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Google Analytics - Part 2: Core Analysis Techniques

10/06/2016

Articles in this series:

Understanding how to interpret and analyse your data will allow you to harness the power of Google Analytics to help inform important business decisions about your website and online strategy. Although Google Analytics provides you with terrific aggregate data that highlights trends and patterns in your website traffic, you need to be able to analyse this data so that you can hypothesise about the underlying causes and adjust your marketing strategy accordingly.

Segmentation

Our first key technique in data analysis is Segmentation. Segmentation refers to the process of separating your data into subsets so that you can better understand the various factors at work. 

Let’s look at an example: Claire owns a boutique travel agency and notices an increase in website traffic between Monday to Friday, but finds it drops off at the weekend. By looking solely at the aggregate data, she isn’t able to understand what might be driving this pattern and therefore how to adjust her marketing endeavours appropriately.

When Claire begins to segregate her data by time of day, device and whether they are new or returning customers she discovers that the upturn in traffic occurs primarily on mobile phones, during the typical hours of commuting, and that the majority of people are new users coming to the website through referrals from social media channels like Facebook. Claire can now use this segregated data to formulate a hypothesis and inform her next marketing campaign. She predicts that there’s new business to be won by publicising a special offer on her company’s social media accounts aimed at tired commuters. During and after the campaign Claire can then test her hypothesis to see if she is receiving an upturn in sales as a consequence.

Data can be segmented into many different subsets through Google Analytics, but here are a few of the most common ones:

  • Date and time
  • Device (e.g. mobile, desktop, tablet) 
  • Marketing channel (e.g. search engines, email, referrals from other websites, paid advertising etc.)
  • Country
  • Customer characteristics (e.g. first-time customers or repeat customers)

Context

The second key technique in data analysis is Context. Applying context to your data allows you to assess whether you’re performing well or not. There are essentially two forms of context:

  • External ContextThis could be industry-standard benchmarks, or wider market trends that are influenced by factors you don’t control, e.g. you may be in the middle of an economic a recession that’s impacting consumer spending.
  • Internal ContextLooking at your data through internal context involves comparing your performance with historical data to see if you are performing better or worse than you have in the past. Looking at historical data can help you formulate internal benchmarks for what you believe to be the level of expectation in the business. These benchmarks can then serve as your Key Performance Indicators (KPI’s) when you review your data in the future.

If you would like to speak to a member of the team about implementing Google Analytics on your website or how you can use it to greater effect, why not get in touch.

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