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Google Analytics Academy: The Importance of Digital Analytics

Have you heard about Google’s new Analytics Academy? It offers extensive training in Google Analytics and data analysis and helps you to prepare for the Google Analytics Individual Qualification. We’ll be covering each unit as we complete them and Unit 2 of the Analytics Academy is called Getting Started with Digital Analytics. It’s all about understanding the true meaning of the term digital analytics in order to be successful with your digital analytics implementation.

What is Digital Analytics?

There are several trends in today’s world that are driving change in the way everyone does business. The internet has made an endless amount of information available to everyone. Smartphones, mobile websites and apps allow everyone to stay connected 24/7. Add cloud computing on top of that and the possibilities are not only infinite, but cheap. People are empowered with more information than ever and it is all instantly available. Because of this, there is more business data than ever to analyze and unless you have a solid system for collecting and distributing this mass amount of data, and the skills to analyze it, then it’s all pointless.

Let’s circle back around and take a look at the actual definition of digital analytics. Avinash Kaushik, entrepreneur, author and public speaker, defines digital analytics as the following:

The analysis of qualitative and quantitative data from your business and competition to drive continual improvement of the online experience that your customers and potential customers have which translates to your desired outcomes, both online and offline.

Now let’s break this definition down into the key elements of digital analytics starting with your customers’ purchase journey. We’ve all seen the traditional purchase funnel with the various stages of customer interaction.

But with the consumer being so much more in control, now more than ever, this linear purchase funnel has become irrelevant. Customers can begin their purchase journey anywhere along the decision path. That means it’s up to marketers to anticipate where customers might pop up along this path and what message they need to hear when they do. That means we need to start looking at customer behavior rather than focusing on the individual channel they’re coming from. In order to analyze customer behavior, we must understand the difference between qualitative and quantitative data.

Qualitative vs. Quantitative Data

Quantitative data is any data that is quantifiable, or can be expressed in quantities.  Quantitative data can tell you the size of your online audience, where they’re located, what they do when they visit your site and the performance of your online marketing strategies.  For a very long time, Analytics software could only collect quantitative data. But we’ve come a long way since then.  Nowadays, analytics software can virtually tell us what you had for breakfast. Okay, maybe it’s not that precise, but it can track mobile applications, cloud based applications, CRM software, video games consoles, and even household appliances.  With these abilities, we can collect an abundance of qualitative data, or rather, data that tells us why. An example of qualitative data could be data collected by a survey, for instance.

Now that we understand the differences between quantitative and qualitative data, let’s look at the last part of Kaushik’s definition of digital analytics. That is, measuring the outcomes of your customers experience.  You must first have a clear understanding of your company’s business objectives in order to implements a measurement strategy. Typically, there are five common business objectives in the online world:

  1. Selling products or services (ecommerce sites)
  2. Collecting user information for sales prospects (lead generation sites)
  3. Engagement and frequent visitation (content publishers)
  4. Helping users find the information they need (online information and support sites)
  5. Drive awareness (branding)

There are key actions on any site that tie back to these objectives. When an action results in one of your business objectives being fully met, this is referred to as a macro conversion.  Sometimes, customers might take an action that brings them closer to meeting one of these objectives, but hasn’t quite reached it. An example might be downloading a coupon or signing up for marketing emails. This is referred to as a micro conversion. Macro and micro conversions both need to be measured in order understand what really drives business objectives being met.

Finally, let’s look at Kaushik’s concept of continual improvement. The data we collect drives continual improvement.  Let’s look at the continual improvement process.

Data drives the continual improvement process but the whole thing starts with measurement and reporting. Measurement is all about collecting the data you need to answer your business questions. Then that data needs to be packaged in a way that makes it readable so it can be sent to decision makers. This can easily be done in Google analytics with pre-made reports and dashboards. The next step in the Continual Improvement Process is to use these reports to analyze your data. These can be as easy as looking at the data in aggregate to identify larger trends or as complicated as segmenting the data down into much smaller subsets of data.  This way you can figure out why the data does, or does not meat your expectations.

The final two steps in the Continual Improvement Process are Testing and Improvement. If your data does not match your expectation for meeting business objectives you’ll need to identify the potential problems and test different solutions to the problems. And finally, repeat.  Repeat what you learned from the whole process and make any necessary improvements.

Let’s look one more time at Avinash Kaushik’s definition of digital analytics:

The analysis of qualitative and quantitative data from your business and competition to drive continual improvement of the online experience that your customers and potential customers have which translates to your desired outcomes, both online and offline.

What did we take from this definition?  We learned that the traditional linear purchase funnel is no longer relevant and that customers can enter the purchase journey at any point on the path. Therefore, marketers need to take quantitative and qualitative data into consideration in order to understand customer behavior and measure the outcomes of the customer experience.  With this data, and an understanding of business objectives, we are able to make continual improvement by measuring, reporting, analyzing and testing.

Are you ready to get started with digital analytics? Stay tuned for more on analysis techniques and creating a measurement plan.

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