Adobe, Inc.

Kumar Mrityunjay Singh

Balaji Krishnamurthy

Nikaash Puri

Mihir Naware

Hyman Chung

Kunal Kumar Jain

Yaman Kumar

William Brandon George

Deepak Pai

Lauren Dest

Joshua Butikofer

Shankar Venkitachalam

Brandon Pack

Simra Shahid

Sukriti Verma

Matthew William Rozen

Pranjal Prasoon

Somesh Singh

Vinh Ngoc Khuc

Vijay Srivastava

James Patrick O'Reilly

Abhinav Chauhan

Mihai Corlan

Timothy Chia-chi Liu

Alexander Klimetschek

Clinton Hansen Goudie-Nice

Shirsh Bansal

Sandipan Karmakar

Sean Steimer

Nimish Srivastav

Florin Silviu Iordache

Horea Bacila

Umang Moorarka

Suman Basetty

Ashish Duggal

Anuj Jain

Praveen Kumar Goyal

Atul Kumar Shrivastava

Rushil Mahajan

Prakash Tripathi

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Determining user affinities for content generation applications

Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Content analytics as part of content creation

A segmentation system utilizes a supervised learning method and a clustering analysis to identify clusters, thereby segmenting a population into groups, where the clusters are associated with various conversion potentials that indicate the probability of an event. The segmentation system employs the supervised learning method to train a model on training data comprising historical conversion data and features associated with members of the group. A subset of features is selected from a ranked order that is determined using weights generated by the supervised learning. A clustering analysis is performed for a population with respect to the subset to generate clusters. A superior cluster is identified based on it having a conversion potential greater than a conversion potential of another cluster. In a marketing context, the system can be employed to identify a superior cluster of users that have a higher conversion potential in response to an advertisement campaign.

Advanced segmentation with superior conversion potential

Techniques and systems are described to enable an analytics system to generate a forecast value for audience impressions of subsequent digital video content. In one example, an analytics system generated a linear regression model using data. The data describes audience segment values for past audience impressions of digital video content and a time series of when the digital video content is made available for consumption. The analytics system generates forecast values for audience impressions of digital video content using the linear regression model. In one example, the analytics system generates a forecast value for audience impressions of a subsequent item of digital video content based on scheduled availability for the subsequent item.

Audience forecasting for digital video content

Methods and systems for managing advertisement (ad) delivery and ad inventory for advertisements (ads) placed in video content are provided. An exemplary method receives campaign criteria for a new advertising campaign, the campaign criteria including at least a desired date range. The method then retrieves indications of unsold ad space inventory. In response to determining, based at least in part on the unsold ad space inventory in the desired date range, that the campaign criteria cannot be satisfied, the method: renders, in a user interface, an interactive ad delivery and distribution report indicating forecasted ad distribution and ad delivery for at least part of the desired date range during which the campaign criteria cannot be satisfied; and redistributes ad delivery by adjusting an ad delivery plan based on adjusted report parameters received via the interface in order to accommodate the new advertising campaign.

Forecasting-based management of advertisement delivery and inventory

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Bengaluru, India

Kumar Mrityunjay Singh