Choices of segmentation bases

Despite the application of segmentation, some critical concerns have been expressed in the literature about the bases used to segment markets. Any criteria choices and measurements need to be evaluated with care. This research has classified market segmentation approaches into four key attributes. First, the descriptive attributes are frequently associated with geographic and socio-demographic segmentation in order to identify ‘who buys.’ Second, the behavioural elements explain what one does and how one does it. Third, psychographic bases demonstrate ‘why they do it’. Finally, the benefit-sought segmentation seeks to understand the reasons ‘why’ people do the things they do (Table 2-1).

Table 2 - 1 Attributes of market segmentation approaches

Attributes Market segmentation approach Purposes of investigations Descriptive attributes Geographic segmentation

Demographic segmentation

To identify ‘who they are’

Behavioural attributes Usage segmentation To understand ‘what they do’,

‘when they do’, and ‘how they

Many marketers use descriptive variables such as geographic and demographic attributes to describe and target customers. This focus is upon understanding who the customer is.

I. Geographic segmentation

A geographic approach involves segmenting consumers into regions, cities or neighbourhoods on the assumption that the majority in a pre-determined area would be likely to react to a particular offer in the same way (McDonald and Dunbar, 2009). With the growth of globalisation, geographic segmentation has become important when firms want to enter international markets (Steenkamp and Ter Hofstede, 2002). There are two key geographic approaches in marketing research: the location of consumers, and the location of resources (Table 2-2).

Table 2 - 2 Geographic-based variables The location of tourists

Area types Metropolitan area, city, town, village, province, country, continent, region urban, sub-urban, rural

Economic regions EU/Non-EU; Developing countries, Emerging economic countries, non-developing countries.

Distance 0-50 km or 51-100 km; near-home or far-home; short-haul or long-haul, respectively

The attractive resources

Types of destination Costal, rural, urban, sea, nature, historical place Climates Winter, Summer, Warm, Snow, Monsoon Source: Modified from McDonald and Dunbar (2009)

First, the location of consumers includes area types, economic regions and travel distances.

These variables are associated with differences in legal, linguistic, communication media, and distribution channels. These approaches are essential for some businesses such as ski resorts, airline businesses and airports. For example, one airline classified its consumers

of geographic delineation, airlines can bring a ‘local flavour’ to their customers, such as menus suitable for local tastes (McDonald and Dunbar, 2009). In this example, geographic segmentation is used to help vary the organisation’s product mix. Second, a number of studies have been carried out on purchasing behaviours in different places (Hamilton et al., 2005). For example, Jarratt (1998) investigated consumer attitudes towards retail trading areas. Consumers were classified by place of purchase: inside or outside of their resident city. The sample demonstrated the relationships between consumers’ attitudes regarding destination choices.

It can be seen that the advantage of geographic segmentation is the simplicity of measurement and access to information. This approach is suitable for some businesses where the data desired is not complicated, and is often used as a foundation for consumer profiles. The use of the geographic approach alone, however, cannot explain causation, and it fails to explain consumer behaviours, which represents data with important further uses.

Geographic segmentation, therefore, is often combined with other variables.

II. Demographic segmentation

Demographics, or socio-demographic segmentation, is usually used to classify customers in terms of their physical and social characteristics, such as age, gender, income, occupation, social class and family life cycle. Besides these demographics, nationality, race, culture, sub-culture, religion and language are also frequently used for consumer classification in marketing research. In addition, relationships between socio-economic, socio-cultural variables and consumption behaviours have been of interest to marketers (Table 2-3).

Table 2 - 3 Demographic variables

Demographic Age, gender, family-life cycle Socio-economic Income, education, occupation

Socio-cultural Nationality, culture, religion, race, sub-cultures Source: Modified from McDonald and Dunbar (2009)

Demographic segmentation is essential for marketers to recognise who the targets are, so that they can communicate with them efficiently. Several studies have indicated associations in socio-demographic and travel behaviours, expenditures and consumption patterns (Bojanic, 2011; Gokovali et al., 2007; Hsu and Lee, 2002). For example, Bojanic (2011) reported that discretionary income correlated with family life cycle (people’s age,

getting married, having children and losing spouses). In addition, Palmer (2012) used consumer expenditure to determine spending patterns within the family life cycle segmentation. Their study revealed that there was a relationship between family life cycle segmentation and expenditure on travel. Some argue, however, that socio-economic criteria are too simplistic and that consumers may be motivated by other factors such as needs, wants or influences (Greene et al., 1989; Yankelovich and Meer, 2006).

