Tourism Market Segmentation in Context of Nepal

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1 Tourism Market Segmentation in Context of Nepal Jivan Nepali Narayan Pd. Kandel IOE, Pulchowk Campus, Lalitpur IOE, Pulchowk Campus, Lalitpur (June, 2013) ABSTRACT As millions more travel abroad each year, the competition to attract these visitors becomes ever fiercer. Yet the money spent by destinations on capturing their interest can be easily wasted if not properly channeled according to a comprehensive new report on tourism market segmentation. Market segmentation is crucial in making sure that national resources are used in the most effective way. Everything they do media selection, destination positioning, branding, editorial, visuals, etc. revolves around the segments that have been identified as the most important. This is why monitoring and managing a portfolio of target segments that meet a destinations tourism objectives has become a critical function. This paper argues that the market segmentation process must be driven by the strategic imperatives of the destination. Further, through adopting a creative combination of post-hoc and a-priori approaches to segmenting markets, destinations may identify emerging niche markets not yet targeted by competitor destinations being helpful for the Government of Nepal to set proper overall market strategies for the tourism within the nation as well as in the international communities. Keywords: Tourism, Market Segmentation, Marketing Strategy, a-priori, data-driven, Data Mining INTRODUCTION The evolution of information technology has enabled collection and storage of huge amounts of data. With the entrance of each tourist, huge amount of data is generated in immigration department each day. As price of data storage are going reducing and processing power are going affordable, we can implement various visualization tools in order to find the similar characteristic group. Segmentation is the process of finding small group bearing similar behavior characteristics. It may be based on geographical, demographical, psychographic or behavioristic. Segmentation can be useful for any business, but this paper will describe why it is valuable to the tourisms industry. Since tourism industry are keen to implementing tourist centric enterprise, they

2 are named with various data segmentation tools. The effective segmentation project consists of several phases 1 : 1. Defining the Problem to be solved Before starting segmentation, we have to define for what purpose we are going to segment the data so that unique data mining model can be defined. Taking to business people is the best way to define the problem to be solved. 2. Assembling and Preprocessing Data If we have to access the data from relational database or from flat file or from spreadsheet or from other sources, we have to perform effective data preprocessing process which involves data extraction, cleaning, enrichment, aggregation. 3. Modeling In this phase the data is presented to a segmentation software which may follow several data mining processes. Sometime the variables chosen are not appropriates ones, and in this case the analyst has to go back and make some changes to the data he is using. 4. Interpreting the Results The results should be brought together into a coherent presentation and presented to business people. The model should be evaluated with respect to problem solving objectives. 5. Applying the Results The ultimate goal of segmentation is finding the group of people bearing similar behavior and address the interest of that group by lunching effective marketing strategies to increase their satisfaction. The five step described here isn t strict, because segmentation process itself isn t linear. This means that one can move back and forth between different phases because the next phase in the sequence often depend on the outcomes associated with preceding one. REASONS FOR APPLYING SEMENTATION IN TOURISM MARKET For more than 25 years segmentation has been proclaimed to be the cornerstone of successful marketing. Ever since the publication of Theodore Levitt s seminal text, segmentation has continued to be promoted as the sine qua non of successful marketing 4. Levitt s original assertion that if you re not thinking segments, you re not thinking marketing has clearly stood the test of time. Put simply, segmentation is critical to competitive advantage and should underpin every decision that needs to be made about all aspects of the tourism marketing mix. A real tourism marketing strategy needs to start with the tourist visitor, not the destination or product. It is clear that the visitor market, by definition, is not homogenous and every visitor or potential visitor does not want the same thing. This immediately gives rise to a segmentation issue. The recognition that the needs and requirements of the visitor market are heterogeneous rather than homogenous should then become the starting point in developing value propositions that are unambiguously targeted at distinct and identifiable groups of visitors who are all similarly motivated in their decision to visit a destination.

