Intellectual Property India Publishes Patent Application for 'Consumer Behaviour Prediction System For Retail Strategy Optimization' Filed by Malla Reddy (MR) Deemed to be University; Malla Reddy Engineering College For Women; Malla Reddy College Of Engineering And Technology; Malla Reddy Vishwavidyapeeth; and Malla Reddy University

MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541122476 A) filed by Malla Reddy (MR) Deemed to be University; Malla Reddy Engineering College For Women; Malla Reddy College Of Engineering And Technology; Malla Reddy Vishwavidyapeeth; and Malla Reddy University, Malkajgiri, Telangana, on Dec. 5, 2025, for 'consumer behaviour prediction system for retail strategy optimization.'

Inventor(s) include Prof. Mandala Sreenivas; Mr. Venkata Ramana S; Dr V Madhusudhana Reddy; Mrs. Sai Kumari Thakur; and Dr. G. Jose Moses.

The application for the patent was published on Jan. 2, under issue no. 01/2026.

According to the abstract released by the Intellectual Property India: "The present invention discloses an intelligent, end-to-end system for predicting consumer behavior and translating those predictions into optimized retail strategies. Traditional business intelligence tools provide only descriptive summaries of sales data, leaving retailers reactive to market fluctuations. The proposed system instead applies advanced machine-learning and decision-optimization techniques to predict purchasing intent, demand shifts, and promotional responsiveness across online and offline channels. The invention integrates transactional records, digital engagement data, loyalty-program histories, product attributes, inventory feeds, and external contextual factors such as holidays, competitor pricing, weather, and local events. A modular prediction engine generates forecasts for purchase propensity, price elasticity, basket composition, and category substitution behavior. Outputs of the model are fed into an optimization layer that produces recommendations for pricing, promotion depth, assortment allocation, and inventory replenishment. Feedback loops measure the realized outcomes of these actions, refining the models continuously. The system operates on scalable infrastructure and supports real-time data refresh, ensuring that strategy decisions remain synchronized with evolving consumer trends. By unifying predictive analytics and prescriptive decisioning in a single platform, the invention enables retailers to reduce over-stocking and lost-sales risk, improve customer engagement, and enhance profitability through dynamic, data-driven retail management."

Disclaimer: Curated by HT Syndication.