The Criteria of Inbound Marketing to Segment and Explain the Domain Authority of the Cellars’ E-Commerce in the Canary Islands

Abstract

This study uses an inbound measuring instrument to segment the cellar’s e-commerce in the Canary Islands. One of the central claims of this paper is that inbound marketing comprises several dimensions such as content resources, SEO, social media, digital maturity, and e-commerce growth, such that it implies superior levels of personalisation, flexibility, service, security, and privacy, product and shop transparency, advanced technology, and data mining. After raising three research questions, this study performed fieldwork based on web analytics procedures, extracting metrics from 59 cellars’ websites thanks to employing a broad range of software. The sampling procedure was non-probabilistic and judgmental, took place in the spring of 2023, and gathered relevant metrics to build an inbound scale. The data prove that the inbound measuring instrument can rank and segment the Canary Islands’ marketplace. Although this study has put forward a tentative explanation and application of the inbound marketing notion and tools, findings suggest a need for a more significant effort to explore how generalizable the created measuring instrument is.

1. Introduction

Several recent studies have addressed the issues of consumer information overload, an outbreak of marketing detox desires, and market saturation [1]. Consequently, traditional marketing has lost competitiveness due to advertising clutter, consumer inattention, and new methods people use to block unwanted messages [2]. It is disgraceful for many marketers that their presumed prospective consumers felt under pressure owing to push marketing strategies whose effect comes down to undesirable interruptions provoked by spam, unwanted phone calls, and massive advertising techniques. As a result, consumers ignore or reject careless offers bombarded without their permission and interest.

In this context, consumers have two options. First, “patience is the plaster for all sores”. Nonetheless, this is not an excellent idea because it does not solve marketing management inefficiencies and marketers’ malpractice. Second, “paradise is always where love dwells”; love’s first duty is to listen. It is high time to highlight the importance of creating an emotional connection with the customer to achieve loyalty by adopting inbound marketing [3]. Therefore, companies are looking for strategies to connect with customers by introducing more direct and emotional technologies. Undoubtedly, inbound marketing leads to better communication with clients and listening to them [4]. The inbound concept has emerged as traditional marketing no longer produces the desired results [5].

Inbound marketing can be defined as “attraction marketing”, which means that a company attracts users and potential customers by creating and sharing valuable, relevant, and quality content [5], search engine optimisation, and public relations marketing [6]. Undoubtedly, inbound marketing is subject to using advanced digital tools in the online environment [2] to reach the highest degree of personalisation and distribute value without being intrusive [7]. Therefore, inbound marketing rests on several main pillars: content resources, Search Engine Optimisation, social media, digital maturity, and e-commerce store growth, such that it reaches the highest standards in terms of personalisation, flexibility and service, security and privacy, product and shop transparent information, advanced technology, and data mining.

Having said what inbound marketing is, to delimit its concept perfectly, it is essential to indicate what does not count as inbound marketing. Outbound marketing is the term used to refer to the predominant traditional marketing approach. According to [2], traditional marketing might be characterised by employing invasive sales marketing techniques directed toward large segments. Conversely, inbound marketing replaces these promotional interruption techniques with an organic and customised approach. While outbound marketing puts effort into searching for customers, inbound marketing prefers being found and maintaining the relationship. Whereas outbound marketing is prone to mass advertising, outbound marketing gives credit to content marketing, SEO, Social Media, data and technologies, responsive and flexible service, transparency, privacy, and security.

The primary objective of this research is to develop a measuring instrument to identify and compare the degree of inbound marketing of the digital content and activities of e-commerce cellars in the Canary Islands. To this end, the first research question is raised: whether, based on their inbound/outbound quality, it is possible to score and hierarchically rank these cellars with web metrics mining. In addition, these criteria will be used to profile the cellar segment in the Canary Islands marketplace. Therefore, a second research question might be: Can cellars be segmented using the inbound marketing criteria? Finally, we aim to explore whether an inbound marketing strategy can effectively enhance the cellars’ authority. Hence, a third research question is raised to answer what inbound metrics and dimensions are more influential in enhancing the cellars’ popularity, relevance, and credibility. Specific research objectives are set regarding the validity of the measuring instrument dimensions to rank the cellars and the possibility of segmenting using these dimensions. Finally, this research explores the determining effect of inbound marketing performance over domain authority.

The last few years have seen an increased interest in inbound marketing. No doubt, inbound marketing needs to receive more research attention insofar as permission marketing, privacy and security, data mining and technology, customisation, flexible distribution, and advanced e-commerce market orientation practises have emerged as priorities in recent years [8,9,10]. This clarifies the need for further research on inbound marketing [11], crediting content, and attraction techniques [12]. Therefore, inbound marketing represents a research gap, given its status as a novel strategic concept [13,14].

Aside from the introduction, this paper is divided into four sections. The initial section, the literature review, focuses on supporting three different hypotheses regarding the criteria to include in the measuring instrument and, in turn, to segment with discriminant criteria. Moreover, the methodological section covers the fieldwork, measurement instruments, and research context. Then, the statistical analysis is presented and showcases the empirical evidence. Similarly, we discuss the obtained evidence and compare it to other research works to gain further understanding. In addition, we reach conclusions by exploring the research questions, providing theoretical reflections, deriving practical implications, and suggesting future lines of action. Finally, a list of bibliographical references will be provided, correctly attributing the sources used in this work.

2. Literature Review

Although a few scholars have attempted to identify the inbound marketing essence, the literature on it still has its roots in vaguely delimited concepts and varied overlapping notions. Too many definitions of inbound marketing emerge from practitioners [15], but they need to be unanimous. Nonetheless, researchers seem to debate over its crucial elements, and it is possible to spot an area of agreement about some descriptive dimensions to account for measuring and assessing the inbound level and quality [16,17]. Therefore, not only can inbound marketing be described, but it can also be measured. On this basis, the following hypothesis is put forward:

H1. 

The inbound dimensions might be used to rank the cellars on the internet.

Opinions are divided as to what inbound dimensions can be considered. Academic proponents of explaining it have pointed out that inbound marketing pays careful attention to security and privacy [18]. Accomplishing the law and preserving individual rights regarding intimacy is prioritised insofar as it falls under the liberty of navigating on the internet, and, undoubtedly, inbound marketing signifies being non-intrusive [15], is relevant to permission marketing [7], and puts emphasis on ushering in the user’s discretion [19]. Therefore, the authors claim that, as far as inbound marketing is concerned, cybersecurity and confidentiality play a crucial role in enhancing best practises [7,20].

In addition, there is a direct connection between digital marketing capability and competencies supported by technological skills and processes, such as those designed to secure access and keep customer data safe [21]. The business strategy can be derived from the IT strategy in an iterative process of IT innovations, where existing technology is replaced by more powerful and modern technology in terms of better integration, higher performance, and greater security [22]. On this basis, the following hypothesis is put forward:

H1 (1). 

The level of security and privacy might be used to rank the cellars on the internet.