There are many advantages of using demographic bases. Marketers prefer to use demographic segmentation because it helps them to identify a target group easily.

Moreover, researchers can collect data conveniently from secondary sources such as a statistics centres, public resources or the Internet. Users, however, have become more individual and act differently than before, so using only information from the descriptive attributes may not be sufficient to develop viable marketing strategies.

2.2.1.2 Behavioural attributes

I. Behaviour-based segmentation

Behavioural attributes aim to group consumers according to their consumption of particular products (Swarbrooke and Horner, 1999). Behavioural segmentation was first demonstrated by Twedt (1964), who provided a basic framework for segmenting people into groups based on usage categories: i.e. heavy-users, light-users and non-users. In the beginning, the qualities of behaviour segmentation research were concerned with issues such as the inefficiency of selection of segmentation scales, and the difficulty of measurement (Wind and Green, 1974). Many marketing studies have used behavioural criteria because these bases can provide more detail than the descriptive attributes (Gunter and Furnham, 1992; Yankelovich and Meer, 2006). For example, Park et al. (2010) classified a group of luxury tourist shoppers based on frequency of shopping and the importance of shopping, in order to understand the reasons why they chose a particular destination. Three types of tourist were identified: infrequent shoppers, sometime shoppers, and frequent shoppers. The target groups were then clustered, based on the degree of importance of shopping, which are non-shoppers, neutral shoppers, and great shoppers. Multi-level segmentation offer a member of advantages over other studies since they provide several dimensions of tourist behaviour.

Since the concept of behaviour-based segmentation was recognised as a valid measurement to identify consumer behaviours, different models have been developed and used in various forms. Several studies have focused on building predictive models of customer behaviour. These models aim to be built for specific groups in order to overcome the difficulties related to one-to-one marketing and personalisation applications (Faraone et al., 2012). Two significant variable bases – need base segmentation and the FRM model – have been established to respond to this transformation. For example, the RFM (Recency, Frequency, and Monetary) segmentation is created for direct marketing regard as a tool for targeting and prediction. Marketers use RFM analysis to mine databases in order to identify the customers who spend the most money and create the biggest value for enterprises (Shim et al., 2012). It is claimed that the RFM analytic model is a very effective value analysis for customer segmentation (Kaymak 2001). Its advantage is to extract characteristics of customers by using fewer criteria (a three-dimension of Recency, Frequency, and Monetary) as cluster attributes so as to reduce the complexity of the customer value analysis model.

2.2.1.3 Psychographic attributes

Psychographic segmentation is a process to classify customers by intrapersonal factors such as perceptions, needs, wants, values, emotion and risk. These bases could assist tourism organisations to understand the reasons why tourists travel and what they were expecting when they were on trips. Despite the fact that a large body of tourism literature uses psychographics to provide markets with more insightful information than geographic and socio-demographic segmentation, the predictive power of these approaches in terms of actual purchasing behaviour remains limited (Yankelovich and Meer, 2006).

One of the most widely popularised approaches for market segmentation, according to psychographic segmentation is the “Values and Lifestyles” scale (VALS) developed by Mitchell (1983). The VALS programme aims to identify typical demographics and buying patterns (Gunter and Furnham, 2014). Consumers are grouped into eight segments:

Survivors, Innovators, Thinkers, Believers, Achievers, Strivers, Experiencers and Makers.

These approaches are still in use today. For instance, Kotler et al. (2010) classified consumers from poor countries (Bottom Of the Pyramid) using the VALS system (Table 2-4), identifying four different groups of shoppers and behaviours. The results show that

groups of customers from dissimilar economic backgrounds and shopping attitudes exhibit differences in consumption and behaviour patterns.

