Metaverse and Fashion: An Analysis of Consumer Online Interest

Abstract

Recent studies have demonstrated the value that the Internet and web applications bring to businesses. Among other tools are those that enable the analysis and monitoring of searches, such as Google Trends, which is currently used by the fashion industry to guide experiential practices in a context of augmented reality and/or virtual reality, and even to predict purchasing behaviours through the metaverse. Data from this tool provide insight into fashion consumer search patterns. Understanding and managing this digital tool is an essential factor in rethinking businesses’ marketing strategies. The aim of this study is to analyse online user search behaviour by analysing and monitoring the terms “metaverse” and “fashion” on Google Trends. A quantitative descriptive cross-sectional method was employed. The results show that there is growing consumer interest in both concepts on the Internet, despite the lack of homogeneity in the behaviour of the five Google search tools.

1. Introduction

One area the fashion industry is exploring in order to drive its renewal and attract consumers is the metaverse. For the fashion industry, this new digital world offers the possibility to generate community and create brand experiences [1].

Fashion is being integrated into the metaverse in various ways: from videogames that allow players to purchase outfits for an avatar in order to customize it, through to applications with built-in augmented reality that makes it easier to visualize a product, and even non-fungible tokens or NFTs, digital assets that represent real-world objects such as art, music, game pieces, etc. They are bought and sold online, often with cryptocurrencies, and represent an interesting opportunity for the industry to increase its revenues [2].

The search for information carried out by consumers online through the new empowerment offered by the Internet and its relationship with purchasing and consumption behaviour is currently attracting significant attention. One of the topics that is currently receiving most attention from marketing specialists is the search for information online carried out by users using the terms “fashion” and “metaverse” [3], as well as its relationship with the purchasing and consumption behaviour of this product category. Here, we investigate whether search analysis and monitoring tools such as Google Trends make it possible to understand or anticipate consumer behaviour to guide digital marketing practices [4]. In this study, we aim to address three research questions: RQ1—Where are the users who most frequently use the terms “metaverse” and “fashion” in their Internet searches located? RQ2—What other words are associated with the terms “metaverse” and “fashion” in Internet searches? RQ3—Are there significant differences in search keywords between the different Google corporate communication platforms?

Google Trends is an application that provides information on Internet search trends based on keywords without specifically indicating social content. It enables the evolution of searches over a certain period (from 2004 to the present) to be graphically visualised and can, among other procedures, analyse social behaviours or possible seasonality. Other applications such as Ubersuggest, Keywordini, Google Keyword Tool, and Follow the Hashtag also provide information on Internet search trends [5].

In this study, we ask whether Google Trends could be useful for companies in the fashion sector to guide their digital marketing practices and even predict consumer behaviour using keywords such as “metaverse” and “fashion”. The metaverse is a place where predominantly young fashion consumers lead lives online, with avatars that can move, talk, and be customised to be seen anywhere [6,7].

This study contributes to scientific progress by being one of the first studies to monitor the absolute values of keyword searches, thus constituting a valuable asset for the analysis of consumer search behaviour [8].

Our findings also provide a guideline for the development of efficient and effective digital marketing. Based on the results, we provide recommendations for companies in the fashion sector on how to improve the use of keywords to obtain better SEO (Search Engine Optimisation) positioning. Companies in turn can benefit from a deeper understanding of user behaviour on online search engines in exceptional periods such as that resulting from the emergence of the metaverse, which represents a fundamental change in the way consumers, brands and businesses will carry out transactions and interact in a seamlessly interconnected space of virtual realities [9].

Despite these previous studies linking Internet search activity and consumption, it is important to realize that consumer behaviour is always complex. This leads us to ask several research questions:

• Where are the users who most frequently use the terms “metaverse” and “fashion” in their Internet searches located?
• What other words are associated with the terms “metaverse” and “fashion” in Internet searches?
• Are there significant differences in search keywords between the different Google corporate communication platforms?

