Accenture Global Solutions Limited

Sanjay Sharma

Nilesh Kumar Gupta

Samik Adhikary

Amitava Dey

Kaustav Pakira

Gurpreet Singh Bawa

Derek P Levesque

Keshav Rastogi

Souvik Chakraborty

Kaushik Sanyal

Ruchika Sachdeva

Elfin Garg

Tejas Anant Wagh

Gaurav Yadav

Reema Malhotra

Rohan Aggarwal

Utkarsh Mittal

Mayank Ahuja

Avinna Kumar Sahoo

Rajarshi Bhadra

Sangitika Rana

Rupesh Kumar

Pinaki Asish Ghosh

Lakshay Chhabra

Madhuleena Adhikary

Akriti Agrawal

Namita Khurana

Prachi Rajesh Sawant

Jain Abhishek Kumar

Arpendu Kumar Ganguly

Abhinav Kumar

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

A data processing and analysis system that optimizes the resources to be used for data storage and refresh events. A partitioner module for a data analysis system can receive a first client criteria and a first client dataset that includes tabular data and calculate scores that are used to generate partitioning strategies. The selected partitioning strategy can be implemented to produce aggregated data that can be stored in an intelligent data mart. The partitions can then be accessed by a data visualization platform for intelligent, dynamic responses to user requests for data analyses and generation of visualizations. By providing synchronous partitioning of data (especially big data) and intelligent refresh, the data can move from the back-end to the front-end with minimal user clicks and minimal latency in performance.

System and method for cloud-based intelligence analysis and visualization generation

A device may receive and transform metric data and share of voice data, associated with digital marketing by an entity, into transformed data, may generate model data from the transformed data, and may divide the model data into training data, test data, and validation data. The device may train models, with the training data, to generate training results, and may process the test data, with the models, to generate test results. The device may process the validation data, with the models, to generate validation results, and may select a first model, a second model, and a third model based on the results. The device may utilize the first model to predict a share of voice, and may utilize the second model to predict a click through rate. The device may utilize the third model to predict a conversion rate, and may perform actions based on the predicted data.

Utilizing machine learning models to generate an optimized digital marketing simulation

A system and method for determining a result that includes a solution for a discrete non-linear optimization model for an optimization problem is disclosed. A non-linear relationship between a key performance indicator (KPI) to be optimized and at least one decision variable is determined using econometric modeling. A discrete optimization framework for the optimization problem is determined using a plurality of discrete values of decision variables of the optimization problem. A binary optimization framework is determined from the determined discrete optimization framework based on automatically revising an optimization equation of the discrete optimization framework. A continuous framework is determined from the determined binary optimization framework based on imposing additional constraints and based on modifying the optimization problem. The result is generated, and an action is controlled based on the result.

Automated planning based on converting discrete frameworks to continuous frameworks

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

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

A system selects a set of advertisement media associated with a product. A set of persistent advertisement media from the set of advertisement media is identified based on one or more tests. A Gaussian Bayesian (GB) network based on the set of advertisement media. A net persistence rate for each of the set of persistent advertisement media is determined based on the GB network. a competitor factor associated with another vendor of the product is computed based on one or more marketing parameters associated with the another vendor. A predicted sales (PS) value associated with each of the set of persistent advertisement media is determined. A persistent advertisement medium is selected in real time based on corresponding PS value. An advertisement is rendered on the selected persistent advertisement medium in real time for marketing the product.

Advertisement rendering

In some implementations, a device may receive unstructured interaction data identifying an interaction of a user with a user device. The device may receive historical unstructured interaction data identifying historical interactions of users and historical unstructured resolution data identifying historical resolutions to the historical interactions. The device may process the historical unstructured interaction data and the historical unstructured resolution data to determine historical structured interaction data and historical structured resolution data. The device may process the unstructured interaction data and the historical structured interaction data to determine pretext identifiers for the interaction of the user. The device may process the pretext identifiers and the historical structured resolution data to generate a resolution network identifying possible resolutions to the interaction of the user. The device may process the pretext identifiers and the resolution network to determine a resolution path identifying a resolution to the interaction of the user.

Utilizing machine learning models for determining an optimized resolution path for an interaction

A wireless signal strength monitoring system employs a wireless signal strength driver model to monitor and maintain wireless signal strengths for mobile devices on a wireless communication network. The monitoring system parses activity logs of network entities including the mobile devices on the wireless communication network to generate a plurality of multilevel token sets. A selected one of the plurality of multilevel token sets is used to generate a wireless signal strength driver model along with topics from clusters of homogenous logs of the activity logs. The drivers of the wireless signal strength are identified from the wireless signal strength driver model. The drivers are used to monitor and improve the wireless signal strengths for the mobile devices on the wireless communication network.

Wireless signal strength optimizer

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

Sanjay Sharma