Abstract
We analyze why the impact of wine tourism in Spain lags behind France and Italy. We argue that a key factor is a lack of attraction of foreign enotourists, especially among tourists who visit a wine PDO region anyway. The international tourist profiles visiting such Spanish wine PDOs are identified and segmented with the ultimate objective of fine-tuning the gastro-touristic offer in these regions. In particular, an innovative two-step Latent Class Model (LCM) with covariates is developed. The first step concerns the sociodemographic profiles of potential international enotourists, which is therefore complementary to existing segmentations of actual enotourists. The second step uses touristic activities in order to profile the possible ways of reaching different types of tourists. The daily expenditure and the tourists' valuation are included as covariates, allowing to predict the probability of class membership, and thus to define priority target profiles for market positioning.
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Introduction and Motivation
International tourists visiting Spain are increasingly looking for new experiences, and gastronomy is one activity that can contribute to addressing these expectations (Berbel-Pineda et al., 2019; Pérez-Priego et al., 2019; Vorobiova et al., 2020; Castillo-Manzano et al., 2021; González-Reverté et al., 2022). The market for gastro-tourism creates several business opportunities (Dancausa et al., 2021), and this is true in particular for wine producing regions, especially those registered as Protected Designations of Origin or PDOs (Molleví et al., 2020). These PDOs create a factor of touristic attraction by linking the wine hallmark to a specific regional area where the tourist can see the full production process of the wine, taste the result, and experience a complete gastro-culinary experience in local restaurants (Marchini et al., 2014; Marco-Lajara et al., 2023).
Wine tourism has many selling points. In the first place, it can give excellent economic returns, especially from foreign visitors, not only for the winemakers themselves but for the tourism business in general (López-Guzmán et al., 2014; Tafel & Szolnoki, 2020). Moreover, it can dynamize rural regions that are not on the traditional tourism map, and is considered a sustainable branch of tourism that could help to alleviate some of the ecological and social pressure caused by more traditional forms of tourism (Ferrer et al., 2023; Martínez-Falcó et al., 2023a; Serra-Cantallops et al., 2021).
Since Spain is a major player both as a wine producer and as a tourism destination, there is an obvious synergy opportunity for wine tourism. However, this opportunity is at present only partially taken advantage of. As will be described in the first part of this contribution, Spain lags quite strongly behind other players on the wine tourism market, in particular France and Italy, both in terms of number of visitors and of economic return. It will be argued that Spanish wine tourism should place a stronger focus on the attraction of foreign visitors, in the first place by convincing foreigners who visit a wine PDO region anyway, for whatever original motivation, to actually participate in some form of wine tourism.
The second part of this contribution consists in a two-step segmentation of these potential foreign wine tourists, using the powerful Latent Class method. In the first step, a detailed identification and quantitative segmentation of the different types of such potential foreign wine tourists is carried out based on socioeconomic features, type of accommodation and the duration the visit. Moreover, the analysis incorporates daily spending and tourists' satisfaction with their visit as covariates, which serves as an additional innovative aspect. These external factors enable the prediction of class membership probability, thus aiding to define target profiles of interest for market positioning. The second segmentation step is based on the main types of touristic activities of interest, with the idea of fine-tuning the possible ways of reaching different tourist profiles with marketing material and offers of activities related to wine tourism based on their main types of (other) touristic activities of interest.
Thus, the research questions (RQ) that will be addressed are the following:
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RQ1: What is the current state of wine tourism in Spain, and how does it compare to France and Italy?
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RQ2: What segments can be identified among foreign visitors to Spanish wine PDO regions who declare an interest in gastronomic activities?
With respect to RQ1, the discussion will be argumentative, based on summary data from official organisms and the academic literature, without pretending to perform a comprehensive quantitative analysis. The focus will lie on the role of international tourists and the direct and indirect economic impact of wine tourism. In the case of indirect impact, we will stress the relation between wine tourism, quality reputation and the selling price of wine.
For RQ2, it should be stressed that we are interested in all foreign visitors who declare their main destination to lie within some Spanish wine PDO region, regardless of whether they actually participate in some form of wine tourism or not. The motivation is that we are interested in the potential for convincing foreigners who visit a Spanish wine PDO region anyway into realizing some form of wine tourism. As described above, RQ2 will be answered through a 2-step Latent Class analysis.
Ultimately, one would like to answer the following:
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RQ3: How can foreign visitors to Spanish wine PDO regions best be convinced to participate in some form of wine tourism activity?
While a full answer to RQ3 lies beyond the scope of this research, we will at least provide some hints, and in particular stress that even traditional beach tourists, who are usually thought of as not being interested in cultural or gastronomical activities, can potentially be reached and convinced to participate in wine tourism.
The structure of this contribution is as follows. In Section “Wine, Tourism, and Wine Tourism in Spain”, an overview is given of the current status of Spain as a tourist destination, as a wine producer, and especially as a wine tourism destination. The main causes of Spain’s enotourism lagging behind Italy and France are identified, and it is argued that the Spanish wine tourism sector should put a stronger effort in attracting foreigners who are visiting a Spanish wine PDO region anyway. An additional brief literature review is contained in Section “Tourism in Spain”, followed by a description of the Latent Class Model that has been applied to perform the segmentation, of the data and the cluster variables, as well as the covariates extracted from them, in Section “From gastrotourism to enoutourism”. The actual segmentation of foreign visitors to Spanish wine PDO regions, based on sociodemographic and accommodation features, first, and on activities and interests, second, is presented in Section “Wine and wine tourism: Spain versus France and Italy”. A general summary and conclusions are given in Section “International wine tourists: analysis and potential benefits”.
