JURNAL OF DECISION MAKING MODEL

Integrating multiplicative preference relations in a multipurpose decision-making model based on fuzzy preference relations

F. Chiclana, F. Herrera ∗, E. Herrera-Viedma

Department of Computer Science and Arti cial Intelligence, University of Granada, 18071 –Granada, Spain

Received 23 July 1998; received in revised form 8 October 1999; accepted 8 December 1999

Abstract

The aim of this paper is to study the integration of multiplicative preference relation as a preference representation structure in fuzzy multipurpose decision-makingproblems. Assumingfuzzy multipurpose decision-makingproblems under di4erent preference representation structures (ordering, utilities and fuzzy preference relations) and using the fuzzy preference relations as uniform representation elements, the multiplicative preference relations are incorporated in the decision problem by means of a transformation function between multiplicative and fuzzy preference relations. A consistency study of this transformation function, which demonstrates that it does not change the informative content of multiplicative preference relation, is shown. As a consequence, a selection process based on fuzzy majority for multipurpose decision-makingproblems under multiplicative preference relations is presented. To design it, an aggregation operator of information, called ordered weighted geometric operator, is introduced, and two choice degrees, the quanti7er-guided dominance degree and the quanti7er-guided non-dominance degree, are de7ned for multiplicative preference relations. c 2001 Elsevier Science B.V. All rights reserved.

Keywords: Multipurpose decision-making; Fuzzy preference relations; Multiplicative preference relations; Fuzzy majority; Selection process

1.       Introduction

Decision makingin situations with multiple criteria and=or persons is a prominent area of research in normative decision theory. This topic has been widely studied [2,8,11,21,23]. We do not distinguish between “persons” and “criteria”, and interpret the decision process in the fuzzy framework of mul-tipurpose decision-making(MPDM) [4], assuming that the fuzzy property of human decisions can be satisfactorily modeled by fuzzy sets theory as in [8,11,13,14]. In an MPDM problem, we have a set of alternatives to be analyzed accordingto di4erent purposes in order to select the best one(s). For each purpose a set of evaluations about the alternatives is known. Then, a classical choice scheme for an MPDM problem follows two steps before it achieves a 7nal decision [4,6,19]: “aggregation” and “exploitation”. The aggregation phase de7nes an (outranking) relation which indicates the global preference between every ordered pair of alternatives, takinginto consideration the di4erent purposes. The exploitation phase transforms the global information about the alternatives into a global rankingof them. This can be done in di4erent ways, the most common one beingthe use of a ranking method to obtain a score function [18]. In [4], we consider MPDM problems where, for each purpose (expert or criterion), the information about the alternatives could be supplied in di4erent ways. With a view to build a more Iexible framework and to give more freedom degree to represent the evaluations, we assumed that they could be provided in any of these three ways: (i) as a preference ordering of the alternatives, (ii) as a fuzzy preference relation and (iii) as a utility function. There we presented a decision process to deal with this decision situation which, before applyingthe classical choice scheme, made the information uniform, usingfuzzy preference relations as the main element of the uniform representation of the evaluations, and then obtained the solution by means of a selection process based on the concept of fuzzy majority [10] and on the Ordered Weighted Averaging (OWA) operator [30]. In this paper, we increase the Iexibility degree of our decision model proposed in [4]. We give a new possibility for representingthe evalutions about the alternatives, i.e., to use multiplicative preference relations. This representation structure of evaluations has been widely used (see [9,20–22,27,29]). In [20,21] Saaty designed a choice scheme, called Analytic Hierarchy Process (AHP), for dealingwith decision problems where the evaluations about the alternatives are provided by means of the multiplicative preference relations. We incorporate the multiplicative preference relations in our decision model presentinga transformation mechanism between multiplicative and fuzzy preference relations and analyzingits consistency. Then, as a consequence, we propose an alternative choice scheme to the classical one designed by Saaty. Followingour selection process given in [4], we design a new choice scheme using the concept of fuzzy majority and a new aggregation operator, called ordered weighted geometric (OWG) operator. In order to do this, the paper is set out as follows. The MPDM problem under four evaluation structures is presented in Section 2. A transformation mechanism between multiplicative and fuzzy preference relations is proposed in Section 3. Section 4 is devoted to presentingthe OWG operator and to design the new scheme choice for dealingwith decision problems under multiplicative preference relations. In Section 5 some concludingremarks are pointed out. Finally, the fuzzy majority concept and the OWA operator are presented in Appendix A.

2. A multiplicative selection model based on fuzzy majority

In the AHP it is assumed that we have a set of m + 1 individual multiplicative preference relations, {A1; A2;:::;Am; B}; where B is the importance matrix. Followingthe scheme of the selection process given in Section 2, we present a selection process based on fuzzy majority to choose the best alternatives from multiplicative preference relations. With a view to design it, we introduce a new aggregation operator guided by fuzzy majority in Section 4.1. In the following subsections we show how to apply this aggregation operator to solve the MPDM problem under multiplicative preference relations representingthe experts’ preferences. 4.1. The ordered weighted geometric operator If we have a set of m multiplicative preference relations, {A1; A2;:::;Am}; to be aggregated, normally, the collective multiplicative preference relation, Ac , which expresses the opinion of the group, is derived by means of the geometric mean, i.e., Ac = [ac ij]; ac ij = m k=1 (ak ij) 1=m: In this context, we can de7ne the ordered weighted geometric (OWG) operator, which provides a family of aggregators having the “and” operator at one extreme, the “or” operator at the other extreme, and the geometric mean as a particular case. The (OWG) operator is based on the OWA operator [30] and on the geometric mean, therefore, it is a special case of OWA operator. It is applied in our selection process to calculate a collective multiplicative preference relation and the quanti7er-guided dominance and non-dominance choice degrees from multiplicative preference relations.

3.        Concluding remarks

In this paper, we have studied how to integrate the multiplicative preference relations in fuzzy MPDM models under di4erent preference representation structures (orderings, utilities and fuzzy preference relations). We have given a consistent method using the fuzzy preference relations as uniform representation element. This study together with our fuzzy MPDM model presented in [4] provides a more Iexible framework to manage di4erent structures of preferences, constitutingan approximate decision model to real decision situations with experts of di4erent knowledge areas. Later, we have provided an alternative choice process to the classical AHP for dealingwith MPDM problems under multiplicative preference relations. The aim of the multiplicative selection model is that it is based on fuzzy majority represented by a fuzzy linguistic quanti7er. Futhermore, to design it, we have introduced a new aggregation operator based on the OWA operators to aggregate multiplicative preference relations, and have extended quanti7er-guided dominance and non-dominance degrees to act with multiplicative preference relations.

