Industry Sector Analysis
With the rapid development of innovative technologies, both data mining and data warehouse processes have become an integral part of the business. In turn, taking into consideration that tourism has advanced towards universal markets since its early stages in the 1960s, this industry cannot function properly without data mining. In terms of tourism, this process plays an essential role, as individuals employed in this industry constantly seek effective methods to plan for required tourism infrastructures, such as accommodation sites and transportation. In other words, as one of the most discussed topics in current research of artificial intelligence and the database field, data mining provides a variety of opportunities for the improvement of services provided in the tourism industry.
Even though data mining can considerably contribute to customer relationship management in marketing or prevention of possible criminal activities in the area of law, tourism benefits from various data mining techniques as well. In fact, the majority of operational, tactical, and strategic decisions in tourism depend on the selection of specific data mining techniques (Pitchayadejanant & Nakpathom, 2018). Currently, it is assumed that the primary data mining methods in the tourism industry include forecasting expenditures of tourists, analysis of tourist profiles, as well as the process of forecasting the number of tourist arrivals.
At the same time, it is essential to note that customer relationship management should be taken into account in the tourism industry in the context of tourism sector. The ability to administer an organization’s interactions with customers in the appropriate manner is expected to result in an increase of satisfied clients. Therefore, the service industry can develop to the fullest extent. However, this goal can only be achieved in case the best data mining techniques are applied.
First, data mining can be used in the tourism industry in order to analyze and reveal all aspects of client profiles. At present time, there is a vast amount of customer data, which should be used properly. At the same time, the failure to achieve this goal can result in poor development of the business and the whole tourism industry. Subsequently, data mining techniques offer the opportunity to extract needed client profiles from huge amounts of customer data (Shapoval et al., 2017). Second, integration of data mining applications is beneficial for improving the level of attracting potential customers by offering special personal promotions and seasonal campaigns. Furthermore, data mining techniques play a crucial role in terms of preparing the most effective action plans on the basis of natural grouping or patterns of customers.
In order to provide a comprehensive and detailed analysis of how data mining is used and has impacted business practices for the sector of tourism, it is important to pay attention to the PESILE model. This framework covers several vital aspects of business, including political, economic, social/cultural, individual, legal, and environmental factors. From the perspective of politics, there are multiple political factors that can impact the way information management technology is implemented. These include, but are not limited to, changes in trade/global trade restrictions, industry regulations, government stability, and tax regulations. When it comes to economic factors that are associated with the way data mining techniques are implemented, they include inflation, cost of living, exchange rates, consumer spending, economic growth, globalization, and labor costs. Most frequently, data mining is used with the purpose to keep track of spending and expenditure in different areas, as well as assess the economic impact of that spending through various metrics.
Moreover, it is tremendously essential to take into consideration socio-cultural factors, as they have a considerable impact on the process of interacting with data mining. Usually, researchers highlight that the main examples of this kind of factors are lifestyle changes, cultural norms, career attitudes, population growth, and health consciousness. For instance, data mining is frequently used by individuals employed in the area of tourism in order to monitor and collect data on public interactions with local tourism organizations. Also, individuals are impacted by the use of data mining techniques significantly. In fact, data mining is the process that is frequently used by travel agents to provide information regarding house prices, property market trends, peak times for restaurants, and the best seasons for enjoying certain types of tourism.
Finally, legal implications and environmental impact should also be analyzed. In terms of legal implications and decisions, they are likely to affect the regulations pertaining to information management technologies, such as data warehouses and data mining. In this case, the rules and regulations protecting data privacy and the freedom of information are considered. Nevertheless, there are numerous problems that should be addressed in order to improve tourism legislation. For example, it is crucial to consider that information management technologies have the potential to be hacked by cybercriminals. In addition, taking into account that laws relating to storage and distribution vary by country, it can be difficult to monitor the type of information being stored. From the perspective of environmental factors, the tourism organization can use information management technology to monitor and analyze emissions from manufacturing, supply chain management, and the logistical impact of transportation/delivery on the environment.
In spite of the fact that data mining has a variety of benefits for the development of business at present time, it is essential to take appropriate measures to predict its impact in the future. The majority of data mining techniques that have been used successfully for tourism-related analysis lately are based on prepackaged software. At the same time, it is important to note that scientists have not developed data mining software especially dealing with tourism yet. Therefore, there is no wonder why individuals employed in this field usually use Bayesian belief networks and case-based reasoning.
Researchers predict that data mining will face considerable development in the next 5-10 years. For instance, tourism experts are expected to use ensemble data mining that is based on more than one method in the process of analyzing data. Moreover, to model complex relationships between inputs and outputs or to find patterns in data, an ANN-based approach is predicted to be used (Irawan et al., 2019). In other words, innovative approaches will be developed in the near future to improve data mining techniques.
