Innovation in destinationmarketing

The use of passive mobile positioning for thesegmentation of repeat visitors in Estonia

Andres KuusikFaculty of Economics and Business Administration,

University of Tartu, Tartu, Estonia

Margus TiruPositium LBS, Tartu, Estonia

Rein AhasDepartment of Geography, University of Tartu, Tartu, Estonia, and

Urmas VarblaneFaculty of Economics and Business Administration,

University of Tartu, Tartu, Estonia

Abstract

Purpose – The purpose of this paper is to demonstrate how technological innovation serves as anenabling factor to innovation in tourism management. The motivation of this paper is related to thequestion of how to innovate destination marketing as a tool to manage long-term customer relationships.

Design/methodology/approach – The authors use mobile positioning-based research methods tomeasure visitors’ behaviour. This provides new data for the detection and measurement of destinationloyalty that could be used as valuable input to improve destination marketing strategy and developnew services.

Findings – The use of mobile positioning helps to improve the quality of data about tourism flows inEstonia. The authors were able to observe and measure the duration, timing, density, seasonality anddynamics of visitations. Further, it allowed also to distinguish repeat visitors. The rich datasetprovided by passive mobile positioning (PMP) allowed the implementation of the proposed, moredetailed, classification of segments of repeat visitors and the identification of not loyal, somewhatloyal, loyal, very loyal, functionally loyal and forced to be loyal visitors. This analysis made it possibleto reveal transit, long-term, one-day and other specific visitors among repeat visitors.

Originality/value – The theoretical novelty of the paper consists in the creation of the innovationmodel of the destination marketing of the country and providing the new approach of segmentation ofrepeat visitors. Empirical novelty is the use of PMP in studying repeat visitations for destinationmarketing. The paper offers new ways for governments to shape service policies and allows tourismindustry firms to offer new services.

Keywords Estonia, Tourism development, Destination marketing, Customer loyalty,Positioning technologies, Passive mobile positioning, Service innovation, Segmentation

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1746-5265.htm

The authors wish to thank the Estonian Information Technology Foundation, Ericsson Ltd,Positium LBS and EMT Ltd for support and funding. The project was supported by TargetFunding Projects SF0180052s07 and SF 0180037s08 of the Ministry of Education and Scienceand Estonian Science Foundation Grants 7562 and 7405.

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Received June 2010Revised December 2010Accepted February 2011

Baltic Journal of ManagementVol. 6 No. 3, 2011pp. 378-399q Emerald Group Publishing Limited1746-5265DOI 10.1108/17465261111168000

IntroductionAs a result of globalisation, countries and places face increased competition. There is acompetition for foreign direct investment, visitors, business locations and residents(Kotler et al., 1999). The growing mobility of capital, people and firms requires places tobe more attractive and, therefore, “place marketing” and “destination marketing” asspecial subfields of marketing have emerged. At the same time, relationship marketingand customer relationship management are gaining more and more importance on thefirm level (Gummesson, 1999). The principle that it is cheaper to hold existingcustomers than get new ones (Rosenberg and Czepiel, 1984) is followed today in most ofcompanies. But it is not the case in destination marketing. Rainisto (2003) and Skinner(2008) assert that destination marketing literature is shifting towards branding.

The authors of this paper underline the need to pay attention also to the long-termrelationship between the host country and visitors. It emphasises the central role ofrepeat visitors in destination marketing. Research carried out by different authors hasrevealed several reasons why marketing and customer management costs are muchlower with loyal customers (Rosenberg and Czepiel, 1984; Buttle, 2004; Reichheld,1993). Loyal and long-term customers are more likely to expand the relationship withthe supplier from both sides (Bowen and Chen, 2001; Buttle, 2004). Loyal customerrelations indicate to the positive attitude of the customer, which leads to positiveword-of-mouth (WOM; Oppermann, 2000; Buttle, 2004; Petrick, 2004, etc.). Theabovementioned reasons are extremely relevant in destination marketing.

The rapid diffusion of the relationship marketing on the firm level is facilitated bythe rapid development of information technology solutions enabling to collect andanalyse data about customer behaviour. Unfortunately, similar developments indestination marketing on the country level are quite modest. Information about touristflows is measured inadequately, concentrating mainly on the data about touristaccommodation. Within last decade, the information and communication technologies(ICT) and geographical information systems (GIS) are advancing surveying methods ingeography and tourism studies (Ahas et al., 2008). One of the emerging subjects ingeographical studies is connected with mobile (cellular) phone positioning datasetsand location-based services (LBS). Mobile positioning data has great potential forapplications in space-time behaviour studies addressed in studying tourismgeography. Preliminary papers appeared trying to use the passive mobilepositioning (PMP) in the process of monitoring visitors (Ahas and Mark, 2005). Thenovelty of the study is in the deeper and better focused use of PMP method as new datasources for detecting and measuring different segments of repeat visitors. It could beused as valuable input for the improvements in the destination marketing strategyand for the development of new services for different segments of repeat visitors.

Therefore, the aim of the paper is to show how the improvements in the mobilepositioning technology provide a basis for the novel segmentation of repeat visitorswhich could be used as the tool to create new analytical basis for the destinationmarketing policy making. Therefore, the second novel aspect of the paper is theoreticaland consists in the creation of an innovation model of the destination marketing of thecountry and providing a new approach of segmentation of repeat visitors.

The second section of the paper provides a methodological framework of the study.The third section presents the literature overview, which covers trends in the researchof repeat visitation and customer loyalty and concludes with the discussion

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of segmentation of repeat visitors. The fourth section explains the PMP method anddata used. The fifth section is devoted to the presentation of the first empirical resultsand provides the discussion about the ways how the obtained results could beinterpreted and used in order to determine segments of repeat visitors. The paperconcludes with policy implications, limitations and future research opportunities.

Methodological frameworkIn this paper, we are trying to figure out how different types of innovation in the processof providing services are interlinked. We intend to show how the improvements in themobile positioning technology (technological innovation) provide a basis for the novelsegmentation of repeat visitors (marketing innovation) and, thus leads to improvementsin the destination marketing policy making of the country (organisational innovation).Therefore, in the process of building up the methodological framework of the paper,we searched for the models which link both technological and non-technological formsof change in the process of service provision.

As a departure point for building the study methodology, we used the four-dimensionalmodel of innovation in services suggested by den Hertog (2000, p. 495). This model isstrongly influenced by the innovation system approach (Gallouj and Savona, 2009) and itsstrength lies in the opening links between different types of innovation (technological,marketing and organisational) in the process of service provision. The model allows alsohighlighting the role of demand and end-users. However, in order to consider the specificneeds of the paper, we had to modify den Hertog model significantly (Figure 1).

