Understanding Unexpected Traffic Spikes from Google My Business: A Case Analysis
In the landscape of digital marketing and website analytics, interpreting traffic data accurately is crucial. Recently, I encountered an intriguing situation where a noticeable spike in website activity was observed, prompting a thorough investigation into its origin and nature. This case study aims to shed light on the possible causes of such anomalies, particularly in relation to Google My Business (GMB) traffic, and offers insights into analyzing and differentiating between genuine user engagement and potential bot activity.
Detection of Traffic Anomaly
While reviewing Google Search Console, I identified a sudden increase in clicks and impressions for one of my websites. Initially suspecting bot traffic, I examined the detailed source data using UTM parameters embedded in my GMB profile link. This helped confirm that the spike appeared to be linked to traffic originating from my Google My Business listing.
Analysis Using Google Analytics 4 (GA4)
Switching over to GA4, I scrutinized the traffic under various reporting segments. Notably, the session primary channel grouping labeled this traffic as “Unassigned.” Interestingly, when analyzing the session source and medium, the traffic reverted to “organic/gmb,” aligning with the GMB URL that triggered the spike in Google Search Console. This inconsistency raised questions regarding the nature of the traffic: Was it legitimate user interaction, or was it bot activity masquerading as organic engagement?
Identifying Key Conversions
Further analysis of key engagement metrics revealed that two form submissions originated from this same unassigned channel and source/medium. A quick review suggested these were genuine leads rather than spam, highlighting that not all sudden traffic spikes are necessarily malicious or irrelevant. This underscores the importance of examining conversion data alongside traffic sources.
Geographical Insights and Concerns
To contextualize the traffic, I reviewed geographic data, focusing on regional and city-level distribution. Interestingly, the activity was concentrated within my local area, including my home state and town. This proximity made it less likely to be typical bot traffic, which often originates from suspicious or unrelated locations. However, I remain cautious, contemplating whether external factors—such as recent infrastructure developments like new data centers in Boston and Revere—could influence traffic patterns either genuinely or artificially.
Reflections and Next Steps
This experience highlights the complexities of interpreting traffic data, especially when anomalies occur. While some spikes can be attributed to legitimate activity—such as increased visibility from a GMB listing—others may require further scrutiny to rule out bot or spam interference.