While the idea of the metaverse has been explored for decades in science fiction and videogames, only in recent years has it gained greater attention and popularity in the fashion industry [10]. In 2022, several fashion brands used the metaverse in their digital marketing campaigns. Therefore, we chose this period as the subject of our fieldwork. It is expected that in the coming years, the technology and infrastructure necessary for the creation of metaverses will continue to advance, and it will be of interest to analyse them in greater depth.

2. Unlocking Consumer Insights: Maximizing Google Trends for SEO Strategy Enhancement

Google is the most powerful and well-known search engine in the world. As of January 2024, Google represented 82% of the market share of leading desktop search engines worldwide, followed by Bing with a 10% share [11]. It publishes buzzword statistics on a separate website called Google Trends, which displays the popularity of specific terms, topics, or keywords by geographical location, time range, categories, and type of web search.

Google Trends searches use only a sample of Google searches due to the large volume of data handled by the platform. Through data sampling, a representative dataset of all Google searches is analysed. To achieve this, the platform normalizes the search data to facilitate comparisons between terms. Following the time and location of the queries, the search results are normalised through two processes: (1) each data point is divided by the total searches for the geographic region and the time interval it represents to compare its relative popularity (this is carried out to prevent locations with the highest search volume from appearing in the top rankings) and (2) the resulting numbers are scaled to a range of 0 to 100 based on the proportion of a topic to the total number of searches on all topics. In this respect, it should be noted that regions that register the same search interest for a term do not always have the same total search volumes.

Google Trends is a Google tool that allows the visualisation of consumer search attitudes of any word or term over time in such a way that the relative value of the volume of searches carried out can be observed. With this tool, it is possible to segment results by geographical level (by country, region, or city), by time (the last hour, the last 7 days, the last 5 years, etc.), by categories (hobbies and free time, shopping, sports, etc.), and by web search (images, news, Google Shopping, and YouTube). It also allows researchers to compare trends of up to five other words at the same time.

Users can download these data directly from the website or obtain a comparative analysis through graphs, figures and tables on specific words or terms [12]. Dergiades et al. [13] state that this “web-related data” can influence business activities and predict possible trends in the market and industry.

In the context of marketing and in relation to consumer behaviour, researchers use Google Trends data to understand consumers’ interest in products, brands, and industry trends. In this regard, Silva et al. [14] state that Google Trends can be considered an indicator of consumer fashion behaviour, with specific examples of brands that could use this tool to improve decision-making, while other authors have shown that it can be used to understand the market performance of new products [15]. Along these lines, Karamitros Dimitrios and Fouskas [16] state that Google Trends can be used to recognize the impact of a company’s brand and customer demand based on empirical research.

Choi and Varian [17] demonstrate through statistical methods (autoregressive models) that Google Trends can predict the present (short-term) events of economic variables such as consumer confidence. These figures are usually officially published several days after the end of each month, so Google Trends can predict them a month in advance and long before the official report comes out.

3. SEO Strategies

The Google Trends tool is very useful for boosting an SEO (Search Engine Optimisation) strategy directly linked to the search activity of consumers. For Orense Fuentes and Rojas Orduña [18], SEO as a discipline is the process by which a web page obtains and maintains notable positions in search engine results pages (SERPs), also called organic results (deriving from a large organic database) or algorithmic (depending on an algorithm for their ordering).

Mauresmo and Petrova [19] offer a guide to help entrepreneurs position their website (SEO strategies) so that they appear among the top search results. To do this, they suggest using keyword monitoring and analysis tools such as Google in order to understand what consumers are searching for on the Internet. This forms the starting point for optimizing domain names on web pages, configuring words for web pages (home page, page sections, and blogs) and also developing other marketing aspects such as blog comments or forum posts, among other things [20].

