Accenture Global Solutions Limited

Nilesh Kumar Gupta

Sanjay Sharma

Samik Adhikary

Elfin Garg

Ruchika Sachdeva

Vijeth Srinivas Hegde

Manish Bachhania

Amitava Dey

Ritu Pramod Dalmia

Rajesh Katta

Pragya Sharma

Bhavin Prafulchandra Mehta

Derek P Levesque

Rohan Aggarwal

Payal Gupta

Santanu Dutta

Arpendu Kumar Ganguly

Daniel Pielak-Watkins

Subha Pattanayak

Kusha Arora

Vijayakumar Thandavakrishnan

Utkarsh Mittal

Avinna Kumar Sahoo

Rajarshi Bhadra

Nakul Puri

Pinaki Asish Ghosh

Akriti Agrawal

Madhuleena Adhikary

Daniel Pielak- Watkins

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support optimization of communications transmitted over a plurality of communication mediums. Historical communications data may be analyzed to identify clusters of users and a model may be constructed based on the clusters. Candidate sequences of communications over a period of time (e.g., sequences of communication successfully triggering events) are identified using metrics (e.g., probabilities, attribution penalties, etc.) derived from the model or other information. The candidate sequences of communications may be determined at a group or cluster level and then tuned or optimized (e.g., using transition sequences, harmonization, entity priors, etc.) for individual users to produce optimized sequences of communications. The optimized sequences of communications may then be transmitted to individual users according to each user's optimized sequence of communications. A feedback loop may be utilized to update the model or optimizations of future sequences of communications.

Systems and methods for optimizing and harmonizing sequences of communications over different mediums

In some examples, personality trait-based customer behavior prediction may include extracting personality features from images associated with a user. Social style features may be extracted from social data associated with the user. Consumer demographics features may be extracted from consumer demographics data associated with the user. Based on a probability analysis of the extracted features, relevant features may be selected from the images, the social data, and the consumer demographics data. Historical purchase features may be extracted from historical purchase data associated with the user. At least one machine learning model may be trained based on the extracted features, and used to generate a next best offer for the user for purchase of a product or a service. A purchase of the product or the service may be performed based on the generated offer.

Personality trait-based customer behavior prediction

In some examples, sequence, frequency, and time interval based journey recommendation may include generating a plurality of clusters of entities, and generating a network that identifies a time interval to a next interaction that leads to success. An estimated time interval to a specified number of conversions may be determined. A success criterion that represents a positive outcome in the estimated time interval may be determined. A historical sequence of events may be partitioned into a plurality of sequences of events leading to success or failure. The plurality of sequence of events may be mapped based on analyzed probabilities, a determined waiting interval, and determined frequency contributions, and evaluated as to whether a mapped sequence of events duration is less than a planned duration. If so, a journey may be generated and include a determined sequence of events, a corresponding frequency, and a corresponding waiting interval.

Sequence, frequency, and time interval based journey recommendation

A data management platform may receive an environment configuration for a data environment to be implemented in a data structure, wherein the environment configuration includes requirements of an application. The data management platform may configure, based on the environment configuration, the data environment, to generate a configured data environment. The data management platform may deploy the configured data environment in the data structure. The data management platform may perform one or more tests on data stored in the configured data environment in the data structure to generate one or more test results based on performing the one or more tests on the data. The data management platform may update, based on the one or more test results, the configured data environment, to generate an updated configured data environment, wherein the updated configured data environment meets the requirements of the application.

Data configuration, management, and testing

A device receives an optimization problem and a target value and input data for the optimization problem, and specifies constraints for the optimization problem based on the input data. The device identifies an optimization space for the optimization problem based on the constraints and the input data, and divides the optimization space into sub-regions based on the constraints and the input data. The device performs optimizations of a set of the sub-regions, and determines a respective distance of each sub-region, of the set of the sub-regions, from the target value. The device selects a particular sub-region that is a shortest distance from the target value, and selects a vector from the particular sub-region. The device executes the optimization problem using the vector as an initial parameter and to generate results, and utilizes the results to recommend one or more decisions or modify a process.

Determining an optimal region in a target value optimization problem and utilizing the optimal region to perform an action

An unsupervised machine learning model can make prediction on time series data. Variance of time-varying parameters for independent variables of the model may be restricted for continuous consecutive time intervals to minimize overfitting. The model may be used in a control system to control other devices or systems. If predictions for the control system are for a higher granularity time interval than the current mode, the time-varying parameters of the model are modified for the higher granularity time interval.

Control system with machine learning time-series modeling

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New Delhi, India

Nilesh Kumar Gupta