Wine, Tourism, and Wine Tourism in Spain
Tourism in Spain
Spain is one of the world’s foremost tourist destinations. In the pre-Covid years, it has become the world's second country both in terms of international arrivals and of income from tourism, with 84 million foreign visitors in 2019 spending an estimated 74,000 million euros (UNWTO, 2020). The year 2022 has shown a full recovery, earlier than expected (Exceltur, 2023). Tourism represents 11–12% of the national GDP and accounts for about 3 million direct jobs (Exceltur, 2023; INE, 2022; TURESPAÑA, 2020). In fact, in several Spanish regions, tourism is the leading economic sector and largely determines the structure of local employment and business networks. However, this does not mean that all is perfect on the Spanish tourism front. First, while tourism is well-developed in certain regions, such as the Canary and Balearic Islands, or the Costa del Sol region of Málaga and Marbella, many of Spain's rural regions lag behind in attracting visitors, and especially foreign visitors. Second, Spain's international tourism is highly seasonal (Gil-Alana et al., 2021) and still largely dominated by cultural city tourism in combination with the persistence of the traditional “sun-and-sand” model (Aguiló et al., 2005). These types of tourism are increasingly perceived as a source of social pressure (Álvarez-Sousa, 2018; García-Hernández et al., 2017; Martín et al., 2018) and ecological damage (Barreal et al., 2023; Cervelló-Royo & Peiró-Signes, 2015; Iamkovaia, 2021; Lazzari et al., 2021), while the economic income from this type of tourism is suboptimal (Bujosa et al., 2015). These issues have led to an increasing desire to develop more sustainable and higher-income forms of tourism with a larger geographical spread (Gobierno de España, 2019), a trend which is reinforced by tourists’ intentions as a consequence of the Covid pandemic (González-Reverté et al., 2022).
From gastrotourism to enoutourism
Within this tendency, gastrotourism is playing an increasingly important role. Gastrotourism is globally on the rise, and in Spain in particular it is one of the main attractors for international tourism. Indeed, estimates (KPMG, 2019) show that 15.5% of international tourism expenditure goes to gastronomy (in 2017), and 15% of international tourists declare gastronomy as a main motivation (in 2016). Luxury gastronomic activities, for instance linked to Michelin stars, have created a new touristic niche market (Castillo-Manzano et al., 2021), and many Spanish regions are trying to gradually replace their traditional “cheap & cheerful” touristic image to a more refined reputation in which gastronomic and culinary quality plays an important role (Daries et al., 2018; Kiatkawsin & Han, 2019). Vice versa, international tourism is playing a key role in the development of gastronomy as one of the new motors of the Spanish economy, which represents 33% of Spanish GDP and 18% of employment (KPMG, 2019).
One of the best-known components of gastrotourism is enotourism. Note that we here follow the UNWTO definition that “Eno-tourism or Wine Tourism (is) a sub-type of Gastronomy Tourism” (UNWTO, 2019), which does not necessarily coincide with how these are studied in the academic literature. For example, Hall and Mitchell (2006) define wine tourism and gastronomic tourism as two (separate but related) subsets of food tourism. In any case, it is clear that a relation exists (Etcheverria, 2016; Croce and Perri (2017), and for our research it suffices to accept that wine tourists are generally interested in gastronomic activities, and vice versa: that at least a significant proportion of tourists who express an interest in gastronomic activities consider wine tourism as a potential gastronomic activity.
Wine and wine tourism: Spain versus France and Italy
Wine-related tourism has grown tremendously in the past decade, worldwide and also in Spain. Spain is one of the world’s main wine-producing countries, ranking in the top-three by volume together with Italy and France (OIV, 2022): Spanish vineyards represent 13% of the world's total productive area, and produce approximately 15% of the world's volume of wine. Spanish wines have long had the reputation of being cheaper than, but also of lesser quality compared to, wines from other top-producing countries such as France or Italy. Considerable efforts have been undertaken in recent decades to modernize the Spanish wine sector (González & Dans, 2018; Martínez-Carrión & Medina-Albaladejo, 2010). These efforts are increasingly acknowledged by wine specialists, however it is probably fair to say that the general perception of Spain's wine quality still lags behind French or Italian wines, especially among non-experts who see country-of-origin as the main quality indicator (Sáenz-Navajas et al., 2014). But Spain is certainly a key player on the international wine market, with 76% of Spain’s voluminous wine production being exported.
The combination of Spain’s leading role in international tourism, the importance of gastrotourism as an attractor of Spain’s international tourism, and of enotourism as one of the most succesful forms of gastrotourism worldwide, as well as Spain’s strong position as a wine producer and exporter, seem to suggest that wine tourism should be booming business in Spain and attract many international visitors. This is in fact not the case. Wine tourism in Spain had a very hesitant start. Plans to develop wine tourism in the famous Rioja region started in the 1970s (López-Guzmán et al., 2013) and were first studied in the scientific literature in the early 1990s (Gilbert, 1992). However, a 2010 survey among wine producers in the Jerez/Sherry region still considered wine tourism to be virtually non-existent (López‐Guzmán et al., 2011). Things then quickly accelerated, and the Spanish Association of Wine Cities ACEVIN estimates that by 2017 a peak of 3.2 million people visited the Spanish wine routes (ACEVIN, 2020). However, this number is in fact rather low in comparison with France and Italy, see Table 1, while Table 2 shows some general indicators of the tourism and wine sector in these three countries. Note that these tables contain the latest available data unaffected by Covid. In France, Atout France, (2017) reports 10 million wine tourists for 2016, a number which has been “surpassed” since (Atout France, 2022). In the case of Italy, Città del Vino, (2022) estimates approximately 15 million visitors in 2019. Regardless of any difference in estimation methodology, the gap between France and Italy, on the hand, and Spain on the other, is clearly important. A similar observation holds in terms of economic impact. Vázquez Vicente et al., (2021) study a selected set of Spanish wine routes and find that wine tourism has a positive effect on economic growth on a selected set of wine routes, although there is no clear evidence that it also has a positive effect on employment generation. Quantitative estimates of the overall economic impact of wine tourism in Spain are rare. Two exceptions are (ACEVIN, 2020) and Martínez-Falcó et al., (2023b), which report very similar numbers for the direct income from wine tourism in Spain (expenses during the actual visit to a winery or museum) for the peak year 2019, namely 85 million € and 92 million €, respectively. The ACEVIN report (ACEVIN, 2020) estimates that this number should be multiplied by a factor of 3 to obtain the total economic impact of wine tourism, which would thus amount to roughly 250–280 million €. More detailed estimations for France, Italy and Germany, as reported in Tafel and Szolnoki, (2020), are 5.2, 2.6 and 5.0 billion €, a factor 10 to 20 higher than for Spain, with a more recent estimate for Italy of 2.5 billion € reported in Città del Vino, (2022).
