Categorizing Online Shopping Behavior from Cosmetics to Electronics: An Analytical Framework

• URL: http://arxiv.org/abs/2010.02503v1
• Date: Tue, 6 Oct 2020 06:16:44 GMT
• Title: Categorizing Online Shopping Behavior from Cosmetics to Electronics: An Analytical Framework
• Authors: Sohini Roychowdhury, Wenxi Li, Ebrahim Alareqi, Akhilesh Pandita, Ao Liu, Joakim Soderberg
• Abstract summary: The proposed framework is extendable to other large e-commerce data sets to obtain automated purchase predictions and descriptive consumer insights. The proposed system achieves 97-99% classification accuracy and recall for user-journey level purchase predictions.
• Score: 3.6726589459214445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study [2.126171264016785]
  We develop a hybrid interpretable model to analyse 454,897 online customers' behavior for a particular product category at the largest online retailer in China, that is JD. Our results reveal that customers' product choice is insensitive to the promised delivery time, but this factor significantly impacts customers' order quantity. We identify product classes for which certain discounting approaches are more effective and provide recommendations on better use of different discounting tools.
  arXiv  Detail & Related papers  (2022-12-01T19:37:29Z)
• Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions [60.87845704495664]
  We propose a framework relying solely on chat-based customer support interactions for predicting the recommendation decision of individual users. For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America. Our results show that, with respective feature interpretability, it is possible to predict the likelihood of a user to recommend a product or service, based solely on the message-wise sentiment evolution of their CS conversations in a fully automated way.
  arXiv  Detail & Related papers  (2022-11-08T00:43:36Z)
• Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity [54.26540039514418]
  This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity.
  arXiv  Detail & Related papers  (2021-11-11T05:36:39Z)
• Aggregated Customer Engagement Model [0.571097144710995]
  E-commerce websites use machine learned ranking models to serve shopping results to customers. New or under-impressed products do not have enough customer engagement signals and end up at a disadvantage when being ranked alongside popular products. We propose a novel method for data curation that aggregates all customer engagements within a day for the same query to use as input training data.
  arXiv  Detail & Related papers  (2021-08-17T20:58:10Z)
• PreSizE: Predicting Size in E-Commerce using Transformers [76.33790223551074]
  PreSizE is a novel deep learning framework which utilizes Transformers for accurate size prediction. We demonstrate that PreSizE is capable of achieving superior prediction performance compared to previous state-of-the-art baselines. As a proof of concept, we demonstrate that size predictions made by PreSizE can be effectively integrated into an existing production recommender system.
  arXiv  Detail & Related papers  (2021-05-04T15:23:59Z)
• Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data [63.942632088208505]
  Super-Apps have changed the way we think about the interactions between users and commerce. This paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior.
  arXiv  Detail & Related papers  (2021-02-19T15:13:06Z)
• Predicting Customer Lifetime Values -- ecommerce use case [0.0]
  This work compares two approaches to predict customer future purchases, first using a 'buy-till-you-die' statistical model to predict customer behavior and later using a neural network on the same dataset and comparing the results.
  arXiv  Detail & Related papers  (2021-02-10T23:17:16Z)
• OPAM: Online Purchasing-behavior Analysis using Machine learning [0.8121462458089141]
  We present a customer purchasing behavior analysis system using supervised, unsupervised and semi-supervised learning methods. The proposed system analyzes session and user-journey level purchasing behaviors to identify customer categories/clusters.
  arXiv  Detail & Related papers  (2021-02-02T17:29:52Z)
• Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting [63.78344280962136]
  Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level. This work seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner.
  arXiv  Detail & Related papers  (2020-09-08T12:10:10Z)
• Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding [53.02059906193556]
  We propose to predict consumers' purchases based on their facial features and purchasing histories. We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers. Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
  arXiv  Detail & Related papers  (2020-07-14T06:06:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.