Examples of personality characteristics in psychographic segmentation?

Why and how would you segment tourism markets?

Market Segmentation in TourismEvery tourist is different. Every tourist feels attracted by different tourist destinations, likes to engage in different activities while on vacation, makes use of different entertainment facilities and complains about different aspects of their vacation. While all tourists are different, some are more similar to each other than others: many people enjoy culture tourism, many tourists like to ski during theirwinter holiday and many tourists require entertainment facilities for children at thedestination. Acknowledging that every tourist is different and that tourism industrycannot possibly cater for each individual separately forms the basis of market segmentation.Smith (1956) introduces the concept of market segmentation as a strategy. He states that "Market segmentation […] consists of viewing a heterogeneous market (one characterized by divergent demand) as a number of smaller homogeneous markets". When segmenting a market, groups of individuals are developed which are similar with respect to some personal characteristic. The particular personal characteristic with respect to which similarity is explored is the segmentation criterion or segmentation base. Segmentation criteria / bases can be socio-demographics (for instance, old versus young tourists), behavioral variables (skiers versus sightseers) or psychographic variables (tourists motivated by rest and relation versus those motivated by action and challenges).Market segmentation can be applied by any unit operating in tourism industry: hotels, travel agencies, tourist attractions, restaurants, and local charities. A tourism destination is the entity for which market segmentation is conducted.The benefit of market segmentation lies in a tourist destination being able to specialize on the needs of a particular group and become the best in catering for this group. In doing so the destination gains a competitive advantage because (1) competition can be reduced from the global market to tourism destinationsspecializing on the same segment (e.g., all ecotourism destinations), (2) efforts can befocused on improving the product in a specific way rather than trying to provide all things to people at high cost (e.g., a family destination is unlikely to need extensive nightlife options), (3) marketing efforts can be focused by developing the most effective message for the segment targeted (e.g., a sun and fun message for young tourists traveling with friends) and by communicating the message through the most effective communication channel for the segment (e.g., in national geographic or other nature magazines for ecotourists), and finally, (4) tourist experiencing a vacation at a destination that suits their special needs are likely to be more satisfied with their stay and, consequently, revisit and advertise the destination among like-minded friends. Or, as Smith stated in his seminal paper (1956): "market segmentationtends to produce depth of market position in the segments that are effectively definedand penetrated. The [organization that] employs market segmentation strived to secure one or more wedge-shaped pieced [of the market cake]."The examples above demonstrate that the expected outcome from market segmentation is competitive advantage. Consequently, the aim of the actual segmentation task is to Group tourists in the way that is of most managerial value. In order for a segment to be managerially useful a number of requirements should be fulfilled:1. The segment should be distinct meaning that members of one segment should beas similar as possible to each other and as different as possible from othersegments.2. The segment should match the strengths of the tourism destination.3. The segment should be identifiable. While female travelers can be identified very easily, identification of those visitors who are motivated by rest and relaxation may not be as simple.4. The segment should be reachable in order to enable destination management to communicate effectively. For instance, surf tourists are likely to read surf magazines which could be used to advertise the destination.5. A segment should be suitable in size. This does not necessarily imply that a biggersegment is better. A tourism destination may choose to target a small niche segment that represents a large enough market for the particular destination and has the advantage of having very distinct requirements.The above criteria for the usefulness of segments have to be considered when one or more of many possible segments are chosen for active targeting.Market segments can be derived in many different ways. All segmentation approaches can be classified as being either a priori (commonsense) segmentationapproaches (Dolnicar 2004a ; Mazanec 2000) or a posteriori (post hoc, data-driven)segmentation approaches (Dolnicar 2004a; Mazanec 2000; Myers and Tauber 1977).The names are indicative of the nature of these two approaches. In the first casedestination management is aware of the segmentation criterion that will produce apotentially useful grouping (commonsense) in advance, before the analysis isundertaken (a priori). In the second case destination management relies on theanalysis of the data (data-driven) to gain insight into the market structure and decidesafter the analysis (a posteriori, post hoc) which segmentation base or grouping is themost suitable one.COMMONSENSE SEGMENTATIONIn the case of commonsense segmentation destination management informsthe data analyst about the personal characteristics believed to be most relevant forsplitting tourists into segments. The choice of personal characteristics can be drivenby experience with