A.      Appendix: Fuzzy majority and OWA operator

The majority is traditionally de7ned as a threshold number of individuals. Fuzzy majority is a soft majority concept expressed by a fuzzy quanti7er, which is manipulated via a fuzzy logic-based calculus of linguistically quanti7ed propositions. In this appendix we present the fuzzy quanti7ers, used for representingthe fuzzy majority, and the OWA operators, used for aggregating information. The OWA operator reIects the fuzzy majority calculatingits weights by means of the fuzzy quanti7ers. A.1. Fuzzy majority Quanti7ers can be used to represent the amount of items satisfying a given predicate. Classic logic is restricted to the use of the two quanti7ers, there exists and for all, that are closely related, respectively, to the or and and connectives. Human discourse is much richer and more diverse in its quanti7ers, e.g. about 5, almost all, a few, many, most, as many as possible, nearly half, at least half. In an attempt to bridge the gap between formal systems and natural discourse and, in turn, to provide a more Iexible knowledge representation tool, Zadeh introduced the concept of fuzzy quanti7ers [31]. Zadeh suggested that the semantic of a fuzzy quanti7er can be captured by usingfuzzy subsets for its representation. He distinguished between two types of fuzzy quanti7ers, absolute and relative. Absolute quanti7ers are used to represent amounts that are absolute in nature such as about 2 or more than 5. These absolute linguistic quanti7ers are closely related to the concept of the count or number of elements. He
Pk (xi; xj) = pk ij denotes the preference degree or intensity of the alternative xi over xj [10,12,14,25]: pk ij = 1 2 indicates indi4erence between xi and xj, pk ij = 1 indicates that xi is absolutely preferred to xj, and pk ij¿1 2 indicates that xi is preferred to xj. In this case, the preference matrix, Pk , is assumed additive reciprocal, i.e., by de7nition [17,25] pk ij+ pk ji = 1 and pk ii = 1 2 . de7ned these quanti7ers as fuzzy subsets of the nonnegative real numbers, R+. In this approach, an absolute quanti7er can be represented by a fuzzy subset Q, such that for any r ∈ R+ the membership degree of r in Q, Q(r), indicates the degree to which the amount r is compatible with the quanti7er represented by Q. Relative quanti7ers, such as most, at least half, can be represented by fuzzy subsets of the unit interval, [0,1]. For any r ∈[0; 1], Q(r) indicates the degree to which the proportion r is compatible with the meaning of the quanti7er it represents. Any quanti7er of natural language can be represented as a relative quanti7er or given the cardinality of the elements considered, as an absolute quanti7er. Functionally, fuzzy quanti- 7ers are usually of one of three types, increasing, decreasing, and unimodal. An increasing-type quanti7er is characterized by the relationship Q(r1)¿Q(r2) if r1¿r2.

The OWA operators 7ll the gap between the operators Min and Max. It can be immediately veri- 7ed that OWA operators are commutative, increasing monotonous and idempotent, but in general not associative. A natural question in the de7nition of the OWA operator is how to obtain the associated weighting vector. In [30], Yager proposed two ways to obtain it. The 7rst approach is to use some kind of learning mechanism usingsome sample data; and the second approach is to try to give some semantics or meaning to the weights. The 7nal possibility has allowed multiple applications on areas of fuzzy and multi-valued logics, evidence theory, design of fuzzy controllers, and the quanti7er-guided aggregations. We are interested in the area of quanti7er-guided aggregations. Our idea is to calculate weights for the aggregation operations (made by means of the OWA operator) usinglinguistic quanti7ers that represent the concept of fuzzy majority. In [30], Yager suggested an interestingway to compute the weights of the OWA aggregation operator using fuzzy quanti7ers, which, in the case of a non-decreasingrelative quanti7er Q, is given by the expression.

References

[1] J. AczSel, Lectures on Functional Equations and Their Applications, Academic Press, New York, 1966

[2] K.J. Arrow, Social Choice and Individual Values, Wiley, New York, 1963

[3] F. Chiclana, F. Herrera, E. Herrera-Viedma, Preference relations as the information representation base in multi-person decision making, Proc. of 6th Int. Conf. on Information Processingand Management of Uncertainly in Knowledge-Based Systems, Granada (1996) pp. 459 – 464.

[4] F. Chiclana, F. Herrera, E. Herrera-Viedma, Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations, Fuzzy Sets and Systems 97 (1998) 33–48.

[5] F. Chiclana, F. Herrera, E. Herrera-Viedma, On the consistency of a general multipurpose decision making integrating di4erent preference structures, Fuzzy Sets and Systems (2000), to appear.

[6] F. Chiclana, F. Herrera, E. Herrera-Viedma, M.C. Poyatos, A clasi7cation method of alternatives for multiple preference orderingcriteria based on fuzzy majority, J. Fuzzy Math. 4 (1996) 801–813.

[7] B. De Baets, B. Van De Walle, E. Kerre, Fuzzy preference structures without incomparability, Fuzzy Sets and Systems 76 (1995) 333–348.

[8] J. Fodor, M. Roubens, Fuzzy Preference Modellingand Multicriteria Decision Support, Kluwer Academic Publishers, Dordrecht, 1994.

[9] B.L. Golden, E.A. Wasil, P.T. Hacker, The Analytic Hierarchy Process, Applications and Studies, Springer, Berlin, 1989.

[10] J. Kacprzyk, Group decision makingwith a fuzzy linguistic majority, Fuzzy Sets and Systems 18 (1986) 105–118.

[11] J. Kacprzyk, M. Fedrizzi, Multiperson Decision Making Models UsingFuzzy Sets and Possibility Theory, Kluwer Academic Publishers, Dordrecht, 1990.

[12] J. Kacprzyk, M. Roubens, Non-Conventional Preference Relations in Decision Making, Springer, Berlin, 1988.

[13] R.D. Luce, P. Suppes, Preferences, utility and subject probability, in: R.D. Luce, et al., (Eds.), Handbook of Mathematical Psychology, Vol. III, Wiley, New York, 1965, pp. 249–410

[14] T. Tanino, On group decision making under fuzzy preferences, in: J. Kacprzyk, M. Fedrizzi (Eds.), Multiperson Decision MakingUsingFuzzy Sets and Possibility Theory, Kluwer Academic Publishers, Dordrecht, 1990, pp. 172–185.

Latar belakang Model proses pengambilan keputusan

Beberapa atribut pengambilan keputusan masalah yang dihadapi dalam berbagai situasi di mana sejumlah alternatif dan tindakan atau calon harus dipilih berdasarkan satu set atribut. Ketika kita mempertimbangkan satu set diskrit alternatif digambarkan oleh beberapa atribut, ada tiga jenis analisis yang dapat dilakukan untuk memberikan dukungan yang signifikan untuk pengambil keputusan:

• Pastikan bahwa pembuat keputusan mengikuti "rasional" perilaku (pilihan normatif) - Fungsi nilai, teori utilitas, jarak ke ideal.

• Berikan beberapa saran berdasarkan akal (tapi tidak terbantahkan) aturan – Perancis Sekolah.

• Cari solusi yang lebih disukai dari keputusan hipotesis parsial - metode interaktif. Analisis pengambilan keputusan disiplin muncul, setelah ada nama hanya sejak Howard (1966). Sangat menarik untuk merenungkan pandangan dari tiga pendiri analisis, karena setiap (; Keeney dan Raiffa 1976 Howard, 1966) pengambilan keputusan. Membandingkan alternatif adalah kunci untuk membuat keputusan. Namun, dalam kasus yang bertentangan alternatif, pembuat keputusan juga harus mempertimbangkan data tidak tepat atau ambigu, yang adalah norma dalam jenis masalah pengambilan keputusan.

Metode Model proses pengambilan keputusan

Multi-atribut metode pengambilan keputusan yang rawan dengan informasi tepat ditentukan. Salah satu metode tersebut adalah metode COPRAS.

3.1. Metode COPRAS umum

1. Pemilihan set yang tersedia atribut yang paling penting, yang menggambarkan alternatif.

2. Mempersiapkan dari pengambilan keputusan matriks X

3. Menentukan bobot atribut qi (Kendall, 1970; Zavadskas, 1987).

4. Normalisasi dari? X matriks pengambilan keputusan. Nilai-nilai normalisasi ini

Matriks

5. Perhitungan tertimbang dinormalisasi pengambilan keputusan matriks? X. tertimbang

nilai normal xji

6. Menghitung jumlah Pj dari nilai-nilai atribut, yang nilainya lebih besar

7. Menghitung jumlah Rj nilai atribut, yang nilainya lebih kecil

8. Menentukan nilai minimal Rj

9. Menghitung bobot masing-masing Qj alternatif

10. Menentukan kriteria optimalitas K

11. Menentukan prioritas proyek.

Kesimpulan Model proses pengambilan keputusan

Dalam kehidupan nyata pemodelan multi-atribut masalah penilaian multi-alternatif atribut nilai-nilai, yang berkaitan dengan masa depan, dapat diungkapkan dalam interval. COPRAS-G baru saja metode yang dikembangkan untuk penilaian alternatif oleh multipleattribute nilai-nilai yang ditentukan dalam interval. Pendekatan ini dimaksudkan untuk mendukung proses pengambilan keputusan dan meningkatkan efisiensi proses penyelesaian. Metode COPRAS-G dapat diterapkan untuk solusi dari berbagai multiattribute diskrit masalah penilaian dalam konstruksi.

