Basic Model of Artificial Intelligence Marketing (AIM) for Predictive Market Intelligence: Insights from Teaching Factory Product Planning
DOI:
https://doi.org/10.47134/jobm.v3i2.173Keywords:
Artificial intelligence marketing, Predictive market intelligence, Teaching factory, Product learningAbstract
This research aims to develop an AI-based marketing Intelligence Model as an effort to support strategic decision-making in the development of Teaching Factory products at the Jember State Polytechnic. The study addresses the challenge of optimizing product planning and market positioning within educational manufacturing environments. An integrated approach combining internal production data from Teaching Factory operations with external market data, including price trends, consumer behavior patterns, and competitor activities, was implemented. The methodology employs K-Means Clustering algorithms for consumer segmentation analysis and advanced forecasting algorithms for demand prediction. The system architecture encompasses comprehensive data collection, preprocessing, modeling, and visualization components delivered through an interactive web-based prototype. Preliminary results demonstrate the model's capability to generate enhanced market segmentation accuracy and reliable demand projections, thereby supporting improved production planning and strategic marketing decisions for Teaching Factory operations. The implementation shows promising potential for educational institutions seeking to optimize their product development and market intelligence capabilities.
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