4. Exploring the Intersection of Metaverse and Fashion: Understanding the Fashion Consumer in the Metaverse

The concept of metaverse has been growing steadily for some years. Many fashion brands are now taking positions in this virtual world. But what exactly are they looking for? The metaverse (also referred to by the generic term meta) is presented as Web 3, immersive and sensory, revolutionising human interaction. It is a challenging project, but today the existing metaverse only has a few million users, 935 million in 2024 [21]. We are still far from the 5 billion or so Internet users. Futurologists propose a vision for 2030 or 2040 [22], but today, fashion brands are already taking positions in the existing metaverse.

The fashion industry is undergoing a historic metamorphosis, driven by a synergy between emerging technologies and the latest innovations of the 6G era. The emergence of blockchain and non-fungible tokens (NFTs) marks a milestone, while disruptive technologies such as artificial technology (AI) and machine learning (ML) are redefining the standards of creativity and efficiency. Virtual reality (VR) and augmented reality (AR) are intertwined with the metaverse, a space where the boundaries between the real and the virtual are blurred, giving rise to fully immersive shopping and design experiences [7].

However, the coming era of 6G connectivity promises to drive this revolution. A comprehensive study of its architecture, applications, technologies, and challenges reveals new opportunities for the fashion industry. From ultra-fast connectivity to the integration of smart sensors into garments and accessories, the 6G era offers unprecedented potential for personalisation and real-time interaction.

With the capacity to transmit data at astonishing speeds and ultra-low latency, the 6G era paves the way for truly immersive shopping experiences in which consumers can interact with products in 3D and receive personalised recommendations in real time. Moreover, the integration of extended reality (XR) technologies into the metaverse promises to take the shopping experience to new levels, allowing users to virtually explore shops and fashion shows from the comfort of their homes.

As the fashion industry adapts to these cutting-edge innovations, a new era of creativity and collaboration is emerging. Designers can take full advantage of AI- and ML-assisted design tools to create unique, sustainable collections, while consumers enjoy a more personalised and immersive shopping experience than ever before [23].

The metaverse is a three-dimensional virtual world inhabited by avatars of real people. The term was coined by Neal Stephenson in his novel Snow Crash (1992), in which avatars of real people inhabited a three-dimensional (3D) virtual world. In 2021, it became one of the most popular technology terms. Indeed, a Google Trends search shows that the word “metaverse” has been actively searched for since early 2021, beginning when Roblox went public on March 10, and subsequently when Nvidia CEO Jensen Huang stated that the company’s next step was to create a metaverse [24] and when Facebook CEO Mark Zuckerberg announced his decision to change the company’s name to Meta [25].

While the meanings that potential stakeholders ascribe to metaverses may differ, certain commonalities are indisputable. Metaverses universally comprise the following four commonalities: (a) a shared social space with avatars to represent users; (b) a world for avatars to inhabit and interact; (c) a space that allows users to own virtual goods as they would physical goods; and (d) a space that allows users to create their virtual property [7].

The idea of the metaverse extends an already existing concept: that of Second Life, a cross-platform online community created in 2003 in which users create avatars and essentially lead a “second life” instead of their main life in the real world [26]. Unsurprisingly, user interest in Second Life waned over time and many other metaverses currently exist or are in development. Facebook’s official name change to Meta in 2021 signalled its commitment to creating a new and potentially dominant metaverse [7].

With the metaverse as a new social platform, both academics and the fashion industry are asking how these new technologies can reshape brands, reinvent the consumer experience, and alter consumer behaviour [7].

In this digital world, forward-thinking fashion brands are increasing digital product lines for this social space: a place where predominantly young consumers lead lives online with avatars that can move, talk, and customize themselves to look the way their creators want. They can own property, wear clothes, and engage in animated activities. NFTs can serve as a decentralised approach to track and establish ownership of virtual goods as they traverse metaverses owned by different corporations [27].

