Adobe, Inc.

Craig Mathis

Kevin Gary Smith

Deepak Pai

Branislav Kveton

Hung Hai Bui

Trevor Paulsen

Nikolaos Vlassis

Ivan Andrus

Scott T Pead

Kristopher Paries

James Harold Brown

Natalee A Villa

Michael Rimer

Joshua Aaron Hansen

J Scott Wellwood

James Nyland

Brandon Hartness

This disclosure relates to providing content (e.g., marketing content) to an individual based on a psychographic profile of an individual. The method includes obtaining a psychographic profile of an individual. The psychographic profile comprises one or more psychographic dimensions associated with the first individual and a psychographic profile group. A psychographic dimension corresponds to at least one of an interest, an attitude, an opinion, a value, a behavior, or an activity of the individual. Content (e.g., marketing content) is received, the content being associated with a psychographic profile group corresponding to the psychographic profile of the individual. The content is provided to the individual based on the associated psychographic profile group of the content and the associated psychographic profile group of the individual.

Techniques for targeting a user based on a psychographic profile

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

Marketing assets are automatically generated from asset components having asset features relevant to target users. Asset delivery event data regarding the delivery of marketing assets is initially collected to identify asset features of delivered marketing assets and user attributes of users receiving the marketing assets. The asset delivery event data is processed using machine-learning techniques to generate a model capable of selecting asset features given a set of user attributes. When a request for a new marketing asset is received for a particular user, user attributes of that user are identified and provided to the model to select a set of asset features. The asset features are used to select asset components, which are combined to form the new marketing asset.

Dynamic marketing asset generation based on user attributes and asset features

Systems and methods disclosed herein compactly store representations of segment-specific interaction data from a real time data stream of data interactions by multiple entities to facilitate segment-specific analytics for particular time periods. Segment rules defining characteristics of entities within a segment are received. A first probabilistic data structure is created representing unique entity IDs included in instances of interaction data in the real time data stream during a first time period. A second probabilistic data structure representing unique entity IDs included in instances of interaction data in the real time data stream during a second time period different from the first time period. The first probabilistic data structure represents only entity IDs of entities within the segment and the second probabilistic data structure represents only entity IDs of entities within the segment. The first and second probabilistic data structures are indexed and stored.

Systems and methods for determining real-time visitor segments

Embodiments of the present invention provide systems, methods, and computer storage media directed to an analytics tool for detecting real-time user or 'visitor' events based on real-time data. More specifically, events are detected based on actions not taken by a user. In this regard, events can be defined and, thereafter, detected based on inactions of a user. In some cases, events are inferred or predicted based on a calculated likelihood of a user not performing an action. Upon determining an event based on an action not being performed by a user, an interested party may be notified thereof such that the interested party can influence, in real-time, visitor conversion.

Real-time calculated and predictive events

Methods and apparatus for anomaly detection in network-site metrics using predictive modeling are described. A method comprises obtaining time-series data for a given time range, wherein the time-series data comprises values for a network-site analytics metric for each of a plurality of sequential time steps across the given time range. The method includes generating a predictive model for the network-site analytics metric based on at least a segment of the time-series data. The method includes using the predictive model to predict an expected value range for the network-site analytics metric for a next time step after the segment and, based on the expected value range, determining whether an actual value for the network-site analytics metric for the next time step is an anomalous value.

Anomaly detection in network-site metrics using predictive modeling

Growing community of inventors

American Fork, UT, United States of America

Craig Mathis