In our methodological framework, contrary to the den Hertog model, the initiatingrole is given to the technological option, which in our case is the existence of the PMPmethod, which is the innovation enabling factor (dimension 1 in Figure 1). The newtechnological option provides an idea to develop new approaches to segmentingrepeat visitors (the arrow with a dotted line in Figure 1). The new approach tothe segmentation of repeat visitors (dimension 2) is based on the existing approaches tothe segmentation of loyal customers (A in Figure 1). PMP enables now to create adatabase of characteristics of repeat visitors (e.g. nationality, visiting frequency,timing and visited regions) (B in Figure 1). Therefore, technological innovation creates,in the form of the abovementioned database, the analytical basis for further marketinginnovation. Hence, based on the new segmentation approach and using the database ofcharacteristics, it is possible to identify new segments of repeat visitors (dimension 3).Finally, after detecting the new segments of repeat visitors, information is created,which could be used in the process of improving destination marketing policy making(dimension 4). This is the organisational innovation, which depends also on thecapabilities, skills and attitude of destination policy makers (C in Figure 1). The studyfocuses on the first three dimensions of the model. Owing to the novelty of the researchtopic, we apply the exploratory approach.

Literature overviewThe nature of repeat visitationRepeat visitation has been frequently viewed as a behavioural outcome or expression ofdestination loyalty (Oppermann, 2000; Hernandez-Lobato et al., 2006; Petrick, 2004, etc.).Therefore, it is relevant to explore destination loyalty and, more generally, customerloyalty to understand the nature of repeat visitation.

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There are multiple approaches to customer loyalty created since Copeland (1923) cameout with his approach to a brand insistence. Until 1970, theories of behavioural loyalty(repeat purchase behaviour) were dominating (Cunningham, 1956; Farley, 1964;Jacoby, 1971; Ehrenberg, 1974; Tucker, 1964; Sheth, 1968; Harary and Lipstein, 1962).These approaches (except Copeland’s) regarded customer loyalty as a stochasticbehavioural phenomenon. These theories did not attempt to explain why customersbehave loyally. Bass (1974) stated that even if behaviour is caused by some variablesbut the bulk of the explanation lies in a multitude of variables, which occur with anunpredictable frequency, then, in practice, the process is stochastic.

During the late 1960s, the popularity of stochastic models dropped and somedeterministic views on loyalty were proposed (McConnell, 1968; Day, 1969; Jacoby andKyner, 1973). These approaches asserted that loyalty does not concern onlybehavioural aspects but it includes also some psychological processes (Kuusik andVarblane, 2009). Contemporary research (Oliver, 1999; Chaudhuri, 1995; Djupe, 2000;Reichheld, 2003; Hofmeyr and Rice, 2000) considers and accents the psychological(mostly attitudinal and emotional) factor of loyalty. For example, Oliver (1999) hasstated that brand loyalty is a deeply held commitment to re-buy or re-patronize apreferred product/service consistently in the future, thereby causing repetitive samebrand set purchasing, despite situational influences and marketing efforts having thepotential to cause switching behaviour.

Figure 1.Model of innovation

in the destinationmarketing of the country

New approach ofsegmentation of repeat

visitors (2)

Identified segments ofrepeat visitors

(3)

Improved destinationmarketing policy

(4)

Technological optionPassive Mobile

Positioning(1) Organisational

innovation

Existing approaches ofsegmentation of loyalcustomers (A)

Characteristics of repeatvisitors available indatabase (B)

Capabilities, skills and attitude ofdestination policy makers (C)

Note: Numbers in Figure 1 represent different dimensions of the innovationprocessSource: Based on den Hertog (2000) and modified by authors

Marketinginnovation

Technologicalinnovation

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There is no exact and well formulated definition for destination loyalty. In theliterature, majority of authors (Kozak, 2001; Petrick et al., 2001; Jang and Feng, 2007)view destination loyalty as an intention to revisit the destination, which is related tothe abovementioned attitudinal loyalty. Chen and Gursoy (2001) defined destinationloyalty as a level of tourists’ perceptions of a destination as a recommendable placewhich according to Reichheld (2003) shows also attitudinal loyalty. Some authors(Hernandez-Lobato et al., 2006; Petrick, 2004; Kyle et al., 2004) have distinguishedattitudinal and behavioural destination loyalty. Milman and Pizam (1995) havesimilarly distinguished interest in revisiting (attitudinal) and likelihood of revisitingthe destination (behavioural loyalty). The paper posits on the approach of Oppermann(1999, 2000) who stated that destination loyalty is a lifelong returning behaviour ofvisitors caused by liking and positive attachment to the destination.

Segmentation of repeat visitors based on customer loyaltyBased on the previous approaches of customer loyalty, it is possible to point out twogeneral types of customer loyalty – behavioural and attitudinal. Behaviourally, loyalcustomers act loyally but have no emotional bond with the brand or the supplier. In thecase of attitudinal loyalty, there is an emotional bond between customer and the brand.With regard to this type of loyalty, it is not important what the customer does but whathe or she feels. Jones and Sasser (1995) call these two kinds of loyalty false or truelong-term loyalty, respectively. Hofmeyr and Rice (2000) divide customers into loyal(behavioural) or committed (attitudinal) customers.

There are several reasons why customers may behave loyally without having anyemotional bond with the supplier. First of all, they could be forced to behave loyally ifthe poor financial status of the customer is limiting his selection of goods (Gronholdtet al., 2000), there is no alternative brand, or there are exit barriers created by thesupplier (Buttle, 2004). Second, in the case of inert loyalty, customers do not switchbecause of cosiness, habit and low involvement – for example, if brand differences arenot very big and important to the customer (Wernerfelt, 1991) or if the customerbelieves that the existing brand is better than another (Oliver, 1999) or if the customersfeels the risk that other brands could be worse than the existing one (Hofmeyr and Rice,2000). Third, in the case of functional loyalty, the customer has a very rational reasonto behave loyally. For example, Wernerfelt (1991) points out cost-based loyalty.

According to Oliver’s (1999) approach, it is possible to distinguish three phases ofattitudinal loyalty. In the case of affective loyalty, the customer has some positivefeelings towards the brand, which has satisfied the needs of customer. If the customerhas an inner urge to prefer a particular brand, it is called conative loyalty. This bond ismuch stronger than in the case of affective loyalty. Active loyalty is the case when thecustomer has an inner urge to prefer a particular brand and he/she is ready toovercome any obstacles to get this brand. It is an enduring desire to maintain a valuedrelationship. According to Reichheld’s (2003) definition of loyalty, it is a willingness ofthe customer to invest into or donate for the strengthening of the relationship with thesupplier.

These two concepts (behavioural and attitudinal) do not exclude each other. A loyallybehaving customer could but not have to be attitudinally loyal. Also, some authors havecombined those two types of loyalty. For example, Dick and Basu (1994) have proposedthree types of loyalty:

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(1) Loyalty represents high attitudinal and high behavioural loyalty.

(2) Latent loyalty is associated with high attitudinal but low behavioural loyalty.

(3) Spurious loyalty represents low attitudinal and high behavioural loyalty.

In Figure 2, all the previously mentioned loyalty types are combined. There is the Dickand Basu’s (1994) approach as the basis with two axes showing the strength ofattitudinal or behavioural loyalty. The location of different loyalty types in Figure 2 isrelative. They are somewhat differentiated by the strength of attitude but not by theextent of repeat behaviour – except for latent loyalty, all others are located on theright-hand side of the Figure. Different authors have not specified the extent of repeatbehaviour in the case of different types of loyalty. Therefore, the locations of differenttypes of loyalty in Figure 2 are conditional.

