Research Overview:
I am an applied methodologist, interested in the intersection of modern probabilistic machine learning and marketing. I focus on problems in customer analytics, preference measurement, and design, with an eye to developing and applying flexible, interpretable, computational tools to drive insights in these domains. I also study how rich, unstructured data like text and images can be used in classic data-driven marketing contexts, like preference measurement, and in contexts which previously have been difficult to study from a data-driven perspective, like branding and design. Methodologically, I am interested in Bayesian nonparametrics, Bayesian computation, deep generative models, and representation learning. 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, as well as to have been a finalist for the Frank M. Bass and Paul Green Awards.
Publications:
Working Papers:
- Detecting Routines in Ride-sharing: Implications For Customer Management
Ryan Dew, Eva Ascarza, Oded Netzer, and Nachum Sicherman
[Show Abstract] [Working Paper]
Routines are central to consumer behavior in many industries, including ride-sharing, where consumers may use the same app to take the same trips on a regular basis. While prior work has established the importance of repeat behavior for marketing, little work has been done to understand the implications of routines, which we define as repeated behavior with a distinct, recurring, temporal structure. Partly, this lack of research stems from the statistical problem of estimating routines. In this paper, we propose a new approach to measuring routine usage, which we apply in the context of ride-sharing. Specifically, we model usage of the platform as an individual-level inhomogeneous Poisson point process, where the rate of usage is determined partly by a Bayesian nonparametric Gaussian process. In estimating this rate function, we leverage a unique cyclical kernel structure, that allows for precise estimation of recurrent behavior. We then use this model to estimate individual-level routines in usage of a ride-sharing service. We show that more routine users tend to be more valuable customers, with high individual-level “routineness” being significantly associated with higher future usage and lower churn rates.
- A Gaussian Process Model of Cross-Category Dynamics in Brand Choice
Ryan Dew, Yuhao Fan
[Show Abstract] [Working Paper]
Understanding individual customers’ sensitivities to prices, promotions, brand, and other aspects of the marketing mix is fundamental to a wide swath of marketing problems, including targeting and pricing. Companies that operate across many product categories have a unique opportunity, insofar as they can use purchasing data from one category to augment their insights in another. Such cross-category insights are especially crucial in situations where purchasing data may be rich in one category, and scarce in another. An important aspect of how consumers behave across categories is dynamics: preferences are not stable over time, and changes in individual-level preference parameters in one category may be indicative of changes in other categories, especially if those changes are driven by external factors. Yet, despite the rich history of modeling cross-category preferences, the marketing literature lacks a framework that flexibly accounts for correlated dynamics, or the cross-category interlinkages of individual-level sensitivity dynamics. In this work, we propose such a framework, leveraging individual-level, latent, multi-output Gaussian processes to build a nonparametric Bayesian choice model that allows information sharing of preference parameters across customers, time, and categories. We apply our model to grocery purchase data, and show that our model detects interesting dynamics of customers’ price sensitivities across multiple categories. Managerially, we show that capturing correlated dynamics yields substantial predictive gains, relative to benchmarks. Moreover, we find that capturing correlated dynamics can have implications for understanding changes in consumers preferences over time, and developing targeted marketing strategies based on those dynamics.
- Decomposing The Impact of a Free Cancellation Program on Customer Booking Behavior
Yuhao Fan, Ryan Dew, Eric T. Bradlow, Peter Fader
[Show Abstract] [Request Paper]
Despite the recent surge in popularity of free cancellation programs across many business settings, the impact of a free cancellation program on customers’ behavior and firm profits remains unclear. Previous research has found that the implementation of a free cancellation program encourages customers to repeat purchase more frequently, spend more on each trip, but also cancel their reservations more often. However, we do not know the relative magnitude of each effect, or which may dominate in terms of the overall impact on firm profit. We investigate this question empirically using data from a hostel booking platform, and a Bayesian semiparametric model that lets us nonparametrically decompose the impact of the free cancellation on customers’ repeat booking propensity, booking timing, spend and cancellation amounts, and propensity to churn. Our model combines both Bayesian nonparametric Gaussian processes to assess intertemporal effects of the program, and Dirichlet processes to capture the rich heterogeneity that exists in this setting. We find that the effect of the program on existing customers varies tremendously across different customer segments, with muted short-run effects for loyal customers, but strong, positive CLV implications. Ultimately, we find the program was beneficial for the platform.
Research in Progress:
- Preference Measurement with Unstructured Data, with Applications to Adaptive Onboarding Surveys
Ryan Dew
[Show Abstract]
A common problem in recommendation engines is the cold start problem: how can we make a recommendation to a new customer, without any prior purchase data? Such problems are particularly salient for increasingly common online subscription businesses, where initial recommendations can shape whether potential customers decide to subscribe, and how their preferences evolve subsequently. The need to assess a new customers' preferences quickly, and without prior purchase data, has led to the increasing prevalence of customer onboarding surveys, wherein companies ask potential or current customers a series of questions aimed at understanding their preferences, without having observed any purchasing. While such onboarding surveys are a relatively recent development in e-commerce, the idea of learning the most information about a customer’s preferences as possible using the fewest questions has been studied extensively in “offline” marketing research, in the context of adaptive conjoint analysis. In this work, I bridge these two domains using a combination of representation learning for unstructured data, and Bayesian optimization for on-the-fly estimation of preferences. I apply this framework both in the context of an on-boarding survey for an online subscription business, and in the context of traditional preference measurement.
- Winning The Attention Race: Analyzing Video Popularity And Content Evolution On TikTok
Zijun Tian, Ryan Dew, and Raghu Iyengar
[Show Abstract]
We explore the empirical regularities governing the diffusion of content on the recently popular social media platform TikTok, including video and hashtag-level drivers of content popularity. TikTok is a unique social media platform focused on the sharing of short videos. Similar to social media platforms like Twitter, TikTok features hashtags that organize user generated content. However, unlike other social media platforms, TikTok also features a special type of hashtag, the challenge, that encourages users to generate (rather than merely share) content matching a particular theme. Combining both classical ideas from the literature on diffusion on social networks with cutting-edge multiview representation learning methods, we explore how these challenges grow in popularity, what factors explain the popularity of both challenges and videos within them, and how the content within hashtags evolves over time. These insights have implications for firm management of social media campaigns.
- Causal Reasoning for Unified Marketing Measurement under Privacy Regulations
with Nicolas Padilla
- Learning Cross-market Style Diffusion with Representation Learning
with Anuj Kapoor
- Touchstreams: Learning Preferences from Feed Navigation
with Yoonduk Kim