Consequently, the role that technology and data play in inbound marketing are relevant. The collection of data and the website’s functionality enable the personalisation of offers and the user experience in e-commerce. Inbound marketing signifies advanced technological usage because technology escorts inbound marketing insofar as the former enables companies to practise the latter. Ref. [23] proposes nine dimensions of the Digital Maturity Model (DMM), where digital affinity and experimentation with digital technologies are already prevalent in companies [24]. However, strategically planned transformation and the use of advanced data analytics in business processes are less common. Therefore, the final nine dimensions of the DMM are (1) customer experience, (2) product innovation, (3) strategy, (4) organisation, (5) process digitalisation, (6) collaboration, (7) information technology, (8) culture and experience, and (9) transformation management [22]. Moreover, inbound marketing can only be performed with technological and data resources and capabilities [25,26]. On this basis, the following hypothesis is put forward:

H1 (2). 

The level of technology and data might be used to rank the cellars on the internet.

Inbound marketing is fine content marketing because this is the most powerful appeal to captivate users. Therefore, practising inbound marketing is subject to the best resource format, such that users feel they need that aesthetic, functional, and exceptional value [27]. For example, inbound marketing happens when customers find newsletters, infographics, and streaming that fulfil their needs despite ignoring them in advance and not seeking them on purpose.

In addition, the Digital Capability Reference Model (DCRM) offers insights into how companies can optimally utilise today’s digital content opportunities. The set of global best practises comprises 12 capabilities and 76 sub-capability components organised into one or more zones: front office, middle office, and back office. All channels, including the front, middle, and back offices, must conform to the strategically developed customer experience journeys [28]. The front office employs digital channels for direct customer interaction to promote and sell products and services [29]. The back office provides the necessary information for customer interaction in the front office. Thus, in the DCRM model, the middle office is responsible for creating personalised content using the analytical insights provided by the back office and delivering it to the front office [30]. Therefore, it relies on a resource-based view, distinguishing a company’s resources and capabilities as the foundational elements of digital marketing and the basis for business success. Indeed, achieving diverse content resources can contribute to inbound marketing. This is because generating diverse and attractive content encourages interaction with users and also generates trust and loyalty [31]. On this basis, the following hypothesis is put forward:

H1 (3). 

The level of content resources might be used to rank the cellars on the internet.

Inbound marketing is related to search engine optimisation. It should be noted that the gap study on digital marketing capabilities has ranked the most critical digital capabilities for both business-to-business (B2B) and business-to-consumer (B2C) companies [25]. According to this study, the most critical digital capability for both is that the company has its own website. Also, when e-commerce or a website adheres effectively to inbound marketing, such as search engine optimisation (SEO) practices, it will result in enhanced visibility and a more substantial presence on the Internet, increasing web traffic [32]. In addition, when an e-commerce or website effectively adheres to inbound marketing, such as search engine optimisation (SEO) practices, it will achieve a more significant online presence. Therefore, it will improve the visibility of the in-search results through website optimisation and other factors, such as online social media content. On this basis, the following hypothesis is put forward:

H1 (4). 

The level of search engine optimisation quality might be used to rank the cellars on the internet.

Naturally, achieving e-commerce growth can be relevant for inbound marketing, which can provide significant benefits to the site, such as (1) attracting more traffic, (2) increasing user satisfaction, (3) enhancing the overall visitor experience, and (4) increasing online ranking, which might increase sales conversions [33]. E-commerce deals with inbound marketing when prioritising customers’ needs over selling and short-term profits [34]. Similarly, e-commerce is inbound when companies perform permission marketing, avoid disturbing their customers with aggressive promotions, and uphold the principles of privacy and data protection to go the extra mile [7,15]. On this basis, the following hypothesis is put forward:

H1 (5). 

The level of e-commerce store growth might be used to rank the cellars on the internet.

Furthermore, it is worth noting that the second most crucial digital capability is multi-channel capability, i.e., the ability to communicate through different digital channels [33]. This challenge is related to the significant gap in theoretical knowledge regarding the topic. However, some studies claim that social media is a channel between the company and the customer [35]. Moreover, social media is involved in inbound marketing because various social media allow for customisation thanks to their characteristics. So, social media increases brand exposure and, therefore, website traffic [36]. In addition, they also produce engagement content. Depending on the social media platform, it can personalise ads according to the target audience and based on specific network features. Finally, they can analyse all the data they collect to develop and design new strategies [37]. On this basis, the following hypothesis is put forward:

H1 (6). 

The number of social media profiles might be used to rank the cellars on the internet.

Therefore, digital capability comprises four overarching traits: (1) social media marketing capability [35]; (2) digital marketing strategy, which relates to the combination of marketing strategy and the use of technology; (3) the digital relationship between the company and its customers emerging in a new collaborative environment [38]; and (4) the leadership capacity of business decision-makers.

Thus, inbound marketing is the most advanced stage of the digital capability model and should be measured using the indicators associated with the highest level of digital maturity. This digital maturity facilitates the ongoing, continual implementation and evolution of a company’s digital transformation. Moreover, it provides a connection between digital orientation, digital intensity, and the business’s overall success [39]. For this reason, the selected indicators can be used to analyse the website’s activity and traffic. Moreover, one of the metrics provides knowledge of the marketing metrics integrated by e-commerce. In addition, it measures the responses and results of users when browsing the website. Not only is inbound marketing conceived and implemented by advanced technological companies, but it is also measured by considering several primary metrics. Ref. [40] acknowledges a wide range of metrics, such as those relative to website traffic and architecture, a variety of user engagement and interaction metrics, and many integrated marketing metrics related to referral, profitability, and conversion rates.

Hence, the level of digital development is relevant to inbound marketing. A high level of digital development allows cellars to efficiently manage their online activities, which can result in (1) a better user experience [41], (2) enhanced online security measures in the digital environment, (3) improved search engine optimisation (SEO), scope, and visibility on the Internet [32,42], and (4) analysing web and social media data in order to implement marketing strategies that are personalised to the users’ preferences [33], thus increasing customer loyalty. Therefore, the level of digital development can improve the reach and ranking of cellars on the Internet. On this basis, the following hypothesis is put forward:

H1 (7). 

The level of digital development might be used to rank the cellars on the internet.

The current literature on inbound marketing suggests that outbound marketing is predominant over the former approach in the context of fierce and desperate competition to grab prospective and distracted customers. However, outbound marketing is less effective and efficient owing to its less advanced technological tools [7,43]. There is a glaring difference between inbound and outbound marketing in terms of approach, as inbound marketing is based on attracting in a non-intrusive form [44], while outbound marketing focuses on mass promotion and disruptive approaches. In addition, there is a difference in both effectiveness and costs; e.g., inbound marketing generates more trust [45], but results take longer to achieve, whereas outbound marketing pays for immediate results. Therefore, inbound marketing criteria discriminate against the approach of outbound marketing. On this basis, the following hypothesis is put forward:

H2. 

The inbound marketing criteria might be used to describe cellars’ segments.

Hence, users highly value privacy and prefer e-commerce cellars with strong privacy, data protection, and compliance policies. Moreover, users prefer websites that offer privacy options and transparency in managing data, such as bank details [46]. It is a fact that new technologies are contributing to the growth and improvement of e-commerce. However, they also expose new risks for customers and entrepreneurs (Singh, 2023), such as data disclosure and privacy violations. Inbound and outbound marketing are distinguished significantly regarding the level of security and privacy, technology, and data. Therefore, using inbound marketing will provide greater trust. Simply put, security relies on technology [20]. On this basis, the following hypotheses are put forward:

H2 (1). 

