Google Inc.

Naomi Ben-Ayoun

Sagnik Nandy

Evan Huang

Madhu Kallazhi Vasu

Jerry Hong

James Lee Wogulis

Paul Nicolas Muret

Bradley David Townsend

Neil Campbell Rhodes

Lucas Paul Hansen

Avi Mehta

Stella Marie Laurenzo

Ashok Babu Amara

Nick Mihailovski

The subject technology provides configurations for propagating information from an initial analytics tracking data communication to subsequent analytics tracking data communications at an analytics server. In one example, the subject technology receives an analytics tracking data communication. The subject technology determines if the received analytics tracking data communication includes state changing information based on analytics tracking data included in the analytics tracking data communication. The subject technology then determines one or more attributes for propagating to subsequent analytics tracking data if the analytics tracking data communication includes state changing information. The state changing information are stored as one or more of the determined one or more attributes. The subject technology stores the analytics tracking data including the determined one or more attributes.

Persist and process analytics data dimensions for server-side sessionization

The subject technology provides configurations for providing aggregated analytics tracking data associated with a dynamically generated session in response to a query for analytics tracking data. A query for analytics tracking data associated with a period of time is received in which the analytics tracking data includes data for tracking activity associated with a web site or application. The subject technology determines analytics tracking data for aggregating according to the period of time in order to associate the aggregated analytics tracking data with a dynamically generated session. The aggregated analytics tracking data associated with the dynamically generated session is then provided in response to the query.

Dynamic sessionization of analytics data

A method and system for aggregating analytics data is discussed. The system differentiates between analytics data that is context sensitive and therefore cannot be reliably updated incrementally (e.g., unique page views, time on site, etc.) and analytics data that is not context sensitive. The system aggregates the context insensitive metrics and dimensions incrementally, while aggregating the context sensitive metrics and dimensions after a specified time duration, such as at the end of the day. It is estimated that less than 10% of all metrics and dimensions are context sensitive. In this way, the aggregator serveris able to improve the freshness of more than 90% of the analytics data (represented by the context insensitive metrics and dimensions) to a shorter period of time than the prior art. Further, this reduces the possibility of over-counting metrics.

System and method for aggregating analytics data

A method for sessionization of visitor activity includes receiving a first activity of a first client and a first client identifier from the first activity of the first client; the first client identifier stored at the first client; creating a first session from the first activity and the first client identifier; receiving a session link associated with the first activity; and creating a visitor-identified session based on the first client identifier, the first activity, and the session link.

Sessionization of visitor activity

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Santa Clara, CA, United States of America

Naomi Ben-Ayoun