Based on the previously presented loyalty groups, the following classification ofrepeat visitors is proposed by the authors.

Commitment, conative and affective loyalty. Oppermann (2000) has stated thatdestination selection and trip planning are high-involved decisions and, therefore,spurious loyalty is quite unlikely to occur. This statement is supported also by theresearch carried out by Hernandez-Lobato et al. (2006) and Kaplanidou and Vogt (2007)that revealed that loyal behaviour is determined strictly by attitudinal loyalty or byintentions to revisit. Alegre and Cladera (2009) found that satisfaction with previousvisitations is a very important factor for the repeat visitation intention. Based onvisitation frequency, Oppermann (1999, 2000) divided loyal visitors into threesub-segments: somewhat loyal, loyal and very loyal. According to him, very loyalvisitors are those who had visited Australia more than six times during the 11-yearperiod – it means that the gap between visits was up to 2.2 years. Loyal visitorsvisited Australia four or five times during that period (the gap between visits was2.2-3.5 years) and somewhat loyal visitors visited Australia two or three times during

Figure 2.Typology of loyalty

Hig

hLo

w

CommitmentActive loyalty

Str

engh

t of t

he p

ositi

ve a

ttitu

de

Extent of repeat behaviour

Low High

Latent loyalty

No loyalty

Conative loyalty

Spurious loyalty- Inert loyalty- Functional loyalty

- Forced loyalty

Affective loyalty

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that period (the gap between visits was longer than 3.5 years). As Oppermann (1999)linked the frequency of revisits to the attitude (liking and attachment), it is possible todraw a parallel with Oliver’s (1999) approach and view very loyal visitors ascommitted, loyal visitors as conatively loyal and somewhat loyal visitors as affectivelyloyal customers.

Spurious loyalty. Contrary to Oppermann (2000), the authors of this paper believe thatdifferent types of spurious loyalty exist also among repeat visitors. For example,Oppermann (1998), Mitchell and Greatorex (1993), Milman and Pizam (1995), Gitelsonand Crompton (1984) and Baloglu (2001) have established that one reason for repeatvisitation is familiarity of the destination. This is related with risk-avoiding behaviour –even a slightly unsatisfied tourist could come back to the same destination because it isstill less risky than to go somewhere else. An unknown destination could involve biggerrisks than a familiar destination. According to that and Hofmeyr’s approach presentedabove, the appearance of the repeat visitation should express also the existence of inertloyalty.

In this paper, it is proposed that functionally loyal could be visitors who have acertain reason to revisit the destination (country) having at the same time noconsiderable positive attitude towards the destination. For example, they could revisita country due to certain regular events, they are interested in or due to shopping toursthey undertake with certain regularity. Forced to be loyal could be for examplebusiness travellers who have regularly business to do in the country they might notlike. For second example, there are some very special types of workers – long distancecar drivers and sailors. It is very likely that repeat visitations of this segment are notassociated with a positive attitude.

Latent and no loyalty. Oppermann (1999) has divided people who have no repeatvisits into the following groups: non-purchasers, disillusioned, unstable and disloyal.The first group includes also latently loyal visitors. The others are related to non-loyalvisitors.

As a conclusion, the different loyalty segments of repeat visitors are shown inFigure 3.

Compared with Figure 2, the locations of different segments in Figure 3 are moreexact. That is because Oppermann has specified the frequency of repeat behaviour fordifferent levels of destination loyalty.

Characteristics of repeat visitation as a measure of destination loyaltyThere are plenty of methods to measure behavioural or attitudinal loyalty. As early as in1978, Jacoby and Chestnut (1978) described about 53 methods to measure loyalty. All ofthem are quite complicated and contain numerous and serious problems. They pointedout two levels of analysis of loyalty: micro- (individual) and macro- (aggregated) levels( Jacoby and Chestnut, 1978). The micro-level is linked with attitudes answeringquestions as to why the customer is loyal and what kind of variables affect his or herloyalty to a certain brand or destination. The macro-level measures only the behaviour –the outcome of attitude. Jones and Sasser (1995) have proposed three measures of loyalty.The first one has to do with measuring behavioural and two others attitudinal loyalty:

. customer’s primary behaviour – recency, frequency and amount of purchase;

. customer’s secondary behaviour – customer referrals, endorsements andspreading the word (WOM); and

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. customer’s intent to repurchase – is the customer ready to repurchase in thefuture.

In tourism literature, several authors have adopted a similar approach. Oppermann(2000) measured the number of revisits, Petrick (2004) measured the number of revisits,WOM and intentions to revisit, Chen and Gursoy (2001) used tourist’s willingness torecommend a destination as an indicator of their loyalty. Valle et al. (2006) havesuccessfully tested a hypothesis that revisiting intention and willingness torecommend are adequate measures of destination loyalty intention.

Owing to the difficulties in measuring affective loyalty, behavioural measures aregenerally utilised more often to measure loyalty (Petrick, 2004). Also, Oppermann (2000)stated that while a composite (behavioural þ attitudinal) measurement of loyalty couldbe the most comprehensive one, it is not necessarily the most practical, since themeasurement of attitudinal loyalty calls for very lengthy questionnaires. Therefore, hesuggested using behavioural characteristics of destination visitation for measuringdestination loyalty. More specifically, he proposed two characteristics: the number ofvisits and frequency of revisits. He presumed that since destination selection and tripplanning are high-involved decisions, spurious loyalty is quite unlikely to occur and,therefore, most of behaviourally loyal visitors are emotionally loyal at the same time.

Still, based on Figure 3, one could conclude that repeat visitation could express theexistence of an attitudinal (the customer likes the destination), functional (it is somehowuseful for the customer to visit the destination), inert (the customer is used to visiting afamiliar and safe destination) and also forced destination loyalty (the visitor is executinga task). It means that in contrast to the statement of Oppermann, the authors of thispaper suppose that the proportion of spuriously loyal visitors could not be so small.

Figure 3.Segments of visitors

based on loyalty

Str

engt

h of

the

posi

tive

attit

ude

Length of the period between revisits

Norevisits

Less than2,2 years

Hig

hLo

wLatentloyalty

Notloyal

Loyal

Somewhatloyal

2,2–3,5years

More than3,5 years

Spuriously loyal:- Inertly loyal- Functionally loyal

- Forced to be loyal

Veryloyal

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Nevertheless, conventional quantitative methods are too limited and restricted todetect different segments of repeat visitors. For example, the traditional statistics ontourist flows, such as border and accommodation statistics do not provide researcherswith information concerning the choice of destination. Also, in majority of EuropeanUnion member states, as well in Estonia, border statistics are no longer recorded.Accommodation statistics often have problems due to the tax violation intentions inEastern European and other countries and overnight stays do not show the dailygeographical movement of persons (Ahas et al., 2008). Besides, to the best knowledge ofthe authors of this paper, only some European countries officially collect data aboutrepeat visitations. Therefore, new technological solutions are vital in order to overcomethe shortage of data for the analysis of repeat visitations. In the following partof the paper, we explore the ability of the PMP method to segment visitors according tothe loyalty types shown in Figure 3.

