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Uncovering the message from the mess of big data

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  • Bendle, Neil T.
  • Wang, Xin (Shane)

Abstract

User-generated content, such as online product reviews, is a valuable source of consumer insight. Such unstructured big data is generated in real-time, is easily accessed, and contains messages consumers want managers to hear. Analyzing such data has potential to revolutionize market research and competitive analysis, but how can the messages be extracted? How can the vast amount of data be condensed into insights to help steer businesses’ strategy? We describe a non-proprietary technique that can be applied by anyone with statistical training. Latent Dirichlet Allocation (LDA) can analyze huge amounts of text and describe the content as focusing on unseen attributes in a specific weighting. For example, a review of a graphic novel might be analyzed to focus 70% on the storyline and 30% on the graphics. Aggregating the content from numerous consumers allows us to understand what is, collectively, on consumers’ minds, and from this we can infer what consumers care about. We can even highlight which attributes are seen positively or negatively. The value of this technique extends well beyond the CMO's office as LDA can map the relative strategic positions of competitors where they matter most: in the minds of consumers.

Suggested Citation

  • Bendle, Neil T. & Wang, Xin (Shane), 2016. "Uncovering the message from the mess of big data," Business Horizons, Elsevier, vol. 59(1), pages 115-124.
    • Handle: RePEc:eee:bushor:v:59:y:2016:i:1:p:115-124
    DOI: 10.1016/j.bushor.2015.10.001
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    References listed on IDEAS

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    1. Thelen, Shawn & Mottner, Sandra & Berman, Barry, 2004. "Data mining: On the trail to marketing gold," Business Horizons, Elsevier, vol. 47(6), pages 25-32.
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    Cited by:

    1. Saarikko, Ted & Westergren, Ulrika H. & Blomquist, Tomas, 2017. "The Internet of Things: Are you ready for what’s coming?," Business Horizons, Elsevier, vol. 60(5), pages 667-676.
    2. Wang, Xin (Shane) & Ryoo, Jun Hyun (Joseph) & Bendle, Neil & Kopalle, Praveen K., 2021. "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, Elsevier, vol. 97(4), pages 658-675.
    3. Sun, Xinbo & Zhang, Qingqiang, 2021. "Building digital incentives for digital customer orientation in platform ecosystems," Journal of Business Research, Elsevier, vol. 137(C), pages 555-566.
    4. Gupta, Shivam & Justy, Théo & Kamboj, Shampy & Kumar, Ajay & Kristoffersen, Eivind, 2021. "Big data and firm marketing performance: Findings from knowledge-based view," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    5. Brunzel, Johannes, 2021. "Making use of quantitative content analysis: Insights from academia and business practice," Business Horizons, Elsevier, vol. 64(4), pages 453-464.
    6. Ziye Shang & Jian Ming Luo, 2022. "Topic Modeling for Hiking Trail Online Reviews: Analysis of the Mutianyu Great Wall," Sustainability, MDPI, vol. 14(6), pages 1-16, March.
    7. Damane Moeti, 2022. "Topic Classification of Central Bank Monetary Policy Statements: Evidence from Latent Dirichlet Allocation in Lesotho," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 10(1), pages 199-227, September.
    8. Spada, Irene & Chiarello, Filippo & Barandoni, Simone & Ruggi, Gianluca & Martini, Antonella & Fantoni, Gualtiero, 2022. "Are universities ready to deliver digital skills and competences? A text mining-based case study of marketing courses in Italy," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

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