The level of security and privacy might be used to describe cellars’ segments.

H2 (2). 

The level of technology and data might be used to describe cellars’ segments.

On the other hand, the content resources that appear on the website or social media can be based on the user’s information consumption preferences towards specific written content (blogs), visual content (videos and posts), or interactive content (memes), which enables greater reach and engagement with users [47]. However, they value the personalisation of content based on their individual preferences, regardless of whether it is very detailed or general [48]. Moreover, the level of content resources can be broken down from the concepts used to describe cellars’ segments [49]. So, level content resources can be employed to segment cellars through specific customer preferences and behaviours with content. On this basis, the following hypothesis is put forward:

H2 (3). 

The level of content resources might be used to describe cellars’ segments.

Furthermore, the distinction between inbound and outbound marketing becomes remarkably clear concerning the quality of search engine optimisation. Thus, SEO lets one know the keywords [50] for which a cellar has been optimised and attracts the most suitable users. Furthermore, inbound marketing requires understanding the link strategy of the e-commerce cellar and developing and improving inbound links to attract only the right users. In addition, knowing the percentage of clicks (CTR) in search results and refining the scope to attract new traffic to the website distinguish good digital practises from outbound performances [51]. On this basis, the following hypothesis is put forward:

H2 (4). 

The level of search engine optimisation quality might be used to describe cellars’ segments.

Likewise, a marked disparity exists regarding e-commerce shop growth in terms of inbound and outbound marketing. It stands to reason that transparent information about products and shops is always demanded by customers [15,26], fine service quality is expected constantly [52], and flexible distribution and personalisation are wanted continuously [17,53,54]. No doubt, commerce development and digital market orientation are in line with inbound marketing policies, and, hence, they distinguish from massive promotions, poor quality, and asymmetric informative policies claimed by outbound policies [7,15]. Therefore, the difference between inbound and outbound marketing rests on effective distribution, clear information, and customised offers so that the latter avoids disturbing and is careless about being intrusive [4,18,55]. On this basis, the following hypothesis is put forward:

H2 (5). 

The level of e-commerce store growth might be used to describe cellar segments.

It is a fact that each social network is aimed at a specific audience because, depending on the platforms on which a cellar has a presence, specific segments could be formed according to the audience and produce engagement content [56]. Undoubtedly, while inbound marketing emphasises the importance of managing social media to reach the appropriate community members to engage, outbound marketing uses social media as an indiscriminate channel to reach [15,57]. For example, a social media hashtag strategy [58] can be designed to influence content segmentation. For this reason, both inbound and outbound marketing differ depending on the kind of social media and how they use them [7,59,60]. Therefore, the number of social media profiles can be used to describe the cellars’ segments. According to Ref. [25], multichannel capability is the most crucial digital maturity indicator. Similarly, Ref. [5] states that digital marketing is akin to inbound marketing, aiming to attract customers by creating high-quality content through social media. In this vein, having several profiles on social media allows for a wider reach and segmentation. On this basis, the following hypothesis is put forward:

H2 (6). 

The number of social media profiles might be used to describe cellar’s segments.

In this way, the level of digital development allows segmentation using the various digital platforms on which the cellar has a presence, and according to the level of automation, from digital tools such as chatbots [61]. Furthermore, inbound and outbound marketing diverge markedly concerning the level of digital development. While the former is data-driven, technologically advanced, and digitally intelligent, the latter attempts to follow a sales approach, needing to be more digitally and technologically literate [62,63]. Inbound and outbound marketing are extremes of the digital maturity index model in which customer experience, product innovation, strategy, organisation, process digitalisation, collaboration, information technology, culture and experience, and transformation management characterise those who perform inbound marketing [21,64]. On this basis, the following hypothesis is put forward:

H2 (7). 

The level of digital development might be used to describe cellars’ segments.

Practitioners use more domain authority than academicians, despite being a vital digital marketing concept. However, as far as domain authority is concerned, inbound marketing plays a precursor role in enhancing popularity, relevance, and credibility. Previous studies have demonstrated that website traffic determines domain authority [65], and traffic indicates attraction. Similarly, research has pointed out that natural outbound linking is how users give credit to a distinctive website [66,67] and, hence, avoid penalties derived from malpractices such as artificial and fake recommendations and intrusive and misleading policies to gain visibility and reach at all costs [68]. Therefore, inbound marketing increases domain authority as the latter reckons the merit of the former defining ingredients. On this basis, the following hypothesis is put forward:

H3. 

The inbound marketing criteria influence cellars’ domain authority.

Previous research has shown that the popularity of a website is related to the level of security and privacy measures [69]. If users perceive that their data are protected and feel that their privacy is a priority, they are more likely to stay on the website longer [45]. This will improve the website’s domain authority and e-commerce ranking in search engines. Google gives credit to those websites with secure systems [70,71], which equates to what inbound marketing claims. On this basis, the following hypothesis is put forward:

H3 (1). 

The level of security and privacy influences cellars’ domain authority.

Moreover, previous studies have demonstrated that website prestige is functional to the technology and data usage level. Technology and data enable the collection of valuable information about user behaviour [72] and their preferences. In this way, they can adapt their content and marketing strategies to their target audience’s interests and provide attractive content to their users to increase the authority of their domain. So, previous research has shown that domain authority is related to the level of content resource credibility [73] and advanced technological capabilities [74]. On this basis, the following hypotheses are put forward:

H3 (2). 

The level of technology and data influences cellars’ domain authority.

H3 (3). 

The level of content resources influences cellars’ domain authority.

Hence, previous studies have demonstrated that website popularity and visibility are caused by search engine optimisation [75]. In turn, successful search engine optimisation can improve domain authority, e.g., through keyword optimisation [50] and an excellent internal and external link structure. It can provide a better ranking in search results, increasing domain authority. On this basis, the following hypothesis is put forward:

H3 (4). 

The level of search engine optimisation quality influences cellars’ domain authority.

In addition, earlier studies have shown that the prestige of a website is associated with the level of e-commerce store growth and popularity. This is similar to what was mentioned above since it improves the visibility [76] of the e-commerce store in search engine results, attracting more web traffic. As traffic comes down to popularity and visibility and e-commerce inbound marketing ushers in fine-quality content and referential outbound links, a causal connection exists between the degree of e-commerce marketing development, market orientation, and domain authority [77]. On this basis, the following hypothesis is put forward:

H3 (5). 

The level of e-commerce store growth influences cellars’ domain authority.

Moreover, previous studies have demonstrated that website popularity is related to the number of social media profiles. A wide range of social media profiles generate diverse content [60], attracting different users and generating trust and loyalty through interaction [78]. Therefore, the more social media profiles the cellar has, the more significant the positive impact on the cellar’s domain authority, as it will increase its visibility, interaction, and online engagement, whereby domain authority is enhanced. On this basis, the following hypothesis is put forward:

H3 (6). 

The number of social media profiles influences cellars’ domain authority.

