Expert Systems with Applications 36 (2009) 7860–7864

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Expert Systems with Applications

journal homepage: www.elsevier .com/locate /eswa

Conjoint analysis for IPTV service

Jiwoong Song, Taewon Jang, So Young Sohn *

Dept of Information and Industrial Engineering, Yonsei University, Shinchondong 134, Seoul, Republic of Korea

a r t i c l e i n f o

Keywords:IPTVConjoint analysisCustomer’s preference

0957-4174/$ - see front matter � 2008 Elsevier Ltd. Adoi:10.1016/j.eswa.2008.11.016

* Corresponding author.E-mail addresses: sohns@yonsei.ac.kr, green-xii@h

a b s t r a c t

Given the upcoming introduction of IPTV service in Korea, it is necessary to develop business models andmarketing strategies to improve customer satisfaction and succeed in market competition. We use con-joint analysis to estimate customer preferences and the relative importance of service factors. Based onresults from total customers’ and clustered customers’ service preferences, we propose marketing strat-egies for service providers.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Recent developments of IT and media technologies have given atremendous push toward the development of convergence serviceslike IPTV (Internet protocol television). Korea has taken a leader-ship role in developing IPTV (Shin, 2007). IPTV will be introducedin Korea in 2008 on a full scale as the related bill was passed. IPTVis a system by which digital television service is delivered usingInternet protocol over a network infrastructure, which may includedelivery by a broadband connection. A general definition of IPTV istelevision content that, instead of being delivered through tradi-tional broadcast and cable formats, is received by the viewerthrough technologies used for computer networks. IPTV is oftenprovided in conjunction with video on demand and may be bun-dled with Internet services such as Web access and VoIP. The com-bination of Internet service and television service makes this a typeof digital convergence (Beck et al., 2007, Papagiannidis, Berry, & Li,2006; She, Hou, Ho, & Xie, 2007).

In the coming years, IPTV is expected to grow rapidly. Many ofthe world’s major telecommunications providers are exploringIPTV as a new revenue opportunity in their existing markets andas a defensive measure against encroachment by more conven-tional cable television services. The IPTV service market in Koreais also expected to grow remarkably, and there will be intensecompetition between traditional TV service providers and IPTVproviders, and also between IPTV providers for subscribers. Underthis market expectation, a full understanding of subscribers’ needsand preferences will be needed to identify market conditions andto maintain future business profits.

The objective of this paper is to provide insights on how broad-casting service providers can attract as many subscribers as possi-ble in the IPTV competition. The conjoint analysis technique is used

ll rights reserved.

anmail.net (S.Y. Sohn).

in this study to measure consumers’ multi-attribute utilityfunction.

Conjoint analysis has become an increasingly popular approachto estimate the benefits received from the attributes of a product.Conjoint analyses inform the researcher about the structure of con-sumers’ preferences, which are obtained from their overall judg-ment of a set of alternative products defined as a combination oflevels of different attributes (Green & Srinivasan, 1978). Greenand Krieger (1991) pointed out the potential usefulness of conjointanalysis to deal with some marketing problems, in particular to de-velop new multi-attribute products with optimal utility levels overother competitive products, to estimate market shares in alterna-tive competitive scenarios, to benefit segmentation, and to designpromotion strategies, among other uses (Green & Krieger, 1991).

This paper is organized as follows. The research design is cov-ered in the following section. The type of data and how data werecollected are also explained in that section. The main part of thepaper is devoted to an explanation of the empirical results. Finally,main conclusions are summarized.

The results of our research are expected to inform potentialIPTV service providers of potential customers’ knowledge and per-ception about IPTV service and to help providers design businessmodels and perform successful marketing based on the needs ofcustomers.

2. Research design

A conjoint model has to be defined to explain how consumerpreferences are formed. Specification of the conjoint preferencemodel involves two steps. First, the functional form for each attri-bute must be specified. Next, functional forms for each attributeare combined into a conjoint preference model for estimation. Inthis study, a cumulative part-value approach was used. This isthe simplest and most frequently used approach (Steenkamp,1987). The cumulative model assumes that the overall evaluations

Table 1Attributes and their levels.

