Troubleshoot Invalid Traffic and Bounce Rates in Google Analytics

  • Updated

Context

When reviewing ad campaign activity with Google Analytics, you might notice the following trends that on the surface alone portray a skewed profile of what’s going on:

  • Discrepancies between Google Analytics and Demandbase Advertising Cloud data
  • High bounce rates

Discrepancies between Google Analytics and Demandbase reported numbers for pageviews and clicks persist because they are different systems. Both systems are asynchronous JavaScript solutions that fire in the browser at different times. The difference contributes to a variety of the discrepancies in reported ad engagement results:

  • Demandbase filters out known bots, creative validators, and creative testing activity from clicks and CTR data reported in ad campaign results. Google Analytics doesn’t automatically filter out such invalid traffic (IVT). 
  • Users often click on an ad by accident and quickly navigate away before the landing page finishes loading. In some scenarios, the activity causes Google Analytics to load enough parts of the page to tally a click, but Demandbase doesn’t. The firing order of each solution can differ, causing discrepancies in what activity is tracked. Likewise, cookie consent managers might treat Google Analytics and Demandbase differently, depending on your company’s cookie policies.  

Even within Google Analytics itself, the reported number of clicks doesn’t equal the reported number of visits because users might click your ad but the click doesn’t necessarily result in a pageview of your landing page. Click-to-land ratio benchmarks can vary greatly, as discussed on the What is the standard Click to Visit ratio? Quora page.

The factors listed above can also inflate the reported bounce rate. See the Google Analytics documentation of bounce rates for tips about how you can get a clearer picture of bounce rate if you suspect it’s too high. 

You can exclude much IVT activity with Google Analytics when you analyze ad and landing page performance. The steps below can help you configure a custom report to filter out much invalid web page activity. The filter recommendations suggest excluding activity that originates from Linux operating systems. Most pageviews served to Linux computers are the result of automated processes like creative validators and bots.

Steps

  1. Open the Admin page of Google Analytics.
  2. Select View Settings.
  3. Enable the Bot Filtering option by ensuring the checkbox by Exclude all hits from known bots and spiders is selected.
  4. In the left navigation pane, go to Customization > Custom Reports.
  5. Click +New Custom Report.
  6. Configure the report for your needs in the Edit Custom Report window, including the following selections and manual entries in the Filters section of the window:
    • Exclude > Audience > Exact > Bot (this option isn’t available in all Google Analytics versions)
    • Exclude > Operating System > Exact > Linux
    • Exclude > Page > Regex > x-bw-preview=1
    • Exclude > Page > Regex > db3id-0-0-0

Google_Analytics_filters_IVT.png

The x-bw-preview=1 and db3id-0-0-0 values filter out IVT because these values are appended to query parameter results based on non-human web page activity such as creative validators.

Was this article helpful?

0 out of 0 found this helpful