Thus, identifying customers buying patterns is the main goal in any industry, including tourism. As a result, data mining techniques are widely used to achieve this goal. For example, these techniques can be utilized with the purpose to create special campaigns for the selected target audience, thereby transforming random customers into loyal customers. Currently, many tourism experts develop customer data profiles to make offered products and services more personalized. Considering the increasingly fierce tourism market, it is assumed that data mining will get a vast amount of attention in the future. Subsequently, a variety of data analysis technologies will be used in the tourism industry in the next 10-15 years. However, current data mining trends, such as forecasting expenditures of tourists, analysis of tourist profiles, as well as the process of forecasting the number of tourist arrivals, will also be taken into consideration.
Founded in 2013, GOOD Travel is a tourism organization that uses data mining principles in order to satisfy the needs of travels across the world. Initially, it was founded by four women from Peru, South Africa, the USA, and New Zealand, which is why this company serves the needs of all people from different parts of the world (“Sustainable & Responsible Tourism”, 2021). In turn, to achieve its main goal, which is to inspire travelers and tourism businesses to transform the travel industry, representatives of this organization use data mining techniques. For instance, it is quite a common practice for this organization to take into consideration segmentation, neural networks, and forecasting (Krsak & Kysela, 2016). In addition, GOOD Travel pays a vast amount of attention to Internet data mining.
In the context of Internet data mining, this tourism organization uses social networks, media sharing, virtual communities, reviews, and blogs. The analysis of this activity demonstrates that the organization has increased the number of loyal customers by targeting individual offers to its customers. Taking into consideration that people employed in this organization have developed a new marketing initiative based on data mining techniques, GOOD Travel managed to arrange trips to over 100 countries in the world (“Sustainable & Responsible Tourism”, 2021). For instance, the SEO specialist has been working with a vast amount of data on the Internet in order to increase raw traffic to the website, thereby attracting the attention of potential customers to the services offered by this tourism organization. Therefore, social media data mining can be described as one of the most effective and potentially used techniques in the area of tourism.
At the same time, it is tremendously important to conduct a SWOT analysis of how the information management technology has impacted GOOD Travel. Hence, it is crucial to consider its strengths, weaknesses, threats, and opportunities. When it comes to the strengths of this organization, it educates, informs, and shares knowledge with travelers and businesses to encourage sustainable and responsible tourism. Moreover, GOOD Travel provides services on the basis of five significant principles, such as openness, passion, learning, transparency, and integrity. From the perspective of weaknesses, this tourism organization struggles with a small amount of personnel and outdated collateral. However, these weaknesses are taken into consideration by the management of the organization. Accordingly, it is assumed that the effect of the weaknesses listed above will be reduced in the near future.
In terms of opportunities that can be used by GOOD Travel to boost its performance on the international market, it is essential to mention the intention to combine positive tourism experience and protection of the environment. In fact, representatives of this organization have had the experience of living in host countries and working in international non-profits. As a result, GOOD Travel has the opportunity to create a brand of an organization that cares about the environment while finding interesting ways to explore the world. Taking into account that protection of the environment is the number one priority of the 21st century, this direction is beneficial for the development of the whole tourism organization.
Lastly, threats should constantly be analyzed as ignoring this field of SWOT analysis can result in bankruptcy. Currently, the global epidemiological situation can be considered the main threat not only for GOOD Travel but also for a variety of other organizations. The outbreak of COVID-10 made numerous countries close the borders due to the fear of the considerable spread of the virus. As a result, the entire tourism industry has been negatively affected by this pandemic. At the same time, it is extremely important to pay attention to economic, technical, social, demographic, and ecological factors.
Thus, in spite of the fact that there are a variety of external and internal factors that can significantly lower the quality of services provided by tourism organizations, data mining techniques are expected to minimize the impact of these factors. In the case of GOOD Travel, this organization is known in different parts of the world due to a high quality of products and services, as well as unique offerings and special campaigns for the selected target audience. Furthermore, this organization pays a vast amount of attention to Internet data mining presented through social networks, media sharing, virtual communities, reviews, and blogs. At present, the mission of GOOD Travel is to promote the significance of tourism under the circumstances of an unstable environment situation. In other words, in addition to leisure activities, this tourism organization encourages people to protect the environment in different parts of the world.
Irawan, H., Akmalia, G., & Masrury, R. Mining tourist’s perception toward Indonesia tourism destination using sentiment analysis and topic modelling. Proceedings of the 2019 4th International Conference on Cloud Computing and Internet of Things – CCIOT 2019, 23(2), 1-15. Web.
Krsak, B., & Kysela, K. (2016). The use of social media and Internet data-mining for the tourist industry. Journal of Tourism & Hospitality, 25(1), 1-15. Web.
Pitchayadejanant, K., & Nakpathom, P. (2018). Data mining approach for arranging and clustering the agro-tourism activities in orchard. Kasetsart Journal of Social Sciences, 39(3), 407-413. Web.
Shapoval, V., Wang, M., Hara, T., & Shioya, H. (2017). Data mining in tourism data analysis: Inbound visitors to Japan. Journal of Travel Research, 57(3), 310-323. Web.
Sustainable & Responsible Tourism. GOOD Travel. (2021). Web.