BACKGROUND

Internet users access millions of websites or applications daily for a variety of purposes. For example, Internet users may access a website or mobile application to make a purchase, locate information, or view entertainment media. Indeed, some Internet users conduct a large portion of their daily tasks using various websites or applications, such as shopping, reading news articles, or finding recipes or restaurants. Users also access the Internet or other networks to transfer files, submit search queries, upload images and other digital media, or send social network posts.

Not only do users routinely access millions of websites or applications a day, but a single website, application, or Uniform Resource Locator may receive thousands to millions of visits or views a day. With such large quantities of network destinations and visits, web administrators and marketers often seek to gather information concerning specific segments of users who visit a website, application, or a collection of websites or applications. In some instances, a web administrator may seek to identify a specific segment of users who have certain characteristics or who have demonstrated a certain pattern of behavior.

Despite the utility of identifying information about specific segments, the amount of analytic data a system may collect for even a single website or application may be unwieldy or too difficult to manage or mine. The amount of data can be particularly problematic for websites or applications that receive thousands or millions of daily visitors or users. Conventional analytics engines often lack the ability to identify and organize captured data in highly specified categories. Even the conventional analytics engines that possess this ability, however, consume significant processing power to create specific segments that satisfy sophisticated query parameters. To identify specific segments for some high-volume websites or applications, some conventional analytics engines or methods may require individually analyzing billions of transactions to identify data representing segments defined by sophisticated query parameters. Without the processing power or time to individually analyze such a large quantity of transactions, some web administrators must manually program code to generate the segment. Such customized segmenting is often time consuming and does not allow the administrator to easily change or revise the segment.

SUMMARY

This disclosure describes one or more embodiments that provide benefits and/or solve some or all of the foregoing (or other) problems with systems and methods that create sequential segments from analytics data that enable investigation of events that occurred before or after a sequence of events—that is, pre-sequence or post-sequence events. For example, certain embodiments of the disclosed systems and methods receive a segment query from a client device that includes both an indication of a sequence of events and a request to identify events that occurred before or after the sequence of events. Upon receiving the segment query, the disclosed systems and methods query an analytics database for a segment of users who performed the sequence of events and for events performed by the segment of users before or after the sequence of events. Based on the query, the disclosed systems and methods provide query results that include analytics data associated with performance of the sequence of events as well as data associated with events that occurred before or after the sequence of events.

Additional features and advantages of one or more embodiments of the present disclosure will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of such example embodiments.