This will improve the website’s domain authority and e-commerce ranking in search engines. A high level of digital development can significantly and positively impact a cellar’s domain authority [73], enabling enhanced website or e-commerce optimisation, data analysis, online marketing strategies, and adapting to digital transformation [64]. This means that the digital level of the website or the overall online presence is related to the domain authority. On this basis, the following hypothesis is put forward:

H3 (7). 

The level of digital development influences cellars’ domain authority.

3. Methodology

This research conducted fieldwork based on web analytics procedures for the cellars’ websites in the Canary Islands. To carry out the web analytics fieldwork, the population of cellars in the Canary Islands was considered the universe. Therefore, 133 cellars in the Canary Islands were included in an initial database. This results in a sample of 70 cellars, which, after data pre-processing, ends up in an actual sample of 59 cellars. The pre-processing task is characterised by cleaning and classifying the data, eliminating duplicates, and identifying missing data and possible inconsistencies between variables. In addition, this final sample consists of cellars with a website, all dedicated to hosting tourist visits and producing wine. It is worth noting that some cellars without their own website were selected only if their presence on the internet was provided, in particular, by a third party. All the selected cellars are e-commerce since they offer tourist visits and intend to sell their products and services on the Internet. Therefore, the difference between the universe and the final actual sample size is due to eliminating data flaws, the requisite digital presence, and minimal maturity, along with an active tourism business related to wine production.

To obtain these data, a judgmental non-probabilistic procedure is used, thanks to the existence of a list or census of cellars in the Canary Islands to which the selection criteria were applied. Surveyors are also randomly assigned to this limited sampling frame once they have undertaken to collect and observe the web pages of each cellar. The fieldwork was carried out with the help of the Tourism Market Research class 22–23 students of the University of Las Palmas de Gran Canaria. It was carried out during the practical sessions of the course at the Faculty of Economics, Business, and Tourism. It was carried out during March, April, and May 2023.

Of the cellars in the sample, 31 belong to Santa Cruz de Tenerife and 28 to the province of Las Palmas de Gran Canaria. Specific information is obtained for each cellar, such as identification characteristics, digital characteristics, and classification data (see

Table 1. Technical sheet of the research.

Sampling procedure	Judgemental sampling procedure
Universe	133 cellars of the Canary Islands
Criteria	Extraction of information from cellars with a website, tourism business activity, and currently open in the Canary Islands
Defined sample	70
Actual sample	59
Date fieldwork was carried out	April, May, and June 2023.
Direct quality control	Personal interactions occurred during the fieldwork sessions in the classroom, where doubts and incidents were solved.
Indirect quality control	Through internal analysis checking the consistency of the data through pre-processing analysis, where data cleaning, elimination of inconsistencies, duplicate cases, and/or missing data were carried out.

).

Before carrying out the fieldwork, we designed a database with a total of seventy-seven variables grouped into eight dimensions (see

Table 2. The variables and dimensions of this study.

Dimensions	Variables	Software
Dimension 1. Cellar identification data	1. Cellar name 2. Address 3. Postal code 4. Locality 5. Municipality 6. Island 7. Community 8. Main activity 9. Type of company 10. Telephone 11. Second telephone.	No software Manually
Dimension 2. Level of digital development	12. Email 13. web URL 14. External web URL 15. Type of web 16. Facebook 17. Twitter 18. Instagram 19. Pinterest 20. YouTube 21. TikTok 22. LinkedIn 23. WhatsApp 24. Vimeo 25. Level of digital development	No software Manually
Dimension 3. Web traffic analysis	26. Traffic 27. Time of visit 28. Bounce rate 29. Number of pages viewed 30. Traffic 31. Time of visit 32. Bounce rate 33. Number of pages viewed	Semrush
Dimension 4. Level of online commercial development according to the GMS programme	Twenty-two variables, all grouped into eight categories, are shown in Table 3.	Grow my store
Dimension 5. Web authority analysis	58. Domain authority 59. Linking root domain 60. Ranking keywords 61. Spam score 62. Domain authority 63. Linking root domain 64. Ranking keywords 65. Spam score	Moz
Dimension 6. Privacy and non-intrusiveness	66. Legal notice 67. Data protection 68. Cookies policy 69. Language accessibility	No software Manually
Dimension 7. Use of technology	70. Spending on technology 71. Number of technologies 72. Technology data 73. Technology expenditure 74. Number of technologies 75. Technology data	Built with
Dimension 8. Content resources	76. Internal resources 77. External resources	Website Auditor

). To be precise, the dimensions are (1) cellar identification data; (2) level of digital development; (3) web traffic analysis; (4) level of online commercial development according to the Grow My Store (GMS) programme; (5) web authority analysis; (6) privacy and non-intrusiveness; (7) use of technology; and (8) content resources.

Thus, dimension 1 refers to the contact information variables: (1) cellar name, (2) address, (3) postal code, (4) town, (5) municipality, (6) island, (7) province, (8) main activity, (9) type of enterprise, (10) telephone, and (11) second telephone. All of them are qualitative and nominal, and the data are obtained by searching for information on the Internet.

Dimension 2 also aims to analyse the cellars’ digital literacy level. For this purpose, the following qualitative dichotomous variables are used: (12) email, (13) web URL, (14) external web URL, (15) type of web, (16) Facebook, (17) Twitter, (18) Instagram, (19) Pinterest, (20) YouTube, (21) TikTok, (22) LinkedIn, (23) WhatsApp, (24) Vimeo, and (25) level of digital development. The data were obtained from a search on the company’s website. Within this dimension, it is worth highlighting variable 15, “Type of website”, which refers to whether the cellar has its own website, which is assigned a 2, or whether it does not have its own website but has a third-party website, which is assigned a 1. If neither of these applies, i.e., the cellar has neither its own website nor a third-party website, it is assigned a 0. Similarly, variable 25, “Level of digital development”, is scored as follows based on the conceptual framework of [79,80]: (0) No website, no Social Media; (1) No website, no Social Media, but mentions in networks; (2) No website, no Social Media, but mentions in SERPS; (3) Has a simple website; (4) Has a basic website and a profile in social media; (5) Has a mobile website, requests information with forms, and allows price inquiries; (6) Has a website that allows commercial transactions.

Dimension 3 therefore identifies the variables that make it possible to analyse the traffic on the websites of wine producers. This dimension examines the following variables: (26) traffic, (27) time of visit, (28) bounce rate, (28) number of pages viewed, (30) traffic, (31) time of visit, (32) bounce rate, (33) number of pages viewed. The difference between these last four variables is that they collect the information from the cellar’s external website in cases where the cellar does not have its own website. The variables are continuous, and the data are obtained from various online SEO tools such as Similarweb and Semrush.

Dimension 4, Grow My Store, is also the largest of all, as it has twenty-two variables, all grouped into eight categories. In addition, there are two other variables: one that measures the overall score level according to whether it is basic (1), advanced (2), or excellent (3), and another that measures the overall score as a percentage, as shown in

Table 3. Variables Grow My Store.

