IB QUIMBY MELTON <GO>When enabled, Confection's BigQuery integration creates a dataset with two columns: UUID and data. The data column includes JSON-formatted data associated with a UUID's site activity. To connect BigQuery to your Confection account, see this quick-start guide.
Use the following query to calculate pageviews by new and returning visitors.
Sample output follows:
| visitor_type | pages_viewed | visitor_count | percent_of_visitor_type |
|---|---|---|---|
| new | 1 | 1641 | 96.81 |
| returning | 3 | 2 | 11.11 |
Confection defines a "new visitor" as a UUID with exactly one pageview within the specified timeframe and no historical pageviews before this timeframe. Conversely, a "returning visitor" is a UUID that has had at least one pageview in any historical timeframe, as well as one or more pageviews within the specified timeframe. This approach provides a much more precise view of visitor behavior compared to other analytics tools.
For example, Google Analytics (GA) uses "sessions" to distinguish between new and returning visitors. Generally, GA ends a session after 30 minutes of inactivity. If a user visits your site again after this period, GA will count this visitor as a new user. Additionally, visits from different campaigns or ad clicks are often interpreted as new users. Sessions also reset at midnight, so a user active from 11:50pm to 12:10am may be counted as two separate users.
These session-based metrics can lead to fragmented user identities and create confusion about whether users are new or returning.
Confection’s approach offers a significant advantage by maintaining a long-term view of user history. A UUID with a historical pageview from a year ago (or ten years ago) will be correctly identified as a returning visitor. This comprehensive view of user engagement provides customers with a clearer understanding of long-term relationships and helps identify loyal users who might not be apparent through short-term tracking. This enhances analytics reports and offers deeper insights into user behavior.
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