Categorizing Online Shopping Behavior from Cosmetics to Electronics: An Analytical Framework (2010.02503v1)

Abstract: A success factor for modern companies in the age of Digital Marketing is to understand how customers think and behave based on their online shopping patterns. While the conventional method of gathering consumer insights through questionnaires and surveys still form the bases of descriptive analytics for market intelligence units, we propose a machine learning framework to automate this process. In this paper we present a modular consumer data analysis platform that processes session level interaction records between users and products to predict session level, user journey level and customer behavior specific patterns leading towards purchase events. We explore the computational framework and provide test results on two Big data sets-cosmetics and consumer electronics of size 2GB and 15GB, respectively. The proposed system achieves 97-99% classification accuracy and recall for user-journey level purchase predictions and categorizes buying behavior into 5 clusters with increasing purchase ratios for both data sets. Thus, the proposed framework is extendable to other large e-commerce data sets to obtain automated purchase predictions and descriptive consumer insights.

• The paper introduces a modular framework that automates feature engineering and predictive modeling for online shopping behavior analysis.
• It employs bidirectional LSTM and Random Forest methods, achieving accuracies between 91% and 99% in classifying user journeys and behavior clusters.
• The framework’s scalable data processing and actionable insights support enhanced market intelligence and targeted marketing strategies.

An Analytical Framework for Predicting Online Shopping Behavior

The paper "Categorizing Online Shopping Behavior from Cosmetics to Electronics: An Analytical Framework" presents a comprehensive approach for automating consumer behavior analysis in e-commerce environments. The authors propose a machine learning framework capable of handling large-scale datasets, specifically targeting online customer behaviors in the cosmetics and electronics sectors. Their work focuses on predicting purchase events using a modular consumer data analysis platform, emphasizing three key areas: session-level interactions, user-journey level patterns, and customer behavior clustering.

Framework and Methodology

The authors delineate a robust, modular workflow designed to automate feature engineering, selection, and predictive modeling on user-product interaction data. This involves the classification of user journeys (with high accuracy and recall values between 97-99%) and the prediction of purchase events through sequence modeling. The feature sets are optimized based on Random Forest and Fisher Score methodologies, leading to distinct patterns of purchasing behavior being identified and categorized into five behavioral clusters.

The framework employs scalable techniques, including Python's Pandas library and Google's BigQuery, to tackle challenges associated with large volumes of e-commerce data. This ensures efficient data processing and transformations, crucial for developing predictive models that achieve high classification accuracy given imbalanced datasets.

Numerical Results

The empirical results reflect the effectiveness of the proposed models with notable classification accuracies and recalls. User journey classifications achieve accuracies of 97-99%, underscoring the reliability of predictive models derived from user engagement data spanning multiple sessions. Meanwhile, session-based predictions via bidirectional LSTM models reach accuracies of 91-97%, offering substantial improvements over baseline sequence models.

Implications and Future Research

The paper's findings carry significant implications for enhancing market intelligence strategies. The categorization of customers into distinct behavioral clusters enables more tailored marketing efforts, inventory management, and customer relationship management. By distinguishing new shoppers from returning decisive shoppers, businesses can deploy targeted promotions to maximize sales conversions.

From a theoretical perspective, this work presents an opportunity to extend the application of sequence models to varied e-commerce data types, further refining customer segmentation techniques. Future research could explore the integration of additional data sources or alternative deep learning architectures to bolster predictive capabilities.

Overall, this work serves as an exemplary model of how machine learning frameworks can transform the analysis of digital consumer behavior, providing actionable insights to drive engagement and retention in an increasingly digital economy.

Conclusion

The paper contributes a scalable, data-driven approach for automated prediction and analysis of online shopping behaviors. Its applicability across different e-commerce domains and high predictive accuracy make it a valuable asset for companies navigating the complexities of digital consumer behavior. The methodologies outlined can inform future developments in AI-driven market intelligence, promising advancements in personalized shopping experiences and optimized resource allocation.