Passive mobile positioning method and dataMethodThere are different methods and approaches for identifying the location of mobiletelephones. Technical solutions vary from handset-based systems with special telephonesoftware to satellite navigation and peer-to-peer positioning tools using Bluetooth. PMPdata concerns the location of phones in network cells that is automatically stored in thememory of service providers (Ahas and Mark, 2005; Ahas et al., 2008). This data sourceoffers a good potential for monitoring the geography and mobility of the population,since mobile phones are widespread and similar standardised data can be used aroundthe globe. Issues of privacy and surveillance are very important aspects of any mobilepositioning data.

For detecting and segmenting tourists in this study, we use data from the billingmemory of roaming calls of the mobile operator Eesti Mobiil Telefon (EMT). Every callactivity has its record with descriptive information such as the time, place, duration,etc. Positium LBS has developed special software Positium Data Mediator forgathering data from systems of mobile operators. The program enables to address theprivacy of subscribers and to hide business secret sensitive issues within a mobileoperator system. The quality control and sampling is handled outside of operatorssystem in Positium LBS server.

The steps of collecting and processing the passive positioning data for this studyare the following:

(1) Positium Data Mediator collects data about the call activities of selectedroaming service users from the billing memory. The selection can berandom from all users or targeted to specific user groups. Call activity isdefined as any active use of telephone: calls, SMS, MMS, GPRS incoming andoutgoing.

(2) This database is processed to make the data pseudonymous, real phonenumbers are transformed by a special one-way algorithm into unique ID,so there would be no computable link to any real persons.

(3) Pseudonymous data are obtained from the operator’s system and transferred tothe servers of Data Mediator. The entries include the following parametersfor every call activity:

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. the pseudonymous ID number for the telephone;

. the time of the call activity;

. the cell ID with the geographical coordinates of the antenna; and

. nationality – the country of origin (contract) of the telephone.

(4) The country of the SIM card registration network is considered the country oforigin or nationality of the person.

(5) The unique ID of the person remains valid as long as this person does notchange his/her phone number. In this way, it is possible to identify repeatingvisits.

(6) The visitor flows in network cells are interpolated geographically for samplingpurposes using accommodation statistics, border crossing data and aquestionnaire survey. After sampling procedures, the data processing iscompleted in Positium Data Mediator. The next steps are user specific.

The segmentation of foreign visitors is based on individual pseudonymous datarecords. Every record consists of the number of call activities, from this informationis calculated the number of days stayed in Estonia. From visitor day data, uniquevisits to Estonia are calculated. A single visit is considered a compact period (numberof days) when the visitor conducts call activities in Estonia. As telephone use is notfrequent for all users, there are often days with no phone use during a visit. If thereare single days without any calls within a compact visit, they are included in the visit(period). Large gaps between call activities are considered a period of time when thevisitor is not staying in the country. A threshold of seven days is used for thispurpose. If the interval without any calls is longer than seven days, the followingperiod is considered a separate visit. If the interval is shorter, the entire period isconsidered a single visit. The study of frequencies of call activities in Estonia showedthat majority (88.8 per cent) of calls were made with an interval of 24 hours. Thosecalls made within 24 hours were probably made during the same (single) visit toEstonia. The rest of 11.2 per cent of calls were distributed over all the study period ofthree years.

Finally, every telephone (pseudonymous ID) in the database has a list of visiteddays and list of unique visits. This data is used for detecting visiting behaviour andfurther for segmentation.

Detecting repeat visitsFor the detection of repeated visits, the research team developed a framework fordetecting and classifying repeat visits on the basis of time interval (Tiru et al., 2010b).A repeat visit is detected if the same telephone (pseudonymous ID) visits Estoniaseveral times. Certainly, the chosen criterion for determining a single visit (seven days)is the preliminary proxy. There is a need to analyse the visiting frequencies of differentnationalities and destinations and to develop a statistical model for selecting the bestinterval for repeat visits.

With such a framework, we identified the persons (pseudonymous IDs) who visitedEstonia more than once within the study period of 4.5 years. The visualisation of thevisiting pattern of one frequent visitor is shown in Figure 4 with one randomly selectedphone. The person made four visits to Estonia within the timeframe covered.

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DataThis paper is based on the data from EMT, the largest Estonian mobile operator,which covers nearly 99.9 per cent of the total land area of Estonia. EMT operates withEricsson network system and Positium Data Mediator is linked to this. Thegeographical preciseness of the positions was determined with Cell ID (Figure 5). Thestudy period was 25 April 2005 till 30 September 2009. Altogether more than 32 millioncall activities with 650,000 ID-s were processed for this paper.

Mobile positioning data involves an issue related to cross-border radio-coverage.Telephones not leaving physically country 1 can be linked with antenna in country 2.This can be accidental or planned use of a “foreign” network. There are specific caseswhen some user groups tend to use “foreign” networks. One example are the sailors,who use any radio coverage from coastal areas nearby. Positium LBS has a developedframework for detecting and excluding those calls and ID-s from the database. Thisframework analyses the number of the calls in border areas (cells) and inland and themovement characteristics of a phone (ID). The foreign visitors who most probablydid not step on the Estonian soil, but used roaming service outside Estonia such as

Figure 4.An example of the visitingprofile (call activities andtiming) of one repeatvisitor

Gapbetween

visits

Gapbetween

visits

Visitation detection

Gapbetween

visits

Gapbetween

visits

First visit Second visit Third visit Fourth visit Fifth visitTime

Cal

ls p

er d

ay

Figure 5.Distribution of EMTantennae networkin Estonia

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passing sailors, accidental border area roaming users are excluded from the analysis.They constitute roughly 11 per cent of all visits and 9.4 per cent of all visitors.

Owing to privacy issues, the database does not contain personal information aboutthe respondents, but only a randomly given ID for every phone. The research teamtogether with the mobile operator and the Estonian Data Protection Inspectoratechecked carefully the accordance of data use with the Estonian legislation and EUdirectives (Directive 95/46/EC, 1995; Directive, 2002/58/EC, 2002). The abovementionedstudy and discussions concluded that the personal privacy of respondents is protected.There is no personal information connected with data and the generalisation level of theanalysis does not allow the identification of single persons on geographical or temporalgrounds. It is not possible to extract individual movement tracks from the data.Nevertheless, there is a concern about issues of privacy and ethics, as any use of mobilepositioning data is very sensitive in this respect.

Results and discussionSpecifying segments of repeat visitorsThe data and methodology used to assess repeating visitors provides following basicstatistics for operations:

. number of repeat visitors;

. number and the sequence of visits made by a single visitor;

. the duration of visit and gap between different visits (based on starting andending dates of visits);

. the nationality of the visitor (based on the country of origin of SIM card); and

. geographical locations of visits: what places were visited (based on cell ID-s).

