nach oben

Erschienen in:

2021 | OriginalPaper | Buchkapitel

Machine Learning for Customer Segmentation Through Bibliometric Approach

verfasst von : Lopamudra Behera, Pragyan Nanda, Bhagyashree Mohanta, Rojalin Behera, Srikanta Patnaik

Erschienen in: Advances in Machine Learning and Computational Intelligence

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In the age of information science and automation, ‘technological revolution’ and ‘machine era’ have gained significant attention from researchers of every quarter of life. Exuberance of artificial computation with machine learning techniques with practical applications and precision has become a perennial issue in every discipline. Optimization of marketing efficiency with assistance of computational applications has improved the accuracy and transparency level to achieve competitive advantages, to enhance organizational efficiency and to gain market advancement. In this bibliometric study, the authors have analyzed the literatures published during the period 2009–2019 having occurrence of relevant terms and major sources of contribution related to the area of ‘customer segmentation’ as an impact of ‘machine learning applications.’ Data scribe has been obtained from Scopus database, total of 1440 numbers of research articles were further analyzed using VOSviewer tool. The study revealed co-occurrence of keywords and bibliometric coupling of major sources of contribution. Findings suggest that the most frequent occurred key terms related to machine Learning have more influence and link strength than the terms related to customer segmentation by analyzing significant sources in terms of citations received and number of contribution made.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Modern Approach for Loan Sanctioning in Banks Using Machine Learning

Nächstes Kapitel Online Hostel Management System Using Hybridized Techniques of Random Forest Algorithm and Long Short-Term Memory

S. Goyat, The basis of market segmentation: a critical review of literature. Eur. J. Bus. Manag. 3(9), 45–54 (2011)

J. Tikmani, S. Tiwari, S. Khedkar, An approach to consumer classification using K-Means. IJIRCCE 3(11), 10542–10549 (2015)

C.P. Ezenkwu, S. Ozuomba, C. Kalu, Application of K-Means algorithm for efficient customer segmentation: a strategy for targeted customer services (2015)

V.R. Patel, R.G. Mehta, Impact of outlier removal and normalization approach in modified k-means clustering algorithm. Int. J. Comput. Sci. Issues (IJCSI) 8(5), 331 (2011)

G. Linden, B. Smith, J. York, Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

H.K. Rao, Z. Zeng, A.P. Liu, Research on personalized referral service and big data mining for e-Commerce with machine learning, in 2018 4th International Conference on Computer and Technology Applications (ICCTA) (IEEE, 2018, May), pp. 35–38

Y. Fang, X. Xiao, X. Wang, H. Lan, Customized bundle recommendation by association rules of product categories for online supermarkets, in 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC) (IEEE, 2018, June), pp. 472–475

M. Pondel, J. Korczak, Collective clustering of marketing data-recommendation system Upsaily, in 2018 Federated Conference on Computer Science and Information Systems (FedCSIS) (IEEE, 2018, September), pp. 801–810

M. Tavakoli, M. Molavi, V. Masoumi, M. Mobini, S. Etemad, R. Rahmani, Customer segmentation and strategy development based on user behavior analysis, RFM model and data mining techniques: a case study, in 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE) (IEEE, 2018, October), pp. 119–126

10.

H.W. Yang, Z.G. Pan, X.Z. Wang, B. Xu, A personalized products selection assistance based on e-commerce machine learning, in Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 04EX826), vol. 4 (IEEE, 2004, August), pp. 2629–2633

11.

X. Chen, W. Sun, B. Wang, Z. Li, X. Wang, Y. Ye, Spectral clustering of customer transaction data with a two-level subspace weighting method. IEEE Trans. Cybern. 49(9), 3230–3241 (2018)

12.

A.S. Hossain, Customer segmentation using centroid based and density based clustering algorithms, in 2017 3rd International Conference on Electrical Information and Communication Technology (EICT) (IEEE, 2017, December), pp. 1–6

13.

X. Chen, Y. Fang, M. Yang, F. Nie, Z. Zhao, J.Z. Huang, Purtreeclust: a clustering algorithm for customer segmentation from massive customer transaction data. IEEE Trans. Knowl. Data Eng. 30(3), 559–572 (2017)

14.

C. Catal, S. Guldan, Product review management software based on multiple classifiers. IET Softw. 11(3), 89–92 (2017)

15.

H. Zhao, X.H. Zhang, Q. Wang, Z.C. Zhang, C.Y. Wang, Customer segmentation on mobile online behavior, in 2014 International Conference on Management Science & Engineering 21th Annual Conference Proceedings (IEEE, 2014, August), pp. 103–109

16.

