Solved: Data Transfer from BigQuery into Analytics Funnel

HGYY · 04-02-2024 10:27 AM

Hi Community!

I have a question regarding the connection between BigQuery and an analytics funnel.

Currently, we are recording user login events, logging each instance of a change in login status, and transmitting these events to BigQuery. Additionally, we are using Google Analytics funnel to extract and analyze these events. However, we've encountered an issue: the number of login events displayed in the funnel is significantly lower than the number of daily user logins reported on Google's Authentication Page.
For example, on April 1st, Google Authentication Page showed 400 login event records(which means 400 distinct users), but the funnel only logged three instances of this event (indicating only three user logins). Furthermore, we also queried BigQuery for the daily count of registered logins, confirming that there are indeed hundreds of logins each day. However, the number of login events in the funnel remains unusually low.

What could be the reason for this?

Poala_Tenorio

There could be several reasons for the discrepancy between the number of login events recorded in Google Analytics funnel and the actual number of login events reported by your authentication system and stored in BigQuery. Here are some potential issues to consider:

Sampling: Google Analytics may be sampling the data, especially if you're dealing with a large volume of events. This means that only a subset of the data is being used for reporting, which can lead to inaccuracies, particularly when dealing with rare events like user logins.
Data Collection Issues: There could be issues with the implementation of the event tracking code or the configuration of the funnel in Google Analytics. Make sure that the events are being captured correctly and that the funnel is configured to include all relevant events.
Data Processing Latency: There may be delays in the processing and reporting of events in Google Analytics, leading to discrepancies in the reported numbers. Check if there are any delays in data processing that could be causing the mismatch.
Data Filtering: Ensure that there are no filters applied in Google Analytics that could be excluding certain events from the funnel analysis. Sometimes filters are inadvertently applied, leading to discrepancies in the reported data.
Sessionization: Google Analytics operates on sessions, which may not always align perfectly with individual login events, especially if users are logging in multiple times within a single session. This could lead to undercounting of login events in the funnel analysis.
Data Sampling in BigQuery: If you're querying BigQuery directly for login events, make sure that the queries are not being affected by data sampling. BigQuery can also sample data for large datasets, which can lead to inaccuracies in the reported counts.
Duplicate Events: Check for any duplicate events being recorded in either Google Analytics or BigQuery, as this can inflate or distort the reported numbers.
Timezone and Date Discrepancies: Ensure that the timestamps of events recorded in Google Analytics and BigQuery are in the same timezone and that there are no discrepancies in date formatting that could lead to missed events.

By investigating these potential issues, you should be able to identify the root cause of the discrepancy between the number of login events reported in Google Analytics funnel and the actual number of login events recorded in your authentication system and stored in BigQuery.

View solution in original post

Poala_Tenorio

There could be several reasons for the discrepancy between the number of login events recorded in Google Analytics funnel and the actual number of login events reported by your authentication system and stored in BigQuery. Here are some potential issues to consider:

Sampling: Google Analytics may be sampling the data, especially if you're dealing with a large volume of events. This means that only a subset of the data is being used for reporting, which can lead to inaccuracies, particularly when dealing with rare events like user logins.
Data Collection Issues: There could be issues with the implementation of the event tracking code or the configuration of the funnel in Google Analytics. Make sure that the events are being captured correctly and that the funnel is configured to include all relevant events.
Data Processing Latency: There may be delays in the processing and reporting of events in Google Analytics, leading to discrepancies in the reported numbers. Check if there are any delays in data processing that could be causing the mismatch.
Data Filtering: Ensure that there are no filters applied in Google Analytics that could be excluding certain events from the funnel analysis. Sometimes filters are inadvertently applied, leading to discrepancies in the reported data.
Sessionization: Google Analytics operates on sessions, which may not always align perfectly with individual login events, especially if users are logging in multiple times within a single session. This could lead to undercounting of login events in the funnel analysis.
Data Sampling in BigQuery: If you're querying BigQuery directly for login events, make sure that the queries are not being affected by data sampling. BigQuery can also sample data for large datasets, which can lead to inaccuracies in the reported counts.
Duplicate Events: Check for any duplicate events being recorded in either Google Analytics or BigQuery, as this can inflate or distort the reported numbers.
Timezone and Date Discrepancies: Ensure that the timestamps of events recorded in Google Analytics and BigQuery are in the same timezone and that there are no discrepancies in date formatting that could lead to missed events.

By investigating these potential issues, you should be able to identify the root cause of the discrepancy between the number of login events reported in Google Analytics funnel and the actual number of login events recorded in your authentication system and stored in BigQuery.

HGYY

Thanks so much for the help! I solve my problem already!

Saurabh2506

Currently, we are recording user login events, logging each instance of a change in login status, and transmitting these events to BigQuery. Additionally, we are using Google Analytics funnel to extract and analyze these events. However, we've encountered an issue: the number of login events displayed in the funnel is significantly lower than the number of daily user logins reported on Google's Authentication Page.
For example, on April 1st, Google Authentication Page showed