Customer oriented intelligent DSS based on two-phased clustering and integrated interval type-2 fuzzy AHP and hesitant fuzzy TOPSIS

Abstract

Firms need to integrate multiple business functions in order to acquire, analyze, model, and evaluate information necessary for better understanding customer behaviors and making data-driven decisions to enhance the customer experience journey. This study proposes a customer oriented intelligent decision support system (IDSS) to ultimately improve the customer experience journey. Besides, a real application study is handled for a multinational company located in Turkey, considering its abrasives product sales for years of 2017 and 2018. For the data utilized in application study, the proposed methodology is constructed for customer segmentation to develop appropriate data-driven marketing strategies for customers with similar values, preferences and other factors for creating customer-centric organizations. In this regard; firstly two-phased clustering process, which involves the hierarchical multivariate average linkage clustering algorithm and partitional k-means clustering algorithm, is used to present the number of clusters on the basis of three variables (expenditure, transaction and unit cost) and then to assign the customers to the related clusters (VIP, Platinum, Gold and Bronze), respectively. Secondly, the performances of company’s departments are ranked according to the preferences of customers from each segment considering 4Ps marketing mix concept via integrated methodology of interval type-2 Fuzzy AHP and hesitant fuzzy TOPSIS.

Keywords

Intelligent decision support system (IDSS)customer experience journey clustering fuzzy multi criteria decision making (MCDM)interval type-2 fuzzy AHP hesitant fuzzy TOPSIS

Get full access to this article

View all access options for this article.

References

Bhatti

, Internet of Equipment: Enhancing customer value and experience, Jónkóping University, Sweden, Master Thesis in Dept of Business Administration, (2019), http://www.diva-portal.org/smash/get/diva2:1319546/FULLTEXT01.pdf

De Keyser

, Schepers

and Konuş

, Multichannel customer segmentation: Does the after-sales channel matter? A replication and extension, International Journal of Research in Marketing 32(4) (2015), 453–456.

Jain

A.K.

, Data clustering: 50 years beyond K-means, Pattern recognition Letters 31(8) (2010), 651–666.

Nagpal

, Jatain

and Gaur

, Review based on data clustering algorithms, in IEEE Conference on Information & Communication Technologies (2013), 298–303.

Pansari

and Kumar

, Customer engagement: the construct, antecedents, and consequences, Journal of the Academy of Marketing Science 45(3) (2017), 294–311.

Ravasan

A.Z.

and Mansouri

, A fuzzy anp based weighted rfm model for customer segmentation in auto insurance sector, in Intelligent Systems: Concepts, Methodologies, Tools, and Applications, M. Khosrow, ed., IGI Global, USA, (2018), 1050–1067.

Öztayşi

, Gokdere

, Simsek

E.N.

and Oner

C.S.

, Anovel approach to segmentation using customer locations data and intelligent techniques, in Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics, S. G. Deshmukh, ed., IGI Global, USA, (2017), 21–39.

Schmitt

, Experience marketing: concepts, frameworks and consumer insights,^®, in Marketing 5(2) (2011), 55–112.

Meyer

and Schwager

, Understanding Customer Experience, Harvard Business Review 85(2) (2007), 117–126.

10.

Homburg

, Jozić

and Kuehnl

, Customer experience management: toward implementing an evolving marketing concept, Journal of the Academy of Marketing Science 45(3) (2017), 377–401.

11.

Chan

C.C.H.

, Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer, Expert Systems with Applications 34(4) (2008), 2754–2762.

12.

Kahraman

, Öztayşi

, Sarı

İ.U.

and Turanoğlu

, Fuzzy analytic hierarchy process with interval type-2 fuzzy sets, Knowledge-Based Systems 59(2014), 48–57.

13.

Kahraman

, Onar

S.C.

and Oztaysi

, Customer Segmentation Method Determination Using Neutrosophic Sets, in Proceedings of the International Conference on Intelligent and Fuzzy Systems (2019), 517–526.

14.

Kahraman

, Onar

S.C.

and Oztaysi

, Fuzzy multicriteria decision-making: a literature review, International Journal of Computational Intelligence Systems 8(4) (2015), 637–666.

15.

Kahraman

, çevik Onar

and Öztayşi

, Present Worth Analysis Using Hesitant Fuzzy Sets, in Proceedings of 16th World Congress of the International Fuzzy Systems Association (IFSA) and 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), (2015), 255–259.

16.

Vuppalapati

, Vuppalapati

, Kedari

, Ilapakurti

, Vuppalapati

J.S.

and Kedari

, Fuzzy Logic Infused Intelligent Scent Dispenser For Creating Memorable Customer Experience of Long-Tail Connected Venues, in Proceedings of IEEE International Conference on Machine Learning and Cybernetics (ICMLC), (2018), 149–154.