Levels Attributes

VOD1 Monthly n 30;000þ only sky wave programs are free ðbasicÞ2 Monthly n 45; 000þ charge for some brand� new programs ðmidÞ3 Monthly n 60;000þ no additional charge for all programs ðhighÞ

Setup cost1 No contractþ installation fee n 10;000 þ set� top box

rental fee n 5000ðnoneÞ2 1 yrcontractþ noinstallationfeeþ set� top box rental fee n 5000ð1 yrÞ3 3 yrcontractþ no installation feeþ free set� top boxð3 yrÞ

Information service1 Choose one service (one)2 Choose two services (two)3 Choose all services (three)

Additive service1 E-commerce (shop)2 Display phone and e-commerce (shop_show)3 Display phone, e-commerce, and home-network service (fullset)

J. Song et al. / Expert Systems with Applications 36 (2009) 7860–7864 7861

are formed by the sum of the values of the separate parts of theattributes.

2.1. IPTV attributes and their levels

According to a recent article about potential user factors driv-ing IPTV adoption, customers wanted the service to be more inter-active, customized, and personalized, i.e., they demanded anincreased level of user control when considering the adoption ofIPTV. Hence, VOD service was selected as the most significantattribute in model design. Through VOD service, a subscriber canaccess any contents and information, and use it their way by thehelp of the set-top box which is designed to recommend programsaccording to the analyzed preferences of service users. Fixed costwas found to be significant in IPTV subscription. Also, special func-tionality and value-added service were important (Lee & Yang,2003; Shin, 2007). In accordance with recent articles on IPTV ser-vice plans of service providers, service attributes, which are re-lated to these factors and available, are information-relatedcontents-providing services (this will be referred to as the infor-mation service) and additive telecommunication-network-basedservices such as home-networks, e-commerce, and display phones,among others (Choi, Han, Jeong, & Park, 2006; Lee, 2007; Ryu,2007).

The first attribute was VOD service. For VOD service, we focusedon the monthly fee and the accessibility of the contents. Subscrib-ers of the conventional TV service paid only one type of basic ser-vice fee and paid additional charges for some particular contents.However, when we investigated the internet service, we found thatsubscribers generally want to use the service with no additionalcharges even though they have to pay a slightly higher basic ser-vice fee (Zubey, Wagner, & Otto, 2002). Therefore, we thought thatthe same demand would possibly apply to IPTV and set the levelsaccordingly. Each level of the first attribute was made as a selec-tion between a large basic fee without an additional fee and a smallbasic fee with a large additional fee.

The second attribute was the setup cost. As mentioned earlier,fixed cost was a significant factor. We wanted to know how to ap-ply this factor for marketing. In the Internet service market in Kor-ea, freeing or reducing the setup cost were often offered under thecondition that the subscriber make a promise to use the service fora fixed period. To see how it could be applied to IPTV service, levelswere arranged as setup-cost-reducing conditions for each givensubscribing period (Zubey et al., 2002).

The next attributes were information services and additive ser-vices, which are regarded as exclusive strong points of IPTV (Becket al., 2007; She et al., 2007). For the third attribute, we set theinformation service to contain education, financial information,and entertainment, which are popular topics and will be popularin Korea. Educational services will offer good academic programsfor customers who are studying for qualifications and tests. Withthese academic programs, the service users can study at home asif they take real classes at school, not merely watching pre-re-corded lectures, but, using the Web, submit assignments, get feed-backs, communicate with the lecturer and other students, and soon (Tian, 2001). Financial information services were selected be-cause of the growing interest in financial assets and investment.Entertainment services will provide special programs about games,sports, and those other areas that will maintain people’s attention.Levels of information service attributes were created to ask respon-dents if they wanted to be provided with one, two, or all services.

The last attribute was the additive services that make IPTV par-ticularly different from conventional TV services. Services assumedto be included were e-commerce, display phone, and home-net-work service. E-commerce will be provided with the personalizedrecommender system, which is able to retrieve optimal products

based on the customer preferences (Lee, Liu, & Lu, 2002). Each levelwas made as a package of these services.

