Adobe, Inc.

Neolane Sas

Kokil Jaidka

Niyati Himanshu Chhaya

Prakhar Gupta

Sumit Shekhar

Tanya Goyal

Frederic Mary

Sajal Rustagi

R Kaushik

Iftikhar Ahamath Burhanuddin

Harvineet Singh

Moumita Sinha

Atanu R Sinha

Meghanath Macha Yadagiri

Ponnurangam Kumaraguru

Vamsi Krishna Bokam

Soham Dan

Yogesh Singh

Various embodiments are directed to assigning offers to marketing deliveries utilizing new features to describe offers in the marketing deliveries. Marketing deliveries can be described at a finer level to thus enhance the effectiveness of building and conducting marketing campaigns. The approaches facilitate matching content to recipients, predicting content performance, and measuring content performance after dispatching a marketing delivery.

Creating meta-descriptors of marketing messages to facilitate in delivery performance analysis, delivery performance prediction and offer selection

This disclosure relates to utilizing a statistical model trained on character dimensions to determine a likelihood of a person purchasing a product. The method may include obtaining user-input data of a first person (e.g., textual-input data, survey-response data, offer information, or clickstream data associated with a first person). A character profile for the first person is derived using the user-input data and a psycholinguistic lexicon. A statistical model is generated based on the derived character profile of the first person. Second user-input data associated with a second person is obtained. The second user-input is applied to the statistical model to determine an output of the model (e.g., a statistical probability value that quantifies, for example, a predicted intention of the second person to purchase a particular product).

Techniques for generating a psychographic profile

Techniques are provided for detecting new topics and themes and assigning new posts to existing topic and/or theme clusters in online community discussions. A post posted to an online community is received and a post feature vector representative of the post is created. The post is compared to a plurality of centroid feature vectors, each centroid feature vector being representative of a respective post cluster and associated with a theme. Upon determining that similarity between the post feature vector and one of a plurality of centroid feature vectors satisfies a minimum similarity threshold, the post is assigned to the post cluster of which the centroid feature vector is representative. Upon determining that similarity between the post feature vector and any of the plurality of centroid feature vectors is below the minimum similarity threshold, a new theme cluster is created and the post is assigned to the new theme cluster.

Segmenting topical discussion themes from user-generated posts

Methods and systems for providing targeted marketing include using consumer-centric indices to identify users who are most conversant with marketing communications. In particular, one or more embodiments generate a model that indicates a probability of user interactions based on dynamic data. The dynamic data indicates a time to action for each user interaction with a marketing communication within an observation window. The model fits the dynamic data to a distribution and determines the parameters of the distribution. Using the parameters of the distribution, one or more embodiments calculate interest scores for users who have received marketing communications. One or more embodiments select a set of users as a target audience based on the interest scores and provide marketing communications to target audience.

Generating and utilizing a conversational index for marketing campaigns

A method for generating predictive insights for authoring messages is provided. The method includes receiving a message to be sent as an input. Key performance indicator (KPI) whose value is to be predicted for the message is identified from the input or marketing tool configuration. A plurality of feature vectors of the message are generated. KPI contributions for the plurality of feature vectors are determined using feature vectors of messages sent in past and tracked KPI values of the messages sent in past. The KPI contribution is a measure of contribution of feature vector to value of the KPI. Value of the KPI for the message is predicted by applying determined KPI contributions to the plurality of feature vectors. Apparatus for substantially performing the method as described herein is also provided.

Generating predictive models for authoring short messages

Natural language notification generation techniques and system are described. In an implementation, natural language notifications are generated to provide insight into alerts related to a metric, underlying causes of the alert from other metrics, and relationships of the metric to other metrics. In this way, a user may gain this insight in an efficient, intuitive, and time effective manner.

Generation of natural language notifications

Embodiments of the present invention relate to a determination of a user's exclusiveness toward a particular brand. User-specific entities are extracted from social media content associated with a user. At least a portion of the user-specific entities are brand-related entities that are specifically relevant to a particular brand. These brand-related entities are analyzed with respect to the user-specific entities extracted from the social media content to determine a level of exclusivity of the user to the brand.

Determining brand exclusiveness of users

Method and apparatus for generating predictive insights for authoring short messages

Social media posts related to a topic are analyzed over time by parsing the posts to identify terms and by statistically analyzing occurrences and co-occurrences of the terms in the posts to derive metrics. A relationship-based structure is updated over time based on the metrics. A relationship-based structure is updated over time based on the metrics. In an example, the relationship-based structure includes weighted nodes and edges. The nodes represent terms in the posts and the edges represent co-occurrences of the terms. The weights of the nodes depend on frequencies of the occurrences, while as the weights of the edges depend on frequencies of the co-occurrences. A trend in the social media posts is detected by identifying a change over time in the relationship-based data structure.

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Bengaluru, India

Kokil Jaidka