einstein
There are four key elements to the platform principles:
Collection: This is when all your data is collected and recorded. Essentially Google Analytics sends “hits” from your website to its servers to be processed. Google Analytics does this by using the Javascript code (which you should place before the closing head tag), which tells the software to begin collecting data.
Configuration: Put simply, configuration is the settings you specify to Google Analytics regarding what type of data you want collected. For example, you may want a filter that removes website interactions that come from your own business.
Processing: Once raw data has been collected, it needs to be processed by your configuration settings. It is important to note, that once raw data has been processed, it can longer be changed or retrieved.
Reporting: Google Analytics merges the data to then generate reports. This is where you will analyze your data.
These principles work together to give us our data model. There are three elements that make up the data model: users, sessions, and interactions.
- Users: the visitors to your website
- Sessions: the time that your users spend on your website
- Interactions: what users are doing on your website (ex. watch a video, make a transaction etc.)
The users, sessions, and interactions exist within a type of hierarchy. Interactions sit on the bottom, sessions in the middle, then users at the top. Perhaps we can even imagine them fitting in one another like a Matryoshka stacking doll. With each doll we remove, we get the chance to see a new doll. This is similar to the data model. We first need to understand the interactions that occurred on the website, which then compile to create sessions, which lastly allow us to understand the users.
"The goal is to turn data into information, and information into insight." -Carly Fiorina
While browsing through the Google Analytics of their Google Merchandise Store, I found a few interesting observations about the data. The first one somewhat surprised me; over half of the sessions on the website were from men. Yet men and women’s conversion rates remained quite similar (4.94% and 4.71% respectively). So it appears that if the women make it to the site, they are as likely to make a purchase as a man. So why are there so few women visiting the site compared to men? What I gather is that the online store is not marketing itself appropriately to women. Perhaps their current advertising practices are simply not as eye catching to women as to men. This is an important statistic because bringing more women to the website would undoubtedly generate more revenue.
Secondly I found the difference between new and returning visitors intriguing. New visitors are more than double the number returning visitors to the website. The conversion rate and the number of transactions, however, is much higher for returning visitors. This tells us that returning customers are more likely to make a purchase than a new customer. So the goal is to increase the number of return visitors, while also keeping the number of new visitors high. There are several ways an online ecommerce site such as the Google Merchandise Store could work on this. Perhaps creating a customer loyalty or rewards program, or other incentives for being a repeat customer.
Sources
31 Essential Quotes on Analytics and Data. (2012, October 25). Retrieved January 12, 2017, from http://www.analyticshero.com/2012/10/25/31-essential-quotes-on-analytics-and-data/
Guppta, K. (2014, November 17). Why Marketers Struggle To Understand Analytics. Retrieved January 12, 2017, from http://www.forbes.com/sites/kaviguppta/2014/11/17/why-marketers-struggle-to-understand-analytics/#45722424112e