Data Mining Techniques for Predicting Impulse Buying Behavior in Online Retail
DOI:
https://doi.org/10.63856/ijis/v2i5/00038Keywords:
Data Mining, Impulse Buying Behavior, Online Retail, Predictive Modeling, Consumer Behavior, Decision Trees, Association Rule MiningAbstract
Impulse buying behavior is common in online shopping, where consumers make unplanned purchases driven by various psychological and environmental stimuli. Understanding this kind of behavior is essential, as it helps online retailers streamline marketing campaigns, product recommendations, and the customer experience. This paper examines how data mining methods can be used to forecast impulse buying behavior in online shopping. We examine the available methods, including classification models, clustering, and association rule mining, and compare their performance in predicting impulse purchases. Using consumer information, including browsing history, demographics, and purchase history, we would suggest a hybrid data-mining model that combines decision-tree classification and association-rule mining to enhance prediction accuracy. Our findings indicate that data mining has substantial potential to improve the precision of impulse-buy forecasts, providing retailers with actionable information to tailor their products and maximize sales. The paper will end by discussing the practical implications, challenges and future of applying data mining in online retail strategies.
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