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.

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.


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.

Working Papers:

• Dynamic Preference Heterogeneity
  Ryan Dew, Yang Li, Asim Ansari
  Revision invited at Journal of Marketing Research.
  [Show Abstract] [Working Paper]
  

  Consumers' preferences change over time, often in tandem with population trends, but frequently exhibiting individual-specific idiosyncrasies. In this paper, we develop a novel Bayesian nonparametric framework based on Hierarchical Gaussian Processes (HGP) for modeling such dynamic heterogeneity. Our specification generalizes previous approaches for estimating dynamics that have been used in the marketing literature by flexibly capturing both the evolution of population trends and individual-level departures from those trends. This allows for sharing of statistical information across individuals, and within individuals over time, and provides rich individual-level insights and efficient inferences regarding preference evolution. We showcase our HGP specification in a choice modeling context, using simulations and real data from two CPG categories. We find that commonly used heterogeneity specifications can lead to significant biases or inefficiencies when dynamic heterogeneity is present, and are unable to represent managerially relevant individual-level parameter trajectories. Our application uncovers robust evidence of dynamic heterogeneity during the Great Recession, and important individual-specific trends that can be leveraged by retailers for optimal targeted pricing.



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

  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:

• 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