JURNAL OF SEGMENTATION AND CUSTOMER STATIFICATION

“Market Segmentation and Its Impact on Customer Satisfaction with Especial Reference to Commercial Bank of Ceylon PLC”

Puwanenthiren Premkanth

Abstract - In this competitive commercial world, an organization has to satisfy the needs and wants of the customers, and has to attract new customers, and hence enhance their business. Customer value is considered as a control element for all business strategies. Therefore, every organization has to emphasize on customer satisfaction. As far as the banks are concerned this phenomenon is very prominent .To carry out this research, defined the Hypotheses as “The Market Segmentation highly positive impact on customer Satisfaction”. The Customer satisfaction with Market Segment has higher positive correlation 0.726. This means that high level of four market segment leads to highly increase in the customer satisfaction. This Co-efficient of determination 0.526 that the customer satisfaction in accounted for by market segment. In this connection hypothesis is accepted.That is market segments and marketing mix has strong impact on customer satisfaction. Keywords : Market Segmentation, Customer Satisfaction, Commercial bank.

1.       INTRODUCTION

Having multi perspective on a concept is a power full source to capitalize the knowledge on a particular concept. According to that the concept of market segmentation will be illustrated in the following manner. “Market segmentation is no accident, but a result of careful planning and execution.” Through above phrase we can smell an initial idea about what is meant by the term Market segmentation. There are so much of strategies and techniques are available for an entrepreneur to segment the market. But this research concentrates on how to tune the market segmentation in ensuring achieving the efficient customer satisfaction. So that research topic falls into marketing field. In growing competitive world marketing plays a vital role in every business firm. Any firms in any industry realized that marketing management is an essential part in their business success. A well prepared counter argument is also believed by lot expert against to previous one. Marketing is not a magic tool which means a firm which produces worst goods can’t become as a market leader by using effective market segmetation. The firstrequirement for market segmentation is particular product or service should read the consumers’ pulse then meeting or exceeding the consumer expectation, then only marketing may play any role in market segmentation.

2.       LITERATURE REVIEW

The paper of W. Boyd et al. (1994) the results of the study reveal that reputation, interest charged on loans, and interest on savings accounts are viewed as having more importance than other criteria such as friendliness of employees, modern facilities, and drivein-service. A study by Clarkson et al. (1990), suggests that the characteristics and financial service requirements of consumers vary with age, and that these differences could be used in developing marketing strategies for such services. Marla Royne Stafford (1996) stated that demographics continue to be one of the most popular and well-accepted bases for segmenting markets and customers. Even if others types of segmentation variables are used a marketer must know and understand demographics to assess the size, reach and efficiency of the market. The general conclusion of this study is that there is a significant relationship between demographics characteristics the service quality perception. However, for income the test statistic was not significant. The psychographic segmentation, in the literature, has been extensively researched. For example Beckett et al. (2000) presents and develops a model through which attends to articulate and classify consumer behaviour in the purchasing a range of financial products and services. Using and placing the two principal factors that motivate and determine individual contracting choices, namely involvement and uncertainty, on to a simple continuum running from high to low it is possible to construct a two-dimensional matrix of consumer behavior. Harrison (1994) concludes that the traditional segmentation variables of age, stage in the family life H cycle and social class have provided little insight into the financial services customer behavior. In order to take full advantage of the factors which could affect take-up and usage of financial services, Harrison develops a multidimensional model. The analysis has suggested four customer segments for financial services on the basis of customers’ own perceived knowledge, confidence and interest in financial maturity, defined by the type and complexity of financial services currently in use. Each of the four segments is distinct in terms of the financial objectives exhibited, motivations for financial services usages and attitudes and behaviour towards financial services. Machauer and Morgner (2001) prefer segmentation by expected benefits and attitudes could enhance a bank’s ability to address the conflict between individual service and cost-saving standardization. Using cluster analysis, segments were formed based on combinations of customer ratings for different attitudinal dimensions and benefits of bank services. The clusters generated in this way were superior in their homogeneity and profile to customer segments gained by referring to demographic differences. Booms and Bitner (1981) suggested 7Ps mix which extended the traditional 4Ps which including 3Ps: Participants, Physical Evidence and process. The 7Ps of marketing mix have been conducted by some researchers in marketing fields (e.g., Low and Tan, 1995; Pheng and Ming, 1997; Melewar and saunder, 2000). Nagar and Rajan (2005) studied the impact of satisfaction and other operational factors utilizing crosssectional data on US retail banks. Despite its importance to the banking industry, limited researches that consider customer requirements and service elements together have been conducted even though considerable researches have been done on service sector (Bolton and Drew, 1991; Parasuraman et al., 1988). Garwin (1988) did a research that considers customer requirements and service product quality separately. A number of models and views have been developed to identify the determinants of retail customer satisfaction in banking industry. Kearsley (1985) in his article discussed the types and uses of computerbased training (CBT) in bank training to achieve better customer satisfaction. Rust and Zahorik (1993) provided a mathematical framework for assessing the value of customer satisfaction. The framework enables managers to determine which customer satisfaction elements have the greatest impact, and how much money should be spent to improve particular customer satisfaction elements. They demonstrated the application of theirapproach in a pilot study of a retail banking market. Athanassopoulos (2000) performed a complete survey on customer satisfaction in retail banking services in Greece. The study proposed an instrument of customer satisfaction that contains service quality and other attributes. The performance implications of the customer satisfaction instrument are also explored. Manrai L. A. and Manrai A. K. (2007) developed and tested some hypotheses regarding the between customer satisfaction and bank service switching behavior as it is mediated by the importance of a particular bank service to a particular customer and by the nature of competitive offerings for different types of banking services available from other banks. Gil et al. (2007), in their research exhibited that services encountered directly and significantly affect perceived service value which is the final antecedent to customer satisfaction in banking industry. In the call center industry the empirical research for SERVQUAL model appears to be scarce. Warrenet.al. (2002) conducted a research to assess a case call center using SERVQUAL model. They found that as a customer never comes into contact with the physical appearance of a call center, the area covered by the tangibles criteria does not apply. Via the telephone, the only dimension of “tangible” contact is customer service representative’s (CSR) voice, which is extremely important in any encounter with a customer through telephone. It is not possible for the customer to evaluate the service level but the customer has to interact with other criteria. Responsiveness, reliability, assurance and empathy are all transmitted by CSR’s voice and are dependent on CSR’s communication skills In another research, Upal M. (2008)30 applied SERVQUAL approach in evaluating customer satisfaction in telecommunication industry in Bangladesh. The traditional SERVQUAL five dimension model was adjusted into four dimension model. These dimensions are responsiveness, assurance, communication and discipline. The research showed that call center agents are vital to the success of any call center. Satisfied employees reinforce customer satisfaction, which in turn reinforced employee satisfaction. In addition to that, customers’ education in the service delivery process contributes to their satisfaction. Managerial orientation also is one of the major forces that drive customer satisfaction. Beckett et al. (2000) draw tentative conclusions as to why consumers appear to remain loyal to the same financial provider, even though in many instances they hold less favourable views toward these service providers. For example, many consumers appear to perceive little differentiation between financial providers, making any change essentially worthless. Secondly, consumers appear to be motivated by convenience or inertia. Finally, consumers associate changing banks with high switching costs in terms of the potential sacrifice and effort involved. Market segmentation and customer satisfaction have been largely affected by banks’ massive involvement in technological banking activities (Keeton, 2001). De Young (1999) found that some consumers willing to pay high service charge since they are receiving e-banking services at the next foot step, however, some people still want to see banks are reducing fees/ charges where they believe in personalized attention instead of large technological investment, which may increase cost Studies on satisfaction of the customers in financial service sectors have been well known among the academicians since the mid of 80s’. Parasuraman et al. (1988) opened a new window of research in service quality by establishing SERVQUAL model. Their model has been replicated in many countries with multidimensional sectors, and found close to a big success. Since, customer satisfaction has close relationship with customer retention especially in markets that are highly competitive and saturated like financial services (Lopez et al. 2007); it is necessary to continuously monitor changes in satisfaction among different segments of customers.

