Intellectual Property India Publishes Patent Application for Machine Learning-Based Predictive Analytics Framework For Financial Evaluation And Return On Investment Assessment Of Digital Marketing Campaigns

MUMBAI, India, Oct. 5 -- Intellectual Property India has published a patent application (202621101054 A) filed by Sneha Suhasrao Palimkar; G. Anusha; Dr. Naresh Ogirala; Sachin Narayan Janvekar; Dr. Deepak Prabhakar Khedkar; Dr Priya Kalyanasundaram; P. Sathish Kumar; Dr Dasari Rajesh Babu; Dr. S. Aruna; Revati Anawardekar; Dr. Kirti Sahu; and S. Mekala on August 21, 2026, for Machine Learning-Based Predictive Analytics Framework For Financial Evaluation And Return On Investment Assessment Of Digital Marketing Campaigns.

Inventors include Sneha Suhasrao Palimkar; G. Anusha; Dr. Naresh Ogirala; Sachin Narayan Janvekar; Dr. Deepak Prabhakar Khedkar; Dr Priya Kalyanasundaram; P. Sathish Kumar; Dr Dasari Rajesh Babu; Dr. S. Aruna; Revati Anawardekar; Dr. Kirti Sahu; and S. Mekala.

The application for the patent was published on October 02, 2026, under issue no. 40/2026.

Abstract: The present invention discloses the development of a machine learning-based predictive analytics framework that will be used for the purpose of financial evaluation and return on investment calculation of digital marketing campaigns. The predictive analytics framework obtains heterogenous data on marketing, customers, expenses, conversions, transactions, and revenue from various digital and enterprise sources, and prepares it into one format to use for analysis. There is a pre-processing module that sanitizes and normalizes the data, and there is a feature engineering module that creates temporal, behavioral, marketing, and financial features. Machine Learning prediction engine estimates expenses, conversions, revenue, cost per acquired customer, customer lifetime value, and performance of the campaign. Financial evaluation engine calculates return on investment, return on ad spend, contribution, and break-even criteria. There is a predictive simulation module for evaluating alternative scenarios of the budget for the campaign, and there is an optimization engine for budget recommendation based on the predictions and risk and constraints. FIG.1

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