Optimizing Online Sales Strategies in the USA Using Machine Learning: Insights from Consumer Behavior

Abstract

The exponential expansion of e-commerce in America has redefined the retail landscape, presenting both opportunities and challenges for online retailers. This research aims to apply machine learning techniques to develop a strategic online sales strategy through deep consumer behavior analysis. This research paper focuses on a consumer behavior analysis based on U.S.-based datasets underlining American consumers' unique characteristics and preferences. The consumer behavior dataset contained complete data on various aspects of the customer's behavior in online retail. The dataset consisted of transaction records for customer purchase history, items purchased, frequency of purchases, and values of transactions. It also contained browsing history data that would point out user interaction patterns, such as visited pages, time spent on each page, and views of different products to draw fine-grained inferences on consumer interest and preference. The analyst implemented accredited and credible models, such as Random Forests, Logistic Regression, and Gradient Boosting Classifiers, that are useful in various analyses of the dataset related to customer behavior. Random Forest turned in a strong performance, having relatively high accuracy, reflecting that it is efficient in picking up complex patterns in the data.  Machine learning can revolutionize the way online retailing is approached in the U.S., as it has the potential to make full use of consumer data on a large scale for more nuanced decision-making. While integrating machine learning algorithms, retailers can develop highly personalized shopping experiences that best meet the preferences and behaviors of individual customers.