the local market or practical considerations. Most tourismdestinations, for instance, use country of origin as asegmentation criterion. Theyprofile tourists from different countries of origin and develop customized marketingstrategies for each country. Even if this method is not the most sophisticated, countryof origin segmentation offers major practical advantages of taking such an approach:most countries of origins speak a different language which requires customizedmessages to be developed anyway, each country of origin has different mediachannels.Commonsense segmentation has a long history in tourism research with manyauthors referring to it as profiling. As early as 1970 tourism researchers didinvestigate systematic differences between commonsense segments with a publicationtitled "Study Shows Older People Travel More and Go Farther" (author unknown)appearing in the Journal of Travel Research. A vast amount of commonsensesegmentation studies have been published since and are continuing to be published.4Dolnicar (2004a) concludes that commonsense segmentation remains the mostcommon form of segmentation study conducted in academic (and most likely alsoindustry) tourism research: 53 percent of all segmentation studies published in the last15 years in the main outlet for tourism segmentation research (the Journal of TravelResearch) were commonsense segmentation studies. Recent examples includeKashyap and Bojanic (2000), who split respondents into business and leisure touristsand investigates differences in value, quality and price perceptions, Israeli (2002),who compares destination images of disabled and not disabled tourists, Klemm(2002), who profiles in detail one particular ethnic minority in the UK with respect totheir vacation preferences, and McKercher (2002), who compares tourists who spendtheir main vacation at a destination with those who only stop on their way through.Other commonsense studies are discussed in Dolnicar (2005).Typical examples of areas in which commonsense segmentation approachesare regularly used include profiling respondents based on their country of origin,profiling certain kinds of tourists (e.g., culture tourists, ecotourists) and profilingtourists who spend a large amount of money at the destination (big spenders). In fact,geographical segmentation such as grouping tourists by the country of origin wereamong the first segmentation schemes to be used (Haley 1968).A step by step outline of commonsense segmentation is given in Figure 1.Commonsense segmentation consists of four distinct steps: first, a segmentationcriterion has to be chosen. For example, destination management may want to attracttourists from Australia. Country of origin represents the segmentation criterion in thiscase. In Step 2 all Australian tourist become members of segment 1 and all othertourists (or a more specific subset of other countries of origin) become segment 2members.Figure 1: Steps in commonsense segmentationAnalyses of variance, t-tests, Chi-square tests or binary logistic regressionsrepresent suitable techniques to test whether Australian tourists are significantlydifferent from other tourists in Step 3. Note that the kind of test used depends on thenumber of characteristics that are tested and the scale of the variables. If manyStep 1: Selection of the segmentation criterion(e.g. age, gender, $ spent, country of origin)Step 2: Grouping respondents into segments by assigning eachrespondent to the respective segmentStep 3: Profiling of segments by identifying in which personalcharacteristics segments differ significantlyStep 4: Managerial assessment of the usefulness of the marketsegments (and formulation of targeted marketing activities).5characteristics are available in the data set the computation of independent tests foreach characteristic overestimates the significance. Therefore, a Bonferroni correctionis necessary on each p-value to account for this systematic overestimation, orresearchers must choose methods, such as binary logistic regression, whichautomatically account for potential interaction effects between variables. The testchosen in Step 3 also needs to be appropriate for the scale of the data. If the profileregarding nominal (e.g., gender, type of vacation), binary (e.g., prior experience withthe destination on a yes - no scale) or ordinal (e.g., income groups, level of expressedsatisfaction) characteristics is tested, analysis of variance and t-tests are not theappropriate tests as they assume metric, normally distributed data. For some ordinaldata this can be shown, but should be demonstrated before a test for metric data isapplied.Finally, in Step 4 destination management has to evaluate whether or not thecommonsense segment of interest (e.g., Australian tourists) does represent anattractive market segment. This evaluation is made using the criteria outlined above.If the segment is attractive, destination management can proceed to customize theservice to best suit the segment needs and develop targeted marketing activities whichwill enable most effective communication with the segment.DATA-DRIVEN SEGMENTATIONData-driven segmentation studies do not have as long a history ascommonsense segmentation studies do. Haley (1968) introduces data-driven marketsegmentation to the field of marketing. While acknowledging the value of geographicand socio-demographic information about consumers, Haley criticizes commonsenseapproaches as being merely descriptive rather than being based on the actual cause ofdifference between individuals and instead proposed to use information about benefitsconsumers seek to form market segments. This approach requires groups ofconsumers to be formed on the basis of more than one characteristic and,consequently requiring different statistical