A Review of Machine Learning Algorithms in Consumer Behavior: the Missing Link in Impulse Buying

EasyChair Preprint 10998

Abstract

This literature review critically examines machine learning algorithms' role in predicting consumer buying behaviors, such as product recommendations, customer segmentation, fraud detection, churn prediction, demand forecasting, and pricing optimization. Despite substantial progress in these areas, a glaring gap exists in the research concerning impulse buying—a global phenomenon reported by 71% of consumers across 27 countries in a 2020 IBM study.

In France and the United States, regret and financial consequences of impulse buying are notable, affecting 47% of French individuals aged 18-34 and accounting for an average monthly spending of $314 in the U.S. as of 2022. The existing literature on impulse buying has undergone significant shifts over seven decades, incorporating psychological and environmental triggers to understand the behavior better. However, most studies veer toward prescriptive models that, paradoxically, might fuel further impulse buying.

The need for analytical models is especially acute. These models offer a counterpoint to prescriptive approaches by focusing on understanding the determinants of impulse buying without necessarily promoting it. As such, they could provide critical insights that benefit both consumers, by enhancing their awareness of impulse buying's post-purchase impact, and vendors, who may need to re-evaluate the long-term profitability of revenue generated from such buying behaviors.

This review emphatically underscores the imperative for dedicated research to fill this analytical void, leveraging machine learning for a more nuanced understanding and predicting of impulse buying, or more practically, Online impulse buying.

Keyphrases: Online impulse buying, consumer behavior, impulse buying, machine learning

@booklet{EasyChair:10998,
  author    = {El Hassan Laachach},
  title     = {A Review of Machine Learning Algorithms in Consumer Behavior: the Missing Link in Impulse Buying},
  howpublished = {EasyChair Preprint 10998},
  year      = {EasyChair, 2023}}