International wine tourists: analysis and potential benefits
A key issue here is that Spanish wine tourism is still largely focused on domestic tourists. According to the same ACEVIN report (ACEVIN, 2020), the proportion of international visitors among all wine tourists in Spain lied around 25% in 2018 and 2019 (logically dropping to around 12% in 2020 due to the Covid pandemic). In France, the percentage of foreign wine tourists lies around 42% (Atout France, 2017), while for Italy, the percentage is estimated at 29% for the year 2021, a number reported as 33% lower than in 2019 due to the Covid situation (Città del Vino, 2022), see also (Colombini, 2015), which estimates foreign wine tourists in Italy at 30–40% already in 2014, and increasing. The importance of foreign tourism in Italian wine routes is also emphasized by Galletto, (2018).
It is also instructive to look at the percentage of international visitors who engage in any form of wine tourism. This is surprisingly low in Spain, with the exception of visitors to the Rioja wine region. For instance, the wine route with the strongest international orientation is the Marco de Jerez route in the Cadiz province. This wine route attracted 240,000 international visitors in 2019, representing 42% of all wine tourists in the region. However, these 240,000 international visitors participating in some activity related to wine tourism represent a mere 8% of all international tourists to the region, while numbers are even below 5% for other wine routes (Vázquez Vicente et al., 2021). In comparison, New World wine-producing countries such as Australia, New Zealand or Canada’s British Columbia broke through the threshold of convincing 10% of all international visitors to participate in some form of wine tourism two decades ago, with peaks of up to 40%, for instance, in South Australia (Williams & Dossa, 2003).
These low international numbers in Spanish wine tourism have different causes. One of them is a perception, shared by Spanish wineries and many researchers, that the effort should lie mainly on attracting domestic, and even largely local, “wine connoisseurs” (Cava Jiménez et al., 2022). The logic is that this focus requires a limited effort from the wineries’ side, for instance avoiding the need to train English-speaking personnel or to foresee accommodation, while such wine lovers are also likely to spend more money during their visit compared to amateurs, thus giving the highest return on investment. The impression is that this is especially true in comparison with foreign visitors, who must take into account the limitations on weight, liquids, and alcohol, in particular, which they are allowed to carry home (Alonso et al., 2015).
However, from the broader perspective of Spanish tourism in general, convincing international tourists who are in or near a Spanish Wine region anyway (regardless of their main motivation, be it cultural city tourism or beach tourism) to actually visit a winery or engage in some other activities related to wine tourism such as visiting a wine museum, would present many advantages, which can be broadly classified into two cross-related categories.
In the first place, it could contribute to relieving the social and ecologic pressure that mass tourism is putting on highly touristic cities such as Barcelona or Granada as well as the beach regions (Mediterrean mainland coast; Balearic and Canary Islands) and contribute to the development of more sustainable forms of tourism, of which wine tourism is considered an exponent (Ferrer et al., 2023; Serra-Cantallops et al., 2021). This could have a larger beneficial effect on rural sustainability, enabling residential resettlement and economic development of regions that are currently suffering depopulation (Hall et al., 2009; Sheridan et al., 2009; Tamarit, 2018).
Second, it could increase the profitability of wine-related businesses in a direct way, since, in spite of the airport limitations mentioned above, international wine tourists in fact tend to spend more than domestic ones (López-Guzmán et al., 2014). Indirect economic benefits are also likely for the Spanish wine industry, as a consequence of improving the image of Spanish wine at an international level, where (with the exception of the most famous regions such as Rioja and Ribera del Duero), as a national product, they are often still considered of lesser quality compared to Italy or France. For instance, Spanish wines are associated with personality traits such as sociable, funny, or nice, whereas Italian wines call up ideas of classy, stylish, elegant, sophisticated, and French wines evoke concepts of refined, delicate, complex, classy and cultured (Rojas-Méndez et al., 2018). In economic terms, Spain lies at the bottom-end of the average price per volume of exported wine amongst all major wine-exporting countries, far behind France and Italy, see Table 2, but also well behind Portugal or New World wine countries such as Argentina, Chile, or Australia (OIV, 2022; Parga-Dans & Alonzo, 2017). In this context, wine tourism could also contribute to the reputation of the Protected Designations of Origin (PDO) brand system, which could dynamize not only the wine sector’s revenues but also radiate secondary benefits from wine to other PDO products such as ham, cheese, or olive oil (Gómez & Molina, 2012; Barreal and Jannes, 2021), thus further contributing to the rural sustainability described above.
Towards attracting international tourists to wine tourism
The general argument of the current manuscript is thus that a major effort should be dedicated to engaging international tourists who are already visiting Spain, and especially Spanish wine PDO regions, into wine tourism. This task involves many challenges in terms of infrastructure, educating specialized and language-capable personnel, and seeking active involvement from local and regional public institutions (Marzo-Navarro and Pedraja‐Iglesias, 2009a; Alonso et al., 2015; Festa et al., 2020). A first step should consist in identifying the types of foreign visitors who could potentially be attracted into participating in some form of wine tourism. With this objective in mind, the research question that will be addressed in this manuscript consists in an identification and segmentation of the various profiles of international visitors to Spanish Wine PDOs, i.e.: of international travellers who declare their main destination to lie withing a Spanish Wine PDO region, regardless of their major motivation (cultural, beach tourism, business), with the only requirement that they declare an interest in gastronomic activities. Given the rudimentary state of foreign enotourism in Spain, as described above, this identification and segmentation is a crucial step in the process of further developing international wine tourism in Spain, and of the broader shift towards more profitable and especially more sustainable forms of tourism in Spain in general.
Two kinds of classifications are proposed. The first one is based on personal (gender, age, country of origin) and travel characteristics (season, length of stay, accommodation type, travel party, main method of transport, main purpose) and its main goal is to obtain a socio-economic and demographic profile of potential international wine tourists. It is worth insisting that this segmentation is of potential foreign wine tourists, i.e.: all foreigners who are visiting a Spanish wine PDO region for any reason, with the only condition that they declare an interest in gastronomic activities., and we therefore use general data about foreign tourists obtained by the Spanish National Statistics Institute. Obviously, the ultimate goal would be convert these potential wine tourists into actual wine tourists (see RQ3). It thus goes without saying that it would be extremely interesting to conduct detailed interviews with foreign wine tourists, for example asking how they learned about the wine tourism activity that they have participated in, or how satisfied they were with the provided information and with the actual wine-related activity, in order to identify the general strengths and weaknesses of the current wine tourism offer and strategy. However, such surveys are necessarily local (typically limited to one or a few wine regions), whereas we are interested in Spain as a whole. Moreover, as a first step, attracting foreign wine tourists requires identifying tourist segments within the potential market and prioritizing strategic target groups.