Recent events such as Metaverse Fashion Week (MVFW) have shown that digital fashion is real and is attracting interest from global brands and fashionistas around the world. Forever 21, for example, introduced the collection of wearables created specifically for the metaverse. For his part, Philipp Plein founded the Museum of NFT Arts, which is open to all users, and launched his first collection for the metaverse with a special catwalk fashion show in collaboration with 3D artist Antoni Tudisco.

For their part, H&M, Gucci, and Ralph Lauren are opening new virtual stores to sell their digital clothing and offer their consumers an immersive shopping experience. Other brands have partnered with gaming platforms, such as Roblox and Fortnite, to launch their digital products. Balenciaga, for example, partnered with Fortnite to give players the ability to purchase branded clothing for their avatars, helping to increase interest in Balenciaga [28].

These global companies are increasingly implementing AI in the design of interactions between their brands and consumers. Many of them are also incorporating NFTs to certify the authenticity of digital images available for purchase [7].

5. The Fashion Consumer in the Metaverse

Currently, the fashion industry is experiencing an inflection point as its core customers shift from millennials to generation Z [29]. By 2025, this generation will account for approximately half of all global sales of personal luxury goods [30]. Individuals born between 1994 and 2010 are the first digital natives, since they have been adopting any technology that facilitates and improves their daily lives from an early age [2]. This is a generation that dominates, by 60%, the metaverse, mainly through video games and applications such as Roblox, Zepeto, or Fortnite [31].

Thanks to hyper-connectivity across devices and platforms, members of this generation communicate effectively on a wide range of issues that, in turn, shape consumer preferences, based primarily on customer experience. Interaction, transparency, and social responsibility are inseparably linked to the way they consume [32].

This generation exhibits particularly unique purchasing and consumption behaviours [33]. Previous studies have shown the influence of the fashion sector’s communication strategies on social networks such as Instagram on their purchasing decisions by generating positive emotions, especially among their female audiences [34].

Other studies have focused on the importance of sustainable initiatives and corporate social responsibility (CSR) strategies in the purchasing decisions of luxury fashion brands for generation Z, as a result of their growing environmental awareness [35,36]. The search for uniqueness and differentiation, on the one hand, and the bandwagon effect, on the other, seem to be the main motivations of generation Z when buying luxury fashion brands [37], which is why this generation takes into account the recommendations of their circle of friends or the influencers they follow [38].

6. Method

In order to identify the interests of consumers regarding the metaverse, this study used a quantitative descriptive cross-sectional method based on external secondary sources in which we analysed data trends to obtain a complete overview of the research.

For this, we used Google Trends to obtain the relative frequency of searches for words related to the terms “metaverse” and “fashion” on the various Google platforms: the web search engine, YouTube, Images, News and Shopping. The tool allowed us to obtain information on the number of searches that have been carried out on the Google page (https://www.google.com) on these two terms.

To analyse whether or not all the analysed platforms behave in the same way when searching for the terms “metaverse” and “fashion”, a Kruskal–Wallis test was carried out. This non-parametric test is used to determine whether a dataset derives from the same population or not. The sets analysed included Google, YouTube, Google Images, Google News, and Google Shopping. The Kruskal–Wallis H test analysis confirms that Google’s five communication platforms do not behave in the same way. The expected result was that the consumer behaviours do not differ according to the platform used, implying that marketers in fashion organisations should not necessarily plan different strategies for each platform. Therefore, the hypothesis is that consumer interest does not seem to vary depending on the content format (video, text, images).

The study scope covers, as work units, the period between 1 January and 31 December 2022, in a global territorial context.

While the proposed method using secondary data and Google Trends provides valuable insights into consumer behaviour related to fashion and the metaverse, it is essential to acknowledge certain limitations. Secondary data, by nature, may not capture real-time or comprehensive consumer interactions, potentially leading to gaps in understanding nuanced behaviours. Additionally, Google Trends data, while indicative of search interest, may not directly reflect actual consumer behaviour or purchasing decisions. Factors such as search volume fluctuations, algorithm changes, and user demographics can influence the data interpretation. Therefore, while the analysis offers valuable trends and patterns, it is important to interpret the findings with caution and consider the inherent limitations of using secondary data sources.