techniques to be used. As Haley (p. 32)states,"All of these methods relate the ratings of each respondent to those of everyother respondent and then seek clusters of individuals with similar rating patterns."About one decade after Haley has proposed data-driven market segmentation,tourism researchers adopted the method and published the first data-drivensegmentation studies in tourism (Calantone, Schewe and Allen 1980; Goodrich 1980;Crask 1981; Mazanec 1984). A large number for data-driven segmentation studies hasbeen published since with recent examples including work by Bieger and Lässer(2002), who construct data-driven segments among Swiss population on the basis of8travel motivations. This study represents data-driven segmentation in its pure formbecause no pre-selection of respondents takes place before the segmentation study isconducted. Contrarily Hsu and Lee (2002) use a subset of the tourist population as astarting point: only motor coach travelers. Among motor coach travelers they furthersegment tourists in a data-driven manner by exploring systematic differences in 55motor coach selection attributes. Further examples are discussed in Dolnicar (2005).The large number of data-driven segmentation studies published in the pasttwo decades has led to a number of reviews of segmentation studies in tourism, someof which focus more on content, some on methodology.Frochot and Morrison (2000) review benefit segmentation studies in tourism.They conclude that benefit segmentation leads to valuable insights in tourism researchin the past, but recommend the following improvements: careful development of thebenefit statements used as the segmentation base (some benefits are generic, but manyare specific to the destination under study), informed choice of the timing (askingtourists before their vacation is less biased by the actual vacation experience), conductbenefit segmentation studies regularly to account for market dynamics and conductthem separately for different seasons.Dolnicar (2002), based on a subset of studies reviewer by Baumann (2000),analyzes methodological aspects of data-driven segmentation studies in tourismconcluding that only a small number of the available algorithms is used by tourismresearchers who prefer either the hierarchical Ward's algorithm or the k-meanspartitioning algorithm. Dolnicar also identifies a number of problematicmethodological standards that have developed in data-driven segmentation in tourism.To avoid data-driven segmentation studies that are of limited scientific and practicalvalue it is important for data analysts and users to be aware of a number of basicprinciples upon which data-driven segmentation is based. These foundations aredescribed in detail in the following section.Foundations of data-driven market segmentationFoundation 1: Market segmentation is an exploratory process. Many statisticaltechniques enable researchers to conduct test that provide one single correct answerfor a research question. For instance, if an analysis of variance is conducted ondestination brand image data, the test results inform the researcher whether or notthere is a significant difference in the way respondents from different countries oforigin perceive a destination. This test result is exactly the same, no matter how oftenthe analysis is repeated. This method is not the case in data-driven marketsegmentation. Market segmentation is a process of discovery, an exploratory process.Aldenderfer and Blashfield (1984) refer to clustering, the algorithm typically used indata-driven market segmentation in tourism, as "little more than plausible algorithmsthat can be used to create clusters of cases." Each algorithm produces a differentgrouping and even repeated computations of one algorithm will not lead to the samesegments. This point is very important to both researchers conducting data-drivenmarket segmentation and managers using segmentation results. As a consequence, thechoice of the segmentation algorithm and the parameters of the algorithm can and dohave a major impact on the results. Data analysts must be aware of the fact that theirselection of a data-driven segmentation procedure is "structure-imposing"(Aldenderfer and Blashfiled 1984) and that segmentation results from one algorithm9are unlike to have revealed the one and only true segmentation solution for any givendata set.Foundation 2: Market segments rarely occur naturally. The exploratory natureof market segmentation leads to a question which has rarely been discussed inmarketing or tourism research: are market segments real and is the data analyst's aimto identify such naturally occurring segment or are market segments an artificialconstruction of groups for a particular purpose. Different authors take distinctlydifferent positions on the matter. The seminal market structure analysis and marketsegmentation studies (Frank, Massy, and Wind 1972; Myers and Tauber 1977) implythat the aim of market segmentation is to find natural groupings. More recently,Mazanec (1997) and Wedel and Kamakura (1998) state explicitly that marketsegmentation typically means that artificial groupings of individuals are constructed.Empirically both cases can occur and represent to extremes on the continuumof highly structured to not structured data sets. These two extreme options have beenreferred to as "true clustering" and "constructive clustering" by Dolnicar and Leisch(2001).Conducting data-driven market segmentationA data-driven segmentation study contains all the components of acommonsense segmentation study. The way in which respondents are grouped is the only difference between the commonsense and the data-driven approach: in commonsense segmentation one criterion is selected which usually is one single variable such as age or gender