In this sense, our analysis is thus complementary to the many existing segmentations of actual wine tourists.
The second classification looks in more detail at the activity interests. The idea is to allow a fine-tuning of the marketing efforts aimed at convincing these foreign tourists to participate in any form of wine tourism, by focusing on their (other) activity interests and, for example, offering them combined packages, or at least simply knowing where to provide promotion material. In both cases, expenses and valuation are used as covariates in order to predict class membership. The idea of these covariates is to center on the importance of, first, economic return, by focusing on those tourists that are likely to have the highest spending; and second, of word-of-mouth and social media marketing as a possible source of the promotion of international wine tourism in Spain, and by extension of the quality of Spanish wines and of the importance of PDOs in general, by focusing on those tourists who have shown a larger degree of satisfaction.
The segmentation method that will be applied is Latent Class Modelling (LCM) based on more than 300,000 questionnaires recorded between 2015 and 2019. LCM is a powerful analytical method, and in combination with the large amount of data analysed, this makes the results extremely robust, while the inclusion of the expenses and satisfaction covariates is an innovative element in wine tourism research.
Further Literature Review
Wine tourism
Global wine tourism is an active and increasing field of research. Classic reviews include Mitchell and Hall, (2006) and Carlsen, (2004). The relation between wine and tourism, and their relevance for rural development in the Mediterranean region, were identified more than 20 years ago by Hall and Mitchell, (2000). More recently, the connection between wineries, gastronomy and tourism has been recently studied from different perspectives (Gómez et al., 2019; Santos et al., 2022). Various papers focus on the wine tourist's experience, from a multisensory (Brochado et al., 2021) or motivation-based experiential point of view (Bruwer & Rueger-Muck, 2019), using “netnographic” tools within an experience economy model (Thanh & Kirova, 2018), or based on a three-dimensional interaction model (Madeira et al., 2019). Other researches center on the role of wine as a strategic sector in tourism (Peris-Ortiz et al., 2016) and its economic impact (Tafel & Szolnoki, 2020), or on the relation between enotourism and gastrotourism, and its potential role in rural development (Bitsani and Kavoura, 2012). The local impact of wine tourism has both positive aspects, namely an opportunity for development (Lopes et al., 2018; Ferreira & Hunter, 2017; Correia & Brito, 2016), as well as negative ones, in terms of local attitude to wine tourists and environmental effects. These negative aspects have long been a source of concern (Poitras & Donald, 2006), and have more recently been examined e.g. in (Xu et al., 2016; Flores & Medeiros, 2016; Sun & Drakeman, 2022). A crucial point in this respect, which is receiving increasing attention, is the adoption of sustainable practices in the wine tourism sector (Nave et al., 2021).
Wine tourism in Spain
Looking at wine tourism in Spain in particular, similar research interests are reflected in the recent academic literature. The general importance of the link between wine, gastronomy and tourism was emphasized in López-Guzmán and Sánchez-Cañizares, (2012) and more recently by Dancausa et al., (2021). Wine tourists’ experiences and motivations were the focus of Cava Jiménez et al., (2022), An and Alarcón, (2021), Alonso and Kok, (2020), Crespi-Vallbona and Mascarilla-Miró, (2020) or Vorobiova et al., (2020). The impact on local development and the crucial role of sustainability was examined, for different regions, by Cruz-Ruiz et al., (2020), Ruiz-Romero de la Cruz et al., (2020), Serra-Cantallops et al., (2021), Vázquez Vicente et al., (2021), Zamarreño-Aramendia et al., (2021), and Martínez-Falcó et al., (2023a). The role of PDO certification was highlighted in Molleví et al., (2020). The impact of Covid-19 on Spanish wine tourism was examined in Marco-Lajara et al., (2022) and Martínez-Falcó et al., (2023b).
Market segmentation and Latent Class Models
From a methodological point of view, market segmentation is well known in economic research as an important tool to determine customer profiles, identify potential target groups, and make commercial decisions. Within the context of wine tourism classification and segmentation, different approaches have been applied (Alebaki and Iakovidou, 2011; see also Gómez et al., 2019; Vitale et al., 2019) since the ground-breaking work of (Hall, 1996). Most of these find 3 categories of wine tourists, which receive various (though similar) names, for example wine lovers, wine interested, and curious tourist; committed consumer, traditional consumer, uninvolved consumer; or formal wine tourists, tourists with an acknowledged interest in wine, and general tourists. Sometimes a fourth category is added (Alebaki and Iakovidou, 2011, and references therein), which then leads to wine lovers; neophytes (with little experience but a specific interest in wine); occasional wine tourist (no specific interest in wine-making but attracted by local gastronomy); and hangers-on (who see wine tourism as just another tourist attraction). For the particular case of wine tourism segmentation in Spain, the local case studies of López-Guzmán et al., (2014) and Marzo‐Navarro and Pedraja‐Iglesias, (2009b, 2010) deserve highlighting. Molina et al., (2015) apply a latent-class based market segmentation of Spanish wine tourism across five PDO regions. They find 4 categories: experts, interested, potentials, and novices, whose descriptions largely overlap with the 4 classes indicated in Alebaki and Iakovidou, (2011), as described above. Although Molina et al., (2015) is methodologically similar to the study presented here, it should be stressed that the focus of the current research is quite different, since we are interested only in international visitors to wine regions, but independently of whether they actually visit a winery or not, whereas Molina et al., (2015) focuses specifically on actual (domestic and international) wine tourists. Wine tourism segmentation studies for Italy include Galletto, (2018) and Vitale et al., (2018).
Other applications of similar Latent Class Models in tourism include Alegre et al., (2011), which uses a latent class approach to classify tourists’ length of stay, while Van der Ark and Richards, (2006) and more recently Singh et al., (2019) study the selection of cultural activities and of hotels, respectively. Scozzafava et al., (2018) apply an LCM segmentation to wine customer segmentation with regard to PDO labelling. LCM models that consider exogenous variables in tourism are rare. Barreal et al., (2021) study valuation and daily expenditure in online ski package purchases, while Barreal and Jannes, (2021) look at different categories of expenditure to compare tourist profiles in protected wine and olive oil areas.
