Digital analytics reports can be highly accurate, but not precise. This is because many users don’t understand the data or how it is gathered, and different tools measure the same data points differently. Data accuracy is impacted by factors outside of the control of the analytics software, such as users blocking cookies or other tracking methods, or internet blips. The best way to think of analytics data is as a poll of user activity.
The accuracy of analytics data can be monitored by comparing it to other sources of data. For example, when using Shopify and Google Analytics 4 together, daily variations in total revenue and orders can vary from 0-13%. In these 24 days, GA4 reported 5.6% less revenue and 5.7% fewer orders. This data is accurate enough to determine which marketing efforts are working, but not precise enough to match sales records.
Accuracy can be improved by pushing data directly to analytics tools (server side). This avoids issues with user browsers and cookies, however it is a complex and time-consuming method to implement. Organizations must consider whether the extra effort is necessary to capture another 2-5% of sales revenue in their analytics reports.
Ultimately, analytics data can be both accurate and useful. By understanding the nuances of data measurement, and monitoring and comparing data sources, organizations can gain valuable insights into their users.
Originally reported by Martech: https://martech.org/accuracy-in-digital-analytics-what-marketers-need-to-know/
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