US 11,887,167 B2
Utilizing machine learning models to generate an optimized digital marketing simulation
Keshav Rastogi, Gurgaon Haryana (IN); Amitava Dey, New Delhi (IN); Lakshay Chhabra, New Delhi (IN); and Sanjay S. Sharma, New Delhi (IN)
Assigned to Accenture Global Solutions Limited, Dublin (IE)
Filed by Accenture Global Solutions Limited, Dublin (IE)
Filed on Jun. 14, 2022, as Appl. No. 17/806,816.
Prior Publication US 2023/0401607 A1, Dec. 14, 2023
Int. Cl. G06Q 30/00 (2023.01); G06Q 30/0241 (2023.01)
CPC G06Q 30/0276 (2013.01) 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, by a device, metric data and share of voice data associated with digital marketing by an entity;
transforming, by the device, the metric data and the share of voice data into transformed data;
performing, by the device, exploratory data analysis techniques on the transformed data to generate model data;
dividing, by the device, the model data into training data, test data, and validation data;
training, by the device, a plurality of machine learning models, with the training data, to generate training results;
processing, by the device, the test data, with the plurality of machine learning models, to generate test results;
processing, by the device, the validation data, with the plurality of machine learning models, to calculate root mean square errors;
processing, by the device, the validation data, with the plurality of machine learning models, to calculate R-squared values;
processing, by the device, the validation data, with the plurality of machine learning models, to calculate mean absolute percentage errors,
wherein the root mean square errors, the R-squared values, and the mean absolute percentage errors correspond to validation results;
selecting, by the device and from the plurality of machine learning models, a first machine learning model, a second machine learning model, and a third machine learning model based on the training results, the test results, and the validation results;
utilizing, by the device, the first machine learning model to predict a share of voice for the entity;
utilizing, by the device, the second machine learning model to predict a click through rate for the entity;
utilizing, by the device, the third machine learning model to predict a conversion rate for the entity; and
performing, by the device, one or more actions based on the share of voice, the click through rate, and the conversion rate.