Based on the presented statistics, it was possible to reveal the following differentsegments of repeat visitors: loyal and not loyal; somewhat loyal, loyal and very loyal;transit; long-term; one-day and visitors who are loyal to certain events.

Loyal and not loyal visitors. Those persons who have conducted more than one visitto Estonia represent 31.2 per cent of all visitors. Data indicate that 68.8 per cent ofvisitors in Estonia are not loyal or latently loyal. The PMP method does not allowdistinguishing latently loyal visitors from not loyal visitors. In the case of loyalvisitors, there could be a rather large share of spuriously loyal visitors who should beexcluded. As mentioned before, it is possible to divide spuriously loyal visitors intothree sub-segments: inertly loyal, functionally loyal and forced to be loyal. With PMPmethod, it is not possible to detect inert loyalty. But ability to find other sub-segmentsis analysed as follows.

Transit visitors (sailors, long haulers and others) are forced to be loyal. Their visitswere made during a limited time and to areas which are considered as “transit”corridors and can be extracted from all visits (Figure 6). For example, Latvianstravelling from Riga to Tallinn to travel by ferry to Helsinki; long-haulers drivingfrom Latvia to Russia in Southern Estonia and in Via Baltica road; seamen who aregoing ashore for a short period of time in Paldiski, Tallinn or other ports innorth-western coast of Estonia; Tallinn airport transfer (though small number). Transitvisitors represent 29.7 per cent of all repeat visitors and 9.3 per cent of all visitors.It must be taken into account that those visitors can also visit Estonia for other

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purposes previously or afterwards. Transit visits are short (depending on a corridor,three to 24 hours).

Though almost one-third of all repeating visitors have made a transit visit toEstonia, only 7.4 per cent of those visitors have travelled to Estonia only on transitvisits. Still, it is not reasonable to make any assumptions based on transit visitsbecause the methodology used does not prove that the visits were actually transitvisits, rather than there is no proof that they were of some other type. For example,only 19.7 per cent of repeating visitors whose at least one visit was a transit visit wasthe first visit to Estonia following other types of visits which refers to a contraryassumption that functional visitors return to Estonia on other purposes.

Long-term visitors to Estonia represent mostly foreigners closely connected toEstonia for some reasons. On the one hand, they are very loyal but, on the other hand,they could be functionally loyal. This group represents foreign workers, students and,to some extent Estonians, using foreign phones for some reason. It is difficult to definehow to detect “long-term” visits as the used criteria could be different. The overalllength of stay can be based on the total visit duration over the period (54 months) orbased on the duration per running year. Residency is defined in most countries as livingin the country for over 182 days (26 weeks) per year. The problem with methodology inthis case is that many foreigners working or studying in Estonia do not use foreignphones, thus these long-term visitors represent only a part of actual foreigners inEstonia. Therefore, it is reasonable to use a much shorter period to define long-termvisitors.

Figure 6.Transit visits in corridorsdefined as transit areas

Density of transit-visits

0 visits per day

Note: Broadcasting areas of antennae are calculated based on Voronoi tessellation cells

1000 visits per day

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Only 0.5 per cent of foreign visitors stayed in Estonia for longer than half a yearwithin the period covered by our dataset (Figure 7). About 6.1 per cent of all repeatingvisitors (1.9 per cent of all visitors) stayed in Estonia on an average for over four weeks.

We view one-day visitors as functionally loyal visitors. One-day visits to Estonia areusually made to Tallinn by shopping tourists from Finland. There are also transittourists whose visit duration in transit corridors is usually ,24 hours. Many businessvisitors also probably visit Estonia (mostly Tallinn) for a single day. The criteria fordifferentiating between shopping-tourists and business visitors amongst one-dayvisitors can be rather complicated as there is no specific pattern of spatio-temporalbehaviour that can be identified. Also, it is very difficult to distinguish businessvisitors from shoppers because both visiting patterns to Estonia are alike. The twomain distinctions can be assumed for both visitor types still beside one-day criteria:shopping-tourists visit Tallinn rather frequently; they do not leave central Tallinnwhere there are special shopping areas for Finnish tourists and many offices forbusiness visitors. Therefore, the combination of frequent visitors who spend mostlyonly one-day in central Tallinn central can be set as criteria for this group. Withouttransit visitors, there are about 1.6 per cent of repeat visitors (0.5 per cent of all visitors)whose length of stay is usually only one-day and who can, thus, be considered asfunctionally loyal shopping or business visitors.

Some repeat visitors are loyal to a specific event. In former studies, we have learnedthat there are some regular events which make very loyal customers return in everyyear (Kuusik et al., 2010). They are functionally loyal because they have a certainreason (an event) for visiting Estonia. For detecting those visitors, 34 periodic eventswere discovered and analysed with the help of PMP using the following procedure:

. Antennas with abnormalities in the number of call activities during the period ofthe study were detected.

. In total, 172 events were discovered by comparing the geographical locations ofdetected antennas and the time periods of abnormalities with the informationobtainable in the internet about the time and location of different events.

. A closer analysis of those events revealed that out of 172 events, 83 were uniqueand from those 34 were periodic (have taken place 2-5 times, altogether 121times).

Figure 7.Distribution of durations

of visits of repeatingvisitors in weeks (per cent

of all repeating visitors)

70.0

60.0

50.0

40.0

30.0

20.0

10.0

0.01 3 5 7 9 11 13 15 17 19 21 23 25 27+

Weeks

Sha

re o

f rep

eatin

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%)

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. In case of periodic events, geographically near located antennas which wereaffected by a larger number of visitors than usually were marked as eventcovering antennas.

. The ID-s that made call activities during the event in the marked antennae wereconsidered as event visitors.

. All visits of those filtered visitors are singled out from the main visitationdatabase of study.

It was revealed that 0.8 per cent of repeat visitors (0.2 per cent of all visitors) whovisited Estonia only when certain events took place and they visited the location whereevents took place. So, one may expect that those visitors were loyal to the events.

Somewhat loyal, loyal and very loyal visitors. After the exclusion of functionally andforced to be loyal visitors, only committed and inertly loyal repeat visitors should beleft. The PMP method provides us with data about the number of visits and timeperiods between visits. The relatively short period of the study (54 months) limits thepossibilities to distinguish somewhat loyal visitors. Following Oppermann’s approach,one could detect very few somewhat loyal visitors (0.3 per cent of repeat visitors)whose time interval between visits is longer than 192 weeks or 3.5 years. In addition,there is also a limited number of “loyal visitors” (1.0 per cent of repeat visitors) whosetime period between visits is from 114 to 192 weeks (see lines O1 and O2 in Figure 8).