3.       OBJECTIVES

This research is conducted with the intention of following objectives. · To evaluate how far bank follow the concept of market segmentation · To analysis the market segmentation in Commercial Bank of Ceylon Plc · To analyze what are they relationship between market segmentation & market mix and customer satisfaction in Commercial Bank of Ceylon Plc.

4.       HYPOTESESS

All activities of Commercial Bank of Ceylon Plc are Mainly providing services to its customers and by this it try to earn profit. This research work based on the servuices provided by the Commercial Bank Ceylon Plc. So, Hypothesis of any research spells that

H1 : The Market Segmentation highly positive impact on customer Satisfaction

5.       METHODOLOGY

To produce the above mentioned research objective, the data for this study was gathered from the Size of the sample is a one of the important determinants in measuring validity of the research customer satisfaction started warily came in the study done by Snow et al. (1996). This study concluded that there were clear differences in the service’s expectations for retail banks in Canada among different ethnic groups. Research continues with Furrer et al. (2000), which reported relationship between segmented customers based on cultural background and their satisfaction. They also developed a Cultural Service Quality Index (CSQI) and established multicultural market segmentation.

6.       RESULTS AND DISCUSSION

a)      Customers Ideas about Commercial Bank of Celon Pl’s Market Segmentation.

According to the below tabulation 3% of Customers agree that the Commercial Bank’s Segmentation is poor (Low). Majority of Customers (82%) say that the Segmentation is effective in the Banking industry. But 15% of Customers argue that the Bank’s segmentation is in the Good category.

b)      Customers Pulse about Commercial Bank of Ceylon Plc

 According to the below tabulation 2%  of Customers satisfy that the Commercial Bank’s Services

c)       Market Segmentation Vs Customer Satisfaction

According to the below tabulation 2 Customers agree that the Poor Segmentation leads to poor Customer satisfaction and 1 customer argue that the Poor Segmentation result to moderate level Customer satisfaction. Leven Customers say Good Segmentation leads to Moderate Customer’s Pulse and 4 customers highly satisfied with good segmentation. But 70 customers highly satisfied with effective Market Segmentation and only 12 customers moderately satisfied with effective Market segmentation.

d)      Under this below part Analysis- Correlation, Regression, Correlation Co efficiencies, ANOVA test and presentation of Scatter PLOT.

REFERENCESS

1. Baker, W.E. And Sinkula, J.M. (1999) “The Synergistic Effect Of Market Orientation And Learning Orientation On Organizational Performance”, Journal Of Academy Of Marketing Science, Vol. 27.

2. Phillip Kotler Marketing Management 11th edition.(India Pearson Education Inc 2003)

3. Schreiber, A.L. and Lenson, B. 2001. Multicultural Marketing: Selling to the New America, IL: NTC Business Books.

4. Skinner, T.J. and Hunter, D. 1997. “Developing Suitable Designators for a Multicultural Society”, Statistical Journal of the UN Economic Commission for Europe, 14 (3), pp. 217-228.

 5. The Australian. 2002. “McDonald’s Meets Religious Needs”, Multicultural Marketing Awards—A Special Advertising Report, 14 November 2002, p. 24.

6. Wainwright, R. 1996. “No. 4: Address the Chinese Avoid”, The Sydney Morning Herald, 14 March 1996, p. 3.

7. Wilkinson, I. and Cheng, C. 1999. “Multicultural Marketing in Australia: Synergy in Diversity”, Journal of International Marketing, 7(3), pp. 106-125.

8. Wilkinson, I. and Cheng, C. 2003. “Multicultural Marketing”, in Rugimbana, R. and Nwankwo, S. (Eds.), Cross-Cultural Marketing, South Melbourne: Thomson.

9. Zenthimal V (1988) “Customer perception of price quality and value a means end model journal of marketing vol 2, pp 2-22

Latar belakang Segmentasi dan kepuasan konsumen

Memiliki multi-perspektif tentang sebuah konsep adalah kekuatan sumber penuh untuk memanfaatkan pengetahuan pada konsep tertentu. Menurut bahwa konsep pasar segmentasi akan digambarkan berikut ini cara. "Segmentasi pasar adalah kebetulan, tapi hasil perencanaan yang matang dan eksekusi. " Melalui kalimat di atas kita dapat mencium awal Ide tentang apa yang dimaksud dengan istilah Market segmentasi. Ada begitu banyak strategi dan teknik yang tersedia untuk pengusaha untuk segmen pasar. Tetapi penelitian ini berkonsentrasi pada bagaimana tune segmentasi pasar dalam memastikan pencapaian kepuasan pelanggan efisien. Sehingga topik penelitian jatuh ke lapangan pemasaran. Dalam pemasaran dunia yang kompetitif berkembang memainkan peran penting di setiap perusahaan bisnis. Setiap perusahaan dalam industri apapun menyadari bahwa manajemen pemasaran merupakan bagian penting dalam mereka keberhasilan bisnis. Argumen kontra siap adalah juga diyakini oleh ahli banyak terhadap satu sebelumnya. Pemasaran bukan alat sulap yang berarti suatu perusahaan yang memproduksi barang terburuk tidak bisa menjadi sebagai pemimpin pasar dengan menggunakan segmetation pasar yang efektif. The firstrequirement untuk segmentasi pasar adalah particula produk atau jasa harus membaca pulsa konsumen kemudian memenuhi atau melampaui harapan konsumen, maka hanya pemasaran mungkin memainkan peran dalam pasar segmentasi.

Metode Segmentasi dan kepuasan konsumen

Untuk menghasilkan penelitian yang disebutkan di atas obyektif, data untuk penelitian ini dikumpulkan dari Ukuran sampel adalah salah satu yang penting penentu dalam mengukur validitas kepuasan pelanggan penelitian mulai waspada datang dalam penelitian ini dilakukan oleh Snow et al. (1996). Penelitian ini menyimpulkan bahwa ada perbedaan yang jelas dalam harapan layanan ini bagi bank ritel di Kanada antara berbagai etnis kelompok. Penelitian berlanjut dengan Furrer dkk. (2000), yang melaporkan hubungan antara tersegmentasi pelanggan berdasarkan latar belakang budaya dan mereka kepuasan. Mereka juga mengembangkan layanan Budaya Kualitas Index (CSQI) dan didirikan multikultural segmentasi pasar.

