The google analytics 4 report with misleading metrics set
The google analytics 4 report with misleading metrics set
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Have you ever implemented a product item list appearance on web page based on Google Analytics 4 (GA4) data, only to discover later that the metrics you used were misleading? This case is surprisingly common, especially if you are not expert of GA4. Let's delve into a situation where a product manager might fall prey to this trap.
Imagine a product manager wants to improve click-through rates (CTRs) for items on a web page. They define a key performance indicator (KPI) to track the conversion rate from item views to item clicks. This seems easy and simple to do, right?
Based on the product manager's findings, the organization will invest time and resources to implement the changes. This investment is justified by the potential to achieve positive outcomes and improve overall organizational performance
Here's the catch: the product manager relies on a standard GA4 report titled "Items purchased by Item list name." They implement the changes and see a promising 2% increase in CTR based on this report. However, before presenting this data to stakeholders, they accidently spotted a crucial detail: the CTR in this report might not reflect what they intended to track as KPI
The issue lies in how GA4 defines metrics. The "Item list click through rate Items added" defined on User level not on Item level. This discrepancy throws the entire analysis into question. The product manager's efforts may have indeed increased user engagement (amount of user with click), but not necessarily clicks (per user)
This scenario highlights the importance of involving data experts in the data analysis process. They can possess the expertise to:
Challenge and spot weakness of any step of collecting and interpreting data
Take in account nuances of any data analysing tool with using metrics and dimensions
Assess risk of bias and inaccurate results
Come up with additional ideas how to validate and double check outcomes
How to properly understand data schema for calculation
How to prove results statistically
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