Methodology & Data
The structure of the analysis presented follows Fig. 1. Survey data about international tourists who arrive at Spanish wine PDOs is obtained from the Spanish National Statistics Institute, and will be described in detail below. The questionnaire records socio-demographic features, travel characteristics, activities, expenses and visit valuation. The first two categories are the core of the first LCM cluster analysis, in which a general segmentation of tourist profiles are defined. Policy makers and companies directly involved in wine tourism would be interested mainly in the actual implications and recommendations based on this classification, in terms of policy, marketing strategy, offering and pricing. While a detailed study of these implications lies beyond the scope of the current paper, the second step of the LCM analysis presented here, based on activities of interest, allows cross-relating the interests of these potential wine tourists, thus giving very relevant hints at how, when and where such marketing efforts could be performed.
Finally, the last two categories (expenses and visit valuation) are used as covariates (in both LCM models) in a second computation step to obtain the evolution and prediction of class membership rates depending on these covariates. These also provide important tools to link marketing and management efforts to specific target groups in terms of image and income.
A brief technical summary of the method applied, namely Latent Class Analysis with covariates, is given in Appendix 1. In the remainder of this section, we will describe the data and variables in detail.
Spain has 853.915 hectares of wine cultivation area covered by Protected Designation of Origin (PDO) quality labels. Note that not all wine produced in a PDO area is automatically commercialized with the PDO label, since there are certain rigorous quality requirements that must be met on top of the geographical requirement. In 2019, the total Spanish wine PDO area was composed of 96 different denominations with a production of 14,568,368 hl (43,26% of Spanish wine production) and a direct economic impact of 2,668 million euros (CECRV, 2020). Figure 2 represents the geographical distribution of Spanish wine PDOs.
![figure 2](http://media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs10708-024-11055-0/MediaObjects/10708_2024_11055_Fig2_HTML.png)
Source: own elaboration based on MAPA, (2020)
Spanish wine PDOs.
The present research is based on microdata that the Spanish National Statistics Institute (INE—Instituto Nacional de Estadística) obtains from international tourists arriving or departing by air, land or sea throughout the whole year (INE, 2020a, b). The study period is from September 2015 to July 2019, which represents a total of 318,765 questionnaires. The questionnaire covers several aspects from individual socio-economic and demographical features to specific activities enjoyed during the stay. Additionally, the questionnaire also includes economic aspects of the holiday itself, such as the expenditures the tourist estimates to have made on transportation, activities or shopping.
This study focuses on the analysis of wine PDO areas. Therefore, only data are taken into account from tourists who declare that their principal destination (city or town) lies within such a wine protected area, and who moreover declare having participated in gastronomic activities during their visit. The percentage declaring such an interest is shown in Table 3. Note that the percentage obtained for Spain as a whole is consistent with the estimate in (KPMG, 2019) mentioned above. It might be surprising that the interest in gastronomic activities is slightly lower for the wine PDO regions than for Spain in its whole. This is probably due to the relatively large presence of wine PDO regions on the coasts, and especially on the Canary Islands, see Fig. 2 above, where sun and beach is the main holiday motivation. This result also implies that the wine PDO system is probably not sufficiently publicized towards foreign tourists, and many of them simply do not realize that they are actually visiting a wine PDO region which could offer interesting gastronomic activities.
Questionnaires with a large number of empty answers or other anomalies are also removed. The result of this statistical pre-processing is that 18,878 questionnaires are taken into account. This very significant number can thus be expected to lead to representative and robust results.
Table 4 summarizes the manifest variables to which the cluster model will be applied in order to define the classes in the two proposed frameworks. Table 4a in particular involves the variables that define the tourist's individual features (gender, age, origin) and the general features of the visit, such as season, accommodation type, length of stay, or general purpose. The ratio or relative frequency of each possible answer (or “level”) within each variable are thus indicated in Table 4a. Note that the levels are assigned arbitrarily to the different possible responses in order to allow a quantitative treatment. Table 4b includes information about the activities that the tourists have carried out during their stay, or declare intending to carry out. The first variable represents the main activity that motivates the visit, while the remaining variables are dichotomous (yes/no) and regard participation in family gatherings, parties, beach activities, sports or shopping, among others.
The covariates included in the research depend on the scope of the model. In particular, the latent class model related to individual and visit characteristics involves the total daily expenditure and the personal valuation (on a scale of 10) of the visit. The second segmentation focuses on activities and therefore the total daily expenditure is substituted by the daily expenditure on activities only, still supplemented with the tourist's valuation of his/her visit. The database records both variables at current prices. In order to make these values comparable, all values of total expenditure and activity payments are actualized through the touristic Consumer Price Index (INE, 2020c). Table 5 gives a statistical summary of these covariates. It should be noted that the valuations present relatively low variability. This is not due to a lack of valid responses, but to the fact that a large majority of visitors give a very high valuation to their stay. It is thus a consistent indicator of consumer satisfaction and a relevant variable for market segmentation.
For statistical safeguarding, a correlation analysis between covariates was performed in order to avoid multicollinearity problems and possible excess information between variables which could provide an incorrect result. This analysis shows that there does not exist a strong correlation between the valuation and either type of expenditure (total or activities only). In the case of the total daily expenditure, the correlation factor is r = -0.031, while for expenditure on activities only it is r = -0.008. A Pearson test confirms that all these results are significant (p-value < 0.01).
Results & Analysis
First of all, the number of classes should be determined (see Appendix 1), in order to maximize the Log-likelihood and minimize the AIC and BIC, while optimizing the relevance and coherence of the obtained information. The analysis considers any number of classes from zero to eight, and the optimum number is found to be four classes for the analysis of the individual and travel features, and five classes for the activities study. The inclusion of both covariates in a multiplicative model is found to outperform any other option (additive, single covariate, or no covariate at all), and so this is the option that will be presented.
Table 6 depicts the percentage of each class with respect to the total sample, as well as the mean value of the covariates within each class. A striking result is that in both models, the class with the highest expenditure is also the one with the lowest valuation (class 4 in the first model, class 5 in the second). Fortunately, these classes are also the smallest by size, while their mean valuations are still very good.
We will now describe the obtained classes in both models in some detail.
Individual and travel features
As mentioned above, the optimal segmentation turns out to present four classes. Figure 3 outlines the proportions for each level of the manifest variable within each class. The main features of each class can be summarized as follows:
-
1
Short getaway: Traditional visitors who arrive by plane and spend a relatively short holiday in hotels. Most tourists belonging to this class come from Europe or the Middle East, and travel in couple, although friends and family groups also are included in this group.
