IDEAS home Printed from https://ideas.repec.org/a/eee/joreco/v81y2024ics0969698924003308.html
   My bibliography  Save this article

Using machine learning to develop customer insights from user-generated content

Author

Listed:
  • Mustak, Mekhail
  • Hallikainen, Heli
  • Laukkanen, Tommi
  • Plé, Loïc
  • Hollebeek, Linda D.
  • Aleem, Majid

Abstract

Uncovering customer insights (CI) is indispensable for contemporary marketing strategies. The widespread availability of user-generated content (UGC) presents a unique opportunity for firms to gain a nuanced understanding of their customers. However, the size and complexity of UGC datasets pose significant challenges for traditional market research methods, limiting their effectiveness in this context. To address this challenge, this study leverages natural language processing (NLP) and machine learning (ML) techniques to extract nuanced insights from UGC. By integrating sentiment analysis and topic modeling algorithms, we analyzed a dataset of approximately four million X posts (formerly tweets) encompassing 20 global brands across industries. The findings reveal primary brand-related emotions and identify the top 10 keywords indicative of brand-related sentiment. Using FedEx as a case study, we identify five prominent areas of customer concern: parcel tracking, small business services, the firm's comparative performance, package delivery dynamics, and customer service. Overall, this study offers a roadmap for academics to navigate the complex landscape of generating CI from UGC datasets. It thus raises pertinent practical implications, including boosting customer service, refining marketing strategies, and better understanding customer needs and preferences, thereby contributing to more effective, more responsive business strategies.

