How to read Google Analytics data

Analytics Consultant Will Smith gives an insight on measures you can take to be confident when analysing your Google Analytics data.

Google Analytics is one of the most widely used analytics platforms, helping show web analysts and marketers how their digital efforts are performing, turning data into actionable insights. But how confident can you be in the data you’re pulling from Google Analytics? When was the last time you did an audit on your setup?

If you’re struggling to think when the last time you conducted an audit of your analytics set up was, the data reports you’re pulling might not be telling you the truth, meaning any data-led strategies you’ve implemented might be misguided.

We’ve put together our top tips for ensuring the data you see within Google Analytics is accurate.

Exclude yourself from the data set

If you haven’t excluded your internal IP address from your dataset, you might find it near impossible to differentiate you and your audience’s interaction onsite. Every time you go onto your website, your activity will be recorded in your data, meaning what you see within Google Analytics isn’t a true representation of your customer’s interaction.

You can add internal IP addresses from the Admin section in Google Analytics, but remember, any changes to internal IP addresses need to be updated!

Add multiple views to your account

One of the best ways to analyse data within Google Analytics, is to segment it. This helps provide context and meaning to the data you’re seeing, adding to the validity that what you’re interpreting is correct.

Creating multiple views is essential to ensuring confidence within your data. Google Analytics works on the basis that once the data has been recorded, there is no changing it. If you only have one view set up, and need to make changes, for example, adding a new filter, or changes to goals, you’re going to be testing on live data. If something goes wrong, there won’t be a way to go back to change the data.

As part of best practice, we’d recommend the following views be added to your account:

  • Mobile View – the rise in popularity in mobile means this little device now dominates in some industries. Having a mobile-only view allows you to quickly identify key trends, understanding how users behave differently from device to device, without sampling your data
  • Unfiltered View – this view will record everything, ensuring you’ve full visibility over interactions with your site. If all of your other views have filters applied, you won’t be able to see what you’ve filtered out, so if you ever needed to investigate any issues, a raw profile is a perfect view to allow you to see all interactions, completely unrestricted
  • Test View – identical setup to the unfiltered view, this view allows you to test out new goals or filter patterns, without effecting the main data. As it says on the tin, by first testing out the setup of any changes to your account, you can be confident in any changes moving forward
  • Main View – as your main view, this is the one you’re going to want to make sure has all the right filters in place, as you’re making any data-led strategic decisions, you’ll want to base them on the data you see within this view

Check that Referral traffic isn’t clogged with Spam traffic

An easy one for many web analysts and marketers to miss, Referral sources are an important channel for driving valuable traffic to your site, but assessing this report might highlight additional traffic you weren’t expecting.

Spam traffic is nothing new but still causes headache for us, causing havoc with data and reducing confidence within our analytics. If it’s been a while since you’ve checked the Referral traffic, you might not have noticed bounce rates are inflated, or that conversion rates are diluted unnaturally.

Luckily, most spam traffic can be easy identified and is simple to filter out. Navigate to the Referral traffic report (under Acquisition tab in Google Analytics) and skim through the list of Referrals. It’s easy to identify spam traffic living amongst legitimate Referral traffic through these key indicators:

  • Bounce rate close to 100%
  • Sudden spike in traffic
  • Zero goal completions or ecommerce transactions

The first step to excluding traffic like this is to ensure settings are adjusted to exclude known bots and spiders from the Admin tab in your GA account. You’ll find the check box under View Settings in your View section. This will filter out most of the spam which is diluting your results, but if you’re noticing stubborn spam sources, adding these to the Referral Exclusions list is a sure-fire way of wiping them out.

No one know your data quite like you do