Kesimpulan Segmentasi dan kepuasan konsumen

a) Argumen pada membuktikan hipotesis

Berdasarkan argumen ini hipotesis yang diajukan adalah terbukti oleh peneliti dengan menggunakan data primer Peneliti dikumpulkan selama proses Metodologi. Empat Segmentasi Pasar campuran yang dimanfaatkan oleh Bank komersial Sri Lanka PLC untuk menyerang Pelanggan dalam cara yang efisien dengan maksud menangkap posisi pemimpin pasar dalam waktu dekat atau di jangka panjang disajikan untuk membuktikan hipotesis pelajaran ini. Mereka Pemasaran campuran diilustrasikan dalam cara seperti yang disebutkan dalam masalah penelitian. Pertama dari semua peneliti menganggap bauran pemasaran untuk mendukung argumennya pada membuktikan hipotesis.

Opsi pertama Marketing Mix adalah "harga": ketua Nike mengatakan "bisnis adalah perang tanpa kehilangan darah ", di bahwa harga perang adalah salah satu senjata populer dalam pemasaran karena merupakan satu-satunya bauran pemasaran lebih mudah untuk mengubah dari yang lain pada saat yang sama waktu itu adalah senjata yang baik untuk menarik pelanggan dengan mudah mendukung produk atau jasa tertentu.

b) Situs Peneliti pada Segmentasi Pasar dan

Kepuasan pelanggan Commercial Bank Sri Lanka PLC yang berjalan di Industri Perbankan mereka dapat disebut sebagai Top Perusahaan di Bank. Istilah "Top Firm" dapat ditafsirkan berarti istilah penuh dengan cara sebagai berikut, Sebuah perusahaan yang memiliki pangsa pasar tertinggi dalam industri tertentu dapat ditujukan sebagai Top Firm dari industri tertentu. Di bawah ini ide porsi Kepuasan Pelanggan pada pembentukan Segmentasi pasar dalam industri di kepentingan posisi pasar dengan tuning kelas yang berbeda dari pemasaran Mix disajikan dengan jelas. Berikut terpilih Bank disarankan Segmentasi Pasar yang tepat strategis dan Pemasaran mix yang digunakan oleh mereka di Pasar Jaffna mereka untuk menyerang Pelanggan dalam cara yang efisien dengan niat menangkap posisi pemimpin pasar di dekat masa depan dan atau jangka panjang. Model mereka disajikan dalam cara seperti yang disebutkan dalam masalah penelitian. Dari Mix, salah satu adalah Harga adalah variabel utama, sisa campuran adalah promosi tempat, produk.

c. Rekomendasi untuk Mengembangkan Pelanggan

Kepuasan Setelah pengujian hipotesis perlu memberikan rekomendasi untuk meningkatkan kepuasan dan mengelola segmen pasar dan bauran pemasaran berikut adalah beberapa rekomendasi untuk meningkatkan pelanggan kepuasan.

JURNAL OF CONSUMER BEHAVIOR

Examining the Impact of Ranking on Consumer Behavior and Search Engine

Revenue Anindya Ghose Department of Information, Operations and Management Sciences, Department of Marketing, Stern School of Business, New York University, New York, New York 10012, aghose@stern.nyu.edu Panagiotis G. Ipeirotis Department of Information, Operations and Management Sciences, Stern School of Business, New York University, New York, New York 10012, panos@stern.nyu.edu Beibei Li Information Systems and Management, Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, beibeili@andrew.cmu.edu

I n this paper, we study the effects of three different kinds of search engine rankings on consumer behavior and search engine revenues: direct ranking effect, interaction effect between ranking and product ratings, and personalized ranking effect. We combine a hierarchical Bayesian model estimated on approximately one million online sessions from Travelocity, together with randomized experiments using a real-world hotel search engine application. Our archival data analysis and randomized experiments are consistent in demonstrating the following: (1) A consumer-utility-based ranking mechanism can lead to a significant increase in overall search engine revenue. (2) Significant interplay occurs between search engine ranking and product ratings. An inferior position on the search engine affects “higher-class” hotels more adversely. On the other hand, hotels with a lower customer rating are more likely to benefit from being placed on the top of the screen. These findings illustrate that product search engines could benefit from directly incorporating signals from social media into their ranking algorithms. (3) Our randomized experiments also reveal that an “active” personalized ranking system (wherein users can interact with and customize the ranking algorithm) leads to higher clicks but lower. 
 purchase propensities and lower search engine revenue compared with a “passive” personalized ranking system (wherein users cannot interact with the ranking algorithm). This result suggests that providing more information during the decision-making process may lead to fewer consumer purchases because of information overload. Therefore, product search engines should not adopt personalized ranking systems by default. Overall, our study unravels the economic impact of ranking and its interaction with social media on product search engines. Keywords: travel search engine; randomized experiments; hierarchical Bayesian methods; information systems; IT policy and management; electronic commerce History: Received May 28, 2012; accepted May 12, 2013, by Lorin Hitt, information systems. Published online in Articles in Advance. 1. Introduction Over the last few decades, search engines have emerged as a significant channel for promoting and selling products. In information search engines (e.g., Google) the ranking of the search results is an immediate signal of the relevance of the result to the query. However, in product search engines, the ranking of the displayed products is often based on criteria such as price, product rating, etc. In such a setting, we may often have multiple, potentially conflicting signals given to the customer about the products’ rankings. For example, if we rank by price, then the cheapest products sometimes have low product ratings, or products appearing on top of the list may be too expensive for the customer. Effectively consumers have to observe multiple, competing ranking signals and come up with their own ranking in their minds; in some settings, the product search engine will also generate personalized results, trying to rank the products according to the preferences of the consumer. In such an environment, we want to understand which factors influence the decision-making process of the customers and the magnitude of that influence. Are consumers influenced by the display ranking order, by the product rating, by price, and to what degree? How does this interplay affect the revenue that a search engine can generate?

1.1.  Related Work

In the last 10 years, the literature in e-commerce has shown the existence of a strong primacy effect in environments wherein consumers make choices among offers displayed in information search engines such as Google, Yahoo, or Bing. Specifically, we have learned that an online position effect exists and that rank order has a significant impact on the clickthrough rates and conversion rates (e.g., Ghose and Yang 2009, Yang and Ghose 2010, Agarwal et al. 2011, Jerath et al. 2011, Rutz and Trusov 2011, Narayanan and Kalyanam 2011, Animesh et al. 2011, Baye et al. 2012, Jeziorski and Segal 2012, Rutz et al. 2012, Abhishek et al. 2013, Ghose et al. 2013a). These papers focused primarily on evaluating the effect of screen position on user behavior, controlling for the quality of the advertisement. However, in product search engines, the observed demand patterns can be influenced by the joint variation in product ratings (either professional rating or user rating) and online screen position. The first goal of our study is to examine the position effect in product search engines, conditional on its interaction with product ratings. Search engines are beginning to adopt signals from social media sites directly into their ranking mechanism design (e.g., Bing Social Search, TripAdvisor). Recent work has found that a utility-based ranking mechanism on product search engines that incorporates multidimensional consumer preferences and social media signals can lead to significant surplus gain for consumers (Ghose et al. 2012). However, given that price was not the top priority considered in the ranking recommendation, whether such a mechanism can actually benefit product search engines is unclear because their revenues are normally commission based. Therefore, the second goal of our study is to examine the effect of different ranking mechanisms on product search engine revenue. Outside of search, one of the most important ways for shoppers to discover products has been through recommendation engines (Chittor 2010). However, although some online retailers use recommendation systems, many product-specific search engines (e.g., travel search engines) still do not provide personalized ranking results in response to consumer queries, presumably because these product search engine companies are unsure whether providing extra information to consumers will lead to an increase in profit. Existing research holds two different opinions on the effects of  personalization. One stream of work is supportive of personalization (e.g., Rossi et al. 1996, Ansari and Mela 2003, Arora and Henderson 2007, Yao and Mela 2011), whereas another stream of work is a bit more skeptical (e.g., Zhang and Wedel 2009, Aral and Walker 2011, Goldfarb and Tucker 2011, Lambrecht and Tucker 2013), suggesting that although personalization can lead to higher customer satisfaction and profits, it will not work as well universally.1 However, none of these papers have examined the effect of information availability and personalization in a search engine context. Koulayev (2014) examines consumers’ costly search behavior on travel search engines through the formation of consideration sets. Ghose et al. (2013b) build a structural econometric model to predict individual consumers’ online footprints on product search engines to improve user experiences under the context of social media overload. Chen and Yao (2012) use secondary data to examine how the sorting and filtering tools on travel search engines influence consumer hotel search. They find these tools result in a significant increase in total search activities, but they also lead to lower overall welfare because of the disproportional engagement induced by the refinement tools. With these findings in mind, our third goal is to examine how different kinds of personalized ranking mechanisms in product search engines affect consumer behavior and search engine revenues. Specifically, does allowing users to interact with the ranking algorithm to proactively personalize their search results lead to more or fewer purchases?

