Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
The Concept of Cognitive Social Media and Cognitive Literary Studies Read chapter Read first chapter

2021 | OriginalPaper | Chapter

16. Social Media: The Dark Horse of Market in Consumer Decision Journey

Authors : Nipun Dhaulta, Sakshi Aggarwal

Published in: Big Data Analytics in Cognitive Social Media and Literary Texts

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

With businesses continually striving to be insightful enough to precisely read the consumers’ desires and subjectively measure their satisfaction, the need for and impact of Big Data Analytics is growing without bounds. The chapter elucidates the proficiency of behavioral predictions enabled through the big data further made available by people on various social media platforms, which guide the marketers throughout the Consumer Decision Journey. These behavioral predictions help the businesses to keep tapping the consumer decision journey through several customer touch-points. Consequently, Big Data Analytics being a massive source of data collection drives marketers toward value creation by comprehending the needs of customers effectively and thus promoting value delivery to customers. With the help of various social media models, the chapter also unravels the benefits of behavioral predictions to not only marketers but also other researchers in the field of health, politics, academia, and so on focusing on personality traits, lifestyle and health of their subjects and not just their buying behavior. The chapter explores the various dimensions of social media platforms and models to demonstrate effective use of technically enhanced opportunities to drive customer satisfaction.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
previous chapter Security and Privacy Challenges for Big Data on Social Media
Literature
Abbas, J., Aman, J., Nurunnabi, M., & Bano, S. (2019). The impact of social media on learning behavior for sustainable education: Evidence of students from selected universities in Pakistan. Sustainability, 11(6), 1683.CrossRef
Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34–49.CrossRef
Baars, H., & Kemper, H. G. (2008). Management support with structured and unstructured data—an integrated business intelligence framework. Information Systems Management, 25(2), 132–148.CrossRef
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45–59.CrossRef
Bi, G., Zheng, B., & Liu, H. (2014) Secondary crisis communication on social media: The role of corporate response and social influence in product-harm crisis. In PACIS (p. 93).
Bleier, A., & Eisenbeiss, M. (2015). The importance of trust for personalized online advertising. Journal of Retailing, 91(3), 390–409.CrossRef
Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017). Online behavioral advertising: A literature review and research agenda. Journal of Advertising, 46(3), 363–376.CrossRef
Broussard, G. (2000). How advertising frequency can work to build online advertising effectiveness. International Journal of Market Research, 42(4), 1–13.
Chan, N. L., & Guillet, B. D. (2011). Investigation of social media marketing: How does the hotel industry in Hong Kong perform in marketing on social media websites? Journal of Travel & Tourism Marketing, 28(4), 345–368.CrossRef
Coles, L. (2014). Marketing with social media: 10 easy steps to success for business. Wiley.
Cvijikj, I. P., & Michahelles, F. (2013). Online engagement factors on Facebook brand pages. Social Network Analysis and Mining, 3(4), 843–861.CrossRef
Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.CrossRef
Edelman, D. C. (2010). Branding in the digital age. Harvard Business Review, 88(12), 62–69.
Fernández, A., del Río, S., López, V., Bawakid, A., del Jesus, M. J., Benítez, J. M., & Herrera, F. (2014). Big Data with Cloud Computing: An insight on the computing environment, MapReduce, and programming frameworks, Wiley Interdisciplinary reviews. Data Mining and Knowledge Discovery, 4(5), 380–409.CrossRef
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.CrossRef
Gârdan, I. P., & Gârdan, D. A. (2014). The G.A.P. model applied to dental healthcare services. In The Proceedings of the International Conference “Marketing-from Information to Decision” (p. 107). Babes Bolyai University.
Ghani, N. A., Hamid, S., Hashem, I. A. T., & Ahmed, E. (2019). Social media big data analytics: A survey. Computers in Human Behavior, 101, 417–428.CrossRef
Goel, S., & Goldstein, D. G. (2014). Predicting individual behavior with social networks. Marketing Science, 33(1), 82–93.CrossRef
Griffiths, M., & McLean, R. (2015). Unleashing corporate communications via social media: A UK study of brand management and conversations with customers. Journal of Customer Behaviour, 14(2), 147–162.CrossRef
Gundecha, P., & Liu, H. (2012). Mining social media: A brief introduction. In New directions in informatics, optimization, logistics, and production (pp. 1–17). Informs.
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38–52.CrossRef