Product information	34. Product details 35. Product Reviews/Product Ratings 36. Product search 37. Product prices
Shop information	38. Opening hours 39. Directions to the shop 40. Geolocation
Personalisation	41. Personalised accounts 42. Wish list and favourites
Shopping without hassle	43. Pick up in shop 44. Online ordering/returns to store
Flexible distribution	45. Shopping basket 46. Next-day delivery 47. Free returns 48. Various methods of payment
Customer service	49. Contact telephone number 50. Live chat 51. Return policy 52. Social Media
Security	53. HTTPS Mobiles
Mobiles	54. Mobile speed 55. Mobile optimised
	56. Overall score level
	57. Total overall score (%)

. As can be seen, this tool aims to measure a series of variables related to the commercial development of online cellars and digital market orientation. All these variables are dichotomous since the answer is whether the company has (1) or does not have (0) what each variable refers to. The Grow My Store tool provides all the data by generating a report based on the web address provided.

For dimension five on web authority analysis, four variables are analysed: (58) domain authority, (59) linking root domain, (60) ranking keywords, and (61) spam score. As for dimension 3, four variables were also created for those cellars that did not have their own website but had a third-party website: (62) domain authority, (63) linking root domain, (64) ranking keywords, and (65) spam score. This dimension aims to measure the SEO quality of the cellars. The variables are continuous and obtained through the MOZ online tool.

Dimensions 6, 7, and 8 include variables that can be used to measure and analyse a wide range of aspects, such as privacy and non-intrusiveness, the use of technology, and the generation of relevant and interesting content for the customer so that he/she feels attracted.

The following monitoring tools were used to analyse the above variables: The Built-With tool was used to analyse the variables in dimension 7. This tool is used to analyse web addresses, generate reports, and provide competitive analysis and intelligence that allows companies to make decisions about their technology adoption [81]. In addition, the Website Auditor tool is used to collect content-related data. Among many other SEO and SEM functions, it can measure the resources a website uses to generate content. These can be from HTLM, JavaScript, CSS, images, videos, Flash, PDF, and others [82]. MOZ is also a tool that measures SEO, inbound, and content marketing variables. It has several free features [83]. The Semrush marketing platform works similarly, analysing SEO, content marketing, market research, advertising, and social networks [84]. Similarweb analyses websites and provides various variables of interest, such as audience, rankings, competitors, or channels [85]. In addition, Grow My Store is a Google tool that provides businesses with a report of their website experience and advice on improving the customer experience and reaching a wider audience. The score is given in four categories: (1) basic (0–25%), (2) standard (25–50%), (3) advanced (50–75%), and (4) excellent (75–100%) [86]. In addition, it measures a total of twenty-two variables within eight categories, as shown in Table 3.

4. Analysis of the Results

The analysis of the results is divided into three sections, referring to the hypotheses formulated in the literature review. Firstly, the preliminary analysis section describes the work conducted to pre-process the data and the steps taken to typify them to rank the variables and work with them later. This is followed by the second section of in-depth analysis, in which, once the data have been standardised and the new variables have been created, we will be able to empirically contrast the second hypothesis as regards the possibility of segmenting the cellars. Finally, the last section refers to the hypothesis (3) empirical contrast to test whether the inbound dimensions determine the cellars’ popularity, relevance, and credibility.

4.1. Preliminary Analysis to Test Hypothesis (1)

Once the content validity has been tackled in the literature review and the quantitative variable description has been shown in the methodology, we must make new variables suitable to rank. For this reason, 16 new variables have been created by carrying out the calculations shown in

Table 4. New Variables.

New Variable	Formula
V1. Security and privacy	Legal + data protection + cookie policy + HTTPS
V2. Technology and data	Technology expenditure + number of technologies + data technology + data technology
V3. Content resources	Internal resources + external resources
V4. SEO quality	(Domain authority + linking root domain + ranking keywords)—spam score
V5. Product information	Product details + product reviews/scores + product search + product price + product price
V6. Shop information	Opening hours + directions to the shop + geolocation
V7. Personalisation	Personalised accounts + wish lists, and favourites
V8. UX shopping simplicity	In-store pickup + online orders/returns to shop
V9. Flexible distribution	Shopping cart + next day delivery + free returns + multiple payment methods
V10. Customer service	Contact telephone numbers + live chat + returns policy + social networks
V11. Mobile optimisation	Mobile speed + mobile optimisation
V12. Online Orientation	V5 + V6 + V7 + V8 + V9 + V10 + V11.
V13. Number of Social Media	Facebook + Instagram + Twitter + Pinterest + YouTube + TikTok + LinkedIn + WhatsApp + Vimeo
V14. Qualitative. UX attraction and interest	0: if no website; 1: if external website; 2: with own website but no traffic; 4: when traffic amounts to 100 but does not give session metrics; 6: if > 200 and gives session metrics; 8: if > 400; 12 if > 600; 18: if > 900; and 20 if > 1000.
V15. Level of digital development	Same as variable 25. “Level of digital development” is unchanged.
V16. Overall Inbound	Sum of all V.

.

After creating the new 16 variables, we conducted a bivariate correlation analysis using Pearson’s coefficient to check the degree of similarity between the cellars. This coefficient denotes the extent of association between two continuous variables. Based on the correlation analysis outcomes presented in

Table 5. Correlation test.

Correlations
		v2	v3	v4	v5	v6	v7	v8	v9	v10	v11	v12	v13	v14	v15	v16
v1	P	0.568 **	0.372 **	0.438 **	0.700 **	0.484 **	0.587 **	0.519 **	0.547 **	0.666 **	0.640 **	0.724 **	0.669 **	0.547 **	0.790 **	0.807 **
	Sig.	0.000	0.004	0.001	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	59	59	59	59	59	59	59	59	59	59	59	59	59	59	59
v2	P	1	0.806 **	0.345 **	0.571 **	0.421 **	0.395 **	0.506 **	0.639 **	0.624 **	0.546 **	0.650 **	0.513 **	0.730 **	0.630 **	0.828 **
	Sig.		0.000	0.008	0.000	0.001	0.002	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N		59	59	59	59	59	59	59	59	59	59	59	59	59	59
v3	P		1	0.190	0.545 **	0.397 **	0.370 **	0.472 **	0.599 **	0.617 **	0.448 **	0.608 **	00.236	0.549 **	0.433 **	0.690 **
	Sig.			0.150	0.000	0.002	0.004	0.000	0.000	0.000	0.000	0.000	0.072	0.000	0.001	0.000
	N			59	59	59	59	59	59	59	59	59	59	59	59	59
v4	P			1	0.321 *	0.094	0.169	0.280 *	0.270 *	0.221	0.154	0.268 *	0.380 **	0.342 **	0.395 **	0.512 **
	Sig.				0.013	0.479	0.201	0.032	0.038	0.093	0.245	0.040	0.003	0.008	0.002	0.000
	N				59	59	59	59	59	59	59	59	59	59	59	59
v5	P				1	0.681 **	0.602 **	0.549 **	0.596 **	0.778 **	0.774 **	0.897 **	0.467 **	0.660 **	0.699 **	0.838 **
	Sig.					0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N					59	59	59	59	59	59	59	59	59	59	59
v6	P					1	0.375 **	0.517 **	0.610 **	0.639 **	0.632 **	0.816 **	0.190	0.532 **	0.581 **	0.656 **
	Sig.						0.003	0.000	0.000	0.000	0.000	0.000	0.150	0.000	0.000	0.000
	N						59	59	59	59	59	59	59	59	59	59
v7	P						1	0.583 **	0.525 **	0.548 **	0.447 **	0.682 **	0.480 **	0.532 **	0.427 **	0.642 **
	Sig.							0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.001	0.000
	N							59	59	59	59	59	59	59	59	59
v8	P							1	0.698 **	0.749 **	0.438 **	0.767 **	0.455 **	0.603 **	0.585 **	0.742 **
	Sig.								0.000	0.000	0.001	0.000	0.000	0.000	0.000	0.000
	N								59	59	59	59	59	59	59	59
v9	P								1	0.666 **	0.519 **	0.811 **	0.284 *	0.545 **	0.547 **	0.768 **
	Sig.									0.000	0.000	0.000	0.029	0.000	0.000	0.000
	N									59	59	59	59	59	59	59
v10	P									1	0.706 **	0.888 **	0.566 **	0.667 **	0.783 **	0.855 **
	Sig.										0.000	0.000	0.000	0.000	0.000	0.000
	N										59	59	59	59	59	59
v11	P										1	0.800 **	0.381 **	0.578 **	0.679 **	0.736 **
	Sig.											0.000	0.003	0.000	0.000	0.000
	N											59	59	59	59	59
v12	P											1	0.475 **	0.720 **	0.759 **	0.919 **
	Sig.												0.000	0.000	0.000	0.000
	N												59	59	59	59
v13	P												1	0.522 **	0.736 **	0.652 **
	Sig.													0.000	0.000	0.000
	N													59	59	59
v14	P													1	0.644 **	0.811 **
	Sig.														0.000	0.000
	N														59	59
v15	P														1	0.842 **
	Sig.															0.000
	N															59

