IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v35y2016i3p341-342.html
   My bibliography  Save this article

Editorial—Marketing Science and Big Data

Author

Listed:
  • Pradeep Chintagunta

    (Booth School of Business, University of Chicago, Chicago, Illinois 60637)

  • Dominique M. Hanssens

    (UCLA Anderson School of Management, University of California Los Angeles, Los Angeles, California 90095)

  • John R. Hauser

    (MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormksc:v:35:y:2016:i:3:p:341-342
    DOI: 10.1287/mksc.2016.0996
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2016.0996
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2016.0996?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
    ---><---

    References listed on IDEAS

    as
    1. Joel Barajas & Ram Akella & Marius Holtan & Aaron Flores, 2016. "Experimental Designs and Estimation for Online Display Advertising Attribution in Marketplaces," Marketing Science, INFORMS, vol. 35(3), pages 465-483, May.
    2. Chun-Yu Ho & Dan Li, 2014. "A mirror of history: China's bond market, 1921–42," Economic History Review, Economic History Society, vol. 67(2), pages 409-434, May.
    3. Daniel M. Ringel & Bernd Skiera, 2016. "Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data," Marketing Science, INFORMS, vol. 35(3), pages 511-534, May.
    4. Aron Culotta & Jennifer Cutler, 2016. "Mining Brand Perceptions from Twitter Social Networks," Marketing Science, INFORMS, vol. 35(3), pages 343-362, May.
    5. Xiao Liu & Param Vir Singh & Kannan Srinivasan, 2016. "A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing," Marketing Science, INFORMS, vol. 35(3), pages 363-388, May.
    6. Bruno J.D. Jacobs & Bas Donkers & Dennis Fok, 2016. "Model-Based Purchase Predictions for Large Assortments," Marketing Science, INFORMS, vol. 35(3), pages 389-404, May.
    7. Michael Trusov & Liye Ma & Zainab Jamal, 2016. "Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting," Marketing Science, INFORMS, vol. 35(3), pages 405-426, May.
    8. Scott A. Neslin & Russell S. Winer, 2014. "The History of Marketing Science: Beginnings," World Scientific Book Chapters, in: Russell S Winer & Scott A Neslin (ed.), THE HISTORY OF MARKETING SCIENCE, chapter 1, pages 1-15, World Scientific Publishing Co. Pte. Ltd..
    9. Michael Braun & Paul Damien, 2016. "Scalable Rejection Sampling for Bayesian Hierarchical Models," Marketing Science, INFORMS, vol. 35(3), pages 427-444, May.
    10. Dongling Huang & Lan Luo, 2016. "Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning," Marketing Science, INFORMS, vol. 35(3), pages 445-464, May.
    11. Shasha Lu & Li Xiao & Min Ding, 2016. "A Video-Based Automated Recommender (VAR) System for Garments," Marketing Science, INFORMS, vol. 35(3), pages 484-510, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Achrol, Ravi S. & Kotler, Philip, 2022. "Distributed marketing networks: The fourth industrial revolution," Journal of Business Research, Elsevier, vol. 150(C), pages 515-527.
    2. Vinay Singh & Brijesh Nanavati & Arpan Kumar Kar & Agam Gupta, 2023. "How to Maximize Clicks for Display Advertisement in Digital Marketing? A Reinforcement Learning Approach," Information Systems Frontiers, Springer, vol. 25(4), pages 1621-1638, August.
    3. 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).
    4. Roozbeh Irani-Kermani & Edward C. Jaenicke & Ardalan Mirshani, 2023. "Accommodating heterogeneity in brand loyalty estimation: application to the U.S. beer retail market," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 820-835, December.
    5. Robert W. Palmatier & Andrew T. Crecelius, 2019. "The “first principles” of marketing strategy," AMS Review, Springer;Academy of Marketing Science, vol. 9(1), pages 5-26, June.
    6. Khai Xiang Chiong & Matthew Shum, 2019. "Random Projection Estimation of Discrete-Choice Models with Large Choice Sets," Management Science, INFORMS, vol. 65(1), pages 256-271, January.
    7. Maximilian Schäfer & Geza Sapi & Szabolcs Lorincz, 2018. "The Effect of Big Data on Recommendation Quality: The Example of Internet Search," Discussion Papers of DIW Berlin 1730, DIW Berlin, German Institute for Economic Research.
    8. Marchand, André & Hennig-Thurau, Thorsten & Flemming, Jan, 2021. "Social media resources and capabilities as strategic determinants of social media performance," International Journal of Research in Marketing, Elsevier, vol. 38(3), pages 549-571.
    9. 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.
    10. John R. Hauser, 2017. "Phenomena, theory, application, data, and methods all have impact," Journal of the Academy of Marketing Science, Springer, vol. 45(1), pages 7-9, January.
    11. Shah, Denish & Murthi, B.P.S., 2021. "Marketing in a data-driven digital world: Implications for the role and scope of marketing," Journal of Business Research, Elsevier, vol. 125(C), pages 772-779.
    12. Piyush Anand & Clarence Lee, 2023. "Using Deep Learning to Overcome Privacy and Scalability Issues in Customer Data Transfer," Marketing Science, INFORMS, vol. 42(1), pages 189-207, January.
    13. Wieringa, Jaap & Kannan, P.K. & Ma, Xiao & Reutterer, Thomas & Risselada, Hans & Skiera, Bernd, 2021. "Data analytics in a privacy-concerned world," Journal of Business Research, Elsevier, vol. 122(C), pages 915-925.
    14. 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.
    15. Kanishka Misra & Eric M. Schwartz & Jacob Abernethy, 2019. "Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments," Marketing Science, INFORMS, vol. 38(2), pages 226-252, March.
    16. Andreas Falke & Harald Hruschka, 2022. "Analyzing browsing across websites by machine learning methods," Journal of Business Economics, Springer, vol. 92(5), pages 829-852, July.

