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Marketing analysis of wineries using social collective behavior from users' temporal activity on Twitter

机译：利用用户临时行为对推特上的酿酒厂的营销分析

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摘要

Marketing professionals face challenges of increasing complexity to adapt classic marketing strategies to the phenomenon of social networks. Companies are currently trying to take advantage of the useful collective knowledge available on social networks to support different types of marketing decisions. The appropriate analysis of this information can offer marketing professionals with important competitive advantages. This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users' temporal activity. Time series of mentions made by individual users to each company's Twitter account are aggregated to obtain collective activity data for the companies, which is a consequence of both the company's and other users' actions. These data are processed using classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, to extract collective temporal behavior patterns and models of the dynamics of customers over time for a single brand and groups of brands. The derived knowledge can be used for different tasks, such as identifying the impact of a marketing campaign on Twitter and comparatively assessing the social behaviors of different brands and groups of brands to assist in making marketing decisions. Our methodology is validated in a case study from the wine market. Twitter data were gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley), and comparative behavior analysis was carried out from the perspective of the use of Twitter as a communication channel for marketing campaigns.
机译：营销专业人员面临越来越复杂的挑战，以适应社会网络现象的经典营销策略。目前正在努力利用社交网络上提供的有用集体知识，以支持不同类型的营销决策。对这些信息的适当分析可以提供具有重要竞争优势的营销专业人士。这项工作提出了一种新的方法，提取了基于用户的时间活动的一组品牌的推特用户的社会集体行为。各个用户对每个公司的推特帐户制定的时间序列是汇总的，以获得公司的集体活动数据，这是公司和其他用户行为的结果。这些数据是使用经典无监督机器学习技术处理，例如时间聚类和隐藏的马尔可夫模型，以提取客户的集体时间行为模式和模型，随着单个品牌和品牌组。派生的知识可以用于不同的任务，例如识别营销活动对Twitter的影响，并相对评估不同品牌和品牌群体的社会行为，以协助营销决策。我们的方法论在葡萄酒市场的案例研究中验证。来自世界各地的四个地区的推特数据与重要的葡萄酒厂（意大利：威尼托，葡萄牙：Porto和Douro Valley，Spain：La Rioja和美国：Napa Valley）和比较行为分析来自使用Twitter作为营销活动的沟通渠道的看法。

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  • 来源
    《Information Processing & Management》 |2020年第5期|102220.1-102220.20|共20页
  • 作者

    Gema Bello-Orgaz; Rus M. Mesas; Carmen Zarco; Victor Rodriguez; Oscar Cordon; David Camacho;

  • 作者单位

    Departamento de Sistemas Informaticos ETSI de Sistemas Informaticos Universidad Politecnica de Madrid Calk de Alan Turing s Madrid 28031 Spain;

    Telefonica de Espana Ronda de la Comunkacion 2 Madrid 28050 Spain;

    Department of Market Research Universidad International de La Rioja Av. de la Paz 137 Logrono La Rioja 26006 Spain;

    Computer Science Department Universidad Autonoma de Madrid Francisco Tomas y Valiente 11 Madrid 28049 Spain;

    Instituto Andaluz Interuniversitario de Ciencia de Datos e Inteligencia Computational (DaSCI) University of Granada C/Daniel Saucedo Aranda s Granada 18071 Spain;

    Departamento de Sistemas Informaticos ETSI de Sistemas Informaticos Universidad Politecnica de Madrid Calk de Alan Turing s Madrid 28031 Spain;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Social networks; Marketing analysis; Temporal Twitter Activity; Social collective behavior; Temporal clustering; Hidden Markov models; Wineries;

    机译：社交网络;营销分析;时间推特活动;社会集体行为;时间聚类;隐马尔可夫模型;酿酒厂;
  • 入库时间 2022-08-18 21:00:45

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