** Correlation is significant at 0.01 (bilateral)/* The correlation is significant at 0.05 (bilateral).

, nearly all the variables have a strong relationship with a significance level of 0.01 or less and a significant relationship with a statistical significance level of 0.05 or less. Variable 3, ‘content resources’, is associated with all inbound marketing variables except for variable 4, known as ‘SEO quality’, and variable 13, referred to as ‘number of social media channels’. Variable 4, named ‘SEO quality’, is not related to variables 6 (also known as ‘shop information’), 7 (‘personalisation’), 10 (‘customer service’), and 11 (‘mobile optimisations’). There is no association between variable 6, which refers to “shop information”, and variable 13, which denotes the “number of social media”. Therefore, the cellars’ inbound marketing levels are similar but slightly different. Otherwise, building a cellar’s ranking would be impossible.

This study aims to create a measurement tool to identify and compare the extent of inbound marketing in the Canaries’ wine cellars. For this purpose, all the variables were standardised and summed up to obtain a single variable encapsulating all the aspects of inbound marketing. It allows sorting the cellars in ascending order and, consequently, ranking them based on their inbound marketing dimensions (see

Table 6. Inbound marketing ranking.

Ranking	Name	Value	Ranking	Name	Value
1	Bodega El Grifo	40.60	31	Bodega La Marzagana	−9.89
2	Territorio Sibarita	34.62	32	Bacardi	−9.93
3	Hermógenes Vera López	23.17	33	S.A. T. Bodegas De Vilaflor	−9.98
4	Vinos Artesanales De Calidad	22.45	34	Bacardi	−9.99
5	La Geria	18.51	35	Vinomelca S.L.	−10.63
6	Bodega San Diego	17.68	36	Havaianas	−10.91
7	Bodegas 7 Islas	17.06	37	Bodegas Aceviño	−11.20
8	Hermanos Mesa	15.28	38	Bodega S.A. T. Prodiflora	−11.34
9	Eugenio Reveron Sierra S.L.	14.56	39	Vinos Artesanales De Calidad S.L.	−11.34
10	Bodegas 7 Islas	12.38	40	Bodega S.A. T. Prodiflora	−11.47
11	Bodegas Los Bermejos	12.34	41	Bodegas Timanfaya	−11.75
12	La Higuera Mayor	11.72	42	S. Coop. Las Eras De Arico	−11.75
13	Bodegas Insulares Tenerife S.A.	11.06	43	Bodegas Antonio Suarez S.L.	−11.88
14	Bodegas Viñatigo	10.88	44	Bodega El Tesoro	−11.88
15	La Despensa	10.71	45	Koka Kaffe Vegueta S.L.	−11.88
16	S.A. T. Bodegas El Hoyo N° 6223	8.90	46	S. Coop. Las Eras De Arico	−11.88
17	Bodegas Tunte	8.47	47	Bodegas Viñamonte	−11.88
18	Bodega De Parrado	7.71	48	Bentayga	−11.88
19	Bodegas Álvaro	7.57	49	Raimundo J. Hernandez Jorge	−11,88
20	Bodegas Guiguan S.L.	6.75	50	Vediamoci S.L.	−11.88
21	Tabaibilla	6.72	51	Bodegas Fronton De Oro	−11.88
22	Bodega Tajinaste	6.50	52	Bodega El Tesoro	−11.88
23	Pernod Ricard Winemakers Spain S.A.	5.56	53	Narciso Luis Hernandez Mora	−11.88
24	Bogega Juan Matias Torres	5.41	54	Sat 6218 Bentayga	−11.88
25	Bodegas Bentayga	3.27	55	Juan Dios	−11.88
26	El Mocanero S.L.	2.57	56	Bodegas La Ovejera	−11.88
27	Bodega Valleoro	2.42	57	Miguel Medina Perdomo	−11.88
28	Mencey Chasna	1.10	58	Miguel Medina Perdomo	−11.88
29	Bodegas Onésimo Pérez Rodríguez	−4.86	59	Cremeria Que Leche S.L.	−11.88
30	Bodegas Manuel Quintana Naranjo	−8.95

). As shown, El Grifo and Territorio Sibarita have the best inbound marketing practices. Therefore, in light of the obtained results, Hypothesis (1), stating that the inbound dimensions might be used to rank the cellars on the internet, is confirmed.

4.2. Analysis to Test Hypothesis (2)

To empirically contrast Hypothesis (2), we employ non-hierarchical K-means cluster analysis. Non-hierarchical clustering methods, instead of hierarchical clustering methods, lend themselves to more rigorous analysis as they are objective and statistically significant. This leads to superior decision-making and optimised solutions. Large databases with numerous cases and variables often employ these methods because they can segment large datasets. Consequently, the influence of anomalies on the results has decreased.

Additionally, the ANOVA table provides insight into the role of each variable (see

Table 7. ANOVA.