Attributes and their levels are described in Table 1. The word atthe end of each level description is the short description of it.

2.2. Conjoint experimental design

Once attributes and attribute levels are selected, they must becombined to form different hypothetical services for surveyrespondents to assign preference ratings. In this study, a full profileapproach was used to design the product profiles. In this approach,the number of hypothetical services was obtained by multiplyingthe number of levels associated with each attribute. This approachcan generate a large number of service profiles (in this case: 81hypothetical services). It is difficult, from a customer’s perspective,to evaluate a large number of product concepts. It is necessary toselect a few profiles, but maintain the effectiveness of sortingand evaluating the relative importance of a product’s multi-dimen-sional attributes. Fractional factorial design was used (Gil & San-chez, 1997).

Fractional factorial designs allow one to gather data on a largenumber of product profiles using a relatively small number ofproduct profiles. When reducing profiles, we need to keep at least15 profiles (the number of parameters times 1.5 profiles). Thisstudy used the marketing experiment function of the SAS 9.1 soft-ware to construct the 15 product profiles used in this study. The 15hypothetical service profiles considered are shown in Table 2.

A survey was made using the 15 profiles shown in Table 2.Respondents were selected in the Shin-chon area, and all were col-lege students. The survey was performed under two assumptions:

(1) College students have more knowledge about IPTV (theyusually have more knowledge about brand-new technolo-gies), so we can obtain more meaningful results from them.

(2) Even though college students are not the main purchasers ofIPTV service, they have decision-making power in the ser-vice purchase.

Each respondent was shown a randomly mixed set of the 15cards describing the IPTV service profiles and was asked to rankthem, according to their own purchasing intention on a scale from1 (most preferred) to 15 (least preferred). This ranking was used toprevent confusion during the survey. Ranks were replaced by pref-erences (preference = 16-rank) for analysis.

Fig. 1 shows the mean preference rating for each profile.

Table 2Hypothetical IPTV service profiles.

OBS VOD Setup Info Additive

1 High None Two Fullset2 Basic None Three Fullset3 Mid 3 yr Three Shop4 High 3 yr Two Shop_show5 Basic None One Shop6 Mid 3 yr Two Fullset7 High 3 yr One Shop8 High 1 yr One Fullset9 Mid None One Shop_show10 High None Three Shop11 Mid 1 yr Three Fullset12 Basic 3 yr One Fullset13 Mid 1 yr Two Shop14 Basic 1 yr Three Shop_show15 Basic 1 yr Two Shop

Fig. 1. Mean preference ratings for hypothetical service profiles.

Table 4Part-worth of the hypothetical profiles.

Type VOD Setup Info Additive

Score 1.078 �1.1345 0 0.55Score �0.9 �1.1345 1 0.55Score �0.18 0.75337 1 �1.1

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2.3. Conjoint model specification

The conjoint model was designed as below. Attributes were allcategorical, so dummy variables were defined appropriately (seeTable 3).

Y ¼ b0 þ b11 � x11 þ b12 � x12 þ b21 � x21 þ b22 � x22 þ b31 � x31

þ b32 � x32 þ b41 � x41 þ b42 � x42;

whereY = preference rating given to each hypothetical serviceb0 = intercept.

Table 3Defined dummy variables for conjoint model.

Variable VOD X11 X12

1 Monthly n 30; 000þ only sky wave programs are free 1 02 Monthly n 45;000þ charge for some brand� new

programs0 1

3 Monthly n 60; 000þ no additional charge for all programs 1 1

Setup cost X21 X221 No contractþ installation fee n 10; 000þ set� top box

rental fee n 50001 0

2 1 yrcontractþ noinstallationfeeþ set� top box rentalfee n 5000

0 1

3 3 yrcontractþ no installation feeþ free set� top box 1 1

Information service X31 X321 Choose one service 1 02 Choose two services 0 13 Choose all services 1 1

Additive service X41 X421 E-commerce 1 02 Display phone and e-commerce 0 13 Display phone, e-commerce, and Home-network service 1 1

The model was estimated using the TRANSREG function in theSAS 9.1 software.

