Abstract Advancements in computing, technology, and their applications to advertising enable marketers to deliver brand messages tailored to individuals and consumer segments. The growth of computational advertising (CA) has created new opportunities but also poses risks in the use of algorithms to generate and optimize the impact of such messages. This article addresses a particular domain influenced by these advancements, namely, automated brand-generated content. We offer an automated brand-generated content (ABC) model that posits two advances. First, rather than solely optimizing consumer data for enhanced impact of automated content, we submit, and provide extra key variables to further illustrate, that there is a desirable balance of both consumer and brand data as inputs to algorithms to serve short- and long-term impact goals. Second, this article guides research by addressing tensions between understanding the relationship between inputs and desired impacts (both short and long term) and proposing a research agenda for future work.

中文翻译：

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在计算广告时代引入自动生成品牌内容的模型

摘要计算，技术及其在广告中的应用方面的进步使营销人员能够交付针对个人和消费者细分市场的品牌信息。计算广告（CA）的增长创造了新的机会，但在使用算法生成和优化此类消息的影响方面也带来了风险。本文介绍了受这些进展影响的特定领域，即自动品牌生成的内容。我们提供了一个自动的品牌生成内容（ABC）模型，该模型具有两个方面的优势。首先，我们提交并提供额外的关键变量（而不是仅优化消费者数据来增强自动化内容的影响），并进一步说明了消费者数据和品牌数据之间的理想平衡，可以作为算法的输入，以服务于短期和长期客户。长期影响目标。第二，