7. Results

7.1. Analysis of Results in Google Trends

As described in Section 2, Google Trends explains, “the numbers reflect search interest in relation to the maximum value of a graph in a given region and period. A value of 100 indicates the maximum popularity of a term, while 50 and 0 indicate that a term is half as popular as the maximum value or that there was not enough data for the term, respectively”. Research Question 1 (RQ1) seeks to identify the geographical locations of users who extensively search for the terms “metaverse” and “fashion” on the Internet. The popularity index for these search terms on Google in 2022 peaked in March, as shown in Figure 1. Previous studies, such as Jun et al. [39], have validated the effectiveness of Google Trends as a suitable tool for analysing user interests across diverse domains.

In terms of location, interest was detected in 14 different regions, with Singapore standing out above the rest. The other locations, in descending order of user interest were Hong Kong, the United Kingdom, Italy, the Netherlands, Spain, France, Australia, the United States, Canada, Germany, India, Argentina, and Brazil. Focusing on cities, London and New York stand out significantly.

To go into greater detail, regarding Research Question 2 (RQ2), which referred to what other words are associated with the terms “metaverse” and “fashion” in Internet searches, we analysed related queries in order to examine the related topics users were interested in. Thus, we observed that the leading web queries were mainly related to both concepts separately. At the company level, only Gucci and Nike appeared, albeit with little relative weight.

Table 1. Queries related to the concept “metaverse and fashion” in Google in 2022.

Related Queries	Relative Scale
fashion week metaverse	100
fashion in the metaverse	44
nft fashion	34
nft	34
nft metaverse	33
digital fashion	26
decentraland	25
what is metaverse	22
metaverse fashion show	22
virtual fashion	21
metaverse fashion week decentraland	19
decentraland fashion week	19
metaverse fashion brand	17
metaverse meaning	13
gucci metaverse	10
what is the metaverse	10
roblox	9
metaverse news	8
nike metaverse	7
business of fashion	7
web3	5
facebook metaverse	5
metaverse fashion council	4
metaverse fashion summit	4
dressx	3

Source: Google Trends (2023).

shows the score for each related query based on a relative scale in which 100 indicates the most frequent search query and a value of 50 indicates queries whose search frequency is half the search frequency of the most popular query, and so on.

Finally, and in relation to the previous table, it should be noted that the three searches that experienced the greatest increase in frequency compared to the previous year were “fashion in the metaverse” (+140%), “decentraland” (110%) and “nft” (70%). As has been shown before, search statistics have a certain explanatory capacity for user’s interests [40]. In this way, it is possible to predict the growth of the phenomenon.

Next, our analysis proposed Research Question 3 (RQ3) to identify whether there are significant differences in search keywords between the different Google corporate communication platforms. Following the examination of the Google search engine, our attention shifted to the second Google tool, YouTube. Figure 2 shows the popularity index throughout 2022 of the search for both concepts at the same time on YouTube, reaching its maximum in March, just as for the web search engine but following a more irregular trend. Interaction with YouTube as a social space permits the detection of new interests in a self-directed mode [41].

In terms of location, interest was seen in eight different regions, with the United Kingdom standing out above the rest. The other locations, in descending order of user interest were Germany, Canada, Italy, Brazil, France, the United States. and India. Focusing on cities, New York primarily stands out.

To go into greater detail, we analysed related queries in order to examine the related topics users were interested in. Thus, the leading web queries were mainly related to the organisation of events (

Table 2. Queries related to the concept “metaverse and fashion” on YouTube in 2022.

Related Queries	Relative Scale
metaverse fashion week	100
metaverse fashion show	50
metaverse fashion week 2022	20

Source: Google Trends (2023).

