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Home | Events Archive | Identifying Market Structure: A Deep Network Representation Learning of Social Engagement
Seminar

Identifying Market Structure: A Deep Network Representation Learning of Social Engagement


  • Location
    Online
  • Date and time

    September 17, 2020
    14:30 - 15:00

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 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. Extant methods using surveys on consumer perceptions or purchase data will be unable to identify the impact that a brand from outside the boundary may have on brands within a product-market. 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 heterogeneous brand-user network and then compress the network into a lower dimensional space using a deep Autoencoder technique. We validate our technique using a novel link-prediction method and visualize the learned representations pictorially. We 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. Joint with Yi Yang and Kunpeng Zhang.

Keywords: AI, Deep Representation Learning, Social Media, Competitive Market Structure, Big Data

Click here to read full paper.

Biography

P. K. Kannan is the Dean’s Chair in Marketing Science at the Robert H. Smith School of Business at the University of Maryland. His research expertise is on marketing modeling, applying statistical, econometric, machine learning, and AI methods to marketing data. His current research stream focuses on digital marketing - mobile marketing, attribution modeling, media mix modeling, new product/service development and customer relationship management (CRM).