or high versus low levels of tourism spending. In data driven segmentation a number of variables which ask respondents about different aspects of the same construct (e.g., a list of travel motives, a list of vacation activities) form the basis of segmentation and a procedure - in tourism research typically aclustering algorithm - is used to assign respondents to segments based on thesimilarity relationships between respondents. Figure 3 illustrates the additional stepsneeded for data-driven segmentation as steps 2a-2c.Figure 3: Steps in data-driven segmentationIn step 2a the data analyst selects one or more segmentation algorithms. Thepredominant algorithms used in tourism research are k-means clustering and Ward'sclustering. Ward's clustering is one form of hierarchical clustering procedures.Hierarchical - more precisely agglomerative hierarchical - clustering proceduresdetermine the similarity between each pair of two respondents and then choose whichtwo respondents are most similar and places them into a group. This process isrepeated until all respondents are in one single group. The disadvantage ofhierarchical algorithms is that they require computations of all pair-wise distances ateach step which can be a limiting factor when working with very large data sets. Thesecond most frequently used data-driven segmentation algorithm in tourism researchis k-means clustering. K-means clustering is an algorithm from the family ofpartitioning techniques. This technique does not require the computation of all pairwise distances. Instead the number of segments to be derived has to be stated inadvance. Random points drawn from the data set represent these segments. In eachStep 1: Selection of the segmentation base(e.g. travel motivations, vacation activities)Step 2: Grouping of respondentsStep 3: Profiling (external validation) of segments by identifyingin which personal characteristics segments differ significantlyStep 3: Managerial assessment of the usefulness of the marketsegments (and formulation of targeted marketing activities).Step 2a: Selection of segmentation algorithm(s)Step 2b: Stability analysisStep 2c: Computation of final segmentation solution13step of the iterative procedure the distance between each of the respondents and the"segment representatives" is computed and the respondent is assigned to the segmentthat best represents his or her responses. For example, if a five segment solution iscomputed, only five distance computations have to be calculated using partitioningtechniques as opposed to as many distance computations as there are respondents inthe sample when using hierarchical techniques.Although k-means and Ward's clustering dominate data-driven segmentationstudies in tourism, a large number of other algorithms is available to the data analyst:a wide range of alternative clustering algorithms (Everitt, Landau, and Leese 2001),neural networks (e.g., Mazanec 1992; Dolnicar 2002), bagged clustering (e.g.,Dolnicar and Leisch 2003), latent class analysis (e.g., Van der Ark and Richards2006), and finite mixture models (Wedel and Kamakura 1998).When selecting an algorithm the data analyst should be aware of theadvantages and disadvantages of the alternative methods and in particular the way inwhich they are known to impose structure on data. Most clustering algorithms allowthe data analyst to define which distance measure should be used. Again, a largenumber of alternative distance measures are available. The data analyst has theresponsibility to select a distance measure suitable for the data scale. For instance,metric and binary data can be analyzed using Euclidean distance. This choice is notnecessarily the case for ordinal data. For a detailed discussion of alternative distancemeasures see Everitt, Landau, and Leese (2001).Another point that should be noted while discussing the selection of a suitableclustering algorithm is the term "factor-cluster segmentation" which appears to havedeveloped in tourism research. Researchers using this approach typically select a largenumber of items, conduct factor analysis to reduce a large number of items to asmaller number of factors and subsequently use factor scores as the basis forsegmentation. This approach has two effects: (1) the original items are actually notused to segment. Consequently, resulting segments cannot be interpreted using theoriginal items, because they emerged from a heavily transformed data space. (3)Factor analyses typically explain between 50 and 60 percent of the informationcontained in the original items. Conducting factor analysis before clustering essentially means that 40 to 50 percent of information is lost. Direct clustering of original items is therefore preferable if the aim of the segmentation study is to develop segments based on the questions asked in the survey (benefits, motivations, and behavior). Sheppard (1996) compares cluster analysis with factor-cluster analysis methods and concludes that factor-cluster analysis is not suitable if the study's aim is to examine heterogeneity among tourists; factor analysis may be a valuable approach for the development of instruments for the entire population assuming homogeneity.Arabie and Hubert (1994) are less diplomatic by stating that "`tandem´ clustering is anoutmoded and statistically insupportable practice" because the nature of the data ischanged dramatically through a factor analytic transformation before segments are explored.Data analysts also should keep in mind that the number of variables that can be analyzed with a sample of a certain size is limited. Although there are no specific rules for non-parametric procedures, a rule of thumb proposed by Formann (1984) provides some helpful guidance: for the case of binary data (yes no questions) the minimal