).

Finally, and in relation to the previous table, the search that experienced the greatest increase in frequency compared to the previous year was “metaverse fashion week” (+550%).

Next, we focused our analysis on the third Google tool mentioned in the method, Google Images. Figure 3 shows the popularity index throughout 2022 of the search for both concepts at the same time in Images, reaching its maximum in May, and following a trend more similar to YouTube than to the Google web search engine.

At the location level, interest was detected in 10 different regions, Hong Kong standing out above the rest. The other locations, in descending order of user interest were the United Kingdom, Italy, the Netherlands, France, India, Canada, Germany, Spain and the United States. Focusing on cities, London and New York stand out, as for the web browser.

To go into greater detail, we analysed related queries in order to examine the related topics users were interested in. Thus, it can be seen, as was the case for YouTube, that the leading online queries were mainly related to the organisation of events (

Table 3. Queries related to the concept “metaverse and fashion” in Google Images in 2022.

Related Queries	Relative Scale
Metaverse Fashion Week	100
Metaverse Fashion Show	61

Source: Google Trends (2023).

).

We next centred our analysis on the fourth Google tool mentioned in the method, Google News. Figure 4 shows the popularity index throughout 2022 of the search for both concepts at the same time in News, reaching its maximum in January. In this case, though, the trend was distinct from those seen in the other selected tools since no searches were carried out by users most of the time. Related to Google News, [39] point out that the search results returned by Google News offers evidence of personalisation based on browsing history. Therefore, this is a factor to consider when analysing user interests using this Google tool.

At the location level, interest was observed in three different regions, with Canada standing out above the rest. The other locations, in descending order of user interest were France and the United States. Focusing on cities, New York stands out, as it did with the previous tools. Regarding related queries, the search did not yield enough data to display results.

Finally, we analysed the fifth and last Google tool mentioned in the method, namely Google Shopping. Figure 5 shows the popularity index throughout 2022 of the search for both concepts at the same time in Shopping, reaching its maximum in November and following a similar trend to Google News, since for much of time there were no searches by users. Regarding digital markets, like Google Shopping, it is important to note that they have several characteristics, the principal two being network effects and economies of scale and scope with low marginal cost [42]. The first one means that the value of the platform increases as the number of users grows. Therefore, and applied to the case of Google Shopping and due to its growth in recent months, the information provided by the tool has become increasingly valuable.

For all other information, location, and related queries, the search did not yield enough data to display results.

7.2. Kruskal–Wallis Test

In order to determine whether all the analysed platforms behaved in the same way or not when searching for the terms “metaverse & fashion”, a Kruskal–Wallis test was carried out. This non-parametric test is used to determine whether a set of data comes from the same population. For this reason, and as shown in

Table 4. Mean ranges, Kruskal–Wallis test.

	V1	N	Mean Range
V2	1	52	176.31
	2	52	132.49
	3	52	147.78
	4	52	97.39
	5	52	98.53
	Total	260

Source: Authors.

, the first step was to assign the average range to each group, defined as:

• Group 1: Google Search.
• Group 2: YouTube.
• Group 3: Google Images.
• Group 4: Google News.
• Group 5: Google Shopping.

As the main result and following the data in

Table 5. Test statistics, Kruskal–Wallis test *.

	V2
Kruskal–Wallis H	48.322
gl	4
Asymptotic sig.	0.000

* Grouping variable: V1. Source: Authors.

, as the p-value (asymptotic significance) is less than 0.05, the null hypothesis that the population medians are equal is rejected. Therefore, we conclude that, with a significance level of 5%, the data differ between the five corporate communication platforms. That is, the population medians are not equal.

The analysis carried out is based on a Kruskal–Wallis test. This is a non-parametric statistical test used to determine whether there are significant differences between two or more independent groups on a continuous or ordinal outcome variable [43]. It is often used as an alternative to the one-way analysis of variance (ANOVA) test when the data do not meet the assumptions of normality and equal variances.