Table 2 - 4 VALs of BOPs Types Definitions

Believers Believers are conservative consumers with a strong belief in traditional values, love their family and communities, have a predictable consumption pattern, and have high brand loyalty

Strivers They are driven by social approval, pursue achievement to impress their colleagues, but have limited supporting resources to move forward; in addition, they buy products to mimic the wealthy.

Makers This group of people like to express themselves through concrete activities, prefer practical and functional products and are not impressed with emotional values.

Survivor They focus on meeting basic needs rather than fulfilling desires because their material resources are lower than other segments. They are cautious consumers who will always look for bargains.

Source: Modified from Kotler et al. (2010)

2.1.2.4 Lifestyle segmentation

Lifestyle segmentation has been extended to the interpretation of consumer behaviour related to similarities in patterns of living, spending time and money (European Travel Commission, 2007a). Lifestyle variables are related directly to purchasing behaviour because they demonstrate more particularly the needs and values that are involved in consumer performance (Blythe, 2008). More importantly, this approach provides rich descriptive detail to develop customer profiles, such as those related to family orientation, price consciousness, self-indulgence and religious beliefs (Blythe, 2008). Researchers, therefore, prefer lifestyle variables to describe consumers compared to traditional demographic variables (Gunter and Furnham, 1992). For example, Spencer and Holecek (2007) segmented tourists based on their lifestyle to explain why tourists behave as they do and why they hold their current attitudes. These variables are helpful in terms of understanding the distinct types of tourist purchasing behaviours and to highlight marketing support. A drawback of using lifestyle segmentation as a criterion to classify tourists, however, is that it is complicated and thus requires more time and money to research (Seaton and Bennett, 2004).

In an alternative to VALS, lifestyle segmentation can be identified through activities, interests and opinions (AIO): Activities report behaviour associated with membership in each event; Interest refers to the degree of excitement; and Opinion is a belief in something

were shopping based on their travel activities and motivations. Three types of shoppers were identified: the Outdoor tourists, the Cultural and Sports tourists, and the Entertainment tourists. The understanding of activity participation can offer data for marketers to forecast volumes of consumers or the spending of money in a market.

Lifestyle segmentation has limitations in terms of the stability of each cohort, however.

Buyers do not necessarily display consistent buying behaviours. Changes in behaviour can occur during the decision-making process which in turn, could cause some problems for marketers for long-term planning (Legoherel, 1998).

2.2.1.5 Benefit attributes

A benefit sought is defined, within tourism marketing, as a process of determining the environmental attributes that encourage an individual to visit a destination (European Travel Commission, 2007b). As the interests of market segmentation shifted from the external person-oriented characteristics to internal consumer characteristics, marketers have been encouraged to find reasons for buying behaviour in respect to attitude, values and needs. These variables are adopted to explain decision-making in relation to a marketplace (Molera and Albaladejo, 2007) and consumer preferences for retail attributes (Kang et al., 2012). The results of benefit segmentation can help explain the way in which people make decisions and choose a destination. In addition, marketers often use benefit segmentation to design tourism products, and to develop facilities and marketing strategies (Lee et al., 2006; Frochot and Morrison, 2000). These outcomes help destination marketers to design and modify facilities and activities to meet the needs of consumers (Frochot and Morrison, 2001). Wells et al., (2010) suggested that benefit sought is a more powerful basis for brand choice. They also revealed the idea that demographic attributes are not very effective in the case of brand choice and in price selection.

Benefit-based variables are considered as a set of needs that cause a person to participate in activities and are related to destination selection. Marketers often use these variables to identify what a customer is seeking and what drives them to a destination (Lee et al., 2006;

Frochot and Morrison, 2000). The study of benefit segmentation aimed to encourage consumer experiences that would persuade consumers to integrate shopping with other travel activities (Park and Yoon, 2009). This could provide a contribution for marketers to make decisions about product developments, prices and distribution. Shoppers often have

more than one motive for choosing a destination, therefore, it may be difficult for marketers to identify all the motivations behind consumption. This could cause confusion in explaining consumer behaviour. Solving this complexity requires sophisticated techniques to measure and analyse data.