sample size should include no less than 2k cases (k = number of variables), preferably 5*2k of respondents.Finally, the most unresolved question in market segmentation remains how toselect the number of segments that best represents the data or most suitably splitsrespondents into managerially useful segments. A large number of heuristics exist to assess the optimal number of clusters but comparative studies show that no single oneof these indices is superior to the others. If the data is well structured, the correct number of clusters will be identified by most heuristic procedures. If the data is not well structured, which is typically the case in the social sciences, heuristics are not helpful to the data analyst. The approach the author finds most useful is based on the above mentioned concepts of segmentation (Figure 2) where data structure is the driving force and stability is the criterion. To determine the number of clusters using the stability criterion, a number of repeated computations are conducted and the agreement across alternative solutions is assessed. The number of clusters that leads to the most stable results over repeated computations wins.OTHER APPROACHES TO CREATING MARKET SEGMENTSAlthough the majority of market segmentation studies in tourism are typicallyclassified as being commonsense segmentation studies or data-driven segmentationstudies, combinations of both approaches are possible and may represent a usefulalternative for tourism managers to explore potentially attractive target segment fortheir purposes. Dolnicar (2004a) gives an overview of such alternative segmentationapproaches. The classification of these approaches (left side of Figure 5) assumes that a two-stage process is taken where the data analyst first creates a commonsense or adata-driven segmentation and then continues with an additional analysis afterwards.For instance, destination management could first split tourists based on their countryof origin and then in the second step either (1) search for distinct groups differing intheir travel motivations (which would represent a Concept 5 segmentation) or (2) split respondents into first time and repeat visitors (Concept 3).Figure 5: A systematics of market segmentation approaches (modified from Dolnicar, 2004a)Which group is described first?A subgroup of the total touristpopulation determined by data-drivensegmentation on multivariate basisA subgroup of the total touristpopulation determined by data-drivensegmentation on multivariate basisCONCEPT 1= commonsense= a priori segmentationCONCEPT 2= data-driven= a posteriori= post-hoc segmentationWhich groups are explored next?A subgroup determined by an a priorior common sense criterionA subgroup determined by data-drivensegmentation on multivariate basisCONCEPT 3commonsense /commonsensesegmentationCONCEPT 4data driven /commonsensesegmentationCONCEPT 5commonsense /data-drivensegmentationCONCEPT 6data-driven /data-drivensegmentationCONCEPT 7Types of touristemerge as cells from across-tabulation of twoindependentlyconductedsegmentation studieswhich could becommonsense ordata-driven.multaneousOf course, managers may be interested in exploring combinations of simultaneously constructed market segments. Combination methods are done by conducting two independent segmentation studies based on different segmentation bases and then simply cross-tabulating the resulting groups. For instance, destination management could construct segments based on motives and segments based on vacation activities independently based on the same data set and then investigate whether these two segmentations are associated and result in interesting vacationtypes. One example for such a simultaneous segmentation study is provided byDolnicar and Mazanec (2000).Note that while such alternative segmentation approaches are useful inexploring potentially interesting target segments they can also be used to externallyvalidate segments. For instance, if country of origin is used as an a priori segmentation criterion, researchers could investigate whether segments of tourists who differ with respect to their tourism motivations are associated with the country of origin grouping.CONCLUSIONMarket segmentation is a strategy any entity in the tourism industry can use tostrengthen their competitive advantage by selecting the most suitable subgroup oftourists to specialize on and target.A wide variety of alternative techniques can be used to identify or constructsegments. Approaches range from simple commonsense segmentations (wheretourists are split on the basis of a predefined personal characteristic) tomultidimensional data-driven approaches where a set of tourist characteristics is usedas the basis for grouping. Once tourists are grouped using the correct and mostsuitable analytical techniques the resulting segmentation solution has to be assessedby the users (tourism managers) who will not only evaluate the segmentation solutionper se but also the fit of potentially interesting segments with the strengths of thetourism destination.Tourism managers can benefit from market segmentation by using it activelyas a method of market structure analysis. In doing so, they can gain valuable insightinto the market and specific sections of the market and identify the most promisingstrategy to gain competitive advantage. Typically such a strategy will not only requiremarket segmentation, but also product positioning. Both approaches will have to beevaluated in view of competitors' segmentation and positioning choices to besuccessful. Segmentation solutions should be computed regularly to ensure thatcurrent market structure is captured.

Examples of personality characteristics in psychographic segmentation?

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