Scholarlink

<p>In the wake of digital transformation, marketers gained access to large amounts of user-generated content and data in which consumers specifically mention and discuss brands, products, and services. This data offers rich information potential and may ultimately provide marketers with the ability to use this data pool to approximate survey-based consumer mindset metrics that mirror consumer attitudes alongside the different levels of the decision-making process. We argue that leveraging this potential may ultimately help marketers overcome common limitations of survey-based metrics and enable companies to observe and track mindset metrics that have been so far inaccessible due to financial and other constraints. To this end, we propose a&#xa0;four-step process that first identifies the key aspects of a&#xa0;mindset metric based on the existing body of developed constructs, then pinpoints potential data sources, and subsequently chooses an adequate data transformation tool.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Mining Consumer Mindset Metrics With User-Generated Content

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月4日

查看全文
  • Raoul V. Kübler,
  • Susanne J. Adler,
  • Lina Welke,
  • Marko Sarstedt,
  • Koen Pauwels

摘要

In the wake of digital transformation, marketers gained access to large amounts of user-generated content and data in which consumers specifically mention and discuss brands, products, and services. This data offers rich information potential and may ultimately provide marketers with the ability to use this data pool to approximate survey-based consumer mindset metrics that mirror consumer attitudes alongside the different levels of the decision-making process. We argue that leveraging this potential may ultimately help marketers overcome common limitations of survey-based metrics and enable companies to observe and track mindset metrics that have been so far inaccessible due to financial and other constraints. To this end, we propose a four-step process that first identifies the key aspects of a mindset metric based on the existing body of developed constructs, then pinpoints potential data sources, and subsequently chooses an adequate data transformation tool.