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From Business Intelligence to Competitive Intelligence: Inferring Competitive Measures Using Augmented Site-Centric Data

Author

Listed:
  • Zhiqiang (Eric) Zheng

    (School of Management, University of Texas at Dallas, Dallas, Texas 75080)

  • Peter Fader

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Balaji Padmanabhan

    (College of Business, University of South Florida, Tampa, Florida 33620)

Abstract

Managers routinely seek to understand firm performance relative to the competitors. Recently, competitive intelligence (CI) has emerged as an important area within business intelligence (BI) where the emphasis is on understanding and measuring a firm's external competitive environment. A requirement of such systems is the availability of the rich data about a firm's competitors, which is typically hard to acquire. This paper proposes a method to incorporate competitive intelligence in BI systems by using less granular and aggregate data, which is usually easier to acquire. We motivate, develop, and validate an approach to infer key competitive measures about customer activities without requiring detailed cross-firm data. Instead, our method derives these competitive measures for online firms from simple “site-centric” data that are commonly available, augmented with aggregate data summaries that may be obtained from syndicated data providers. Based on data provided by comScore Networks, we show empirically that our method performs well in inferring several key diagnostic competitive measures---the penetration , market share , and the share of wallet ---for various online retailers.

Suggested Citation

  • Zhiqiang (Eric) Zheng & Peter Fader & Balaji Padmanabhan, 2012. "From Business Intelligence to Competitive Intelligence: Inferring Competitive Measures Using Augmented Site-Centric Data," Information Systems Research, INFORMS, vol. 23(3-part-1), pages 698-720, September.
    • Handle: RePEc:inm:orisre:v:23:y:2012:i:3-part-1:p:698-720
    DOI: 10.1287/isre.1110.0385
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    References listed on IDEAS

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    2. Stefan Feuerriegel & Nicolas Prollochs, 2018. "Investor Reaction to Financial Disclosures Across Topics: An Application of Latent Dirichlet Allocation," Papers 1805.03308, arXiv.org.
    3. Oliver Schaer & Nikolaos Kourentzes & Robert Fildes, 2022. "Predictive competitive intelligence with prerelease online search traffic," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3823-3839, October.
    4. Chi-Yen Yin, 2018. "Measuring organizational impacts by integrating competitive intelligence into executive information system," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 533-547, March.
    5. Damangir, Sina & Du, Rex Yuxing & Hu, Ye, 2018. "Uncovering Patterns of Product Co-consideration: A Case Study of Online Vehicle Price Quote Request Data," Journal of Interactive Marketing, Elsevier, vol. 42(C), pages 1-17.
    6. Fred P. Hoffman, 2018. "Considerations for Successfully Investing in Commercial Intelligence and Knowledge Management," International Journal of Management, Knowledge and Learning, International School for Social and Business Studies, Celje, Slovenia, vol. 7(1), pages 5-18.
    7. Simona Mina & Felicia Surugiu & Ioana Surugiu & Viorela Georgiana Cristea, 2014. "Generating competitive intelligence within higher education institutions. Case study in Constanta Maritime University," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 7(1), pages 84-93, June.
    8. Zhou, Meihua & Angelopoulos, Spyros & Ou, Carol & Liu, Hongwei & Liang, Zhouyang, 2023. "Optimization of dynamic product offerings on online marketplaces: A network theory perspective," Other publications TiSEM 75d71155-88bf-4ff7-aba1-9, Tilburg University, School of Economics and Management.
    9. Shi, Ruixia & Chen, Hongyu & Sethi, Suresh P., 2019. "A generalized count model on customers' purchases in O2O market," International Journal of Production Economics, Elsevier, vol. 215(C), pages 121-130.
    10. Symeon Symeonidis & Georgios Peikos & Avi Arampatzis, 2022. "Unsupervised consumer intention and sentiment mining from microblogging data as a business intelligence tool," Operational Research, Springer, vol. 22(5), pages 6007-6036, November.
    11. Pfohl, Hans-Christian & Yahsi, Burak & Kurnaz, Tamer, 2015. "The Impact of Industry 4.0 on the Supply Chain," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Innovations and Strategies for Logistics and Supply Chains: Technologies, Business Models and Risk Management. Proceedings of the Hamburg Internationa, volume 20, pages 31-58, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    12. Rikhardsson, Pall & Yigitbasioglu, Ogan, 2018. "Business intelligence & analytics in management accounting research: Status and future focus," International Journal of Accounting Information Systems, Elsevier, vol. 29(C), pages 37-58.
    13. Gautam Pant & Olivia R. L. Sheng, 2015. "Web Footprints of Firms: Using Online Isomorphism for Competitor Identification," Information Systems Research, INFORMS, vol. 26(1), pages 188-209, March.

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