Video is one of the fastest growing online services offered to consumers. The rapid growth of online video consumption brings new opportunities for marketing executives and researchers to analyze consumer behavior. However, video introduces new challenges. Specifically, analyzing unstructured video data presents formidable methodological challenges that limit the current use of multimedia data to generate marketing insights.

To address this challenge, the authors propose a novel video feature framework based on machine learning and computer vision techniques, which helps marketers predict and understand the consumption of online video from a content-based perspective. The authors apply this frame-work to two unique datasets: one provided by Masterclass.com, consisting of 771 online videos and more than 2.6 million viewing records from 225,580 consumers, and another from Crash Course, consisting of 1,127 videos focusing on more traditional education disciplines.

The analyses show that the framework proposed in this paper can be used to accurately predict both individual-level consumer behavior and aggregate video popularity in these two very different contexts. The authors discuss how their findings and methods can be used to advance management and marketing research with unstructured video data in other contexts such as video marketing and entertainment analytics.

视频是提供给消费者的增长最快的在线服务之一。在线视频消费的快速增长为营销主管和研究人员分析消费者行为带来了新的机会。然而，视频带来了新的挑战。具体来说，分析非结构化视频数据带来了巨大的方法论挑战，限制了当前使用多媒体数据来产生营销洞察力。

为了应对这一挑战，作者提出了一种基于机器学习和计算机视觉技术的新型视频特征框架，帮助营销人员从基于内容的角度预测和理解在线视频的消费。作者将此框架应用于两个独特的数据集：一个由 Masterclass.com 提供，包含来自 225,580 个消费者的 771 个在线视频和超过 260 万条观看记录，另一个来自 Crash Course，包含 1,127 个专注于更传统教育的视频学科。

分析表明，本文提出的框架可用于在这两种截然不同的环境中准确预测个人层面的消费者行为和聚合视频流行度。作者讨论了如何将他们的发现和方法用于在视频营销和娱乐分析等其他环境中利用非结构化视频数据推进管理和营销研究。