当前位置: X-MOL 学术Inf. Process. Manag. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Marketing analysis of wineries using social collective behavior from users’ temporal activity on Twitter
Information Processing & Management ( IF 8.6 ) Pub Date : 2020-02-18 , DOI: 10.1016/j.ipm.2020.102220
Gema Bello-Orgaz , Rus M. Mesas , Carmen Zarco , Victor Rodriguez , Oscar Cordón , David Camacho

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.



中文翻译:

利用用户在Twitter上的时间活动中的社会集体行为对酒厂进行营销分析

营销专业人员面临着越来越复杂的挑战,以使经典营销策略适应社交网络现象。公司目前正在尝试利用社交网络上可用的有用的集体知识来支持不同类型的营销决策。对该信息进行适当的分析可以为市场营销专业人员提供重要的竞争优势。这项工作提出了一种新的方法,可以根据用户的时间活动提取有关一组品牌的Twitter用户的社会集体行为。汇总个人用户对每个公司的Twitter帐户提及的时间序列,以获得公司的集体活动数据,这是公司和其他用户的行为共同产生的结果。这些数据使用经典的无监督机器学习技术(例如时间聚类和隐马尔可夫模型)进行处理,以提取单个品牌和品牌组随时间变化的集体时间行为模式和客户动态模型。派生的知识可用于不同的任务,例如识别营销活动对Twitter的影响,并比较评估不同品牌和品牌组的社会行为,以帮助制定营销决策。我们的方法在葡萄酒市场的案例研究中得到了验证。Twitter数据收集自世界各地不同国家的四个地区,这些地区都有重要的酿酒厂(意大利:威尼托,葡萄牙:波尔图和杜罗河谷,西班牙:拉里奥哈和美国:纳帕谷),

更新日期:2020-04-21
down
wechat
bug