The latest disruptive technologies are supporting the marketing mix elements as they can improve the businesses’ interactive engagement with prospective customers, and enhance their personalization of services. They may also provide secure pricing options.
Many firms are evolving from their passive, rigid, and product-centric state to a more flexible, dynamic, and customer-centric environment. Technology is enabling data-driven companies to monitor and detect any changes in consumer sentiment. Savvy technology giants including Facebook, Amazon, Microsoft and Google are capturing (and analyzing) the online and mobile activity of prospective customers. Their analytics captures the consumers’ interactions with brands and companies through digital media. Big data is enabling them to target and re-target individuals and online communities with instantaneous pricing and access options, across multiple channels (via web-site activity, mobile,video, social media, e-commerce, among others).
Mobile tracking technologies are being utilized by big technology conglomerates as they gather information on the consumer behaviours, including their shopping habits, lifestyle preferences , et cetera. Businesses have learnt how to take advantage of on-demand, real-time information from sensors, radio frequency identification and other location tracking devices to better understand their marketing environments at a more granular level (Storey and Song, 2017). This way business could come up with personalised products and services, that are demanded by individual customers. From a business perspective, it is important to acquire this data, quickly, and in high velocities.
Many businesses are already benefiting of the programmatic advertising environment; where buyers and sellers of digital advertising connect online to exchange available inventory (Busch,2016; Stevens et al., 2016). The challenge for tomorrow’s businesses is to recognize the value of smart technologies as effective tools that can help them analyse their marketing environment; that comprise their customers as well as their competitors.
The predictive-analytical tools can examine different scenarios as they can anticipate what will happen, when it will happen, and can explain why it happens. These technologies can monetise data by identifying revenue generating opportunities and cost savings.
Other innovations, including; blockchain’s distributed ledger technologies are improving data privacy. This technology involves the verification and the secure recording of transactions among an interconnected set of users. Blockchain tracks the ownership of assets before, during, and after any online transaction. Therefore, this technology could be used by different businesses to facilitate their transactions with marketplace stakeholders, including; suppliers, intermediaries, and consumers across borders. The block chain will probably be more convenient than other payment options, in terms of time and money. Therefore, blockchain’s ledger technology can possibly lead to better customer service levels and operational efficiencies for businesses.
The smart tourism technologies, including big data analytics are shifting how organisations collect, analyze and utilise and distribute data. A thorough literature review suggests that the crunching of big data analytics is generating meaningful insights and supporting tourism marketers in their decision making. Moreover,other technologies, including the programmatic advertising and block chain are helping them to improve their financial and strategic performance, whilst minimizing costs. Table 1 illustrates how smart tourism businesses are capturing, analysing and distributing data.
Table 1. Data-driven approaches for smart tourism
Emerging Trends and Future Research
Tomorrow’s tourism businesses will be serving customers from geographically-diverse regions. There will be more travellers from emerging markets and developing economies. The tourism service providers will have to cater to different demographics, including senior citizens and individuals with special needs; as the populations are getting older in many countries.
Therefore, smart technologies can be used to anticipate the discerned consumers’ requirements. For instance, the use of programmatic advertising will probably increase the individuals’ intuitive shopping experiences and can tap into the individuals’ discretionary purchases.
It is very likely, that the third-party retailers will continue to form part of the distribution mix. However, many service providers will be using their direct channels to reach out to their targeted customers.
The sales of products will continue to rely on mobile devices with increased consumer interactions through speech and voice recognition software. The service providers may possibly rely on artificial intelligence and other forms of cognitive learning capabilities, like machine learning and deep learning.
The businesses’ distributive systems could interface with virtual reality software to help online intermediaries to merchandise their products in captivating customer experiences. Many online prospects may use blockchain’s secure technology to purchase tourism products, in the foreseeable future.
This contribution calls for further empirical research that could explore smart tourism innovations for individuals and organisations, including; mobile social networking, mobile visualisation, personalization and behavioural modelling for mobile apps, programmatic advertising, blockchain, AI, and the internet of things, among other areas.
References
Busch, O. (2016), “The programmatic advertising principle”, In Programmatic Advertising (pp. 3-15). Springer, Cham, Switzerland.
Camilleri, M.A. (2018) Data-Driven Marketing and Disruptive Technologies. Working Paper 08/2018, Department of Corporate Communication, University of Malta.
Stevens, A., Rau, A., and McIntyre, M. (2016), “Integrated campaign planning in a programmatic world”, In Programmatic Advertising (pp. 193-210), Springer, Cham, Switzerland.
Storey, V. C., and Song, I. Y. (2017), “Big data technologies and Management: What conceptual modeling can do?”, Data and Knowledge Engineering, Vol. 108, pp. 50-67.
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