-
2
European adventurers: This group arrives mainly by road, train or ship during the warm season and spends a long time in non-hotel accommodation. They come in couple or in family from nearby countries to enjoy their holidays. The age of this class is the highest.
-
3
Non-commercial visit: Long stay in non-market accommodation. This group involves younger people compared with the two previous groups, who arrive by plane throughout the whole year and mostly travel alone to enjoy their holidays or to satisfy personal motivations.
-
4
Whirlwind visit: Short stays mainly in hotels by visitors who are typically 25–44 years old and arrive by plane, mainly from non-European countries. Their prime visit aim can be both touristic or for business purposes.
The color bars indicate the proportion within that particular class of the corresponding level, as defined in Table 4a.
The tourists’ valuation and total daily expenditure can be used as predictors for the probability of class membership. As indicated earlier, this is interesting because it allows to focus on the potential customers that are likely to spend most money during their visit, and/or to give a high valuation to their trip and hence to stimulate more future visitors, for example through positive online reviews or word-of-mouth marketing. Appendix 2 details the LCM with both variables as multiplicative covariates, which–as observed before–are statistically significant and robust.
Figure 4 depicts the influence of these covariates (on a continuous scale) on the probability of class membership. Class 1 registers a dominant membership probability when the daily expenditure is low-to-average and the valuation relatively high. Class 2 is most relevant in the lower expenditures range. Class 3 reaches its highest probability for high valuation and an average-to-high total expenditure (around 150 € per day). However, even at its peak, this probability never exceeds 60%. Finally, Class 4 acquires an increasing membership probability as the expenditure increases.
From a purely economic point of view, Class 4 is thus the most interesting, followed by Class 3, since these are the most likely categories at high expenditure rates, while Class 4 also has by far the highest average spending, see Table 6 above. Classes 1 and 2 are more relevant in the low expenditure corner (note that Class 2 also has a markedly lower average expenditure, see again Table 6). However, this goes paired with high valuation, and can therefore nevertheless be interesting from a marketing point of view, in terms of attracting more future visitors.
A straight mapping between the classes obtained here and traditional segmentations of wine tourism makes little sense, since the latter are usually mainly determined by psychographic variables directly related to interest and knowledge of wine, as well as previous experience with wine tourism as such, and no such variables were available for the present study. Nonetheless, some partial correspondences can be identified, mainly based on the demographic characteristic of age and the spending pattern. Comparison with Molina et al., (2015) suggests that our category of “non-commercial visitors” could potentially become wine tourism “novices” or “potentials”. Wine tourism “experts” are most likely to originate from our “European adventurers” and “whirlwind visitors”. Finally, wine tourism “interested” could essentially come from any of the categories obtained here.
Activities
Tourists’ interest in particular classes of activities will now be used as a second step in the segmentation. This could have been combined with the previous characterization based on demographic and travel features, to obtain a more precise characterization of tourist categories. However, this has intentionally been avoided. The idea is that it is one thing to know the general demographic and economic characteristics of typical tourist profiles, but it is a separate thing to understand how to attract and convince these to participate in wine tourism. Obviously, a segmentation of each group obtained in step 1 based on their preferred activities could also have been performed. While this could give more detailed hints for marketing strategies, it would also unnecessarily clutter the present manuscript. It has therefore been preferred to perform this second segmentation step with the whole group of interest, i.e.: based on all the valid questionnaire responses, as described in Section “From gastrotourism to enoutourism”.
In this case, the optimal segmentation model consists of five classes. Figure 5 plots the proportion of each activity interest within the different classes, which can be described as follows:
-
1
Sun & beach: The main motivation for their visit is to enjoy the sun and beach, but there is also a significant proportion of shopping or party activities among the members of this class. However, generally speaking, this class shows little interest in sports or leisure.
-
2
Culture & leisure: The main purpose of the visit within this class is given by cultural and leisure activities. Members of this class also enjoy going to the beach or shopping, but show no particular interest in rural or sports activities.
-
3
Family: The prime goal is to visit their family or relatives. The members of this class also enjoy parties and shopping, but are rarely interested in rural activities or sports.
-
4
Allround active: This group shows high ratios of demand for all types of activities (except family visits and sports), while the main motive of travel is heterogeneous.
-
5
Others: Members of this group show low levels of demand for all the suggested activity items, and define their prime purpose of travel mainly as “other”.
As in the previous model, the covariates (valuation and expenditures on activities) can be used as predictors for the probability of class membership. These conditional probabilities are shown in Fig. 6. Class 1 acquires a dominant probability of membership for visitors who declare a low expenditure level on activities combined with a high valuation, or vice versa. Classes 2 and 3 record a higher probability when the tourist declares a high level of both expenditure and valuation, or a combined low level in both covariates. Note that both these classes show a relatively low sensitivity to the covariates. This is even more true of Class 4. Finally, Class 5 represents the smallest proportion of the sample (see Table 6), but is nevertheless relevant because it is quite sensitive to the values of the covariates. This is true especially for the expenditure on activities, where this class reaches a membership probability of roughly 40% in the upper range of the recorded expenditures.
As explained earlier, the main interest of this second segmentation step is to obtain ideas of how and where to promote wine tourism. In this respect, Category 2 (Culture & leisure) is relatively straightforward. Note that “culture” is a relevant indicator for interest in wine tourism, see Molina et al., (2015). These tourists could be informed of wine tourism options, for example, through leaflets and posters at local museums and historical landmarks. Also, these tourists are the most likely to visit tourism information offices, which should therefore promote wine tourism actively. The type of wine tourism activities offered to this category should obviously match their cultural interest, for example through guided tours including historical information about wine-making, or through active promotion of wine museums.
A category which seems harder to convince of the interest for wine tourism is Class 1 (Sun & beach). However, since this category also has a significant interest in shopping, specialized gastronomic shops with a wine section could be interesting. Also, local souvenir shops, commercial centers, and supermarkets, could be suggested to open sections or even promotion stands with regional products and information about the local PDO. Category 3 (Family) could be reached through efforts of information for the local population of the importance and options within wine tourism. Categories 4 (Allround active) and 5 (Others) show a high interest in all, respectively none, of the activities questioned about, and are therefore harder to target specifically.