Cluster			Error		F	Sig.
	Quadratic Mean	d.f.	Quadratic Mean	d.f.
V1. Security and privacy	8.211	1	2.781	57	2.952	0.091
V2. Technology and data	208.994	1	3.753	57	55.691	<0.001
V3. Content resources	41,775,915.314	1	164,576.917	57	253.838	<0.001
V4. SEO quality	1.919	1	6.008	57	0.319	0.574
V5. Product information	18.213	1	1.785	57	10.206	0.002
V6. Shop information	8.707	1	1.441	57	6.042	0.017
V7. Personalisation	2.710	1	0.400	57	6.769	0.012
V8. UX shopping simplicity	5.034	1	0.412	57	12.205	<0.001
V9. Flexible distribution	17.086	1	0.890	57	19.205	<0.001
V10. Customer service	10.679	1	0.614	57	17.400	<0.001
V11. Mobile optimisation	4.605	1	0.529	57	8.710	0.005
V12. Online orientation	426.648	1	24.261	57	17.586	<0.001
V13. Number of Social Media	0.025	1	3.404	57	0.007	0.932
V14. Qualitative. UX attraction and interest	470.010	1	45.188	57	10.401	0.002
V15. Level of digital development	15.415	1	5.548	57	2.778	0.101
V16. Overall Inbound	2928.553	1	132.569	57	22.091	<0.001

), which, in this case, serves as segmentation criteria to create segments of cases with diverse factor profiles. A factor is considered significant when its value is less than or equal to 0.05. Upon conducting this analysis, all segmentation criteria were found to be significant except for variables 1 (security and privacy), 4 (SEO quality), 13 (number of Social Media), and 15 (level of digital development).

As shown in

Table 8. Clusters.

Final Clusters
	Cluster
	1	2
V1. Security and privacy	3.50	1.44
V2. Technology and data	10.05	−0.35
V3. Content resources	4821.00	171.18
V4. SEO quality	0.96	−0.03
V5. Product information	4.00	0.93
V6. Shop information	3.00	0.88
V7. Personalisation	1.50	0.32
V8. UX shopping simplicity	2.00	0.39
V9. Flexible distribution	3.50	0.53
V10. Customer service	4.00	1.65
V11. Mobile optimisation	2.00	0.46
V12. Online orientation	20.00	5.14
V13. Number of Social Media	1.50	1.39
V14. Qualitative. UX attraction and interest	20.00	4.40
V15. Level of digital development	5	2
V16. Overall Inbound	37.61	−1.32
Number of cases in every segment
	Clusters	Cellars
Clusters	1	2
	2	57
Valid cases		59
Lost		1

, most of the studied cellars are concentrated in Cluster 2. On the other hand, Cluster 1 is comprised of cellars with the most effective inbound marketing practices. These two cellars are El Grifo and Territorio Sibarita, situated on the island of Lanzarote. This finding is consistent with the top-ranked cellars indicated in Section 1 of the analysis of the results. Therefore, the findings show convergent validity and suggest that it is possible to segment cellars based on inbound marketing. Therefore, Hypothesis (2), stating that the inbound marketing criteria might be used to describe cellars’ segments, can be confirmed.

4.3. Analysis to Test Hypothesis (3)

To test Hypothesis (3), a multivariate causal analysis is performed using the statistical technique of linear regression. The dependent variable is domain authority, and the independent variables are all except the SEO quality variable and the “general inbound marketing” variable, as both include the dependent variable. After performing the analysis, it is observed that the R-squared has a value of 0.529 (see

Table 9. t-statistics linear regression.

Model	R	R-Squared	St. Error	Durbin-Watson
1	0.728 a	0.529	13.56111	1.824
Model		B	Error	Beta (St. Cef.)	t	Sig.
(Constant)		6.49	5.91		1.09	0.27
V1. Security and privacy		3.07	2.09	0.30	1.47	0.14
V2. Technology and data		0.63	1.79	0.09	0.35	0.72
V3. Content resources		−0.001	0.00	−0.05	−0.25	0.80
V5. Product information		5.11	2.69	0.42	1.89	0.06
V6. Shop information		−1.42	2.38	−0.10	−0.59	0.55
V7. Personalization		−10.27	4.36	−0.39	−2.35	0.02
V8. Simplicity UX purchase		1.06	4.70	0.04	0.22	0.82
V9. Flexible distribution		6.36	3.04	0.39	2.08	0.04
V10. Customer service		−4.81	5.11	−0.24	−0.94	0.35
V11. Mobile optimisation		−3.73	4.21	−0.16	−0.88	0.38
V13. Number of Social Media		1.97	2.02	0.20	0.97	0.33
V14. Qualitative experience		0.512	0.45	0.21	1.12	0.26
V15. Level of digital development		−0.12	1.91	−0.017	−0.06	0.94

^a For an exponential expression: 1¹.

). Likewise, it is also observed that the Durbin-Watson statistic has a value of 1.824, so it is affirmed that there is no autocorrelation problem.

Notably, out of all variables entered, only two are significant, i.e., variable 7, “customisation”, and variable 9, “flexible distribution”, as their t-statistic values are more significant than 2. Hence, the need for more customisation and specification towards the customer, along with flexible distribution, mainly determines domain authority. Therefore, Hypothesis (3) confirms that the inbound marketing criteria influence cellars’ domain authority.

Table 10. The hypotheses contrast.

H1. The inbound dimensions might be used to rank the cellars on the internet.	Accepted
H1 (1). The level of security and privacy might be used to rank the cellars on the internet.	Accepted
H1 (2). The level of technology and data might be used to rank the cellars on the internet.	Accepted
H1 (3). The level of content resources might be used to rank the cellars on the internet.	Rejected
H1 (4). The level of search engine optimisation quality might be used to rank the cellars on the internet.	Rejected
H1 (5). The level of e-commerce store growth might be used to rank the cellars on the internet.	Accepted
H1 (6). The number of social media profiles might be used to rank the cellars on the internet.	Accepted
H1 (7). The level of digital development might be used to rank the cellars on the internet.	Accepted
H2. The inbound marketing criteria might be used to describe cellars’ segments.	Accepted
H2 (1). The level of security and privacy might be used to describe cellars’ segments.	Rejected
H2 (2). The level of technology and data might be used to describe cellars’ segments.	Accepted
H2 (3). The level of content resources might be used to describe cellars’ segments.	Accepted
H2 (4). The level of search engine optimisation quality might be used to describe cellars’ segments.	Rejected
H2 (5). The level of e-commerce store growth might be used to describe cellars’ segments.	Accepted
H2 (6). The number of social media profiles might be used to describe cellars’ segments.	Rejected
H2 (7). The level of digital development might be used to describe cellars’ segments.	Rejected
H3. The inbound marketing criteria influence cellars’ domain authority.	Accepted
H3 (1). The level of security and privacy influences cellars’ domain authority.	Rejected
H3 (2). The level of technology and data influences cellars’ domain authority.	Rejected
H3 (3). The level of content resources influences cellars’ domain authority.	Rejected
H3 (4). The level of search engine optimisation quality influences cellars’ domain authority.	Rejected
H3 (5). The level of e-commerce store growth influences cellars’ domain authority.	Rejected
H3 (6). The number of social media profiles influences cellars’ domain authority.	Rejected
H3 (7). The level of digital development influences cellars’ domain authority.	Rejected

shows information about the empirical contrast of the hypotheses, and it can be seen that while thirteen hypotheses are rejected, eight hypotheses are verified.

5. Discussion

This study argues that inbound marketing dimensions can be employed to rank cellars on the Internet because they define and devise metrics to describe and measure them. Previous studies showed how to refer to inbound marketing and suggested that it is a relevant notion and philosophy [87,88], but none had found out how to operate with it until now. Inbound marketing overcomes its preceding oversimplifications and vague definitions to pave the path for a well-founded concept to rank businesses and organisations.