The Kruskal–Wallis test ranks the data from all groups together, calculates the sum of ranks for each group, and compares these sums to determine whether they differ significantly [44]. The test is based on the null hypothesis that the medians of all groups are equal.

To conduct the Kruskal–Wallis test, we carried out the following steps:

• Rank the data from all groups together.
• Calculate the sum of ranks for each group.
• Calculate the test statistic, H, using the formula:

H = [(12/(n(n + 1))) × sum(Tj^2)] − 3(n + 1)

where Tj is the sum of ranks in group j and n is the total number of observations.

4.

Calculate the degrees of freedom (df) using the formula:

df = k − 1

where k is the number of groups.

5.

Determine the p-value using the appropriate distribution table on the statistical software.

In our case, the p-value is less than the level of significance (e.g., 0.05), so we can reject the null hypothesis and conclude that there are significant differences between the analysed platforms.

As already indicated, and to conclude, the Kruskal–Wallis H test confirms that Google’s five communication platforms do not behave in the same way. Google’s platforms exhibit different search behaviours due to various factors. First, each platform is designed to meet different search needs. For example, Google Search is designed to provide quick answers to general questions and queries, while YouTube focuses on video results and Google Maps on geographic information [45]. Moreover, the platforms also use different ranking algorithms and metrics to rank and display search results. Each platform has its own set of ranking factors, which can include relevance, authority, freshness, and geographic location [45].

On the other hand, each platform has different usage patterns and audiences. Users may have different search intentions and expectations on each platform. For example, a user searching for information on a topic on Google Search may have a different intention than a user searching for the same thing on YouTube or Google News.

Finally, search behaviour can also be affected by personalisation. Google uses user data, including location, search history, and user activity, to personalize search results. This means that the search results may be different for different users [46].

As we have seen for the “metaverse” and “fashion” searches, Google’s platforms exhibit different search behaviours due to a combination of factors, including user intent, personalisation, differences in ranking algorithms, and the usage patterns and audiences of each platform.

7.3. Words Related to the Terms “Metaverse & Fashion”

The results of the word cloud analysis related to the concepts “metaverse” and “fashion” carried out using the five Google search tools and showing the results displayed and expressing the frequency of appearance of the words extracted and derived from the word analysis in a two-dimensional space in the form of a cloud for easy recognition, are shown in Figure 6.

The results of the word cloud visualisation show the frequency of occurrence of the word according to its size and location. As shown in the results of the frequency analysis, the high frequency of “nft metaverse” is significant and locates it in the middle. It is followed, in order of relevance, by words such as “digital fashion”, “nft fashion”, “what is metaverse”, and “decentraland”.

These types of word cloud graphics make it easy for companies to recognize the most relevant search terms, which can help them when creating marketing strategies connected to the search topic. They help to understand the results of the study in a faster and more intuitive way and encourage creativity.

8. Conclusions and Discussion

Fashion brands seek to work on brand awareness and on/off-line cross-channel options (coupons, dual sales) and provide value enhancement through a virtual offer. Today, the main interest of fashion brands is to take positions in the emerging market that new technologies allow, including the metaverse. It is worth noting that the metaverse is still in its nascent stage and the extent to which fashion brands will be able to capitalize on it remains to be seen [27,40].

The metaverse has the potential to change consumer behaviour in several ways. The immersive and interactive nature of the metaverse can create new opportunities for fashion brands to engage with customers and build relationships. It can also change how consumers discover, learn about, and purchase products [24]. In the metaverse, consumers can interact with digital products in a more realistic and engaging way, allowing them to obtain a better sense of how a product will look and feel before they make a purchase. This can help to reduce the risk of buyer’s remorse and increase customer satisfaction [23]. The metaverse can also create new opportunities for social commerce in which consumers can discover and purchase products through their social networks. Additionally, the metaverse can also support new forms of commerce, such as virtual item trading and digital currency.

