About Me:

I am an Assistant Professor of Marketing at the Wharton School of the University of Pennsylvania. My research explores how machine learning and Bayesian statistical methodologies can solve real world marketing problems. At Wharton, I teach the class Data and Analysis for Marketing Decisions, for which I was awarded the Wharton Teaching Excellence award in 2019.

Interested in my research or in collaborating? Feel free to email me at ryandew@wharton.upenn.edu. You can also find me on LinkedIn and on Google Scholar.

For more about my background, check out my CV.

Research Overview:

I study how modern statistical and computational methods can enhance the capacity of marketing managers to make data-driven decisions. I am particularly interested in topics in marketing analytics, customer relationship management, and preference measurement, with a focus on understanding dynamics in consumer purchasing patterns. I am also interested in incorporating visual and textual data into marketing models, with a specific interest in understanding the interplay between the stated and visual identities of brands, and in bringing big data to bear on firms' design decisions. Methodologically, I focus on techniques from machine learning, Bayesian nonparametrics, and Bayesian econometrics. I'm honored to have received several awards for my research, including the 2018 INFORMS Society for Marketing Science Doctoral Dissertation Award, and the 2018 Marketing Section of the American Statistical Association's Doctoral Research Award.


Publications:

• Bayesian Nonparametric Customer Base Analysis with Model-based Visualizations
  Ryan Dew and Asim Ansari
  Marketing Science, 2018
  [Show Abstract] [Paper] [Code Notebook] [Replication Data] [Stan Code]
  

  Modern marketers are responsible for understanding and managing customer spending behavior across many different products. Dynamics in spending result from both predictable customer-level effects, which are characterized by interpurchase time, customer lifetime, and past purchase frequency, as well as calendar time effects, which are driven by managerial actions such as product changes and promotions, and by general trends and random shocks. Understanding these dynamics in spending is further complicated by a lack of knowledge of all of the factors that influence spending for a given product, a problem exacerbated in large multiproduct firms by information asymmetries that can exist between the product teams that execute marketing actions and the marketing analytics team responsible for customer base analysis. A comprehensive understanding of customer base dynamics therefore requires a modeling framework that flexibly integrates both known and unknown calendar time determinants of spending with the individual-level effects that robustly predict spend activity. In this paper, we develop a Bayesian nonparametric framework based on Gaussian process priors to understand and predict customer spending. Our model separates out calendar time effects from individual-level dynamics by modeling both sets of factors as unknown latent functions that jointly determine spend propensity. The primary output of our Gaussian Process Propensity Model (GPPM) is a set of estimated curves that provides a visual and easily comprehensible representation of purchasing dynamics, which we call the model-based dashboard. We illustrate the utility of our modeling framework on data from two popular free-to-play mobile video games. We show how the GPPM's model-based dashboard can be useful for assessing patterns and disruptions in spending. We also show how the GPPM exhibits superior forecasting ability compared to existing customer base analysis benchmarks, including hazard and buy-till-you-die models.



• Modeling Dynamic Heterogeneity using Gaussian Processes
  Ryan Dew, Asim Ansari, Yang Li
  Journal of Marketing Research, Forthcoming
  [Show Abstract] [Paper]
  

  Marketing research relies on individual-level estimates to understand the rich heterogeneity that exists in consumers, firms, and products. While much of the literature focuses on capturing static cross-sectional heterogeneity, little research has been done on modeling dynamic heterogeneity, or the heterogeneous evolution of individual-level model parameters. In this work, we propose a novel framework for capturing the dynamics of heterogeneity, using individual-level, latent, Bayesian nonparametric Gaussian processes. Similar to standard heterogeneity specifications, our Gaussian Process Dynamic Heterogeneity (GPDH) specification models individual-level parameters as flexible variations around population-level trends, allowing for sharing of statistical information both across individuals and within individuals over time. This hierarchical structure provides precise individual-level insights regarding parameter dynamics. We show that GPDH nests existing heterogeneity specifications, and that not flexibly capturing individual-level dynamics may result in biased parameter estimates. Substantively, we apply GPDH to two problems: understanding preference dynamics, and modeling the evolution of online reviews. Across both applications, we find robust evidence of dynamic heterogeneity, and illustrate GPDH's rich managerial insights, with implications for targeting, pricing, and market structure analysis.

