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Academic Reference Monograph OPEN ACCESS

PREDICTIVE BUYING INTELLIGENCE PLATFORM (PBIP)

Anna Kistner
Master of Science in Business Analytics at California State University East Bay Palo Alto, California
tajet 2023
VOL. 5 / NO. 10 OCTOBER
FULL TEXT PP. 01-55
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VOLUME 5
ISSUE 10
YEAR 2023
PAGES 01-55
RESEARCH OVERVIEW

Abstract

This paper presents a methodological foundation for the Predictive Buying Intelligence Platform (PBIP), designed to address a critical inefficiency in merchandise planning within the luxury fashion industry. The proposed framework advances a comprehensive demand-forecasting approach that replaces traditional professional intuition with algorithmic analysis of 48 variables organized across five key dimensions: social signals (including TikTok virality), competitive intelligence, brand metrics, macroeconomic factors, and operational indicators. The document details a weighted scoring algorithm, a decision-making matrix, and a phased implementation protocol for embedding the system into existing retailer business processes. Particular attention is given to empirical validation, demonstrating forecast accuracy improvements up to 87% and a material increase in margin performance. The study is intended for retail top management, merchandising directors, buyers, merchandisers, planners and analysts focused on supply-chain digital transformation and the reduction of commercial risk.

RESEARCH TOPICS

Keywords

luxury retail demand forecasting algorithmic scoring inventory management buying predictive analytics virality coefficient operational efficiency

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