Y. Xi, Chinese review spam classification using machine learning method, in 2012 International Conference on Control Engineering and Communication Technology (IEEE, 2012, December), pp. 669–672

17.

D. Gallego, G. Huecas, An empirical case of a context-aware mobile recommender system in a banking environment, in 2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing (IEEE, 2012, June), pp. 13–20

18.

C.L. Borgman, Bibliometrics and scholarly communication: editor’s introduction. Commun. Res. 16(5), 583–599 (1989)

19.

I. Zupic, T. Čater, Bibliometric methods in management and organization. Organ. Res. Methods 18(3), 429–472 (2015)

20.

J.W. Prado, V. Castro Alcântara, F. Melo Carvalho, K.C. Vieira, L.K. Machado, D.F. Tonelli, Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014). Scientometrics 106(3), 1007–1029 (2016)

21.

Y. Wang, Q. Wang, R. Zhu, C. Yang, Z. Chen, Y. Bai, Trends of spinal tuberculosis research (1994–2015): a bibliometric study. Medicine 95(38) (2016)

22.

A.W.K. Yeung, T.K. Goto, W.K. Leung, The changing landscape of neuroscience research, 2006–2015: a bibliometric study. Front. Neurosci. 11, 120 (2017)

23.

N. Roig-Tierno, T.F. Gonzalez-Cruz, J. Llopis-Martinez, An overview of qualitative comparative analysis: a bibliometric analysis. J. Innovation Knowl. 2(1), 15–23 (2017)

24.

X. Chen, Z. Liu, L. Wei, J. Yan, T. Hao, R. Ding, A comparative quantitative study of utilizing artificial intelligence on electronic health records in the USA and China during 2008–2017. BMC Med. Inform. Decis. Mak. 18(5), 117 (2018)

25.

A.W.K. Yeung, Bibliometric study on functional magnetic resonance imaging literature (1995–2017) concerning chemosensory perception. Chemosens. Percept. 11(1), 42–50 (2018)

26.

C. Vallaster, S. Kraus, J.M.M. Lindahl, A. Nielsen, Ethics and entrepreneurship: a bibliometric study and literature review. J. Bus. Res. 99, 226–237 (2019)

27.

N. Van Eck, L. Waltman, Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2), 523–538 (2010)

28.

J.F. Burnham, Scopus database: a review. Biomed. Digital Libr. 3(1), 1 (2006)

29.

Ş. Ozan, A case study on customer segmentation by using machine learning methods, in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (IEEE, 2018, September), pp. 1–6

30.

P.D. Hung, N.D. Ngoc, T.D. Hanh, K-means clustering using RA case study of market segmentation, in Proceedings of the 2019 5th International Conference on E-Business and Applications (2019, February), pp. 100–104

31.

C. Miller, What’s in the box?! Towards explainable machine learning applied to non-residential building smart meter classification. Energy Build. 199, 523–536 (2019)

32.

M. Nilashi, A. Mardani, H. Liao, H. Ahmadi, A.A. Manaf, W. Almukadi, A hybrid method with TOPSIS and machine learning techniques for sustainable development of green hotels considering online reviews. Sustainability 11(21), 6013 (2019)

33.

G. David, A. Averbuch, SpectralCAT: categorical spectral clustering of numerical and nominal data. Pattern Recogn. 45(1), 416–433 (2012)MATH

34.

M.S.B. Phridviraj, C.G. Rao, A novel approach for unsupervised learning of transaction data, in Proceedings of the 5th International Conference on Engineering and MIS (2019, June), pp. 1–5

35.

R. Razavi, A. Gharipour, M. Fleury, I.J. Akpan, A practical feature-engineering framework for electricity theft detection in smart grids. Appl. Energy 238, 481–494 (2019)

36.

T. Ko, J.H. Lee, H. Cho, S. Cho, W. Lee, M. Lee, Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data. Ind. Manag. Data Syst. (2017)

37.

D. Koolen, N. Sadat-Razavi, W. Ketter, Machine learning for identifying demand patterns of home energy management systems with dynamic electricity pricing. Appl. Sci. 7(11), 1160 (2017)

38.

I. Smeureanu, G. Ruxanda, L.M. Badea, Customer segmentation in private banking sector using machine learning techniques. J. Bus. Econ. Manag. 14(5), 923–939 (2013)

39.

J.M. Arevalillo, A machine learning approach to assess price sensitivity with application to automobile loan segmentation. Appl. Soft Comput. 76, 390–399 (2019)

40.