17.

Akburak

, Yel

and Senvar

, An Unsupervised Data Mining Approach for Clustering Customers of Abrasive Manufacturer, in Proceedings of the International Conference on Intelligent and Fuzzy Systems, (2019), 416–422.

18.

Arnott

and Pervan

, Eight key issues for the decision support systems discipline, Decision Support Systems 44(3) (2008), 657–672.

19.

Edelman

D.C.

and Singer

, Competing on customer journeys, Harvard Business Review 93(11) (2015), 88–100.

20.

Nauck

, Ruta

, Spott

and Azvine

, A tool for intelligent customer analytics, Proceedings of the 3rd International IEEE Conference Intelligent Systems, (2006), 518–521.

21.

Nauck

, Ruta

, Spott

and Azvine

, Predictive customer analytics and real-time business intelligence, in: Service Chain Management, C. Voudouris, D. Lesaint, G. Owusu, eds., Springer, Berlin, Heidelberg, (2008), 205–214.

22.

Rajagopal

, Customer data clustering using data mining technique, International Journal of Database Management Systems (IJDMS) 3(4) (2011), 1–11.

23.

Pham

D.T.

and Afify

A.A.

, Clustering techniques and their applications in engineering, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 221(11) (2007), 1445–1459.

24.

Camilleri

and Miah

, A Consumer Analytics Framework for Enabling Data-Driven Marketing Intervention Methods. Proceedings of the 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), (2017), 8–12.

25.

Heshmati

, Saeednia

and Badizadeh

, Designing a customer-experience-management model for the banking-services sector, Journal of Islamic Marketing 10(3) (2019), 790–810.

26.

Zogo

E.E.

and Jayawardhena

, Online shopping experience in an emerging e-retailing market: Towards a conceptual model, J Consumer Behav 17 (2018), 379–392. https://doi.org/10.1002/cb.1715

27.

Yusoff

F.H.B.

and Rosman

N.L.A.B.

, A Case Study of Customers’ Payment BehaviourAnalytics on Paying Electricity with RFM Analysis and K-Means, in Proceedings of the International Conference on Soft Computing in Data Science, (2019), 40–55.

28.

Murtagh

and Contreras

, Algorithms for hierarchical clustering: an overview, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2(1) (2012), 86–97.

29.

Beaudon

and Soulier

, Customer Experience Analytics in Insurance: Trajectory, Service Interaction and Contextual Data, in Proceedings of the International Conference on Information Technology & Systems (2019), 187–198.

30.

Phillips-Wren

and Hoskisson

, An analytical journey towards big data, Journal of Decision Systems 24(1) (2015), 87–102.

31.

Abbasimehr

and Shabani

, A new methodology for customer behavior analysis using time series clustering: A case study on a bank’s customers, Kybernetes, Vol. ahead-of-print (No. ahead-of-print.), (2019), https://doi.org/10.1108/K-09-2018-0506

32.

Simon

H.A.

, The New Science of Management Decision, 3rd edn, Prentice Hall (1977).

33.

Güçdemir

and Selim

, Integrating multi-criteria decision making and clustering for business customer segmentation, Industrial Management & Data Systems 115(6) (2015), 1022–1040.

34.

Lee

J.H.

and Park

S.C.

, Intelligent profitable customers segmentation system based on business intelligence tools, Expert Systems with Applications 29(1) (2005), 145–152.

35.

Han

, Pei

and Kamber

, Data mining: concepts and techniques. Elsevier, (2011).

36.

Kannan

J.R.

, Sabitha

, Karthik

and Shanthini

, Mouse Movement Pattern Based Analysis of Customer Behavior (CBA-MMP) Using Cloud Data Analytics, Wireless Personal Communications, (2020), 1–17, https://doi.org/10.1007/s11277-020-07055-1

37.

Spiess

, T’Joens

, Dragnea

, Spencer

and Philippart

, Using big data to improve customer experience and business performance, Bell Labs Technical Journal 18(4) (2014), 3–17.

38.

and Lin

, Research on customer segmentation model by clustering, in: Proceedings of the 7th International Conference on Electronic Commerce, ACM, (2005), 316–318.

39.

Fernandes

, Vinagre

and Cortez

, A proactive intelligent decision support system for predicting the popularity of online news, in Proceedings of the Portuguese Conference on Artificial Intelligence, (2015), 535–546.

40.

Lemon

K.N.

and Verhoef

P.C.

, Understanding customer experience throughout the customer journey, Journal of Marketing 80(6) (2016), 69–96.

41.

Kashwan

K.R.

and Velu

C.M.

, Customer segmentation using clustering and data mining techniques, International Journal of Computer Theory and Engineering 5(6) (2013), 856–861.

42.