Hennig-Thurau, T., Malthouse, E. C., Friege, C., Gensler, S., Lobschat, L., Rangaswamy, A., & Skiera, B. (2010). The impact of new media on customer relationships. Journal of Service Research, 13(3), 311–330.CrossRef
Hudson, S., & Thal, K. (2013). The impact of social media on the consumer decision process: Implications for tourism marketing. Journal of Travel & Tourism Marketing, 30(1–2), 156–160.CrossRef
Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013). January big data: Issues and challenges moving forward. In 2013 46th Hawaii international conference on system sciences (pp. 995–1004). IEEE.
Khan, Z., & Vorley, T. (2017) Big data text analytics: An enabler of knowledge management. Journal of Knowledge Management.
Kumar, V., & Gupta, S. (2016). Conceptualizing the evolution and future of advertising. Journal of Advertising, 45(3), 302–317.CrossRef
Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press.
Manca, S., Caviglione, L., & Raffaghelli, J. (2016). Big data for social media learning analytics: Potentials and challenges. Journal of e-Learning and Knowledge Society, 12(2).
McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2014). Big data and social media analytics. Psychological Methods, 9(4), 403–425.CrossRef
Moniruzzaman, A. B. M., & Hossain, S. A. (2013). Nosql database: New era of databases for big data analytics-classification, characteristics and comparison. arXiv:​1307.​0191.
Neslin, S. A., Grewal, D., Leghorn, R., Shankar, V., Teerling, M. L., Thomas, J. S., & Verhoef, P. C. (2006). Challenges and opportunities in multichannel customer management. Journal of Service Research, 9(2), 95–112.CrossRef
OECD, DDIBD. (2015). For growth and well-being: Big data for growth and well-being.
Pan, B., MacLaurin, T., & Crotts, J. C. (2007) Travel blogs and the implications for destination marketing. Journal of Travel Research, 46(1), 35–45.
Parashara, A., Parasharb, A., & Goyalc, S. (2018). Big data analysis using machine learning approach to compute data. Data Intensive Computing Applications for Big Data, 29, 133.
Phillips, L., Dowling, C., Shaffer, K., Hodas, N., & Volkova, S. (2017). Using social media to predict the future: A systematic literature review. arXiv:​1706.​06134.
Preoţiuc-Pietro, D., Liu, Y., Hopkins, D., & Ungar, L. (July 2017) Beyond binary labels: Political ideology prediction of twitter users. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers) (pp. 729–740).
Schindler, R. M., & Bickart, B. (2005). Published word of mouth: Referable, consumer-generated information on the internet. Online Consumer Psychology: Understanding and Influencing Consumer Behavior in the Virtual World, 32, 35–61.
Schultz, D. (2016). The future of advertising or whatever we’re going to call it. Journal of Advertising, 45(3), 276–285.CrossRef
Shostack, L. (1984). Designing services that deliver. Harvard Business Review, 62(1), 133–139.
Simpao, A. F., Ahumada, L. M., Gálvez, J. A., & Rehman, M. A. (2014). A review of analytics and clinical informatics in health care. Journal of Medical Systems, 38(4), 45.CrossRef
Skoric, M. M., Liu, J., & Jaidka, K. (2020). Electoral and public opinion forecasts with social media data: A meta-analysis. Information, 11(4), 187.
Smith, T., Coyle, J. R., Lightfoot, E., & Scott, A. (2007). Reconsidering models of influence: The relationship between consumer social networks and word-of-mouth effectiveness. Journal of advertising research, 47(4), 387–397.
Song, S. K., Kim, D. J., Hwang, M., Kim, J., Jeong, D. H., Lee, S., Jung, H., & Sung, W. (December 2013). Prescriptive analytics system for improving research power. In 2013 IEEE 16th international conference on computational science and engineering (pp. 1144–1145).
Tsou, M. H. (2015). Research challenges and opportunities in mapping social media and Big Data. Cartography and Geographic Information Science, 42(sup1), 70–74.CrossRef
Verhoef, P. C., Stephen, A. T., Kannan, P. K., Luo, X., Abhishek, V., Andrews, M., et al. (2017). Consumer connectivity in a complex, technology-enabled, and mobile-oriented world with smart products. Journal of Interactive Marketing, 40, 1–8.
Vieira, C., Parsons, P., & Byrd, V. (2018). Visual learning analytics of educational data: A systematic literature review and research agenda. Computers & Education, 122, 119–135.CrossRef
Wen, M., Xia, Z., & Vasthimal, D. K. (October 2019). Practical lessons from predicting new user demographics for ad targeting. In 2nd workshop on online recommender systems and user modeling (pp. 59–67).
Young, S. D. (2014). Behavioral insights on big data: Using social media for predicting biomedical outcomes. Trends in Microbiology, 22(11), 601–602.CrossRef
Zachos, G., Paraskevopoulou-Kollia, E. A., & Anagnostopoulos, I. (2018). Social media use in higher education: A review. Education Sciences, 8(4), 194.CrossRef
Metadata
Title
Social Media: The Dark Horse of Market in Consumer Decision Journey
Authors
Nipun Dhaulta
Sakshi Aggarwal
Copyright Year
2021
Publisher
Springer Singapore
Book
Big Data Analytics in Cognitive Social Media and Literary Texts

Print ISBN: 978-981-16-4728-4

Electronic ISBN: 978-981-16-4729-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-16-4729-1_16

Premium Partner

    Image Credits