3. Empirical analysis

3.1. Estimation

Results from the analysis are shown in Tables 4 and 5. Table 4describes the part-worth of each level of the attributes. Table 5 isa rearranged form of Table 4. Fig. 2 is the graph description ofthe part-worth in Table 5.

Results from Fig. 2 indicate that customers who want to use thecontents with no additional charge allow a higher basic service fee.For setup cost, results indicate that customers who preferred alower setup cost allowed a longer service period commitment.For information services, customers preferred it to be providedwith all available information. This result may reflect Korean con-sumer preferences, which favor the so-called full option in pur-chasing everything. For additive services, customers preferred thepackage including e-commerce and display phones the most, andfull-service next, which contained e-commerce, display phones,and home-network service. This result can be interpreted as cus-tomers not having enough knowledge or experience about home-network service yet, especially as a service bundled with TV.

Table 6 shows the weighted part-worth of attributes’ levels.This indicates how much the level of each attribute affects thepreference of the entire service. As a result, values in Table 6 arethe multiplication of part-worth values and weights of the respon-dents. The wider the range between the maximum value and theminimum value for an attribute, the more influential it is in decid-ing the service preference. The relative importance of attributes isdescribed in Fig. 3. Results in Fig. 3 indicate that customers tend togive more importance to the VOD service (34%) and largely thesame priority to the rest when considering subscribing to theservice.

Score 1.078 0.75337 0 1.04Score �0.9 �1.1345 �1 �1.1Score �0.18 0.75337 0 0.55Score 1.078 0.75337 �1 �1.1Score 1.078 0.38116 �1 0.55Score �0.18 �1.1345 �1 1.04Score 1.078 �1.1345 1 �1.1Score �0.18 0.38116 1 0.55Score �0.9 0.75337 �1 0.55Score �0.18 0.38116 0 �1.1Score �0.9 0.38116 1 1.04Score �0.9 0.38116 0 �1.1

Table 5Values of attribute levels.

VOD Basic Middle High

Value �0.9 �0.18 1.078Setup None 1 yr 3 yrValue �1.1345 0.38116 0.75337Info One Two ThreeValue �1 0 1Additive Shop Shop_show FullsetValue �1.1 1.04 0.55

Fig. 2. Graph description of the part-worth of each attribute.

Table 6Value of each attribute considering weights of the attributes by each respondent.

VOD Basic Middle High

Value �1.9163 �0.383266 2.2953Setup None 1 yr 3 yrValue �1.7062 0.5732487 1.1330Info One Two ThreeValue �1.4373 0 1.4373Additive Shop Shop_show FullsetValue �1.6089 1.5212063 0.8045

Fig. 3. Relative importance for attributes.

Table 7Four hypothetical service models for choice simulation.

Service VOD Setup (yr) Info Additive

A Basic 3 One ShopB Middle 1 Three Shop_showC High 3 Three Full setD High 3 Two Shop_show

Table 8Preference ranking for four hypothetical services.

Service VOD Setup (yr) Info Additive 1st rank

A Basic 3 One Shop 22B Middle 1 Three Shop_show 26C High 3 Three Full set 4D High 3 Two Shop_show 11

Fig. 4.1. Clustering by current TV service.

J. Song et al. / Expert Systems with Applications 36 (2009) 7860–7864 7863

3.2. Choice simulation

An interesting analysis that can be derived from conjoint tech-niques is the possibility of choice simulation of the market share ofdifferent hypothetical services. Selection was made by taking intoaccount that the services included might be realistic. Therefore,four hypothetical service models were selected (Table 7).

Service A was offered at no initial cost (for a 3 yr promise) andlow VOD service. One type of information service and e-commercewere provided. Service B included the medium setup cost (for a1 yr promise), medium VOD service, all types of information ser-vices, and the e-commerce/display phone package. Service C in-cluded high VOD service where the subscriber can use allcontents with an expensive basic fee, all types of information ser-vices, and the full package additive service (e-commerce, displayphone, and home-network service) with no setup cost. Service D

contained high VOD, two types of information services, e-com-merce/display phone package with a 3-yr commitment (i.e. freesetup cost). Results from the calculation are shown in Table 8.