1.1.  Contributions and Results

 We situate our study in a travel search engine context, looking specifically at consumer selection of a hotel. We first apply archival data analysis to gain insights into the product-rating effects and ranking effects on consumers’ click and purchase behaviors. Using a panel data set from November 2008 to January 2009 containing approximately one million online user search sessions—including detailed information on consumer searches, clicks, and transactions obtained from Travelocity—we propose a hierarchical Bayesian framework in which we build a simultaneous equation model to jointly examine the interrelationship between consumers’ click and purchase behavior, search engine ranking decisions, and customers’ ratings. Toward the first goal, we examine the variation in the ratings of different hotels (both hotel “class” rating and customer rating) at the same rank on the travel search engine over time. In addition, our data setting has variation in the rank of the same hotel over time because the same hotel appears at different positions at different points in time. Controlling for room prices, such variation allows us to model the interaction effect of hotel class and customer ratings with rank and to measure its effect on demand. Toward the second goal, we examine how different ranking mechanisms affect the search engine revenue. We achieve this goal by conducting a set of policy experiments. We consider six different ranking designs: utility, conversion rate (CR), clickthrough rate (CTR), price, customer rating, and the Travelocity default algorithms. Then we estimate our model and predict future search engine revenues under each ranking mechanism. Toward our third goal, we examine how different levels of personalized ranking mechanisms affect consumer behavior and search engine revenue. Particularly, we compare two types of personalization mechanisms used to drive the ranking of results in response to a query: active personalized ranking and passive personalized ranking. In our context, a ranking system that allows consumers to proactively interact with the recommendation algorithm prior to the display of results from a search query is classified as “active.” By contrast, a ranking system that does not allow customers to interact with the recommendation algorithm is classified as “passive.” As of today, no hotel search engine has explicitly adopted a personalization-based approach to hotel ranking because they are still grappling with the issue of whether such an approach is useful.2 Hence, to our knowledge, no archival data in any product search engine have information on the effect of personalized ranking on user behavior. Therefore, we designed randomized experiments using a hotel search engine application that we built. Our randomized experimental results are based on a total of 900 unique user responses over a two-week period via the Amazon Mechanical Turk (AMT) crowdsourcing platform. We use a customized behavior-tracking system to observe the detailed information of consumers’ search, evaluation, and purchase decisionmaking process. By manipulating the default ranking method and by enabling or disabling a variety of personalization features on the hotel search engine website, we are able to study the effect of personalized ranking on consumer behavior. Our archival data analysis and randomized experiments are consistent in demonstrating the following: (1) A utility-based ranking mechanism can lead to a significant increase in the overall search engine revenue. (2) Significant interplay occurs between search engine ranking and product ratings. An inferior rank affects “higher-class” hotels more adversely. On the other hand, hotels with a lower customer rating are more likely to benefit from being placed on the top of the screen. These findings illustrate that product search engines could benefit from directly incorporating signals from online social media into the ranking algorithms. (3) Our randomized experiments also reveal that an active personalized ranking mechanism that enables consumers to specify both search context and individual preferences leads to more clicks but lower purchase propensities and lower search engine revenue, compared with passive personalized ranking mechanisms. A plausible explanation is related to theories of consumer cognitive cost. Prior theoretical work has shown that information overload and nonnegligible search costs can discourage decision makers from evaluating choices, leading to a scenario where they make no choices at all (Kuksov and Villas-Boas 2010). Our empirical finding dovetails with the theoretical conclusion by Kuksov and Villas-Boas that providing more information can actually lead to fewer purchases. It is also consistent with Dzyabura (2014), who shows that consumers who do not have well-formed preferences at the start of their search may be better off with uncertainty about product attribute levels rather than perfect knowledge of the attributes of all available products. Therefore, although an active personalized ranking recommendation may help consumers discover what they want to buy, product search engines should not ubiquitously adopt it. Two recent studies that are closely related to the current paper are Ghose et al. (2012) and Ghose and Yang (2009). However, this current paper distinguishes itself from the two previous studies in the following ways: (1) Ghose et al. (2012) do not focus on how rankings can benefit the search engine companies (in addition to the customers)—i.e., is it profitable for a search engine company to implement a utility-based ranking mechanism? In particular, we focus on examining whether the utility-based ranking leads to a significant improvement in the CTR, CR, and the total revenue for search engines. (2) Ghose et al. (2012) examine the direct WOM (word-ofmouth) effect on demand, without considering the impact of rank on the search engine. However, in our paper, we examine the WOM effect conditional on the ranking position of the product on search engines. We focus on examining the interaction effect between ranking and product ratings (both professional hotel class rating and online customer rating). Our findings illustrate that product search engines could benefit from directly incorporating signals from online social media into the ranking algorithms. (3) In Ghose et al. (2012), the authors did not focus on personalized rankings. Our randomized experiments reveal that an active personalized ranking mechanism that enables consumers to specify both search context and individual preferences leads to more clicks but lower purchase propensities and lower search engine revenues, compared with a passive personalized ranking mechanism. Therefore, although active personalized ranking recommendation may help consumers discover what they want to buy, it should not be adopted ubiquitously. (4) Ghose and Yang (2009) study the effect of keyword ranking on CTR and CR in the sponsored search context. However, our current paper looks at a different research context of ranking in product search engines. Compared with Ghose and Yang (2009), we make two method-based improvements in this paper. First, in addition to CTR, CR, and ranking, we model customer rating as a fourth dependent variable in the simultaneous model framework. Second, we allow for unobserved heterogeneity in all time-varying covariates. Our model fitness comparison results show that the model with full heterogeneity on all time-varying variables provides the overall best performance.

1.       Data

 Our data set consists of detailed information on a total of 969,033 online sessions from Travelocity.com, including consumer searches, clicks, and conversions that occurred within these sessions between November 2008 and January 2009. In addition, we have hotelrelated information, such as hotel class, brand, online reviewer rating, and number of reviews. We collected customer reviews from Travelocity.com. We collected the online reviews and reviewers’ information on a daily basis up to January 31, 2009 (the last date of transactions in our database). This process provides us with a final data set containing 29,222 weekly observations for 2,117 hotels in the United States.3 We define an “online session” to capture a set of activities by an online user, identified by a unique cookie. In our data, a starting indicator and an ending indicator with a corresponding time stamp (provided by the company) can characterize each unique online session. More specifically, a typical online session involves the initialization of the session, the search query, the results (in a particular rank order) returned from that search query, the sorting method, the click(s) on hotel(s) if any exist, the login and actual transaction(s) if any conversion occurs, and the termination of the session. The ending indicator marks the termination of a session.

