Customer segmentation in a large database of an online customized fashion business
Robotics and Computer-Integrated ManufacturingPublished 16 January 2015
Pedro Quelhas Brito, Carlos Soares, Sérgio F. de Almeida, Ana Monte, Michel Byvoet
Citations98
SJR quartileQ1
SJR score2.89
SNIP3.82
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The goal of this project was to investigate two different data mining approaches for customer segmentation: clustering and subgroup discovery, and obtained models that allowed a better understanding of customer preferences in a highly customized fashion manufacturer/e-tailor.
Abstract
Data mining (DM) techniques have been used to solve marketing and manufacturing problems in the fashion industry. These approaches are expected to be particularly important for highly customized industries because the diversity of products sold makes it harder to find clear patterns of customer preferences. The goal of this project was to investigate two different data mining approaches for customer segmentation: clustering and subgroup discovery. The models obtained produced six market segments and 49 rules that allowed a better understanding of customer preferences in a highly customized fashion manufacturer/e-tailor. The scope and limitations of these clustering DM techniques will lead to further methodological refinements.
Keywords
Business, Management and Accounting
ACM Computing SurveysData clustering
13,184 Citations1999Anil K. Jain, M. Narasimha Murty +1 more
An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Expert Systems with ApplicationsA simple and fast algorithm for K-medoids clustering
2,041 Citations2008Hae-Sang Park, Chi‐Hyuck Jun
Experimental results show that the proposed algorithm takes a significantly reduced time in computation with comparable performance against the partitioning around medoids.
Machine LearningThe CN2 Induction Algorithm
2,015 Citations1989Peter Clark, Tim Niblett
A description and empirical evaluation of a new induction system, CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present.
Journal of MarketingProduct Differentiation and Market Segmentation as Alternative Marketing Strategies
1,606 Citations1956Wendell R. Smith
Big DataData Science and its Relationship to Big Data and Data-Driven Decision Making
1,533 Citations2013Foster Provost, Tom Fawcett
It is argued that there are good reasons why it has been hard to pin down exactly what is data science, and that for data science to serve business effectively, it is important to understand its relationships to other important related concepts, and to begin to identify the fundamental principles underlying data science.
Journal of Marketing ResearchA Probabilistic Choice Model for Market Segmentation and Elasticity Structure
1,254 Citations1989Wagner A. Kamakura, Gary J. Russell
Machine LearningThe CN2 induction algorithm
994 Citations1989Peter Clark, Tim Niblett
A description and empirical evaluation of a new induction system, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present, is presented.
Lecture notes in computer scienceRule induction with CN2: Some recent improvements
866 Citations1991Peter Clark, Robin Boswell
Improvements to the CN2 algorithm are described, including the use of the Laplacian error estimate as an alternative evaluation function and it is shown how unordered as well as ordered rules can be generated.
Marketing ScienceMarket Segmentation, Self-Selection, and Product Line Design
733 Citations1984K. Sridhar Moorthy
Management ScienceMarket Segmentation and Product Technology Selection for Remanufacturable Products
697 Citations2005Laurens Debo, L. Beril Toktay +1 more
This paper solves the joint pricing and production technology selection problem faced by a manufacturer that considers introducing a remanufacturable product in a market that consists of heterogeneous consumers.
Lecture notes in computer scienceAn algorithm for multi-relational discovery of subgroups
545 Citations1997Stefan Wrobel
An algorithm is described for finding statistically unusual subgroups in a multi-relation database that uses optimistic estimate and minimal support pruning, an optimal refinement operator and sampling to ensure efficiency and can easily be parallelized.
Relational Data Mining
505 Citations2006Sašo Džeroski
International Journal of Research in MarketingInternational market segmentation: issues and perspectives
382 Citations2002Jan-Benedict E.M. Steenkamp, Frenkel Ter Hofstede
Journal of Business ResearchSegmenting consumers based on the benefits and risks of Internet shopping
346 Citations2003Amit Bhatnagar, Sanjoy Ghose
An analytical model is developed to examine the role perceived benefits and risks of online shopping play in forming consumer preferences for online shopping and hypothesize how the two dimensions of risks will vary across the segment characteristics.