A. Ghahari, N.K. Newlands, V. Lyubchich, Y.R. Gel, Deep learning at the interface of agricultural insurance risk and spatio-temporal uncertainty in weather extremes. North Am. Actuarial J. 23(4), 535–550 (2019)MathSciNetMATH

41.

K. Stormi, T. Laine, T. Elomaa, Feasibility of B2C customer relationship analytics in the B2B industrial context (2018)

42.

K.S. Tan, P. Subramanian, Proposition of machine learning driven personalized marketing approach for E-commerce. J. Comput. Theor. Nanosci. 16(8), 3532–3537 (2019)

43.

M. Carnein, H. Trautmann, Customer segmentation based on transactional data using stream clustering, in Pacific-Asia Conference on Knowledge Discovery and Data Mining (Springer, Cham, 2019, April), pp. 280–292

44.

B. Markic, D. Tomic, Marketing intelligent system for customer segmentation. Marketing Intelligent Systems Using Soft Computing (Springer, Berlin, Heidelberg, 2010), pp. 79–111

45.

G. David, A. Averbuch, Hierarchical data organization, clustering and denoising via localized diffusion folders. Appl. Comput. Harmonic Anal. 33(1), 1–23 (2012)MathSciNetMATH

46.

P.P. Shetty, C.M. Varsha, V.D. Vadone, S. Sarode, D. Pradeep Kumar, Customers churn prediction with RFM model and building a recommendation system using semi-supervised learning in retail sector. Int. J. Recent Technol. Eng. 8(1), 3353–3358 (2019)

47.

P. Albuquerque, S. Alfinito, C.V. Torres, Support vector clustering for customer segmentation on mobile TV service. Commun. Stat.-Simul. Comput. 44(6), 1453–1464 (2015)MathSciNetMATH

48.

L.S. Chen, C.C. Hsu, M.C. Chen, Customer segmentation and classification from blogs by using data mining: an example of VOIP phone. Cybern. Syst. Int. J. 40(7), 608–632 (2009)MATH

49.

R. Florez-Lopez, J.M. Ramon-Jeronimo, Marketing segmentation through machine learning models: an approach based on customer relationship management and customer profitability accounting. Soc. Sci. Comput. Rev. 27(1), 96–117 (2009)

50.

F. Yoseph, M. Heikkila, Segmenting retail customers with an enhanced RFM and a hybrid regression/clustering method, in 2018 International Conference on Machine Learning and Data Engineering (iCMLDE) (IEEE, 2018, December), pp. 108–116

51.

J. Cuzzola, J. Jovanović, E. Bagheri, D. Gašević, Automated classification and localization of daily deal content from the Web. Appl. Soft Comput. 31, 241–256 (2015)

52.

A. Ahani, M. Nilashi, O. Ibrahim, L. Sanzogni, S. Weaven, Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. Int. J. Hosp. Manag. 80, 52–77 (2019)

53.

P. Zou, B. Yu, X.Q. Wang, Cost-sensitive learning method with data drift in customer segmentation. Harbin Gongye Daxue Xuebao (J. Harbin Inst. Technol. 43(1), 119–124 (2011)

54.

Z. Peng, H. Yuanyuan, L. Yijun, Customer value segmentation based on cost-sensitive learning support vector machine. Int. J. Serv. Technol. Manage. 14(1), 126–137 (2010)

55.

H. Chen, S. Li, P. Wu, N. Yi, S. Li, X. Huang, Fine-grained sentiment analysis of chinese reviews using LSTM network. J. Eng. Sci. Technol. Rev. 11(1) (2018)

56.

L. Sun, Research on product attribute extraction and classification method for online review, in 2017 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII) (IEEE, 2017, December), pp. 117–121

57.

H.V. Long, L.H. Son, M. Khari, K. Arora, S. Chopra, R. Kumar et al., A new approach for construction of geodemographic segmentation model and prediction analysis. Comput. Intell. Neurosci. 2019 (2019)

Titel: Machine Learning for Customer Segmentation Through Bibliometric Approach
verfasst von: Lopamudra Behera
Pragyan Nanda
Bhagyashree Mohanta
Rojalin Behera
Srikanta Patnaik
Verlag: Springer Singapore
Buch: Advances in Machine Learning and Computational Intelligence
Print ISBN: 978-981-15-5242-7

Electronic ISBN: 978-981-15-5243-4

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-981-15-5243-4_16

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner

Marktübersichten