Customer Purchase Behavior Prediction in E-commerce: A Conceptual Framework and Research Agenda

Cirqueira, Douglas; Hofer, Markus; Nedbal, Dietmar; Helfert, Markus; Bezbradica, Marija

doi:10.1007/978-3-030-48861-1_8

Cited by 23 publications

(15 citation statements)

References 41 publications

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“…Digital platforms in retail and banking have enabled customers to experience convenience through personalization and tailored technologies for shopping and performing transactions [1][2][3][4]. However, the convenience is also accompanied by the danger of frauds [5,6].…”

Section: Introductionmentioning

confidence: 99%

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Cirqueira

Nedbal

Helfert

et al. 2020

Lecture Notes in Computer Science

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Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder's requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenariobased design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection.

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Section: Introductionmentioning

confidence: 99%

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Cirqueira

Nedbal

Helfert

et al. 2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

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“…Determining which customers holds the most value can help solve the problems of Teguh Inc. One problem that we faced was determining which combination attributes will provide us with the best results in predicting the segment level. The better the segment level, the better the likelihood of predicting customers with the criteria of the most valuable [12]. BIBLIOGRAPHY…”

Section: Discussionmentioning

confidence: 99%

Customer Value and Data Mining in Segmentation Analysis

Gunandi

Awang

Alhawad

et al. 2023

IJITCSA

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Customer Value is the accessed value that a customer has to an organization. In Business, the customer is always right. This statement gives us the impression that all customers are viewed as equal in terms of potential value. Each customer is treated differently according to how much profit they can bring to a company. We use various Data Mining techniques to determine who are these customers and how we can acquire more customers like them who can bring more profit. A loyal customer will be treated differently than a customer that rarely do business with the organization. These customers are usually given bonus gifts and special offers as a form of thanks for their loyalty thus further strengthening that bond. Companies need a way to determine which of their hundreds of thousands of customers are deserving of this attention. Customer Value Segments are used in this specific situation.

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“…Yuanzhu and et al present a study that implements the Apriori algorithm and C5.0 which are considered as association rules; also, decision tree techniques for data mining [25]. It has been used to help managers or decision maker people to extract knowledge "from" and 'about' customers in order to determine their preferences, allowing enterprises to develop the correct goods and achieve a competitive advantage.…”

Section: Apriori Pt Association Rule Algorithmmentioning

confidence: 99%

Data Mining Model for Predicting Customer Purchase Behavior in E-Commerce Context

Alghanam¹,

Al-Khatib²,

Hiari³

2022

IJACSA

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Nowadays e-commerce environment plays an important role to exchange commodity knowledge between consumers commonly with others. Accurately predicting customer purchase patterns in the e-commerce market is one of the critical applications of data mining. In order to achieve high profit in e-commerce, the relationship between customer and merchandise are very important. Moreover, many e-commerce websites increase rapidly and instantly and competition has become just a mouse-click away. That is why the importance of staying in the business, and improving the profit needs to accurately predict purchase behavior and target their customers with personalized services according to their preferences. In this paper, a data mining model has been proposed to enhance the accuracy of predicting and to find association rules for frequent item sets. Also, K-means clustering algorithm has been used to reduce the size of the dataset in order to enhance the runtime for the proposed model. The proposed model has used four different classifiers which are C4.5, J48, CS-MC4, and MLR. Also, Apriori algorithm to provide recommendations for items based on previous purchases. The proposed model has been tested on Northwind trader's dataset and the results archives accuracy equal 95.2% when the number of clusters were 8.

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Customer Purchase Behavior Prediction in E-commerce: A Conceptual Framework and Research Agenda

Cited by 23 publications

References 41 publications

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Customer Value and Data Mining in Segmentation Analysis

Data Mining Model for Predicting Customer Purchase Behavior in E-Commerce Context

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