3 Implementing a tourism marketing plan based on needs-based segmentation is difficult, but not impossible 2. Yet, many destinations have still not developed effective segmentation strategies, largely because many of the conventional techniques are no longer suitable for an increasingly sophisticated, discerning and fragmented tourism market. THE DATA TO BE SEGMENTED A huge amount of the data generated by immigration department can t be analyze using traditional techniques, by using manual data analysis. This is why different data mining techniques ought to be applied. As mentioned before information about the visiting of each tourist were stored on the database. Tourist details records include their demographic characteristics along with purpose of visit, total duration stay and tentative places of visit. These information help us to find out the best places for particular group according to age, gender, type of visit or in country prospective. These data are often not suitable for segmentation as goal of segmentation is to find the pattern based on the group rather than each tourist 3. So, raw data should be collected and aggregated on group level. These information also help on addressing the need of the visitor with less miss rate. SEGMENTATION APPROACHES Commonsense Segmentation Market segments can be derived in many different ways. All segmentation approaches can be classified as being either a priori (commonsense) segmentation approaches or a posteriori (post hoc, data-driven) segmentation approaches. The names are indicative of the nature of these two approaches. In the first case destination management is aware of the segmentation criterion that will produce a potentially useful grouping (commonsense) in advance, before the analysis is undertaken (a priori). In the second case destination management relies on the analysis of the data (data-driven) to gain insight into the market structure and decides after the analysis (a posteriori, post hoc) which segmentation base or grouping is the most suitable one. Commonsense segmentation has a long history in tourism research with many authors referring to it as profiling. As early as 1970 tourism researchers did investigate systematic differences between commonsense segments with a publication titled Study Shows Older People Travel More and Go Farther (author unknown) appearing in the Journal of Travel Research. Typical examples of areas in which commonsense segmentation approaches are regularly used include profiling respondents based on their country of origin, profiling certain kinds of tourists (e.g., culture tourists, eco-tourists) and profiling tourists 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 were among the first segmentation schemes to be used. The segments generated by this method can be compared based on: socio-demographic personal characteristics (age, gender), behavioral characteristics (duration of stay, travel party, kind of holiday, accommodation, expenditures) and psychographic personal characteristics (travel motives). Data-driven Segmentation Data-driven method can be taken as the segmentation to the field of marketing. While

4 acknowledging the value of geographic and socio-demographic information about consumers, information about benefits consumers seek form market segments. This approach requires groups of consumers to be formed on the basis of more than one characteristic and, consequently requiring different statistical techniques to be used. To sum up, the implications of all the foundations are the following 4 (1) data analysts and managers need to be aware of the exploratory nature of data-driven market segmentation and not over interpret the value of one single segmentation solution which was not based on thorough preliminary data structure analysis, (2) repeated computations of segmentation solutions can easily be undertaken to assess the stability of alternative solutions, (3) stability analysis will inform the data analyst and manager about the nature of the derived segmentation, whether it reveals true clusters, identified stable artificial groupings or represents an artificial construction of the most suitable grouping in a data set with very little structure, (4) it is not necessary for all segments derived from a segmentation solution to be valid. For a tourism destination searching for a niche, it is perfectly sufficient to have identified or constructed one market segment which has high external validity, (5) market segmentation is not independent. A successful market segmentation strategy is in line with the tourism destinations positioning and differentiation strategy, thus accounting for the particular strengths of the destination and the competitive environment, and (6) data-driven segmentation studies have to be repeated regularly to ensure validity of the insight gained into the market structure at any point in time. BUILDING DATA-DRIVEN MARKET SEGMENTATION TO AID EFFECTIVE DESTINATION MARKETING A data-driven segmentation study contains all the components of a commonsense 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 a clustering algorithm - is used to assign respondents to segments based on the similarity relationships between respondents The approach that we defined to make effective segmentation models is shown in table 4. In step 2a, the data analyst selects one or more segmentation algorithms. The predominant algorithms used in tourism research are k-means clustering and Ward s clustering. Ward s clustering is one form of hierarchical clustering procedures. Hierarchical more precisely agglomerative hierarchical - clustering procedures determine the similarity between each pair of two respondents and then choose which two respondents are most similar and places them into a group. This process is repeated until all respondents are in one single group.

5 Table 1: Steps in Data-driven Segmentation Step 1: Selection of the Segmentation Base (e.g. Travel Motivations, Vacation Activities) Step 2: Grouping of repondents Step 2(a): Selection of segmentation algorithms(s) Step 2(b): Stability Analysis Step 2(c): Computation of final segmentation solution economic criteria. There has therefore, been a growing emphasis in marketing on the human behavioral sciences which has led to segmentation approaches seeking to measure less tangible consumer characteristics such as lifestyle, personality, image and benefits. The following table presents a summary of the main approaches to market segmentation within marketing and consumer behaviour literature 5. Basis for Segmentation Step 3: Profiling (external validation) of segments by identifying in which personal characteristics segments differ significantly Step 4: Managerial assessments of the usefulness of the market segments (and formulation of targeted market activities) Early approaches to segmentation included the use of demographic, geographic and behavioral characteristics of consumers. However, there is evidence of changing consumer behaviour within contemporary affluent societies, including an increased emphasis on the personalization of consumer behaviour patterns, which are not well explained by socio-demographic and