Not only do we demonstrate that inbound marketing is a valid instrument to measure good practices, but it is also helpful to distinguish segments by considering specific criteria. Earlier studies had indicated the crucial variables to distinguish between inbound and outbound policies [15,89]. However, they had yet to utilise knowledge to pin down homogeneous groups for marketers to implement differentiated policies. Nevertheless, this study could not say that security and privacy, SEO quality, the number of social media sites, and the level of digital development could be used to segment cellars. This contradicts some existing research, whose findings assert the importance of these variables to distinguish between inbound and outbound marketing [90]. Paradoxically, digital technology is not vital for supporting differentiated digital marketing strategies. However, it does mean that we do not highlight the importance of keeping e-commerce up to date [91]. This contradiction raises questions about why security and privacy cannot be used to segment, and a possible answer might refer to the minimal standards met by all the cellars. In other words, if security and privacy are strongly regulated and followed, their level of development must be quite similar and, hence, cannot be used to distinguish different segments. Similarly, if SEO quality and the level of digital development are not differentiating the cellars, it must be because they are grouped within the same Regulatory Councils, which might determine the same outbound links and digital resources and capabilities. In this vein, Ref. [73] claims that SEO appears to be less influential in inbound marketing, and, hence, our obtained results are consistent. Finally, a point that can be made is that it is not the number of social media accounts that matters the most, but rather the quality of their content and community management. In addition, the most likely explanation is that social media shows the same differences across the cellars.

While it may well be true that domain authority is a valid metric for practitioners [92], it could have been more academically clear how inbound marketing improves the presumed visibility, popularity, and relevance granted by those brands reaching the highest scores. Therefore, previous usage shows inconclusive evidence and insufficient proof of the causal relationship between inbound marketing and domain authority. Although many authors would agree that domain authority is crucial in digital marketing terms [73], the explanations were merely descriptive and devoid of theoretical applications. We should acknowledge that there are only two crucial determining variables for domain authority: personalisation and flexible distribution. The key to understanding it might rest on their similar notions, insofar as both variables linking to domain authority refer to customisation. No doubt, this finding coincides with other research works, highlighting keywords, originality, and intelligent content adaptation as the most suitable tools to reach qualified users [93]. To put it simply, it is the inbound customisation that directly leads to an increase in domain authority, and, undoubtedly, it is confirmed by other research works [93,94,95].

6. Conclusions

To summarise, this study defines and describes, with clarity and detail, the concept of inbound marketing. The principal reason for doing it is pragmatic, as it elaborates a measuring instrument to rank the cellars, segment their e-commerce activities, and account for the causal effect on domain authority. The inbound application takes place in the Canary Islands, with a long-lasting winery tradition. However, the evolution has not been as linear and continuous as in other Spanish regions. It defines inbound marketing as attraction marketing and describes a list of fifteen variables ranging from security and privacy, technology and data, content resources, SEO quality, product and shop information, personalisation, user experience simplicity, customer service, mobile optimisation, online orientation, social media profiles, attraction, and level of digital development. These inbound ingredients are strongly correlated and can refer to the same concept in different dimensions. Moreover, these dimensions can be used to rank the cellars; most can be considered criteria to segment, and only a few are determining factors to enhance their popularity, relevance, and credibility.

This study allows extrapolating the inbound marketing measuring instrument to other industries and markets for several reasons. First, the software used to extract the metrics is available worldwide and either free or cheap to contract. Second, as the inbound measuring instrument developed in this study consisted of quantitative metrics, the variables are measurable and subject to mathematical calculation. Simply put, the inbound marketing measuring instrument and segmentation can be replicated in similar industries and organisations with minimal digital maturity.

In light of these findings, we may provide some practical applications. Concerning the ranking, it seems logical to address a benchmarking strategy against the best cellars in Lanzarote and Tenerife. Secondly, marketers should acknowledge the crucial criteria for distinguishing different cellar segments for segmentation purposes. To be specific, marketers should pay careful attention to content resources, technology, and data because these two dimensions are the most discriminating variables to segment. In this vein, we recommend that inbound marketers work on content creation and brand-focused campaigns to guide enotourism professionals to carry out inbound marketing strategies and attract potential customers of cellar e-commerce on the Internet. In addition, flexible distribution, customer service, and e-commerce growth emerge as crucial regarding segmentation.

Conversely, as the number of social media, the level of digital development, and SEO quality are not making differences between cellars, it seems logical to recommend that once digital marketers ensure a minimum level of performance, they turn their attention to other more significant variables to differentiate.

Thirdly, it seems advantageous to bear in mind that both show two sides of the same coin: personalisation and flexible distribution, notwithstanding a couple of variables for enhancing domain authority. These inbound marketing practises affect the relevance, reliability, quality, and popularity of e-commerce, determining the appearance of results in search engines.

The results achieved can have a wide range of future research implications. Firstly, the term ‘brand-content focused’ should be applied and studied, which refers to creating an authentic and relevant brand for customers by deeply understanding their needs and creating personalised digital experiences. For this, we recommend using Google’s free “Grow My Store” tool, which generates a comprehensive report on the current state of e-commerce for inbound marketing dimensions, including personalisation, security, technology, and valuable shop and product content. Thus, it can measure and analyse the results to optimise the e-commerce design and provide a positive user experience. Nevertheless, new software is yet to be created beyond this Google tool for specific inbound purposes. Second, as professionals must be aware of trends and continuously adapt their inbound marketing strategy to the target market’s needs, we might further research what role influencers can play. In addition, we should study how highly qualified community managers can handle social media networks with maximum efficacy and efficiency, regardless of the number of social media profiles. On the other hand, it might be useful to develop and estimate an inbound marketing model that distinguishes the antecedent or causal structural relationships between the different inbound variables or factors, i.e., estimating a structural equation model.

It is essential to note the limitations of this academic work. Firstly, we highlight the missing cases since the preliminary database initially had 133 cellars across the Canary Islands. However, after the fieldwork and pre-processing of data, we were left with only 59 cellars in Tenerife, Gran Canaria, Lanzarote, La Palma, and El Hierro. Nevertheless, the final selected sampling units were the most representative of the Canary Islands and had the best practices. However, conducting fieldwork with a probabilistic sampling procedure is advisable for a more outstanding market. Secondly, it highlights the problem with using free SEO and SEM tools. Although they are essential for finding data like traffic and time of visit, many of these tools limit queries to a maximum of 3 to 5 daily searches or do not provide data for small websites. However, we have provided a more straightforward definition and description of the concept of inbound marketing through a theoretical review of the term. Consequently, the content validity of our measurement instrument has been assessed. In addition, convergent validity has been explored through the correlation analysis carried out for the different dimensions of inbound marketing. Therefore, we have not employed statistically advanced techniques, such as confirmatory factor analysis, which will validate the inbound marketing measurement instrument more strongly. To overcome the identified limitations or gain more knowledge, future research should increase the sample size by contacting the cellars’ web administrators to decrease the sampling error. Furthermore, extending this measurement instrument to other application sectors could allow for the analysis of inbound marketing activities in e-commerce. In this regard, a prospective line of research will emphasise the importance of inbound marketing in gaining insights into the functioning of e-commerce and the digital development of cellars and enoturism.