Bristol Research (University of Bristol)Subgroup Discovery with CN2-SD
309 Citations2004Nada Lavrač, Branko Kavšek +2 more
Knowledge and Information SystemsAn overview on subgroup discovery: foundations and applications
281 Citations2010Francisco Herrera, C. J. Carmona +2 more
This review focuses on the foundations, algorithms, and advanced studies together with the applications of subgroup discovery presented throughout the specialised bibliography.
Marketing LettersMarket segmentation with choice-based conjoint analysis
267 Citations1995Wayne S. DeSarbo, Venkatram Ramaswamy +1 more
Journal of Computer ScienceComputational Complexity between K-Means and K-Medoids Clustering Algorithms for Normal and Uniform Distributions of Data Points
253 Citations2010Velmurugan
The average time taken by K-Means algorithm is greater than the time takenby K-Medoids algorithm for both the case of normal and uniform distributions, and the r esults proved to be satisfactory.
Computers & Operations ResearchIntegration of self-organizing feature map and K-means algorithm for market segmentation
246 Citations2002R.J. Kuo, Leyton Ho +1 more
The simulation results indicate that the proposed scheme is slightly better than the conventional two-stage method with respect to the rate of misclassification, and the real-world data on the basis of Wilk's Lambda and discriminant analysis.
Medical Entomology and ZoologyMarket Segmentation: How to Do It, How to Profit from It
218 Citations1998Malcolm McDonald, Ian Dunbar
Journal of Artificial Intelligence ResearchExpert-Guided Subgroup Discovery: Methodology and Application
204 Citations2002Dragan Gamberger, Nada Lavrač
The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search algorithm, using a novel parametrized definition of rule quality which is analyzed in detail.
Expert Systems with ApplicationsA purchase-based market segmentation methodology
191 Citations2004Chieh-Yuan Tsai, Chia-Chi Chiu
A novel market segmentation methodology based on product specific variables such as purchased items and the associative monetary expenses from the transactional history of customers to resolve problems of traditional segmentation.
International Journal of Production EconomicsSales forecasts in clothing industry: The key success factor of the supply chain management
189 Citations2010Sébastien Thomassey
Different forecasting models which perform more accurate and more reliable sales forecasts are proposed which rely on advanced methods such as fuzzy logic, neural networks and data mining for the supply chain and the reduction of the bullwhip effect.
Electronic Commerce Research and ApplicationsCustomer segmentation of multiple category data in e-commerce using a soft-clustering approach
154 Citations2010Roung-Shiunn Wu, Po-Hsuan Chou
A soft clustering method that uses a latent mixed-class membership clustering approach to classify online customers based on their purchasing data across categories, and yields more promising results than hard clustering and greater within-segment clustering quality than the finite mixture model.
Expert Systems with ApplicationsSegmentation of the on-line shopping market using neural networks
127 Citations1999Alfredo Vellido, Paulo Lisböa +1 more
The Self-Organizing Map (SOM), an unsupervised neural network model devised by Kohonen, is proposed as a flexible clustering model able to accommodate both Finer Se segmentation and Normative Segmentation approaches, and a cluster-partition is proposed and analysed.
Machine LearningDecision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned
101 Citations2004Nada Lavrač, Bojan Cestnik +2 more
Three case studies are used to present the lessons learned in solving problems requiring actionable knowledge generation for decision support, and different subgroup discovery approaches are outlined.
Lecture notes in economics and mathematical systemsA Theory of Market Segmentation
84 Citations1976Henry J. Claycamp, William F. Massy
Data Mining Explained: A Manager's Guide to Customer-Centric Business Intelligence
70 Citations2001Rhonda Delmater, Monte Hancock
Robotics and Computer-Integrated ManufacturingA new support vector model-based imperialist competitive algorithm for time estimation in new product development projects
61 Citations2012Seyed Meysam Mousavi, Reza Tavakkoli‐Moghaddam +3 more
A new model integrating the SVR and the ICA for time estimation in NPD projects, in which ICA is used to tune the parameters of the S VR, and results indicate that the presented model achieves high estimation accuracy and leads to effective prediction.
Expert Systems with ApplicationsMining customer knowledge to implement online shopping and home delivery for hypermarkets
48 Citations2010Shu-Hsien Liao, Yin-ju Chen +1 more
This study combines online shopping and home delivery, and attempts to use association rules to determine unknown bundling of fresh products and non-fresh products in a hypermarket.
