With rapid technological developments, product-market boundaries have become more dynamic. Consequently, competition for products and services is emerging outside the product-market boundaries traditionally defined based on SIC and NAICS classification codes. Identifying these fluid product-market boundaries is critical for firms not only to compete effectively within a market, but also to identify lurking threats and latent opportunities outside market boundaries. Newly available big data on social media engagement presents such an opportunity. We propose a deep network representation learning framework to capture latent relationships among thousands of brands and across many categories, using millions of social media users' brand engagement data. We build a brand-user network and then compress the network into a lower dimensional space using a deep Autoencoder technique. We evaluate our approach quantitatively and qualitatively, and visually display the market structure using the learned representations of brands. We validate the learned brand relationships using multiple external data sources. We also illustrate how our method can capture the dynamic changes of product market boundaries using two well-known events – the acquisition of Whole Foods by Amazon and the introduction of the Model 3 by Tesla – and how managers can use the insights that emerge from our analysis.

随着技术的快速发展，产品市场边界变得更加动态。因此，产品和服务的竞争正在出现在传统上基于 SIC 和 NAICS 分类代码定义的产品市场边界之外。识别这些流动的产品市场边界对于公司不仅在市场内有效竞争而且识别市场边界外的潜在威胁和潜在机会至关重要。社交媒体参与的新可用大数据提供了这样的机会。我们提出了一个深度网络表示学习框架，使用数百万社交媒体用户的品牌参与数据来捕获数千个品牌之间和多个类别之间的潜在关系。我们构建了一个品牌用户网络，然后使用深度自动编码器技术将网络压缩到一个较低维度的空间中。我们定量和定性地评估我们的方法，并使用学习到的品牌表征直观地展示市场结构。我们使用多个外部数据源验证学习到的品牌关系。我们还说明了我们的方法如何使用两个众所周知的事件来捕捉产品市场边界的动态变化——亚马逊收购 Whole Foods 和特斯拉推出 Model 3——以及管理人员如何利用我们的洞察力分析。我们使用多个外部数据源验证学习到的品牌关系。我们还说明了我们的方法如何使用两个众所周知的事件来捕捉产品市场边界的动态变化——亚马逊收购 Whole Foods 和特斯拉推出 Model 3——以及管理人员如何利用我们的洞察力分析。我们使用多个外部数据源验证学习到的品牌关系。我们还说明了我们的方法如何使用两个众所周知的事件来捕捉产品市场边界的动态变化——亚马逊收购 Whole Foods 和特斯拉推出 Model 3——以及管理人员如何利用我们的洞察力分析。