Google and Adobe Analytics are powerful tools for tracking the success of your Digital Marketing campaigns. However, despite your best efforts, inaccurate campaign tracking can occur. These inaccuracies can negatively impact attribution, operational efficiency, and return on ad spend (ROAS).
Moreover, the marketing team is in a race against time to spot and fix the issue as the impact of the low data quality on marketing activities grows with each passing day.
In this blog post, we will explain how to spot tracking issues using Google or Adobe Analytics.
Let's get started!
To explain, let's consider a marketing campaign-called "Ship4Free" which aims to attract customers by promoting free shipping.
It will run on Google Search through paid ads, an Email Newsletter, and organic posts on Social Media.
After a couple of days, the marketing team wants to view the results. However, when they log into Google or Adobe Analytics, they may encounter one of two issues:
Consider the following three possibilities:
For the rest of the article, let's assume that all pages, events, apps, and websites, have the necessary analytics tags for counting page views. It's essential to note that identifying data quality problems caused by misfiring or missing tags is a separate use case itself.
Here are some examples of missing Campaign or wrong Channel data from Adobe and Google Analytics reports.
Data-driven practices are only as good as the data they're based on. That's why bad data quality can break business leaders' trust in Digital Analytics. After all, if the data is wrong, the insights derived from it will also be wrong.
The theme of data quality problems came up during the recent market research we conducted with over 40 digital marketing and analytics professionals.
Below is a quick take on the importance of data quality as of today.
Attribution is the process of assigning credit for a conversion to a particular campaign, ad, keyword, or other touchpoints. Attribution analysis involves analyzing this data to improve marketing effectiveness.
Despite its importance, attribution data is often inaccurate or incomplete, which can lead to false conclusions and suboptimal decisions.
Wrong raw data for marketing activities is the bane of every performance marketer's existence. If a campaign is not tracked correctly, its attribution credit will be assigned inaccurately. This can lead to unreliable attribution analysis that does not reflect reality and can result in incorrect decisions based on that analysis.
➡️Cleaning up any campaign tracking problems as fast as possible is crucial to ensuring reliable insights. Failing to do so can lead to untrustworthy analysis results, rendering them useless for decision-making purposes.
Operational debt refers to the additional effort marketing teams will be forced to make in the future due to inaccuracies in marketing attribution and performance data.
When marketing teams try to measure the success of future marketing activities, they may encounter faulty benchmarking data or be forced to spend significant time adjusting the comparison window at a very granular level.
In a highly competitive industry such as retail, there is little time to pause activities and correct past performance data. As a result, the operational debt is not resolved and poses a risk to future operations.
➡️Marketing teams should prioritize accuracy in data collection. Failure to do so can result in additional expenses and complications down the line.
The use of lookalike audiences is a standard tool among advertisers. They enable advertisers to reach new potential customers who share similar attributes with their existing customer base.
Lookalike audiences are generated using data from an advertiser's customer list, for example, a CDP. Then, a predictive model identifies individuals that are likely to be interested in the advertiser's products or services based on this data.
However, these data models can only be as accurate as the data used to create them. If marketing campaigns are not tracked accurately, the resulting data models used to create lookalike audiences will be less accurate, negatively impacting their performance, and marketers risk wasting their ad spend.
➡️A lower ROAS
Inaccurate attribution analysis leads to the misallocation of resources. Lower operational efficiency of the marketing team means more time spent on day-to-day tasks rather than long-term strategic initiatives that drive return. Finally, audience models with a lower conversion rate have, by definition, a direct impact on the return on ad spend.
It's highly unlikely that all of the visitors who directly land on your website typed in the exact URL Ronaldo mentioned in his latest Instagram story. Therefore, if you notice a spike in direct traffic, it's a sign that one or more of your campaigns may have a tracking issue. The direct traffic channel usually follows a steady and predictable trendline, with visitors landing on the homepage in most cases.
But, don't dismiss the chance of the Referral traffic increase trend to be the result of a legit and popular website referring to yours!
Paid and organic traffic show different user behaviors. Typically, paid traffic comes with a higher bounce rate - or a lower Engaged visits rate in GA4. At the same time, when paid traffic lands without a campaign tracking code, the analytics platform can mistakenly classify the visit under the correct channel but as organic instead of paid. Consequently,
However, the moment an organic channel starts reporting a total that includes paid traffic, the overall bounce rate of the channel increases, because the segment of paid traffic skews the trend and the total no longer reflects the organic user behavior accurately.
Sometimes untracked campaigns retain the referrer information. A high increase in Referral traffic is an indicator of an untracked campaign, which can be verified by checking the full referrer URL. Certain referrers can be easily identified as ad networks. E.g. if the referrer contains ‘googleads’, it’s almost certainly a mistracked campaign.
When campaign tracking is not (properly) implemented, Google and Adobe Analytics can identify the paid channel correctly if the incoming traffic matches default or user-configured rules. For example:
To detect such cases, search for 'undefined' or 'not set' values in the Campaign (Google Analytics) or Tracking Code (Adobe Analytics) dimension.
Actions to take
Web analytics tools sometimes automatically remove all tracking parameters from the 'URL' dimension, and store their values in dedicated dimensions - only when implementation is done correctly. Therefore, the existence of tracking parameters in the Landing Page or Page URL reports is a clear sign of a mistracked campaign.
Here are some examples of improper campaign tracking parameters implementations:
Regardless of whether you use Google or Adobe Analytics alerts, both systems have some constraints.
What if you don't want to bother with creating (and maintaining) a myriad of alerts?
Well, Baresquare lets you skip a step!