Segmentation studies

In order to gain insight in applications of segmentation techniques, a literature research is conducted. A considerable amount of literature has been published on consumer segmentation. Table 3 compares relevant consumer segmentation studies. The studies are classified on subject (shopping centers yes/no), variables and whether multi channel shopping is integrated. Omni channel buying behaviour has not been implemented in segmentation research yet, multi-channel shopping was integrated in some segmentation methods in 2004 and 2008. The studies on consumer segmentation are discussed below. For an explanation about the segmentation methods see sections 3.2 and 3.3.

TABLE 3, SEGMENTATION STUDIES

AREA VARIABLES AUTHOR (YEAR)

SHOPPING CENTER GEO GRAPHIC SOCIO- DEMOGRAPHIC PSYCHO GRAPHIC BEHAVIOUR BUYING PROCESS ACTIVITIES BENEFITS PRODUCT CATEGORIES MALL ATTRIBUTES MULTI CHANNEL

Bloch et al. (1994) x x x x x x

In 1994, Bloch, Ridgway & Dawson published a paper in which they identified shopping center related shopping orientations by exploring differences in shopping center habitat activity patterns. 600 consumers were divided into groups based on their shopping behaviour. These groups were clustered using hierarchical as well as non-hierarchical cluster analyses. First, Ward’s method is used to determine the number of clusters. Followed by forming clusters using a K-means cluster algorithm. With this research, four clusters of shopping center shoppers were formed, namely enthusiasts (higher than average value on every activity dimension), traditionalists (higher score than average on mall-focused activities and relatively high on product purchasing), grazers (high tendency to pass time in the mall browsing and eating) and minimalists (low participation in all activities). Significant clusters were found based on the intensity of mall shopping.

A few years later in 1996, Jarret identified a set of variables that is relevant and appropriate for shopper segmentation. Through a telephone interview, information was collected from 931 consumers in three Australian trading areas. With cluster analysis, six shopper types were identified based on the consumers’

importance of the shopping offer (variety, price, quality, comparative, shopping and value), the shopping environment (progressive, exciting, clean, attractive and interest) and the shopping service (friendly, helpful, parking and information). These shopper typologies are: ‘have to’ shoppers (low scores importance of shopping offer, service and environment), ‘moderate’ shoppers (moderate scores importance of shopping offer, service and environment), ‘service’ shoppers (moderate scores on importance of shopping offer and environment and high score on importance of shopping service) , ‘experiential’ shoppers (high scores on the importance of shopping offer, service and environment), practical shoppers (moderate scores on importance of shopping environment) and ‘product focused’ shoppers (moderate score on importance of shopping offer and low scores on importance of shopping service and environment). These developed shopper typologies were used to develop a retail strategy.

Another research was conducted by Frasquet, Gil & Mollá (2001), who used the consumer choice modelling method to analyse two objectives. The first objective was to analyse the perceived value on shopping-centre

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selection. The second objective was to investigate benefits of adopting a segmentation approach in the study of consumer preference for a shopping center. A survey was conducted in shopping centers of Valencia.

Segments were formed by analysing answers on twenty items of the value scale. In the first place, demographic segmentation criteria were chosen. This has two advantages. First, it produces segments which are easy to identify and measure, and second consumers’ wants and preferences are often linked to demographic characteristics. Ward’s method was used to choose the number of clusters, followed by K-means clustering for clustering the final segments. Variables which were used to identify the segments were age, occupation and marital status.

Reynolds, Ganesh & Luckett (2002) researched differences between factory outlets and traditional malls through consumer segmentation. The importance of mall attributes, mall essentials, entertainment and convenience were rated by respondents. Through a mall intercept study, data was collected from 1097 traditional mall shoppers and 827 outlet shoppers. A multistep-cluster analysis (first Ward’s method, then K-means) was conducted to form clusters. Through this research, five shopper segments were found for the traditional mall as well as the factory outlet ‘the enthusiasts’, ‘the basic’, ‘the apathetic’, ‘the destination’ and

‘the serious’. Unique for the factory outlet was the cluster ‘brand seekers’.

Multi channel shopping behaviour was studied by Bhatnagar and Ghose (2004). They used a latent class modelling approach to segment online shoppers based on their purchase behaviour across several product categories. Data was collected from 1330 respondents through an online survey. In the online survey, respondents had to evaluate online shops in general on 11 attributes. Within this study consumers are segmented based on benefits, to gain insight in benefits that respondents perceive from online shopping. For describing the segments, variables such as age, education, gender, income, marital status and internet experience were used. A Latent Class modelling approach was used for the segmentation research. An important finding of this research was that web shoppers do not consider getting the lowest price as an important attribute. Respondents who did search online often did not buy online because of their perception about security and sensitive information.

Another study on multi channel shopping behaviour was conducted by Keen, Wetzels, Ruyter & Feiberg (2004).