However, it is shown in Figure 8 that there are no visually detectable breakpointsfor segmentation of repeat visitors in the points Oppermann suggested. Instead, thereare clear peaks seen to distinguish repeat visitors whose average periods betweenvisits are shorter than 52 or 108 weeks (one or two years). So, we decided to rescaleOppermann’s approach and assume that the visitors whose average period betweenvisits is longer than two years (right form line L2 in Figure 8) are somewhat loyal (6.5per cent of repeat visitors and 2.0 per cent of total visitors) and the visitors who revisitEstonia on average every year (between lines L1 and L2 in Figure 8) are loyal (19.2 percent of repeat visitors and 6 per cent of total visitors). Thus, 36.1 per cent of repeatvisitors (11.3 per cent of all visitors) should be called very loyal visitors because theyvisit Estonia very frequently – at least once per year. It could be caused by thecloseness of neighbours. It is relatively less time consuming for repeat visitors fromFinland, Latvia or even Sweden to come to Estonia compared with the Oppermann’s

Figure 8.Average periods betweenvisits to Estonia

0

10,000

5,000

15,000

20,000

25,000

Weeks

Num

ber

of v

isito

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L1

L2 O1 O2

1 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221

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case about visitors to Australia. It means also that the nature of visits in case ofEstonia may be different from the case of holiday resort Oppermann analysed. Figure 8shows that peaks appear with certain regularity and annual is the strongestregularity pattern. It may be caused by the regularity of holidays, visits to fixed eventshappening annually, national holidays, etc.

It is also important to remember that all the three last segments could include alsoinertly loyal visitors who are not detectable with the PMP method and also otherpossible sub-segments of visitors we are not aware of as yet. Figure 9 shows thedifferent types of repeat visitors of Estonia and their distribution.

The distribution of visitors of Estonia reveals the problems in distinguishingbetween latently loyal and not loyal visitors by using the PMP method. But wesucceeded in detecting spuriously and forced to be loyal groups of visitors. Data aboutthe visiting frequency allowed us to differentiate between somewhat loyal, loyal andvery loyal visitors of Estonia.

Conclusion and policy implicationsIn this paper, we tried to show how the improvements in the mobile positioning technology(technological innovation) provides a basis for the novel segmentation of repeat visitors(marketing innovation) and, thus, leads to improvements in the destination marketingpolicy making of the country (organisational innovation). The theoretical novelty ofthe paper consists in the creation of the innovation model for the destination marketing ofthe country and providing the new approach to the segmentation of repeat visitors.

In the process of model building, we used the four-dimensional model of innovationin services by den Hertog (2000) as the point of departure. However, in contrast to theden Hertog model, the initiating role was given to the technological innovation – in ourcase, the PMP method as the innovation enabling factor. PMP provides a database ofcharacteristics of repeat visitors like their nationality, visiting frequency, timing, etc.

Figure 9.Distribution of visitors

of Estonia intothe loyalty segments

Str

engt

h of

the

posi

tive

attit

ude

Length of the period between revisits

Norevisits

Till 1 year

Hig

hLo

w

Latentlyloyal

Notloyal

Loyal6.0%

Somewhatloyal2.0%

1-2 yearsMore than2 years

Spuriously loyal (11.9%):

- Functionally loyal-Workers and students 1.9%-One-day visitors 0.5%-Event visitors 0.2%

- Forced to be loyal 9.3%

Veryloyal

11.3%

68.8%

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Those characteristics could be used in order to identify new segments of repeat visitorsand thus it serves as the input to the process of improvement of destination marketingpolicy making.

Another theoretical novelty of the paper concerns the improvement of the existingclassification and constructing the new approach to the segmentation of repeat visitors.The authors extended Dick and Basu’s (1994) two-dimensional approach to loyalty bydeepening the classification of spurious loyalty and loyalty into more detailed groups.This classification was later used as the basis for classifying repeat visitors. Amongthe spuriously loyal visitors, we distinguished inertly, functionally and forced to beloyal segments of visitors. The group of loyal visitors was divided into somewhat loyaland very loyal groups of visitors. It provided a more detailed and diverse overview ofthe different groups of visitors.

In the empirical part of the paper, the results clearly indicate to the high potential ofthe use of the PMP method in order to improve the quality of data about tourism flowsin Estonia. The PMP method enables to observe and measure the duration, timing,density, seasonality and dynamics of visitations. Moreover, it allows alsodistinguishing repeat visitors. Repeat visitors could be segmented by their countriesof origin, frequency of visitation, seasonality, etc. In addition, the local destinations andevents most loved by repeat visitors and their movement trajectories could beidentified. The diverse dataset provided by the PMP allows implementing the moredetailed classification of segments of repeat visitors proposed by us.

The use of the PMP method provides prerequisites for creating the new focus indestination marketing of the country. The Estonian Government could diversify itstourism strategy by addressing multiple segments of repeat visitors. Even if the aim ofthe Estonian tourism policy has been to extend the duration of the repeat visits, it wasimpossible to measure the outcome of the policy so far. Consequently, it was alsoimpossible to set clear tactical goals as to how to target different segments of repeatvisitors. The PMP method could be used as a tool to obtain valuable feedbackinformation and, therefore, implement more specific tactics to extend the duration ofrepeat visits of multiple segments of visitors.

Our current analysis already succeeded in identifying the segments of repeat visitorslike sailors and long-distance car drivers. They have a potential to be turned into loyalclients to Estonia’s service providers (shopping centres, taxis serving harbours, mobileoperators serving their phones, etc.). Estonia’s tourism strategy currently completelyfails to address those issues. Adequately developed policy instruments targeting thosesegments may turn these visitors more useful for Estonia. For example, they could beinvolved into promoting Estonia by spreading the message in their native countries.This is only one out of many ways how to use the technological innovation of datacollection process in the improvement of destination marketing.

LimitationsWe are aware of the numerous limitations concerning the PMP method. Thoselimitations can be divided into four major groups: privacy, availability of datasampling and geographical precision. Privacy issues are the most critical aspect inusing the data, because mobile phones become very intimate objects for users. Owingto the privacy issues, we do not include personal features of phone users in our PMPdataset. Second, the availability of data is also a crucial issue. Mobile operators

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“sell trust” to their subscribers and due to privacy concerns and business secrets, theydo not want to expose data for researchers willingly.

The third aspect regarding the limitations is related to sampling. PMP data is a newsource of data and, therefore, raises many questions about sampling. The basicsampling question is: who uses phones? There are huge differences in phone penetrationbetween different age groups and nationalities. Other questions are related to the use ofphones during travelling and the prices of roaming service (Ahas, 2010). The specialalgorithms were developed by Positium LBS to compensate for the possible biases inmobile positioning data (Tiru et al., 2010a). The algorithm is upgraded with indexes froman annual phone use survey. In calculating repeated visitations, the length of periodwhen a person keeps the same phone number is also crucial. In today’s business practice,people tend to keep the same number even when they change their operator and the EUregulation supports the policy of “keeping the same phone numbers”. It certainly helpsidentify repeat visitations.

The fourth limitation is related to the problem of the network antennae beingdistributed unequally throughout the country and with the network coverage alsobeing different, there is an unequal spatial accuracy – dense regions such as urbanareas and roads with heavy traffic have much higher antennae density than ruralareas.

The algorithm developed for calculating repeated visitations (Tiru et al., 2010a) alsoneeds development and critical evaluation. The length of the reference period isimportant, as a longer period makes it possible to detect more repeated visitations. Theinterval between two separate trips today is seven days, but there is a need to developthis fixed frame into a more flexible and “personalised” calculation method.