In general terms, it should be reminded that only questionnaires declaring an interest in gastronomical activities have been retained. Therefore, obvious steps towards the promotion of wine tourism include convincing local restaurants, as well as souvenir shops offering gastronomic products, to actively promote local (wine and other) PDO products. This could be achieved by including some basic information on the menu card, for instance that a certain plate has been prepared using a particular local PDO product, up to recommending some specific local PDO wine for each meal on the menu card. Thematic festivals and events promoting these local PDOs could be organized, ideally during high tourism season.
Discussion and Conclusions
Research question RQ1, regarding the current state of wine tourism in Spain, has been answered by demonstrating that this sector in Spain still lags far behind Italy and France in terms of number of wine tourists and especially of economic revenue. It has been argued that this is (at least partially) due the low contribution of foreign wine tourists. In this sense, it is suggested that, as a first step, marketing efforts should be undertaken to convince foreign visitors visiting a Spanish PDO wine region anyway to participate in some form of wine tourism. A first curious result is that the percentage of foreign tourists with an interest in gastronomic activities is in fact lower in wine PDO regions than in Spain as a whole (12.97 versus 15.47%). There is thus clearly a lack of information and promotion of the PDO concept towards foreign tourists.
As a preliminary step in order to define concrete marketing targeting strategies, it is necessary to identify the different segments among foreign tourists who might potentially be interested in participating in an activity related to wine tourism (research question RQ2). We have taken the generalization that any tourism declaring an interest in gastronomic activities could potentially be interested in wine tourism in particular, and developed an innovative two-step Latent Class model with covariates, related to such international visitors to Spanish wine PDO regions who have declared an interest in gastronomic activities. The first model is based on the individual features of the visitor as well as general characteristics of their visit, thus providing a mainly sociodemographic characterization of different tourist profiles who could potentially be convinced to participate in wine tourism. This model has shown four classes with strongly marked differences in terms of length of stay, type of accommodation or age. The second model developed here focuses on a segmentation by activities, which gives useful clues of how to concretely market wine tourism towards each tourist class, depending on their activities of interest. In this model, five classes were found, again quite clearly differentiated based on their interest in certain activities, from Sun & Beach over Culture & Leisure to Shopping.
Curiously, in both models, the class with the highest daily expenditure turned out to be the one with the lowest valuation. Moreover, within the activities model, the class which spends most on activities is in fact the one that declares the lowest interest in activities. So, this seems to be the class which spends most per individual activity. It could therefore constitute an interesting but complicated niche market. Companies targeting this class should offer high-value activities, for example related to Michelin-starred restaurants (Castillo-Manzano et al., 2021), since the willingness to pay for them is high, although the expectation is also high.
More generally, from a tourism-economic point of view, the inclusion of the expenditures and the valuation as external variables is particularly interesting. Within the LCM methodology, these external variables were included as covariates and used as predictors for class membership. This allows to focus on target groups that are likely to spend more during their holiday and/or promote further visits. Generally speaking, it has been found that the valuation has a relatively small influence on the probability of membership of one class or another. But rather than indifference, this in fact demonstrates the high valuation that visitors have of the Spanish touristic sector in wine PDO regions, with an average valuation of almost 9/10 and relatively small variability.
The expenditure is more strongly determinant for the class membership probability. For example, the fourth class within the model based on individual and travel features becomes dominant at high expenditure ranges. This class, which we have called “whirlwind visitors”, is characterised by high expenditure levels during short stays. Their willingness to spend substantial amounts of money could therefore be exploited within the wine tourism sector as a relevant market niche to improve revenues.
From the activities point of view, tourists with an interest in culture and leisure form an obvious target group. These are quite likely to be interested in wine tourism, and moreover are likely to achieve high expenses on activities and give good valuations, if the stakeholders decide to promote this segment.
Research Question 3 was concerned with how to convert these models into actual marketing strategies targeted at the different potential foreign wine tourist segments. As already warned in the Introduction, a full answer to this RQ3 lies beyond the scope of this paper, however we have tried to make some relevant comments, with a particular focus on the “sun & beach” class of tourists. It is probably unsurprising that this class within the activities model is the one with the lowest expenditure on activities. This is particularly relevant because this has traditionally been the dominant tourism model offered by Spain. It is clear that the types of wine tourism activity aimed at this class should not lie within the high-value/high-cost range targeted at the more demanding class of tourists described above. In fact, this category might at first sight seem the hardest to convince to participate in wine tourism, and should thus perhaps not be targeted as a priority. However, from the point of view defended here, this conclusion would be wrong. Indeed, sun & beach tourism is perceived as causing a substantial amount of social and ecological pressure, while providing limited economic benefits. In recent decades, a substantial effort is being undertaken in Spain to evolve towards higher-value and more sustainable forms of tourism. Wine tourism (and gastrotourism in general) could play an important role in this trend. In this context, it should be observed that these sun & beach tourists also have a significant interest in shopping. It has thus been suggested to reach out to them through local commercial centers, souvenir shops, and supermarkets, which could give a preferential treatment to local (wine and other) PDO products, or make promotion for wine tourism through leaflets and discount vouchers. Also, because this category of tourists is not likely to be actively and spontaneously seeking wine tourism opportunities, it is crucial to increase the visibility of wine tourism. Some suggestions that we have made in this context, and which align with case-studies in the literature, are for example that restaurants could include information about the use of local products on the menu card, specifically regarding PDO wines and other products (López-Guzmán & Sánchez-Cañizares, 2012), as well as the organization of festivals and events, ideally during high foreign tourism season, promoting wine routes and other gastronomic tourism opportunities (for local examples in Spain, see Cava Jiménez et al, 2022; Molleví et al., 2020; Vorobiova et al., 2020; or Bitsani and Kavoura, 2012, for a similar study in Italy), with a particular focus on synergies between complementary gastronomic products such as wine and ham or olive oil (Barreal & Jannes, 2021; Dancausa et al., 2021). Note, in this context, that in many regions such festivals already exist, but they are often little-known beyond the local population (Vorobiova et al., 2020).