We count a “display” for a hotel if that hotel appears visible to a consumer on the webpage in an online search session. Meanwhile, we count a “click” if a consumer selects the hotel and a “conversion” if a consumer has completed the payment in that online session. We only consider sessions with at least one display.4 A display can lead to a click, but it may not lead to a purchase. Each hotel that counts for a display is associated with a page number and a screen position, which capture the corresponding page order and (within-page) rank order of that hotel in the search results. Note that Travelocity only shows 25 hotels per page when it displays the hotel search results on a webpage.5 This design restricts the rank order for each hotel within the range from 1 to 25. Meanwhile, to facilitate consumer search, Travelocity provides a sorting criterion called “Travelocity Pick” by default. It also provides multiple alternative sorting criteria: price, hotel class, hotel name, and customer review rating. To capture consumers’ particular sorting preferences that may potentially influence the position effect, we include a set of control variables in our study to indicate how frequently a hotel appears in a result list under different sorting criteria. In particular, we use a vector (SpecialSort) that contains six control variables to capture the frequency of six sorting criteria that consumers use during their searches: default (DFT), price ascending (PRA), class descending (CLD), class ascending (CLA), city name ascending (CNA), and hotel name ascending (HNA). In summary, each observation in our data set contains the hotel ID, week ID, number of competing hotels, number of displays, number of clicks, number of conversions, average screen position (i.e., rank on the result page), average page number, and corresponding hotel characteristics in that week. For a better understanding of the variables in our setting, we present the definitions and the summary statistics of our data variables in Table 1.

1.       Empirical Model In this section,

 we discuss how we develop our simultaneous model in a hierarchical Bayesian framework. Then we describe how we apply the Markov chain Monte Carlo (MCMC) methods (Rossi and Allenby 2003) to empirically identify the effects of product quality and ranking position on consumer search and purchase behavior. More specifically, our model is motivated by the work of Ghose and Yang (2009). The general idea is as follows: We propose to build a simultaneous equations model of clickthrough, conversion, rank, and customer rating. We model the clickthrough and conversion behavior as a function of hotel brand, price, rank, page, sorting criteria, customer rating, and hotel characteristics. The rank of a hotel is modeled as a function of hotel brand, price, sorting criteria, customer rating, hotel characteristics, and performance metrics such as previous conversion rate. The customer rating of a hotel is modeled as a function of hotel brand, price, rank, page, sorting criteria, and hotel characteristics. Each function contains an unobserved error that is normally distributed with mean zero. To capture the unobserved covariation among clickthroughs, conversions, rank, and customer rating, we assume the four error terms are correlated and follow the multivariate normal distribution with mean zero. We describe our model next.

1.       Empirical Analyses and Results

To estimate our model, we applied the MCMC methods using a Metropolis–Hastings algorithm with a random walk chain (Chib and Greenberg 1995). In particular, we ran the MCMC chain for 80,000 iterations and used the last 40,000 iterations to compute the mean and standard deviation of the posterior distribution of the model parameters. We provide more details on the MCMC estimation algorithm in Online Appendix D.

rank, suggesting the negative effect of rank order on conversion rate also increases at a decreasing rate. As expected, Price has a negative effect on hotel demand, whereas Class has a positive effect on hotel demand. The online WOM-related variables, Rating and ReviewCnt, have a statistically significant and positive effect on hotel demand. We also found similar trends in the interaction effects between Ranking and Price/Class/Rating, suggesting higher-class hotels and more expensive hotels are more sensitive to the online ranking effect. And hotels that receive lower ratings from users benefit more when placed on the top of the screen. The total number of hotels in a certain market, H, has a negative effect on hotel-level conversion rate. Intuitively, the higher the number of choices there are available to consumers, lower the probability of buying from any given hotel. Thus, on average, the conversion rate for each hotel decreases.

1.       Conclusions and Implications

In this paper, we focus on investigating three major issues that product search engines are increasingly facing: the direct effect of ranking mechanism on consumer behavior and search engine revenue; the interaction effect of ranking and product ratings; and what kind of personalized ranking mechanism, if any, to adopt. Toward these objectives, we combine archival data analysis with randomized experiments based on a hotel search engine application that we designed. By manipulating the default ranking method and enabling or disabling a variety of active personalization features on the hotel search engine website, we are able to analyze consumer behavior and search engine revenue under different scenarios. Our experimental results on ranking are consistent with those from the Bayesian model-based archival data analysis, suggesting a significant and causal effect of search engine ranking on consumer click and purchase behavior. In addition to a significant surplus gain found by a previous study (Ghose et al. 2012), a consumer-utility-based ranking mechanism yields the highest purchase propensity and the highest search engine overall revenue compared with existing benchmark systems, such as ranking based on price or star ratings. Moreover, an inferior screen position tends to more adversely affect luxury hotels and more expensive hotels. Hotels with lower reputations benefit more from being placed at the top of the search results. This finding illustrates the need for product search engines to directly incorporate signals from online social media into the ranking algorithms. We are beginning to see much of this interplay between search and social media happening in information search engines. Google began to incorporate tweets and other social media status updates into its real-time search function and then decided to create its own version of the Facebook “Like” button— the Google +1—and have it show up in search results. In another example of the interplay between social media and search, Microsoft’s search engine Bing is now incorporating Facebook updates in its results. Our experimental results on personalized ranking show the availability of excess personalization capabilities during the decision-making process may discourage consumers from searching, evaluating, and making final choices. In particular, we find that although active personalized ranking, compared with passive personalized ranking, can attract more online attention from consumers, it leads to a lower purchase propensity and lower search engine revenue. This finding suggests that personalized ranking should not be adopted blindly and the level of personalization should be carefully designed based on the search context. Our research sheds light on how consumers search, evaluate choices, and make purchase decisions in response to differences in product search engine designs. We provide empirical and experimental evidence for future studies to build on when designing an efficient ranking system and dynamically modeling consumer behavior on product shopping sites. A good ranking mechanism can reduce consumers’ search costs, improve clickthrough rates and conversion rates of products, and improve revenue for search engines. Our work has some limitations, some of which we are striving to address in our ongoing work. First, although the AMT platform provides an efficient and cost-friendly framework for randomized experimental design, the inherent heterogeneity in the Internet population makes controlling for subject characteristics across different treatment groups difficult. The randomization process can alleviate this concern to a large extent. However, robustness tests based on offline subjects as well would be helpful. Our current experiments focus on the type of consumers who can make, at most, one purchase in each online shopping session. To better understand the counterintuitive finding that an active personalized ranking mechanism leads to lower conversion rates, one can extend our experimental design to make a comparison with consumers who are allowed to make multiple purchases in a given session. In addition, a study of how the content of consumer search, such as the length and type of search keyword, interacts with the ranking effect would be interesting. Moreover, with regard to examining the ranking mechanism, one can expand the research scope by taking into account consumers’ social network neighbors’ search and purchase behavior. This expansion would allow one to test the impact of social-signal-based ranking mechanisms on product search engines. Notwithstanding these limitations, we believe our paper paves the way for future research in this exciting area at the intersection of social media and search engines.

Acknowledgments

 For helpful comments, the authors thank Jason Chan, Sam Hui, Prasanna Tambe, Duncan Simester, Yong Tan, and Russ Winer, as well as seminar participants at Carnegie Mellon University, Cheung Kong Graduate School of Business, Columbia University, Harvard Business School, the Massachusetts Institute of Technology, University of Texas at Austin, University of Connecticut, University of Texas at Dallas Marketing Conference, the Wharton School, University of California, Los Angeles, and New York University. Anindya Ghose and Panagiotis G. Ipeirotis acknowledge funding from the Google Faculty Research Award in 2012.

