Accenture Global Solutions Limited

Amitava Dey

Sanjay Sharma

Nilesh Kumar Gupta

Samik Adhikary

Keshav Rastogi

Utkarsh Mittal

Sangitika Rana

Lakshay Chhabra

Madhuleena Adhikary

Namita Khurana

Mayank Ahuja

Arpendu Kumar Ganguly

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

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New Delhi, India

Amitava Dey