Future researchBeing aware of all the abovementioned limitations, the PMP method and data used inthis study is still a promising source for marketing studies and opens up newperspectives for application. The study had an exploratory character focusing on theuse of the new technological solutions in the determining segments of repeat visitors.The paper presents only a very simple identification of segments based on a fewparameters. The future research needs to be oriented already toward applying methodsof multivariate statistics and econometrics on the data in order to support statisticallythe existence of segments of repeat visitors outlined by the authors.

Also, the method offers opportunities of going much deeper. Different criteria couldbe combined to extract specific visitor segments. Already current research revealeddifferences in peaks compared to Oppermann’s results. They could be explained by thedifferent distances between the host country and the countries of origin of visitors,period of vacation, cultural events, national holidays, etc. Therefore, future research isneeded toward the deeper analysis of visitors by their country of origin, motivation andtiming of visit, etc. Focusing on the motivation of visits enables to analyse the impactof changes in the socio-economic and political regulations both in host and homecountry on the behaviour of repeat visitors. For example, it is possible to detect theinfluence of price shocks (e.g. increase in alcohol, tobacco, fuel taxation) on the numberof functionally loyal repeat visitors and frequency of visits.

Owing to the exploratory character of research, our obtained results could be usedin describing the flows of repeat visitors on a general level, which is not sufficient

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to design specific aims of the tourism marketing strategy. Future research should bedirected to the process of figuring out the economic value of different segments of repeatvisitors. For these purposes, we may use the PMP method finding out the duration ofvisits of different segments of repeat visitors. It provides an indirect measure about theirexpenditures during their visit. Another way is to collect direct data from the repeatvisitors themselves by using questionnaires. It provides us with additional data todistinguish segments according their potential to use “word-of-mouth” among theirfellow citizens in their home country. By being aware of the economic value of differentsegments of repeat visitors, one could launch the specific tourism marketing strategybuilding process.

References

Ahas, R. (2010), “Mobile positioning in mobility studies”, in Buscher, M., Urry, J. andWitchger, K. (Eds), Mobile Methods, Routledge, London, pp. 183-99.

Ahas, R. and Mark, U. (2005), “Location based services: new challenges for planning and publicadministration?”, Futures, Vol. 37 No. 6, pp. 547-61.

Ahas, R., Aasa, A., Roose, A., Mark, U. and Silm, S. (2008), “Evaluating passive mobilepositioning data for tourism surveys: an Estonian case study”, Tourism Management,Vol. 29 No. 3, pp. 469-86.

Alegre, J. and Cladera, M. (2009), “Analysing the effect of satisfaction and previousvisits on tourist intentions to return”, European Journal of Marketing, Vol. 43 Nos 5/6,pp. 670-85.

Baloglu, S. (2001), “Image variations of Turkey by familiarity index: informational andexperimental dimensions”, Tourism Management, Vol. 22, pp. 127-33.

Bass, F.M. (1974), “The theory of stochastic preference and brand switching”, Journal ofmarketing Research, Vol. 11 No. 1, pp. 1-20.

Bowen, J.T. and Chen, S.-L. (2001), “The relationship between customer loyalty and customersatisfaction”, International Journal of Contemporary Hospitality Management, Vol. 13No. 5, pp. 213-17.

Buttle, F. (2004), Customer Relationship Management: Concepts and Tools, Elsevier, Amsterdam.

Chaudhuri, A. (1995), “Brand equity or double jeopardy”, Journal of Product & BrandManagement, Vol. 4 No. 1, pp. 26-32.

Chen, J.S. and Gursoy, D. (2001), “An investigadion of tourists’ destination loyalty andpreferences”, International Journal of Contemporary HospitalityManagement, Vol. 13 No. 2,pp. 79-85.

Copeland, M.T. (1923), “Relation of consumer buying habits to marketing methods”,Harvard Business Review, Vol. 1, pp. 282-9.

Cunningham, R.M. (1956), “Brand loyalty: what, where, how much?”, Harward Business Review,Vol. 34, pp. 116-28.

Day, G.S. (1969), “A two-dimensional concept of brand loyalty”, Journal of Advertising Research,Vol. 9 No. 3, pp. 29-35.

den Hertog, P. (2000), “Knowledge-intensive business services as co-producers of innovation”,International Journal of Innovation Management, Vol. 4, pp. 491-528.

Dick, A. and Basu, K. (1994), “Customer loyalty: toward an integrated conceptual framework”,Journal of Marketing Science, Vol. 22 No. 2, pp. 99-113.

BJM6,3

396

Directive 95/46/EC (1995), “Directive 95/46/EC of the European Parliament and of the Council of24 October 1995 on the protection of individuals with regard to the processing of personaldata and on the free movement of such data”, available at: http://eur-ex.europa.eu/LexUriServ/LexUriServ. do?uri¼CELEX:31995L0046:EN:HTML

Directive 2002/58/EC (2002), “Directive 2002/58/EC of the European Parliament and of theCouncil of 12 July 2002 concerning the processing of personal data and the protection ofprivacy in the electronic communications sector (directive on privacy and electroniccommunications)”, available at: http://eur-lex.europa.eu/LexUriServ/site/en/oj/2002/l_201/l_ 20120020731 en00370 047.pdf

Djupe, P.A. (2000), “Religious brand loyalty and political loyalties”, Journal for the ScientificStudy of Religion, Vol. 39 No. 1, pp. 78-90.

Ehrenberg, A.S.C. (1974), “Repetitive advertising and the consumer”, Journal of AdvertisingResearch, Vol. 14 No. 2, pp. 25-34.

Farley, J.U. (1964), “Why does ‘brand loyalty’ vary over products?”, Journal of MarketingResearch, November, pp. 9-14.

Gallouj, F. and Savona, M. (2009), “Innovation in services: a review of the debate and a researchagenda”, Journal of Evolutionary Economics, Vol. 19 No. 2, pp. 149-72.

Gitelson, R.J. and Crompton, J.L. (1984), “Insights into the repeat vacation phenomenon”,Annals of Tourism Research, Vol. 11 No. 2, pp. 199-217.

Gronholdt, L., Martensen, A. and Kristensen, K. (2000), “The relationship between customersatisfaction and loyalty: cross-industry differences”, Total Quality Management, Vol. 11Nos 4-6, pp. 509-14.

Gummesson, E. (1999), Total Relationship Marketing, Butterworth-Heinemann, Oxford, p. 281.

Harary, F. and Lipstein, B. (1962), “The dynamics of brand loyalty: a Markovian approach”,Operations Research, Vol. 10 No. 1, pp. 19-40.

Hernandez-Lobato, L., Solis-Radilla, M.M., Moliner-Tena, A. and Sanchez-Garcia, J. (2006),“Tourism destination image, satisfaction and loyalty: a study in Ixtapa-Zihuatanejo”,Mexico Tourism Geographies, Vol. 8 No. 4, pp. 343-58.

