Adobe, Inc.

Ivan Andrus

Trevor Paulsen

David Wilcox

William Brandon George

Fan Du

Ritwik Sinha

Branislav Kveton

John Bevil Bates

Hung Hai Bui

Sunav Choudhary

Sanket Vaibhav Mehta

Craig Mathis

Nikolaos Vlassis

Brian Lee Jarvis

Nathan A Purser

Said Kobeissi

Chris Challis

Kristopher Paries

Prithvi Bhutani

Michael Rimer

Harvineet Singh

Vladislav Turchenko

Dirk DeHart

Determination of high value customer journey sequences is performed by determining customer interactions that are most frequent as length N=1 sub-sequences, recursively determining most frequent length N+1 sub-sequences that start with the length N sub-sequences, determining a first count indicating how often one of the sub-sequences appears in the sequences, determining a second count indicating how often the one sub-sequence resulted in the goal, and using the counts to determine the most or least effective sub-sequences for achieving the goal.

Intelligent customer journey mining and mapping

The present disclosure relates to performing attribution modeling in real time using user-specified segments of touchpoint data retrieved from a database using a user-specified attribution model. For example, in one or more embodiments, a system stores raw touchpoint data in a database comprising an aggregator and a plurality of nodes. In particular, each node stores touchpoint data associated with a different user. Upon receiving a first query, the system can, in real time, generate and provide a first digital attribution report based on the stored touchpoint data. Upon receiving a second query, the system can generate a second digital attribution report for a user-specified segment of the touchpoint data represented in the first digital attribution report. Specifically, the system retrieves touchpoint data associated with the user-specified segment from the nodes of the database and uses the aggregator to combine the data to generate the second digital attribution report.

Performing query-time attribution modeling based on user-specified segments

The present disclosure relates to performing attribution modeling in real time using touchpoint data that correspond to arbitrary analytics parameters (e.g., a user-specified dimension) and are retrieved from a database using an attribution model. For example, in one or more embodiments, a system stores raw data in an analytics database that comprises an aggregator and a plurality of nodes. In particular, each node stores touchpoint data associated with a different user. Upon receiving a query, the system can, in real time, retrieve subsets of the touchpoint data that correspond to a user-specified dimension in accordance with an attribution model. The system then combines the subsets of touchpoint data using the aggregator and generates the digital attribution report using the combined data.

Performing attribution modeling for arbitrary analytics parameters

Systems and methods are disclosed herein for providing a user interface representing differences between segments of end users. The systems and methods receive user input on a user interface identifying a first segment, the first segment being a subset of the end users having a particular characteristic, determine differences between the first segment and a second segment, and represent, on the user interface, the differences between the first segment and the second segment based on relative significances of the differences. The marketer using the user interface is able to quickly and easily identify the metrics, dimensions, and/or relationships to other segments that most distinguish the compared segments from one another.

Audience comparison

The present disclosure relates to performing attribution channel modeling in real time using touchpoint data that corresponds to a user-specified set of channels and is retrieved from a database using a user-specified attribution model. For example, in one or more embodiments, a system stores raw data in an attribution database that comprises an aggregator and a plurality of nodes. In particular, each node stores touchpoint data associated with a different user. Upon receiving a query, the system can, in real time, retrieve subsets of the touchpoint data that corresponds to a user-defined set of distribution channels in accordance with a user-specified attribution model. The system then combines the subsets of touchpoint data using the aggregator and generates the digital attribution report using the combined data.

Performing query-time attribution channel modeling

Growing community of inventors

Saratoga Springs, UT, United States of America

Ivan Andrus