OPAM: Online Purchasing-behavior Analysis using Machine learning
- URL: http://arxiv.org/abs/2102.01625v1
- Date: Tue, 2 Feb 2021 17:29:52 GMT
- Title: OPAM: Online Purchasing-behavior Analysis using Machine learning
- Authors: Sohini Roychowdhury, Ebrahim Alareqi, Wenxi Li
- Abstract summary: 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.
- Score: 0.8121462458089141
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Customer purchasing behavior analysis plays a key role in developing
insightful communication strategies between online vendors and their customers.
To support the recent increase in online shopping trends, in this work, 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 that can be useful for targeted consumer insights at scale.
We observe higher sensitivity to the design of online shopping portals for
session-level purchasing prediction with accuracy/recall in range
91-98%/73-99%, respectively. The user-journey level analysis demonstrates five
unique user clusters, wherein 'New Shoppers' are most predictable and
'Impulsive Shoppers' are most unique with low viewing and high carting
behaviors for purchases. Further, cluster transformation metrics and partial
label learning demonstrates the robustness of each user cluster to
new/unlabelled events. Thus, customer clusters can aid strategic targeted nudge
models.
Related papers
- Emulating Full Client Participation: A Long-Term Client Selection Strategy for Federated Learning [48.94952630292219]
We propose a novel client selection strategy designed to emulate the performance achieved with full client participation.
In a single round, we select clients by minimizing the gradient-space estimation error between the client subset and the full client set.
In multi-round selection, we introduce a novel individual fairness constraint, which ensures that clients with similar data distributions have similar frequencies of being selected.
arXiv Detail & Related papers (2024-05-22T12:27:24Z) - An Exploration of Clustering Algorithms for Customer Segmentation in the
UK Retail Market [0.0]
We aim to develop a customer segmentation model to improve decision-making processes in the retail market industry.
To achieve this, we employed a UK-based online retail dataset obtained from the UCI machine learning repository.
The results showed the GMM outperformed other approaches, with a Silhouette Score of 0.80.
arXiv Detail & Related papers (2024-02-06T15:58:14Z) - Retail store customer behavior analysis system: Design and
Implementation [2.215731214298625]
We propose a framework that includes three primary parts: mathematical modeling of customer behaviors, behavior analysis using an efficient deep learning based system, and individual and group behavior visualization.
Each module and the entire system were validated using data from actual situations in a retail store.
arXiv Detail & Related papers (2023-09-05T06:26:57Z) - 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) - 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) - Unsatisfied Today, Satisfied Tomorrow: a simulation framework for
performance evaluation of crowdsourcing-based network monitoring [68.8204255655161]
We propose an empirical framework tailored to assess the quality of the detection of under-performing cells.
The framework simulates both the processes of satisfaction surveys delivery and users satisfaction prediction.
We use the simulation framework to test empirically the performance of under-performing sites detection in general scenarios.
arXiv Detail & Related papers (2020-10-30T10:03:48Z) - Categorizing Online Shopping Behavior from Cosmetics to Electronics: An
Analytical Framework [3.6726589459214445]
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.
arXiv Detail & Related papers (2020-10-06T06:16:44Z) - 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.