6 Basis Geographic Demographic Psychographic/Lifestyle Benefits Usage Loyalty Situation Behavioural Description Dividing a market into different geographical units such as nation, states, regions, cities or neighborhoods. For example tourism markets may be segmented into international and domestic visitors. Dividing a market based on demographic variables such as age, gender, family size, family life-cycle, income, occupation, education, religion or nationality. For example Temples like Pashupatinath may be target for Hindu people while Chaitya, Gumba may be the target for Buddhist. Dividing markets based on consumer values, attitudes, interests, opinions. For example adventure tourism operators may target consumers who have a strong interest in outdoor pursuits, bungee, and paragliding while epicureans are the target market for food and wine trails and cooking schools. Dividing the market into groups according to the different benefits that consumers seek from the product or service. An example of benefit segmentation can be seen in the rise of spa resorts targeting consumers who seek rejuvenation and improved health and well being from their holiday experience. Dividing markets based on usage patterns such as non-user, ex-user, potential user, first-time user, regular user, high volume user. For example destination-marketing programs may use one message strategy to communicate with repeat visitors and a different approach for people who have never visited. Dividing markets based on brand loyalty to a particular hotel chain or destination. For example the establishment of the branches of famous hotels like Taj Hotel, Hotel Sheratan, Emirates Palace in Kathmandu, Pokhara, Chitwan, Bardia etc. Related to usage segmentation, situation segmentation divides markets on the basis of the consumption or purchase situation of consumers. For example travelers may find one destination suitable for a short break and a different style of holiday appropriate for a long holiday. Dividing markets based on consumer s knowledge of, attitude toward, uses for and responses to a product or service.

7 RESULTS & DISCUSSION The natural endowments and cultural heritage of Nepal provide a solid foundation for tourism market segments development. Specifically, poor marketing has led to inability to capture higher-value demand. This low-value demand has limited the incentives for new investments in infrastructure, discouraging innovation and adoption of best practices. Based on the data sets available from the Nepal Tourism Board and other Travel Agencies situated in Kathmandu & Lalitpur, we segmented the arrivals of international tourists on the basis of country of origin, purpose of visit & travel partners using Orange Data Visualization framework 7. Figure 1: Tourists Segmentation based on country of origin Figure 2: Tourists Segmentation based on purpose of visit Figure 3: Tourists Segmentation based on travel partner

8 Figure 4: Tourists Segmentation based on their satisfaction level As shown in figures, the segmentation of the customers into different clusters makes the better visualization about the tourists & more informatic showing different tourism market segments 8. According to the pie-chart of the purpose of visit in Figure 2, the tourists arriving for the rafting are very low, almost zero, whereas, there are world-class rafting river sites in Nepal including Trishuli, Bhotekoshi etc. So, the segmentation analysis helps the travel agencies, tourism boards, Government of Nepal to find the effective market areas within the tourism industry which could highly contribute to the country s economy unlike just 4.3% of total GDP as of CONCLUSIONS Market segmentation is a strategy any entity in the tourism industry can use to strengthen their competitive advantage by selecting the most suitable subgroup of tourists to specialize on and target. A wide variety of alternative techniques can be used to identify or construct segments. Approaches range from simple commonsense segmentations (where tourists are split on the basis of a predefined personal characteristic) to multidimensional data-driven approaches where a set of tourist characteristics is used as the basis for grouping 10. Once tourists are grouped using the correct and most suitable analytical techniques the resulting segmentation solution has to be assessed by the users (tourism managers) who will not only evaluate the segmentation solution but also the fit of potentially interesting segments with the strengths of the tourism destination. Tourism managers can benefit from market segmentation by using it actively as a method of market structure analysis. In doing so, they can gain valuable insight into the market and specific sections of the market and identify the most promising strategy to gain competitive advantage. Typically such a strategy will not only require market segmentation, but also product positioning. Both approaches will have to be evaluated in view of competitors segmentation and positioning choices to be successful. Segmentation solutions should be computed regularly to ensure that current market structure is captured. The market segmentation technique can be applied by any unit operating in tourism industry: hotels, travel agencies, tourist attractions, restaurants, local charities as well as the Government of Nepal. REFERENCES [1] Hanlan J., Fuller D., Wilde S. J. (2006). Segmenting tourism markets: a critical review. Southern Cross University, Melbourne, Australia. [2] Barahi S., Fan R., Hung P., Malla P., Yeung R. (2011). Tourism Cluster in Nepal - Microeconomics of Competitiveness. Harvard Business School, USA.

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