Finally, from a technical point of view, the models developed here can easily be adapted to consider additional variables, both manifest or external (covariates). For instance, the market segmentation presented here was focused on all visitors to Spanish wine PDO regions, in order to provide some general idea of the potential of this relatively young intersection between wine and tourism. Clearly, this forms just a first step within the segmentation–targeting–positioning model of marketing (Kotler et al, 2017). The main limitation of the present study is precisely that it mostly limits itself to the first step of this model. All foreign tourists with an interest in gastronomic activities have been considered as potential wine tourists. Clearly, a further refinement of this target group prior to a concrete segmentation would be useful, although we speculate that the general characteristics of the obtained segments would not vary strongly. Likewise, it would be very interesting to perform a more detailed comparison between potential and actual wine tourists, and to interview actual (foreign) wine tourists with respect to how they found out about the win tourist activity, their degree of satisfaction, and their recommendations, based on primary data collection, as is regularly being carried out in several Spanish wine PDO regions (see e.g., Cruz-Ruiz et al., 2020; Crespi-Vallbona and Mascarilla-Miró, 2020; Ruiz-Romero de la Cruz et al., 2020; Serra-Cantallops et al., 2021). Further developments of the Latent Class Model could be used to analyse such questions more specifically directed towards actual wine tourism activities in these PDO areas.
Likewise, the variables studied here could also be applied to other areas or types of protected labelling, such as olive oil or cheese. Actual recommendations for the concrete development of gastronomic and wine tourism strategies must necessarily have a more local character, based on the concrete range of products and involved agents in a particular region. In this sense, a second limitation of the present research is that these local characteristics have been smeared out by using the whole of Spain as the statistical target area. In particular, this implies that important tourism destinations such as the Canary and Balearic Islands, and the Mediterranean coast in general, have acquired a strong weight due to the large number or foreign tourists. On the contrary, inland wine-producing regions in, for example, Aragon or Castilla-la-Mancha, have a comparatively lighter weight, because they are less visited by foreign tourists, even though these might precisely be thought of as priority targets for rural development. However, the main objective of the present research is to contribute to the transformation of the existing Spanish tourism market, with its heavy social and ecologic impact, into a higher-income and more sustainable type of tourism, while also contributing to a higher quality perception of Spanish wines (Rojas-Méndez et al., 2018), and ultimately higher economic returns for Spanish wine producers (González & Dans, 2018; Marco-Lajara et al., 2023). Wine tourism could play a crucial role in this transformation. The authors hope that the research presented here can positively contribute to this transformation.
Change history
20 July 2024
A Correction to this paper has been published: https://doi.org/10.1007/s10708-024-11091-w
Abbreviations
- AIC:
-
Akaike Information Criterion
- BIC:
-
Bayesian Information Criterion
- LCM:
-
Latent Class Model
- PDO:
-
Protected Denomination of Origin
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Acknowledgements
The authors received financial support from projects PID2019-108679RB-I00/AEI/10.13039/501100011033 (JB) and PID2020-118159GB-C44 (GJ) of the Spanish MICINN. JB also receives a grant mobility from the Complutense University of Madrid (Requalification MV24/21) financed by the Spanish Ministry of Universities and cofunded by the European Union’s Next Generation program.
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Appendices
Appendix 1: Latent Class Analysis
Latent Class Analysis (LCA) is a statistical technique that attempts to map into a single unobserved (“latent”) discrete variable the results of a series of observed (“manifest”) variables. This allows to segment the observations into Latent Classes according to the different values of the latent variable, which translate into related observed manifest variables values. This method is particularly useful when the observed variables are categorical.
We will briefly review the Basic Latent Class Model (BLCM), and give some rudimentary indications regarding the (more advanced) Latent Class Regression Model. The latter allows for the inclusion of covariates, such as the expenditures and valuation that are the subject of this study. The actual calculatons have been performed with the poLCA package in R, see Linzer and Lewis, (2011).
We will use the following symbols. N represents the sample size, J the number of manifest variables, Kj the number of possible values for each manifest variable j and we define
such that
and
the conditional probability in class r for the outcome k on variable j. r = 1,…,R represents the class index, where
The overall (cross-class) density function is obtained as
where pr is the proportion (or a priori probability) corresponding to class r (such that \({\sum }_{r=1}^{R}{p}_{r}=1\)).
The actual BLCM is implemented through a Maximum Likelihood estimation, by calculating estimators \(\widehat{{p}_{r}}\) and \(\widehat{{\pi }_{jrk}}\) in an iterative way, and thus maximizing the log-likelihood function obtained from the density function (6). Using these estimators, the probability for a sample element i with manifest variable values Yi to belong to class r can be obtained as
The number of classes is not determined by the procedure just described. In fact, merely maximizing the likelihood would lead to an unjustified increase of the number of classes. This has to be compensated by considering some penalization for over-fitting. The two criteria that are most often used for this purpose are the Akaike Information Criterion (AIC – Akaike, 1973) and the Bayesian Information Criterion (BIC – Schwarz, 1978):
where \(\Lambda\) is the maximum log-likelihood and \(\Phi =R{\sum }_{j}\left({K}_{j}-1\right)+\left(R-1\right)\) is the number of parameters to be estimated by the model. Note that there is no universally agreed-upon algorithmic procedure that always leads to the “best” number of classes. This number of classes needs to be determined by the researcher based on the AIC and BIC criteria just described while taking into account that the main objective is explanatory power. The determination of the number of classes can require a certain amount of trial-and-error and critical evaluation of the obtained results.
Likewise, it should be evaluated whether the model improves by the incorporation of covariates or not. For this purpose, the previous model can be extended into a Latent Class Regression Model (LCRM) by the inclusion of S external factors (the covariates Xi). These serve as predictors for the class membership probability of each sample element. The class proportions pr of the BLCM are replaced by pri: the probability that an element i belongs to class r, depending on the covariate values for that element.
The LCM now calculates estimators \(\widehat{{\beta }_{r}}\) and \(\widehat{{\pi }_{jrk}}\), and the probability that a sample element i with manifest variable values Yi belongs to class r is obtained as a straightforward extension from the basic case, namely
To summarize, there is a certain amount of heuristics in determining the variables to be taken into account, as well as in determining the ideal number of classes. But once these have been decided upon, the LCM procedure is a fully automatic and extremely powerful segmentation method.
Appendix 2: LCM with covariates
Table 7
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Jannes, G., Barreal, J. Attraction opportunities for enotourism among international travellers to Spanish wine PDO regions. GeoJournal 89, 58 (2024). https://doi.org/10.1007/s10708-024-11055-0
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DOI: https://doi.org/10.1007/s10708-024-11055-0