Suggested Citation

  • Mustak, Mekhail & Hallikainen, Heli & Laukkanen, Tommi & Plé, Loïc & Hollebeek, Linda D. & Aleem, Majid, 2024. "Using machine learning to develop customer insights from user-generated content," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:joreco:v:81:y:2024:i:c:s0969698924003308
    DOI: 10.1016/j.jretconser.2024.104034
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969698924003308
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jretconser.2024.104034?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kühl, N. & Mühlthaler, M. & Goutier, Marc, 2020. "Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 130106, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Kannan, P.K. & Li, Hongshuang “Alice”, 2017. "Digital marketing: A framework, review and research agenda," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 22-45.
    3. Guo, Junpeng & Wang, Xiaopan & Wu, Yi, 2020. "Positive emotion bias: Role of emotional content from online customer reviews in purchase decisions," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    4. Schaeffer, Satu Elisa & Rodriguez Sanchez, Sara Veronica, 2020. "Forecasting client retention — A machine-learning approach," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    5. Dang, Tri Vi & Xu, Zhaoxia, 2018. "Market Sentiment and Innovation Activities," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1135-1161, June.
    6. Aron Culotta & Jennifer Cutler, 2016. "Mining Brand Perceptions from Twitter Social Networks," Marketing Science, INFORMS, vol. 35(3), pages 343-362, May.
    7. Nilashi, Mehrbakhsh & Ahmadi, Hossein & Arji, Goli & Alsalem, Khalaf Okab & Samad, Sarminah & Ghabban, Fahad & Alzahrani, Ahmed Omar & Ahani, Ali & Alarood, Ala Abdulsalam, 2021. "Big social data and customer decision making in vegetarian restaurants: A combined machine learning method," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    8. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    9. Rangan Gupta, 2018. "Manager Sentiment and Stock Market Volatility," Working Papers 201853, University of Pretoria, Department of Economics.
    10. Roelen-Blasberg, Tobias & Habel, Johannes & Klarmann, Martin, 2023. "Automated inference of product attributes and their importance from user-generated content: Can we replace traditional market research?," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 164-188.
    11. Salminen, Joni & Kandpal, Chandrashekhar & Kamel, Ahmed Mohamed & Jung, Soon-gyo & Jansen, Bernard J., 2022. "Creating and detecting fake reviews of online products," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    12. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    13. Zaghloul, Maha & Barakat, Sherif & Rezk, Amira, 2024. "Predicting E-commerce customer satisfaction: Traditional machine learning vs. deep learning approaches," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    14. Niklas Kühl & Marius Mühlthaler & Marc Goutier, 2020. "Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(2), pages 351-367, June.
    15. Manthiou, Aikaterini & Hickman, Ellie & Klaus, Phil, 2020. "Beyond good and bad: Challenging the suggested role of emotions in customer experience (CX) research," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    16. Alantari, Huwail J. & Currim, Imran S. & Deng, Yiting & Singh, Sameer, 2022. "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 1-19.
    17. Abbie Griffin & John R. Hauser, 1993. "The Voice of the Customer," Marketing Science, INFORMS, vol. 12(1), pages 1-27.
    18. Adnen Ben Nasr & Matteo Bonato & Riza Demirer & Rangan Gupta, 2019. "Investor Sentiment and Crash Risk in Safe Havens," Journal of Economics and Behavioral Studies, AMH International, vol. 10(6), pages 97-108.
    19. Geng Cui & Man Leung Wong & Hon-Kwong Lui, 2006. "Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming," Management Science, INFORMS, vol. 52(4), pages 597-612, April.
    20. Li-Chen Cheng & Chi-Lun Huang, 2020. "Exploring contextual factors from consumer reviews affecting movie sales: an opinion mining approach," Electronic Commerce Research, Springer, vol. 20(4), pages 807-832, December.
    21. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    22. Nicholas Apergis & Arusha Cooray & Mobeen Ur Rehman, 2018. "Do Energy Prices Affect U.S. Investor Sentiment?," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 19(2), pages 125-140, April.
    23. Choudhary, Anshika & Arora, Anuja, 2024. "Assessment of bidirectional transformer encoder model and attention based bidirectional LSTM language models for fake news detection," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    24. Hartmann, Jochen & Huppertz, Juliana & Schamp, Christina & Heitmann, Mark, 2019. "Comparing automated text classification methods," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 20-38.
    25. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    26. Kumar, Avinash & Chakraborty, Shibashish & Bala, Pradip Kumar, 2023. "Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    27. Stieglitz, Stefan & Mirbabaie, Milad & Ross, Björn & Neuberger, Christoph, 2018. "Social media analytics – Challenges in topic discovery, data collection, and data preparation," International Journal of Information Management, Elsevier, vol. 39(C), pages 156-168.
    28. Praveen, S.V. & Gajjar, Pranshav & Ray, Rajeev Kumar & Dutt, Ashutosh, 2024. "Crafting clarity: Leveraging large language models to decode consumer reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    29. Linda D. Hollebeek & V. Kumar & Rajendra K. Srivastava & Moira K. Clark, 2023. "Moving the stakeholder journey forward," Journal of the Academy of Marketing Science, Springer, vol. 51(1), pages 23-49, January.
    30. Roma, Paolo & Aloini, Davide, 2019. "How does brand-related user-generated content differ across social media? Evidence reloaded," Journal of Business Research, Elsevier, vol. 96(C), pages 322-339.
    31. Pradeep Chintagunta & Dominique M. Hanssens & John R. Hauser, 2016. "Editorial—Marketing Science and Big Data," Marketing Science, INFORMS, vol. 35(3), pages 341-342, May.
    32. Carlson, Keith & Kopalle, Praveen K. & Riddell, Allen & Rockmore, Daniel & Vana, Prasad, 2023. "Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 54-74.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    2. Kadić-Maglajlić, Selma & Lages, Cristiana R. & Pantano, Eleonora, 2024. "No time to lie: Examining the identity of pro-vaccination and anti-vaccination supporters through user-generated content," Social Science & Medicine, Elsevier, vol. 347(C).
    3. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
    4. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    5. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    6. Mariani, Marcello M. & Borghi, Matteo & Laker, Benjamin, 2023. "Do submission devices influence online review ratings differently across different types of platforms? A big data analysis," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    7. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
    8. Zhao, Xiangqi & Huang, Zhe, 2024. "A method for exploring consumer satisfaction factors using online reviews: A study on anti-cold drugs," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    9. Dawn Iacobucci & Maria Petrescu & Anjala Krishen & Michael Bendixen, 2019. "The state of marketing analytics in research and practice," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 152-181, September.
    10. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    11. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    12. Alzate, Miriam & Arce-Urriza, Marta & Cebollada, Javier, 2022. "Mining the text of online consumer reviews to analyze brand image and brand positioning," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    13. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    14. Kullak, Franziska S. & Baier, Daniel & Woratschek, Herbert, 2023. "How do customers meet their needs in in-store and online fashion shopping? A comparative study based on the jobs-to-be-done theory," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    15. Katsumata, Sotaro & Nishimoto, Akihiro & Kannan, P.K., 2023. "Brand competitiveness and resilience to exogenous shock: Usage of smartphone apps during the COVID-19 pandemic," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    16. Huang, Ming-Hui & Rust, Roland T., 2022. "A Framework for Collaborative Artificial Intelligence in Marketing," Journal of Retailing, Elsevier, vol. 98(2), pages 209-223.
    17. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    18. Bag, Sujoy & Tiwari, Manoj Kumar & Chan, Felix T.S., 2019. "Predicting the consumer's purchase intention of durable goods: An attribute-level analysis," Journal of Business Research, Elsevier, vol. 94(C), pages 408-419.
    19. Xin (Shane) Wang & Neil Bendle & Yinjie Pan, 2024. "Beyond text: Marketing strategy in a world turned upside down," Journal of the Academy of Marketing Science, Springer, vol. 52(4), pages 939-954, July.
    20. Rosita Capurro & Michele Galeotti & Stefano Garzella, 2018. ""Mondo reale-tradizionale" e "mondo digitale", strategie aziendali e web intelligence: il futuro del controllo e della gestione delle informazioni," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(2 Suppl.), pages 83-111.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:joreco:v:81:y:2024:i:c:s0969698924003308. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-retailing-and-consumer-services .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.