The research was conducted to investigate the structure for consumer preferences in making product purchases. According to this research, the structure of the consumer decision-making process depends on the retail format and price of the desired product. Keen et al. (2004) analysed three channels: shop, catalogue and the internet. Two product categories were considered, CD’s and personal computer. Data was collected from 281 shopping center shoppers in a suburb of Chicago with the shopping center intercept technique. Through conjoint analyses, the structure of the decision and the importance of attributes in the decision-making process were estimated. Clusters were formed through hierarchical as well as k-means clustering in two stages.

Interesting is the segmentation study of Ruiz, Chebat & Hansen (2004). This study is based on a methodology developed by Bloch et al. (1994). Shoppers are segmented on the basis of their performed activities during their shopping center visit. Variables such as perception, emotions and motivations were used to extend the data. 889 questionnaires were collected in a shopping center in Eastern Canada. A series of Yes and No questions related to activities performed in the shopping center during their visit were answered through a questionnaire among mall customers. Visitors were also asked about the frequency of their visits and the number of purchases. The p-median model was used to find a structure in the dataset and to estimate the optimal number of clusters. Chi-square tests were conducted to test the significance of the segments. The base variables for the segments were differences in activity patterns (do exercise, talk with other customers, browse, take a snack, go to the bank, unplanned purchase, purchase). Descriptive variables were used to describe segments and these variables are classified into these groups: geographic (postal code), socio-demographic (age, mother tongue, sex, annual income, education level, number of children under eighteen at home and occupation), psychographic (perceptions, emotions, atmospheric variables, approach avoidance reactions, motivations, non-economic costs) and related benefits sought.

Konuş et al. (2008) analysed the multi channel shopping behaviour of Dutch consumers through segmentation.

The segmentation study of Konuş et al. (2008) can therefore give a good basis for researching omni channel shopping behaviour during the customer journey of Dutch consumers. The research focuses on two phases of

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the buying process, the search and the purchase phase. Consumers are segmented based on their attitude towards several channels as search and purchase alternatives. A survey was conducted among 364 Dutch consumers in a research panel. Three types of channels (brick and mortar shop, the internet and catalogues) were evaluated by the consumers in terms of their appropriateness for the two phases. The latent-class analysis technique in combination with the Bayesian information criterion (BIC) was used and three segments were found. The multichannel enthusiasts, uninvolved shoppers and shop-focused consumers. Several descriptive variables (shopping enjoyment, loyalty and innovativeness) were used to predict which persons belong to which segments. The research did not find significant relationships with socio-demographics. This confirms prior findings that consumer behaviour is driven more by psychographics. The results demonstrate that segment membership is affected by hedonic and economic variables.

In 2009, Gilboa identified four shopper types based on the shopping behaviour of Isreali shopping center visitors. Behaviours were divided into three categories: visiting patterns, motivations for trips to the mall and activities engaged in during the visit. This segmentation study labelled four types of customers: disloyal, family bonders, minimalists and mall enthusiasts. Data from 636 Israeli consumers was obtained in order to form these shopper types. Research variables for shopping center visits were motivation, activities performed during the visit, visiting patterns and personal details. To find out whether Israeli consumers can be divided into distinct groups of consumers, a TwoStep cluster analysis was conducted. This analysis combines the hierarchical analysis method of Ward with the non-hierarchical k-means clustering procedure in order to optimize the cluster solutions. The TwoStep cluster analysis was most suitable for this study because both categorical and continuous variables can be used.

3.5 CONCLUSION

This chapter has investigated consumer segmentation, in order to answer the main question of this chapter:

‘How can the customer journey and omni channel shopping be implemented in consumer segmentation?’ The customer journey and omni channel shopping are explained in chapter 2. Now it is time to implement omni channel shopping in combination with the customer journey in the segmentation strategy.

In the recent years, there has been an increasing amount of literature on consumer segmentation. However, omni channel shopping behaviour is not researched (yet). In their study Konuş et al. (2008) found consumer typologies based on consumers’ channel orientation. This research was conducted over two phases of the shopping process, namely the purchase phase and the information search phase and they investigated three channels (brick and mortar shop, internet and catalogues). From this research we have learned how consumer segments can be formed based on channel orientation.

Within our research, the customer journey has five phases; stimulation, search for information, purchase, delivery and after sales service. For this research, both online and offline channels must be selected. Even though some channel behaviour is easy to predict, it is interesting to gain information about channel usage during the customer journey of consumer target groups. It is assumed that consumers choose from a set of channels for every phase separately.

Collected data can be analysed by several cluster analysis techniques, in order to discover omni channel consumer segments. There is no information about the shopper typologies before the segmentation study is conducted, therefore the segmentation base is post-hoc. First of all, variables for clustering need to be selected. From literature research we have learned that clustering consumers on socio-demographics does not result in interesting clusters. In addition, interesting cluster can be found by using information about consumers’ behaviour, activities and benefits. From studies we have learned that it is interesting to form clusters based on channel selection during several phases of the customer journey. TwoStep clustering is the most suitable method for analysing clusters in this research, because we probably need to handle a large dataset. Thereby, TwoStep clustering can handle several types of variables on different scales (ordinal, ratio and nominal values). In addition, it is not necessary to determine the number of clusters before cluster analysis take place.

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