Abirami

and Pattabiraman

, Data mining approach for intelligent customer behavior analysis for a retail store, in Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC–16’), Springer, (2016), 283–291.

43.

Bahrami

, Bozkaya

and Balcisoy

, Using Behavioral Analytics to Predict Customer Invoice Payment, Big Data 8(2) (2020), 25–37.

44.

Namvar

, Gholamian

M.R.

and KhakAbi

, A two phase clustering method for intelligent customer segmentation, in Proceedings of the IEEE International Conference on Intelligent Systems, Modelling and Simulation, (2010), 215–219.

45.

Moreno

M.N.

, Lucas

J.P.

and López

VF.

, uzzy Data-Mining Hybrid Methods for Recommender Systems, in: Soft Computing for Business Intelligence, Studies in Computational Intelligence, R. Espin, R. Pérez, A. Cobo, J. Marx, A. Valdés, eds., Springer, Berlin,Heidelberg, (2014), 327–344.

46.

Sun

, Morris

J.G.

, Xu

, Zhu

and Xie

, iCARE: A framework for big data-based banking customer analytics, IBM Journal of Research and Development 58(5/6) (2014), 1–4.

47.

Verma

and Singh

, An intelligent approach to Big Data analytics for sustainable retail environment using Apriori-MapReduce framework, Industrial Management & Data Systems 117(7) (2017), 1503–1520.

48.

Abbas

O.A.

, Comparisons Between Data Clustering Algorithms, International Arab Journal of Information Technology (IAJIT) 5(3) (2008), 320–325.

49.

Senvar

and Kahraman

, Type-2 fuzzy process capability indices for non-normal processes, Journal of Intelligent & Fuzzy Systems 27(2) (2014), 769–781.

50.

Senvar

, Otay

and Bolturk

, Hospital site selection via hesitant fuzzy TOPSIS, IFAC-Papers On Line 49(12) (2016), 1140–1145.

51.

Anitha

and Patil

M.M.

, RFM model for Customer Purchase Behavior using K-Means Algorithm, Journal of King Saud University-Computer and Information Sciences, in press, (2019), https://doi.org/10.1016/j.jksuci.2019.12.011Get

52.

Sneath

P.H.

, Numerical Taxonomy, in: Bergey’s Manual^® of Systematic Bacteriology, D.J. Brenner, N.R. Krieg J.T. Staley, G.M. Garrity eds., Springer, Boston, MA, (2005), 39–42.

53.

Klaus

, The case of Amazon. com: towards a conceptual framework of online customer service experience (OCSE) using the emerging consensus technique (ECT), Journal of Services Marketing 27(6) (2013), 443–457.

54.

Klaus

and Maklan

, Towards a better measure of customer experience, International Journal of Market Research 55(2) (2013), 227–246.

55.

Venkatesan

, Petersen

J.A.

and Guissoni

, Measuring and managing customer engagement value through the customer journey, in: Customer Engagement Marketing, Palmatier

, V.Kumar

, Harmeling

, eds., Palgrave, Macmillan, Cham, (2018), 53–74.

56.

Palmatier

R.W.

, Kumar

and Harmeling

C.M.

, eds., Customer engagement marketing, Springer, (2017).

57.

SAS/STAT 9.2 user’s guide, (2009), 2nd ed., Cary, N.C.: SAS Institute.

58.

Cevik Onar

, Oztaysi

and Kahraman

, Strategic decision selection using hesitant fuzzy TOPSIS and interval type-2 fuzzy AHP: a case study, International Journal of Computational Intelligence Systems 7(5) (2014), 1002–1021.

59.

Lan

, Zhang

, Zhong

R.Y.

and Huang

G.Q.

, A customer satisfaction evaluation model for logistics services using fuzzy analytic hierarchy process, Industrial Management & Data Systems 116(5) (2016), 1024–1042.

60.

Masood

, Ali

, Arshad

, Qamar

A.M.

, Kamal

and Rehman

, Customer segmentation and analysis of a mobile telecommunication company of Pakistan using two phase clustering algorithm, in Proceedings of 8th International Conference on Digital Information Management (2013), 137–142.

61.

Kim

S.Y.

, Jung

T.S.

, Suh

E.H.

and Hwang

H.S.

, Customer segmentation and strategy development based on customer lifetime value: A case study, Expert Systems with Applications 31(1) (2006), 101–107.

62.

and Tian

, Queuing System Optimization based on Customer Experience of Chinese Commercial Intelligent Bank Branches, in Proceedings of the 2019 3rd International Conference on Management Engineering, Software Engineering and Service Sciences, (2019), 140–144.

63.

Shi

, Wen

and Xia

, An Intelligent Recommendation System based on Customer Segmentation, International Journal of Research 2(11) (2015), 78–90.