      References

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Latar belakang Perilaku konsumen

Selama beberapa dekade terakhir, search engine memiliki muncul sebagai saluran yang signifikan untuk mempromosikan dan menjual produk. Di mesin pencari informasi (misalnya, Google) peringkat hasil pencarian adalah langsung sinyal relevansi hasil untuk query. Namun, dalam mesin pencari produk, peringkat produk yang ditampilkan sering didasarkan pada kriteria seperti seperti harga, wisatawan produk, dll Dalam kondisi seperti ini, kami mungkin sering memiliki beberapa, sinyal berpotensi bertentangan diberikan kepada pelanggan tentang peringkat produk '. Sebagai contoh, jika kita peringkat berdasarkan harga, maka produk termurah kadang-kadang memiliki peringkat produk rendah, atau produk muncul di atas daftar mungkin terlalu mahal bagi pelanggan. Efektif konsumen harus mengamati beberapa, bersaing sinyal Peringkat dan datang dengan peringkat mereka sendiri dalam pikiran mereka; dalam beberapa pengaturan, mesin pencari produk juga akan menghasilkan hasil personalisasi, mencoba untuk peringkat produk menurut preferensi konsumen.

Dalam lingkungan seperti itu, kita ingin memahami faktor yang mempengaruhi proses pengambilan keputusan pelanggan dan besarnya pengaruh itu. Adalah konsumen dipengaruhi oleh peringkat display order, dengan rating produk, berdasarkan harga, dan untuk apa Gelar? Bagaimana interaksi ini mempengaruhi pendapatan bahwa sebuah mesin pencari dapat menghasilkan?

Metode Perilaku Konsumen

Pada bagian ini, kita membahas bagaimana kita mengembangkan simultan kami model dalam kerangka Bayesian hirarkis. Kemudian kita menggambarkan bagaimana kita menerapkan rantai Markov Monte Carlo (MCMC) metode (Rossi dan Allenby 2003) secara empiris mengidentifikasi efek dari produk kualitas dan posisi peringkat pada pencarian konsumen dan membeli perilaku. Lebih khusus, model kami 4 Dalam beberapa kasus, pengguna dapat memulai sesi dan mencari umum informasi wisata, seperti kawasan kota, daripada pencarian untuk setiap hotel; dengan demikian, tidak ada hotel akan ditampilkan pada halaman Web manapun. Kita mengecualikan sesi tersebut dari analisis kami. 5 Baru-baru ini, Travelocity upgrade desain halaman web dengan menunjukkan 10 hotel per halaman. Namun, selama periode waktu pemeriksaan kami, Jumlah itu masih 25. Termotivasi oleh karya Ghose dan Yang (2009). Ide umum adalah sebagai berikut: Kami mengusulkan untuk membangun sebuah model persamaan simultan klik per tayang, konversi, rank, dan dari pelanggan. Kita model klik per tayang dan konversi perilaku sebagai fungsi merek hotel, harga, peringkat, halaman, kriteria penyortiran, pelanggan Peringkat, dan hotel karakteristik. Pangkat Hotel dimodelkan sebagai fungsi hotel merek, harga, pemilahan kriteria, dari pelanggan, karakteristik Hotel, dan metrik kinerja seperti konversi sebelumnya tingkat. Rating pelanggan dari sebuah hotel dimodelkan sebagai fungsi dari hotel merek, harga, peringkat, halaman, menyortir kriteria, dan karakteristik Hotel. Setiap fungsi mengandung kesalahan tidak teramati yang biasanya didistribusikan dengan maksud nol. Untuk menangkap covariation teramati antara jumlah klik, konversi, peringkat, dan pelanggan peringkat, kita asumsikan empat hal error berkorelasi dan mengikuti distribusi normal multivariat.

Kesimpulan Perilaku Konsumen

Dalam tulisan ini, kita fokus pada menyelidiki tiga besar isu bahwa mesin pencari produk yang semakin menghadapi: efek langsung dari mekanisme pada konsumen perilaku dan mesin pencari pendapatan; interaksi pengaruh penilaian peringkat dan produk; dan apa jenis personalisasi mekanisme peringkat, jika ada, untuk mengadopsi. Menuju tujuan ini, kami menggabungkan arsip analisis data dengan percobaan acak berdasarkan aplikasi mesin pencari hotel yang kami dirancang. Dengan memanipulasi metode peringkat default dan mengaktifkan atau menonaktifkan berbagai personalisasi aktif fitur pada hotel Situs Web mesin pencari, kita mampu menganalisis perilaku konsumen dan mencari pendapatan mesin di bawah skenario yang berbeda.

            Hasil eksperimen kami pada personalisasi peringkat menunjukkan ketersediaan kemampuan personalisasi kelebihan selama proses pengambilan keputusan dapat mencegah konsumen dari mencari, mengevaluasi, dan membuat pilihan akhir. Secara khusus, kami menemukan bahwa meskipun aktif peringkat pribadi, dibandingkan dengan pasif pribadi ranking, dapat menarik lebih banyak secara online perhatian dari konsumen, itu mengarah ke pembelian lebih rendah kecenderungan dan pendapatan mesin pencari yang lebih rendah. Ini temuan menunjukkan bahwa peringkat pribadi seharusnya tidak diadopsi membabi buta dan tingkat personalisasi harus hati-hati dirancang berdasarkan pencarian konteks. Penelitian kami menyoroti bagaimana konsumenpencarian, mengevaluasi pilihan, dan membuat keputusan pembelian dalam menanggapi perbedaan mesin pencari produk desain. Kami memberikan bukti empiris dan eksperimental untuk studi masa depan untuk membangun ketika merancang sistem ranking yang efisien dan dinamis pemodelan perilaku konsumen di situs belanja produk. Mekanisme peringkat yang baik dapat mengurangi konsumen biaya pencarian, meningkatkan rasio klik per tayang dan konversi tingkat produk, dan meningkatkan pendapatan untuk mesin pencari.

            Pekerjaan kami memiliki beberapa keterbatasan, beberapa di antaranya kami berusaha untuk mengatasi dalam pekerjaan berkelanjutan kami. pertama, meskipun platform AMT menyediakan efisien dan Kerangka ramah-biaya untuk acak eksperimental desain, heterogenitas yang melekat di Internet . Populasi membuat mengendalikan karakteristik subjek di kelompok perlakuan yang berbeda sulit. Proses pengacakan dapat meringankan kekhawatiran ini untuk sebagian besar. Namun, tes ketahanan berdasarkan pada mata pelajaran offline juga akan sangat membantu. Kami eksperimen saat fokus pada jenis konsumen yang dapat membuat, paling banyak, satu pembelian secara online di setiap sesi belanja. Untuk lebih memahami berlawanan menemukan bahwa peringkat pribadi yang aktif. Mekanisme mengarah untuk menurunkan tingkat konversi, satu dapat memperpanjang desain eksperimental kami untuk membuat perbandingan dengan konsumen yang diperbolehkan untuk membuat beberapa pembelian dalam sesi yang diberikan. Selain itu, studi bagaimana isi pencarian konsumen, seperti panjang dan jenis pencarian kata kunci, berinteraksi dengan efek peringkat akan menarik. Selain itu, dengan Berkenaan dengan memeriksa mekanisme peringkat, satu kaleng memperluas cakupan penelitian dengan memperhatikan konsumen akun ' jaringan sosial tetangga 'pencarian dan pembelian perilaku. Ekspansi ini akan memungkinkan seseorang untuk menguji dampak sosial-sinyal berbasis mekanisme peringkat di search engine produk. Meskipun keterbatasan ini, kami percaya kami kertas membuka jalan untuk penelitian masa depan di daerah ini menarik di persimpangan media sosial dan mesin pencari.