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Social Media Metrics and Analysis

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Social Media Marketing in Tourism and Hospitality

Abstract

Information Technology progress and Social media spread, as well as Mobile Social Media development, examined in previous Chapters, contribute to the increasing availability of a large amount of multimedia structured and unstructured content about customers and prospects (called “Big data”). Travel organizations able to gather, analyze, and interpret this information have the opportunity to enhance customers’ knowledge, and consequently, to improve service differentiation and personalization. The synchronization with various target markets allows creating a competitive advantage and increasing financial and operational performance. Therefore, a key issue turns out to be the definition of the most appropriate social media metrics able to evaluate social media performance and, if combined with other measures, to support and improve business strategies.

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Notes

  1. 1.

    For major insights on the topic of “Internet of things” see Chap. 5, Sect. 5.2 “From virtual reality to augmented reality”.

  2. 2.

    The survey of PhocusWright considers travelers’ experience on the basis of the number of trips per year (1–2, 3–5, 6 or more). 39 % of travelers defined as expert (6 or more trips) agree/strongly agree with the affirmation: “I expect travel websites to reference my past activities to personalize offers for me” (PhocusWright 2013).

  3. 3.

    The topic of the adoption of IT by travel companies is examined in Chap. 4, Sect. 4.3.4 “Electronic Customer Relationship Management (eCRM) in tourism and hospitality”.

  4. 4.

    McAfee and Brynjolfsson (2012) created a team of researchers with the MIT Center of Digital Business and Mckinsey to understand if a proper use of Big data can increase companies’ performance. They found that data-driven companies were characterized by better financial and operational performance indicators.

  5. 5.

    The volume of data is relative and depends on the type of organization.

  6. 6.

    For major insights see the special issue of the Journal of Web Semantics (2012) vol. 6(1). Berners-Lee and Fischetti (1999) defined semantic web as follows: “I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web—the content, links, and transactions between people and computers. A “Semantic Web”, which makes this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The “intelligent agents” people have touted for ages will finally materialize” (Berners-Lee and Fischetti 1999).

  7. 7.

    The topic of privacy implications will be examined in Sect. 6.5.

  8. 8.

    For major insights about the topic of sentiment analysis consult Sect. 6.3.

  9. 9.

    Churn rate is the percentage of subscribers to a service that decide to interrupt their subscription in a given time period.

  10. 10.

    Main social media like Facebook, Twitter, Youtube, etc. calculate automatically some outcome metrics (i.e. Reach, Page engagement, etc.).

  11. 11.

    For major insights about crowdsourcing see Chap. 4.

  12. 12.

    For more information consult https://www.facebook.com/business/news/pageinsights.

  13. 13.

    For a review of literature of the concepts of opinion mining and sentiment analysis see Pang and Lee (2008).

  14. 14.

    Automatic sentiment analysis adopts “data scraping”: a technique that allows organizations to analyze unstructured data on third-party websites by means of a web crawler. It is a program searching content on web pages according to predetermined keywords. The crawler spider “traverses the web site starting from a given set of initial URLs and follows the links matching a given pattern to a certain depth” (Banic et al. 2013).

  15. 15.

    The analysis reported in this section was conducted in July 2014.

  16. 16.

    The difference between organic and paid likes is the following: organic likes are the total number of people who liked the Page from unpaid distribution, while paid likes are the total number of people who liked the Page from a paid ad (web or mobile) or a sponsored story (Facebook, July 2014).

  17. 17.

    The difference between organic and paid reach is the following: organic reach is the total number of unique people who were shown the post through unpaid distribution, while paid reach is the total number of unique people who were shown the post as a result of ads (Facebook, July 2014).

  18. 18.

    Location-based services and Mobile Social Media have been examined in Chap. 5.

  19. 19.

    For further insights on Facebook advertising see Chap. 4.

  20. 20.

    For major insights about Web privacy violations see Eltoweissy et al. (2003).

  21. 21.

    Eltoweissy et al. (2003) identifies the following dimensions of We Privacy: information, collection, usage, storage, disclosure, security, access control, monitoring, policy changes.

  22. 22.

    See European Union (1995), Tene (2013) “the reform processes fail to address challenges to the definition of personal data and science of de-identification”.

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  1. Department of Law, Economics and Cultures, University of Insubria, Como, Italy

    Roberta Minazzi

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Minazzi, R. (2015). Social Media Metrics and Analysis. In: Social Media Marketing in Tourism and Hospitality. Springer, Cham. https://doi.org/10.1007/978-3-319-05182-6_6

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