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首页> 外文期刊>Journal of Business Research >Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach
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Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach

机译：预测社交媒体绩效指标并评估对品牌建设的影响：一种数据挖掘方法

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This study presents a research approach using data mining for predicting the performance metrics of posts published in brands' Facebook pages. Twelve posts' performance metrics extracted from a cosmetic company's page including 790 publications were modeled, with the two best results achieving a mean absolute percentage error of around 27%. One of them, the "Lifetime Post Consumers" model was assessed using sensitivity analysis to understand how each of the seven input features influenced it (category, page total likes, type, month, hour, weekday, paid). The type of content was considered the most relevant feature for the model, with a relevance of 36%. A status post captures around twice the attention of the remaining three types (link, photo, video). We have drawn a decision process flow from the "Lifetime Post Consumers" model, which by complementing the sensitivity analysis information may be used to support manager's decisions on whether to publish a post. (C) 2016 Elsevier Inc. All rights reserved.
机译：这项研究提出了一种使用数据挖掘来预测品牌Facebook页面上发布的帖子的绩效指标的研究方法。从化妆品公司的页面（包括790种出版物）中提取的十二个帖子的绩效指标进行了建模，两个最佳结果的平均绝对百分比误差约为27％。其中之一是使用敏感性分析对“终生消费者的终身”模型进行了评估，以了解七个输入功能（类别，页面总喜欢次数，类型，月，小时，工作日，已付费）如何对其产生影响。内容类型被认为是模型中最相关的功能，相关性为36％。状态讯息会吸引其余三种类型（链接，照片，视频）的注意力的两倍。我们从“终生邮政消费者”模型中得出了决策流程，该模型通过补充敏感性分析信息可用于支持经理关于是否发布职位的决策。 （C）2016 Elsevier Inc.保留所有权利。

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