Working Papers:

• Letting Logos Speak: Leveraging Multiview Representation Learning for Data-Driven Logo Design
  Ryan Dew, Asim Ansari, Olivier Toubia
  [Show Abstract] [Working Paper] [Explore Our Data] [Personality-based Logo Generator]
  

  Logos serve a fundamental role in branding as the visual figurehead of the brand. Yet, due to the difficulty of using unstructured image data, prior research on logo design has been largely limited to non-quantitative studies. In this work, we explore logo design from a data-driven perspective. In particular, we aim to answer several key questions: first, to what degree can logos represent a brand's personality? Second, what are the key visual elements in logos that elicit brand and firm relevant associations, such as brand personality traits? Finally, given text describing a firm's brand or function, can we suggest features of a logo that elicit the firm's desired image? To answer these questions, we develop a novel logo feature extraction algorithm, that uses modern image processing tools to decompose unstructured pixel-level image data into meaningful visual features. We then analyze the links between firm identity and the features of logos through a deep, multiview generative model, which links visual features of logos with textual descriptions of firms and consumer ratings of brand personality by learning representations of brand identity. We apply our modeling framework on a dataset of hundreds of logos, textual descriptions from firms' websites, third party descriptions of firms, and consumer evaluations of brand personality to explore these questions.

Research in Progress:

• Nonparametric Modeling of Cross-Category Preferences
  Joint work with Jeremy Fan


• Dynamic Contextual Recommendation Systems
  Joint work with Yegor Tkachenko and Asim Ansari
  [Show Abstract]
  

  We propose and evaluate a variety of recommender system architectures of increasing complexity based on deep neural nets in the context of an online subscription game service. The flexibility of the proposed architecture allows for easy and scalable fusion of user, game, and decision context static and dynamic covariates across data types, such as user game-playing behavior, textual game descriptions, social media data like Reddit comments, and seasonal events.



• Customer-Centric Data Fusion
  Joint work with Oded Netzer
  [Show Abstract]
  

  Marketers face a deluge of data on all aspects of their customers, including what they are buying, how they are using their products, and what they are saying about their purchases. We seek to address the problem of understanding general patterns across many modes of customer behavior, by modeling consumers through a set of latent, inferred personas that are related to behaviors across all domains of interest. Our customer-centric approach allows firms to leverage data across domains, even in the presence of missing data, to better understand and predict customer behavior in any particular domain, using existing probabilistic models of customer behavior. We illustrate the expressive capacity and managerial utility of such a framework through an application to rental car transaction and textual feedback data.
  
  This work was a winner of the Deming Center's 2016 Doctoral Fellowship.



• Habitual Usage and Customer Relationship Management
  Joint work with Eva Ascarza, Oded Netzer, and Nachum Sicherman


• Implications of Service Quality and Expectations on Customer Churn
  Joint work with Eva Ascarza, Oded Netzer, and Nachum Sicherman


• Bayesian Optimization of Online Ad Bidding
  Ryan Dew, Kinshuk Jerath, Jim Prinzi
  [Show Abstract]
  

  Bidding arises in many aspects of digital advertising. While theory suggests optimal bidding strategies, in practice, firms use a variety of heuristics to set their bids. This approach is often based on guesswork, without learning over time. In this work, we investigate an alternative approach, rooted in Gaussian Process-based Bayesian optimization, where we explore the space of possible bids, while exploiting the information we acquire. Unlike in more classical multi-armed bandit settings, in bidding, the space of actions is continuous, which poses a unique methodological challenge for online optimization.