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Applying Quantitative Marketing Techniques to the Internet

Published: 01 March 2001 Publication History

Abstract

Quantitative models have proved valuable in predicting consumer behavior in the offline world. These same techniques can be adapted to predict online actions. The use of diffusion models provides a firm foundation to implement and forecast viral marketing strategies. Choice models can predict purchases at online stores and shopbots. Hierarchical Bayesian models provide a framework for implementing versioning and price-segmentation strategies. Bayesian updating is a natural tool for profiling users with clickstream data. A key challenge for practitioners of Internet marketing is to extract value from the huge volume of data that can be collected. These techniques illustrate how this information can be leveraged to create better decisions.

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  • (2024)Efficient combinations of dual incentives on social networks to achieve viral spreadElectronic Commerce Research10.1007/s10660-022-09668-z24:4(2381-2404)Online publication date: 1-Dec-2024
  • (2022)Blazing the Trail: Considering Browsing Path Dependence in Online Service Response StrategyInformation Systems Frontiers10.1007/s10796-022-10311-325:4(1605-1619)Online publication date: 21-Jul-2022
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Published In

cover image Interfaces
Interfaces  Volume 31, Issue 2
March 2001
156 pages
ISSN:0092-2102
EISSN:1526-551X
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INFORMS

Linthicum, MD, United States

Publication History

Published: 01 March 2001

Author Tags

  1. Marketing--choice models
  2. Marketing--promotion

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  • (2024)Understanding how the Antecedents of Satisfaction with TikTok Influence Continuous Use IntentionProceedings of the 2024 11th Multidisciplinary International Social Networks Conference10.1145/3675669.3675684(183-190)Online publication date: 21-Aug-2024
  • (2024)Efficient combinations of dual incentives on social networks to achieve viral spreadElectronic Commerce Research10.1007/s10660-022-09668-z24:4(2381-2404)Online publication date: 1-Dec-2024
  • (2022)Blazing the Trail: Considering Browsing Path Dependence in Online Service Response StrategyInformation Systems Frontiers10.1007/s10796-022-10311-325:4(1605-1619)Online publication date: 21-Jul-2022
  • (2016)Electronic word of mouth (eWOM) in the movie industryProceedings of the 18th Annual International Conference on Electronic Commerce: e-Commerce in Smart connected World10.1145/2971603.2971610(1-7)Online publication date: 17-Aug-2016
  • (2016)Estimating product-choice probabilities from recency and frequency of page viewsKnowledge-Based Systems10.1016/j.knosys.2016.02.00699:C(157-167)Online publication date: 1-May-2016
  • (2014)Selling vs. ProfilingInformation Systems Research10.5555/2895238.289524325:2(285-306)Online publication date: 1-Jun-2014
  • (2011)Accelerated Learning of User ProfilesManagement Science10.1287/mnsc.1100.126657:2(215-239)Online publication date: 1-Feb-2011
  • (2011)BlogsProceedings of the 2011 iConference10.1145/1940761.1940809(348-355)Online publication date: 8-Feb-2011
  • (2007)The dynamics of viral marketingACM Transactions on the Web10.1145/1232722.12327271:1(5-es)Online publication date: 1-May-2007
  • (2006)The dynamics of viral marketingProceedings of the 7th ACM conference on Electronic commerce10.1145/1134707.1134732(228-237)Online publication date: 11-Jun-2006
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