Hofmeyr, J. and Rice, B. (2000), Commitment-led Marketing, Wiley, Chichester, p. 299.

Jacoby, J.A. (1971), “Model of multi-brand loyalty”, Journal of Advertising Research, Vol. 11 No. 3,pp. 25-31.

Jacoby, J. and Chestnut, R.W. (1978), Brand Loyalty Measurement and Management, Wiley,New York, NY, p. 157.

Jacoby, J. and Kyner, D.B. (1973), “Brand loyalty vs repeat purchasing behavior”, Journal ofMarketing Research, Vol. 10, pp. 1-9.

Jang, S. and Feng, R. (2007), “Temporal destination revisit intention: the effects of noveltyseeking and satisfaction”, Tourism Management, Vol. 28 No. 2, pp. 580-90.

Jones, T.O. and Sasser, W.E. Jr (1995), “Why satisfied costumers defect”, Harvard BusinessReview, Vol. 73 No. 6, pp. 88-99.

Kaplanidou, K. and Vogt, C. (2007), “The interrelationship between sport event and destinationimage and sport tourists’ behaviours”, Journal of Sport and Tourism, Vol. 12 Nos 3/4,pp. 183-206.

Kotler, P., Asplund, C., Rein, I. and Heider, D. (1999), Marketing Places Europe: AttractingInvestments, Industries, Residents and Visitors to European Cities, Communities,Regions and Nations, Pearson Education, London.

PMP for thesegmentation of

repeat visitors

397

Kozak, M. (2001), “Repeaters’ behavior at two distinct destinations”, Annals of Tourism Reseach,Vol. 28 No. 3, pp. 784-807.

Kuusik, A. and Varblane, U. (2009), “How to avoid customers leaving: the case of the Estoniantelecommunication industry”, Baltic Journal of Management, Vol. 4 No. 1, pp. 66-79.

Kuusik, A., Ahas, R. and Tiru, M. (2010), “The Ability of tourism events to generate destinationloyalty towards the country: an Estonian case study”, in Maeltsamees, S. and Reiljan, J. (Eds),Discussions of Estonian Economic Policy XVIII, Berliner Wissenchafts-Verlag, Berlin,pp. 140-55.

Kyle, G., Graefe, A., Manning, R. and Bacon, J. (2004), “Predictors of behavioral loyalty amonghikers along the appalachian trail”, Leisure Sciences, Vol. 26, pp. 99-118.

McConnell, D.J. (1968), “The development of brand loyalty: an experimental study”, Journal ofMarketing Research, Vol. 5, pp. 13-19.

Milman, A. and Pizam, A. (1995), “The role of awareness and familiarity with a destination: theCentral Florida case”, Journal of Travel Research, Vol. 33 No. 3, pp. 21-7.

Mitchell, V.W. and Greatorex, M. (1993), “Risk perception and reduction in the purchase ofconsumer services”, The Service Industries Journal, Vol. 13 No. 4, pp. 179-200.

Oliver, R.L. (1999), “Whence consumer loyalty?”, Journal of Marketing, Vol. 63, pp. 33-44.

Oppermann, M. (1998), “Destination threshold potential and the law of repeat visitation”,Journal of Travel Research, Vol. 37 No. 2, pp. 131-7.

Oppermann, M. (1999), “Predicting destination choice – a discussion of destination loyalty”,Journal of Vacation Marketing, Vol. 5 No. 1, pp. 51-65.

Oppermann, M. (2000), “Tourism destination loyalty”, Journal of Travel Research, Vol. 39,pp. 78-84.

Petrick, J.F. (2004), “Are loyal visitors desired visitors?”, Tourism Management, Vol. 25,pp. 463-70.

Petrick, J.F., Morais, D.D. and Norman, W.C. (2001), “An examination of the determinants ofentertainment vacationers’ intentions to revisit”, Journal of Travel Research, Vol. 40 No. 1,pp. 41-8.

Rainisto, S.K. (2003), “Success factors of place marketing: a study of place marketing practices innorthern Europe and the United States”, doctoral dissertation, Helsinki University ofTechnology, Institute of Strategy and International Business, Espoo.

Reichheld, F.F. (1993), “Loyalty-based management”, Harvard Business Review, Vol. 71 No. 2,pp. 64-72.

Reichheld, F.F. (2003), “The one number you need to grow”, Harward Business Review, Vol. 81No. 12, pp. 46-55.

Rosenberg, L. and Czepiel, J.A. (1984), “A marketing approach to customer retention”, Journal ofConsumer Marketing, Vol. 1, pp. 45-51.

Sheth, J.N. (1968), “A factor analytical model of brand loyalty”, Journal of Marketing Research,Vol. 5, pp. 395-404.

Skinner, H. (2008), “The emergence and development of place marketing’s confused identity”,Journal of Marketing Management, Vol. 24 Nos 9/10, pp. 915-28.

Tiru, M., Kuusik, A., Lamp, M.-L. and Ahas, R. (2010a), “LBS in marketing and tourismmanagement: measuring destination loyalty with mobile positioning data”, Journal ofLocation Based Services, Vol. 4 No. 2, pp. 120-40.

BJM6,3

398

Tiru, M., Saluveer, E., Ahas, R. and Aasa, A. (2010b), “Web-based monitoring tool for assessingspace-time mobility of tourists using mobile positioning data: Positium Barometer”,Journal of Urban Technology, Vol. 17 No. 1, pp. 71-89.

Tucker, W.T. (1964), “The development of brand loyalty”, Journal of Marketing Research,August, pp. 32-5.

Valle, P.O., Silva, J.A., Mendes, J. and Guerreiro, M. (2006), “Tourist satisfaction and destinationloyalty intention: a structural and categorical analysis”, International Journal of BusinessScience and Applied Management, Vol. 1 No. 1, pp. 25-44.

Wernerfelt, B. (1991), “Brand loyalty and market equilibrium”, Marketing Science, Vol. 10 No. 3,pp. 229-45.

About the authorsAndres Kuusik is a Lecturer of Marketing at the University of Tartu (Estonia). His main researchtopics include customer relationship management, customer behaviour, loyalty and branding.His latest research is mainly concerned with the customer loyalty problems intelecommunication sector Andres Kuusik is the corresponding author and can be contacted at:andres.kuusik@ut.ee

Margus Tiru is currently working at Positium LBS, a spin-off company of the University ofTartu, developing spatio-temporal behaviour research algorithms for applications in planning,marketing and economics. Margus Tiru is also a PhD student in the Mobility Research Group,University of Tartu, where his main research topic is the use of GIS algorithms based on datafrom different sources of ICT.

Rein Ahas is a Professor of Human Geography in the Department of Geography, Faculty ofScience and Technology, University of Tartu (Estonia). His main research topics include mobilitystudies, tourism geography and mobile positioning-based methods in geography.

Urmas Varblane is a Professor of International Business and Innovation and Vice-dean ofResearch in the Faculty of Economics and Business Administration, University of Tartu(Estonia). His main research topics include innovation process and policy, knowledge andtechnology transfer and impact of foreign direct investments on host country.

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