However, the metaverse can also have a negative impact on consumer behaviour. The immersive nature of the metaverse can make it difficult for consumers to distinguish between the virtual and real world, leading to addiction and escapism [35]. Additionally, virtual environments can also facilitate the spread of misinformation and disinformation [47], and companies will have to address these issues. In RQ1, we analysed the location of users who most frequently use the terms “metaverse” and “fashion” in their Internet searches.

Overall, the metaverse has the potential to change consumer behaviour in ways that are both positive and negative. It is important for fashion brands and companies to understand how the metaverse is likely to change consumer behaviour in order to create effective strategies for engaging with customers in this new digital environment. This study aims to provide a primary tool to fashion brands by analysing Internet search behaviour for the terms “metaverse” and “fashion”.

Despite the growing number of studies that have emerged on tools that allow the analysis and monitoring of searches, such as Google Trends, and that are currently used by various organisations to guide digital marketing practices and even to predict purchasing behaviour, this is, to our knowledge, one of the first studies to monitor absolute values of keyword searches. As such, it constitutes a valuable asset for the analysis of consumer search behaviour.

The literature review revealed the opportunities that new information and communication technologies offer marketing professionals to improve their strategies. Along this line, the metaverse stands out as one of the tools already currently being used, with an exponential growth trend in the short term. The present study therefore analysed interest in this concept in the fashion sector. For this, the concepts of metaverse and fashion were taken into account.

The data collected through Google Trends throughout 2022 worldwide allowed us to conclude that there is growing consumer interest in both terms on the Internet, despite the fact that the behaviour of the five Google search tools is nonhomogeneous.

Aligned with the results of other previous studies [14,39], we have shown that the purpose of using Big Data is evolving from monitoring to predicting based on the possibility of accurately forecasting using time series analysis models. In short, our findings offer guidelines for the development of efficient and effective digital marketing, both for operators in the fashion sector and those responsible for the development of new technologies in digital marketing. RQ2 examined what other words are associated with the terms “metaverse” and “fashion” in Internet searches, as the complexity of defining concepts pushes people to use different combinations of concepts to specify the meaning they want to give to their searches.

To address RQ3, in order to determine whether all the analysed platforms behaved in the same way or not when searching with the terms “metaverse & fashion”, a Kruskal–Wallis H test was carried out and confirmed that Google’s five communication platforms do not behave in the same way. Google’s platforms exhibit different search behaviours due to different factors. Firstly, each platform is designed to meet different search needs. Secondly, each platform has different usage patterns and audiences. Finally, search behaviour can also be affected by personalisation.

The present study highlights the advantages of developing distinct strategies for each of the five Google platforms analysed in terms of SEO. By tailoring strategies to each platform, brands can potentially increase their popularity and click-through rates, establishing a framework for more successful SEO strategies. Moreover, fashion brands can elevate their digital marketing approaches by customizing content and campaigns to align with the unique user behaviour on specific platforms such as Google, YouTube, Google Images, Google News, and Google Shopping. Understanding the preferences and characteristics of users on these platforms enables brands to create targeted and compelling content that resonates with their audience. Through the analysis of platform-specific trends, engagement metrics and user demographics, fashion brands can optimize their marketing endeavours to enhance reach and impact. By integrating platform-specific strategies that cater to the behaviour of users on each channel, brands can boost brand visibility, engagement levels, and conversion rates in the digital landscape.

This research has a number of limitations that offer possibilities for future research. Firstly, only the analysis and monitoring tool Google Trends was used. Therefore, to generalize and confirm the results obtained in this study, it could be replicated with other applications such as Keywordini, Google Keyword Tool, or Follow the Hashtag [5]. Moreover, to broaden the research, other keywords could be included in the analysis. Finally, it would be useful to study and compare user reaction to those keywords, including industry-specific brands.