    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. 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.
    2. 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.
    3. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    4. Ratchford, Brian & Soysal, Gonca & Zentner, Alejandro & Gauri, Dinesh K., 2022. "Online and offline retailing: What we know and directions for future research," Journal of Retailing, Elsevier, vol. 98(1), pages 152-177.
    5. Djonata Schiessl & Helison Bertoli Alves Dias & José Carlos Korelo, 2022. "Artificial intelligence in marketing: a network analysis and future agenda," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 207-218, September.
    6. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    7. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
    8. Khai Xiang Chiong & Matthew Shum, 2019. "Random Projection Estimation of Discrete-Choice Models with Large Choice Sets," Management Science, INFORMS, vol. 65(1), pages 256-271, January.
    9. Pamuksuz, Utku & Yun, Joseph T. & Humphreys, Ashlee, 2021. "A Brand-New Look at You: Predicting Brand Personality in Social Media Networks with Machine Learning," Journal of Interactive Marketing, Elsevier, vol. 56(C), pages 55-69.
    10. Ning Zhong & David A. Schweidel, 2020. "Capturing Changes in Social Media Content: A Multiple Latent Changepoint Topic Model," Marketing Science, INFORMS, vol. 39(4), pages 827-846, July.
    11. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    12. 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.
    13. Xuan Gong & Yunchan Zhu & Rizwan Ali & Ruijin Guo, 2019. "Capturing Associations and Sustainable Competitiveness of Brands from Social Tags," Sustainability, MDPI, vol. 11(6), pages 1-20, March.
    14. Maximilian Matthe & Daniel M. Ringel & Bernd Skiera, 2023. "Mapping Market Structure Evolution," Marketing Science, INFORMS, vol. 42(3), pages 589-613, May.
    15. Adam N. Smith & Jim E. Griffin, 2023. "Shrinkage priors for high-dimensional demand estimation," Quantitative Marketing and Economics (QME), Springer, vol. 21(1), pages 95-146, March.
    16. Shrihari Sridhar & Eric Fang, 2019. "New vistas for marketing strategy: digital, data-rich, and developing market (D3) environments," Journal of the Academy of Marketing Science, Springer, vol. 47(6), pages 977-985, November.
    17. Bruno Jacobs & Dennis Fok & Bas Donkers, 2021. "Understanding Large-Scale Dynamic Purchase Behavior," Marketing Science, INFORMS, vol. 40(5), pages 844-870, September.
    18. 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.
    19. Saridakis, Charalampos & Katsikeas, Constantine S. & Angelidou, Sofia & Oikonomidou, Maria & Pratikakis, Polyvios, 2023. "Mining Twitter lists to extract brand-related associative information for celebrity endorsement," European Journal of Operational Research, Elsevier, vol. 311(1), pages 316-332.
    20. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.

    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:inm:ormksc:v:35:y:2016:i:3:p:341-342. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.