3.3. Cluster analysis

Another interesting outcome of conjoint analysis is the possibil-ity of designing market segments that allow IPTV service providersto implement differentiated marketing strategies. That is, respon-dents are grouped in clusters depending on their characteristics.Respondents’ current TV service, their current Internet service,and their family size were selected for cluster analysis, becausethey provided some differences. The results are as follows.

3.3.1. People who watch ordinary TV or cable TVPeople who watch ordinary TV service gave more importance to

the VOD service. This is possibly because they want to watch all theprograms without thinking about additional costs. They were notplacing importance on the information service from IPTV. It lookslike they did not expect much from the access to informationthrough the IPTV service. Cable TV watchers had a tendency to givesomewhat equal importance to all attributes, but gave a little moreimportance to the VOD service. Therefore, it could be a good mar-keting strategy to ask what kind of TV service consumers are using,and emphasize VOD if they are ordinary TV watchers or explain thediverse services in detail if they are cable TV watchers (see Fig. 4.1).

3.3.2. Internet service typeWireless internet users showed a marked tendency to be insen-

sitive to the VOD service. Rather, they gave more priority to addi-tive services. This could be interpreted as wireless internet usersbeing considered early-adopters, so we could guess that theywanted to use brand-new technologies. It would be a good market-ing strategy for service providers to advertise the additive services

Fig. 4.3. Clustering by familysize.

Fig. 4.2. Clustering by current Internet service.

7864 J. Song et al. / Expert Systems with Applications 36 (2009) 7860–7864

of IPTV by offering early adopters the additive services at a cheaperprice (see Fig. 4.2).

3.3.3. Number of family membersFor a customer who lives alone or lives with his/her roommate,

he/she had a tendency to be sensitive to the VOD service and gaveless priority to additive services. This is because they tended to notwatch TV regularly and not for long periods of time. They wantedto see the program they wanted when they turned on their TV. Forcustomers who live with relatively large families, they seemed toexpect more benefits from the additive services of IPTV (seeFig. 4.3).

4. Conclusion

In this study, we used a conjoint designed experiment to esti-mate customers’ preferences about the IPTV service that will beintroduced soon. Four attributes were used in the conjoint design:VOD service; setup cost; information services; and additive ser-vices. Based on results showing the level of perception and knowl-edge that customers have about IPTV, this study suggested amarketing strategy for service sellers.

Results indicate that customers give the most importance (34%)to the VOD service when considering subscription to the IPTV ser-vice. They prefer to watch programs without thinking about addi-tional charges. So, future research could study how muchsubscribers will pay to use programs without additional charges.Customers put similar importance (23%) on setup costs and infor-mation services. Because they prefer not to pay for setup, it is ex-pected that service providers can retain customers using theirservice for a longer period by offering installation and set-topbox with no charge. Customers prefer to have full availability ofinformation services. It would be better for service providers to

advertise the diversity as well as the specialty of the contents ofinformation services. Currently, customers put relatively lessimportance on additive services. They seem to consider first theservices they can obtain by ‘watching’ TV when thinking aboutsubscribing to IPTV. However, service providers can take advantageof early-adopters to advertise additive services. Furthermore, thenumber of IPTV customers will incrementally grow. Chances arethat potential customers will pay more attention to additive ser-vices through their exposure to additive services such as e-com-merce, display phones, home-network service, and other servicesthat will be developed. Then the additive services will become animportant factor in their decision to subscribe to IPTV. Additiveservices will be a big strength for IPTV service providers in winningthe competition for customers with conventional TV serviceproviders.

Limitations of our study include two factors. First, we could notput many attributes into the model in the design process. Design-ing an adaptive conjoint model for IPTV would be a good objectivefor future research. Second, this study was based on a surveyincluding a relatively small number of respondents consideringthe entire number of potential customers of IPTV service. Resultsmight not indicate the preferences of the whole market. After theservice is introduced and data accumulate, advanced researchcan be performed with CRM techniques such as logit models, pro-bit models, and neural networks. These are areas for further study.

Acknowledgement

Dongoh Kang helped with the survey analysis part.

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