Journal of Marketing ResearchA Theory of Market Segmentation
39 Citations1968Henry J. Claycamp, William F. Massy
A normative theory of market segmentation is developed that takes account of major implementation problems and is presented as a multistage mathematical model of the full range of segmentation possibilities from the perfectly discriminating monopolist to the mass marketer.
Expert Systems with ApplicationsSegmenting customers by transaction data with concept hierarchy
37 Citations2011Fang‐Ming Hsu, Li-Pang Lu +1 more
This study proposes a hierarchical clustering method to segment customers by their transaction data with concept hierarchy of consumed items and rendered better results than other traditional methods.
Adapting classification rule induction to subgroup discovery
32 Citations2003Nada Lavrač, Peter Flach +2 more
Experimental evaluation of the CN2-SD subgroup discovery algorithm on 17 UCI data sets demonstrates substantial reduction of the number of induced rules, increased rule coverage and rule significance, as well as slight improvements in terms of the area under the ROC curve.
An adaptive approach for online segmentation of multi-dimensional mobile data
24 Citations2012Tian Guo, Zhixian Yan +1 more
An adaptive model for segmenting real-time accelerometer data from smartphones is designed, which is able to dynamically select suitable dimensions to build a model, and adaptively pick up a proper model.
Lecture notes in computer scienceApplications and research problems of subgroup mining
19 Citations1999Willi Klösgen
This paper gives a short introduction into the methods of subgroup mining, especially the main preprocessing, data mining and postprocessing steps are discussed in more detail for two applications.
Expert Systems with ApplicationsMaking CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection
18 Citations2007José-Ramón Cano, Francisco Herrera +2 more
This paper proposes the use of instance selection algorithms for scaling down the data sets before the subgroup discovery task, and shows that CN2-SD can be executed on large data set sizes pre-processed, maintaining and improving the quality of the subgroups discovered.
Lecture notes in computer scienceSubgroup Discovery Techniques and Applications
17 Citations2005Nada Lavrač
Two case studies from medicine and functional genomics are used to present the lessons learned in solving problems requiring actionable knowledge generation for decision support.
Lecture notes in computer scienceGenerating Actionable Knowledge by Expert-Guided Subgroup Discovery
14 Citations2002Dragan Gamberger, Nada Lavrač
The disadvantages of using standard classification rule learning for this task are discussed, and a subgroup discovery approach proposed which uses a novel definition of rule quality.
Frontiers in artificial intelligence and applicationsVoltage Sag Source Location From Extracted Rules Using Subgroup Discovery
9 Citations2008Barrera Víctor, López Beatriz +2 more
This work presents a set of rules to determine the voltage sag source location in electric power systems that can be easily interpreted by a domain expert, allowing the formulation of heuristic classification rules with high accuracy.
Borås Academic Digital Archive (University of Borås)Inpulsive Buying Behaviour : The Role of Feelings When Shopping for Online Fashion
7 Citations2013Malin Sundström, Jenny Balkow +3 more
Using Subgroup Discovery to Analyze the UK Traffic Data
6 Citations2004Branko Kavÿsek, Nada Lavraÿc
The modifications needed for the adaptation of the CN2 rule learner to subgroup discovery are summarized and its application to a real-life data set - the UK traffic data - is presented, confirming its appropriateness for sub group discovery in real- life applications through experimental comparison with the CN1 rule learning algorithm as well as through the evaluation of an expert.
Advances in Methodology and StatisticsUsing subgroup discovery to analyze the UK traffic data
4 Citations2004Branko Kavšek, Nada Lavrač
The modifications needed for the adaptation of the CN2 rule learner to subgroup discovery are summarized and its application to a real-life data set - the UK traffic data - is presented, confirming its appropriateness for sub group discovery in real- life applications through experimental comparison with the CN1 rule learning algorithm as well as through the evaluation of an expert.
Robotics and Computer-Integrated ManufacturingMarketing advantages via new manufacturing technologies
2 Citations1990Wulff Plinke, Michael Kleinaltenkamp
Computer-integrated manufacturing is generally seen as the manufacturing technology of the future but it rather requires an effective marketing orientation to transform those technological innovations into competitive advantages.