Henrique Laurino dos Santos
Empirical methodologist in quantitative Marketing
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Since concluding my doctorate at the Wharton School, University of Pennsylvania, I am set to join the Saïd Business School of Oxford University as an associate professor. Catch me in the UK starting summer 2025.
My research focuses on momentum-based marketing policies, whether that be controlling wearout in digital advertising, detecting community formation in adoption networks, or parsing the interplay between new product adoptions and user reviews. I am interested in how endogenous dynamic patterns emerge among consumers, marketers and products.
Methodologically, I work on problems around Bayesian machine learning and stochastic processes. I have a long-running love affair with Hawkes processes (to the dismay of my friends and family).
You can reach me by email at hlauri@wharton.upenn.edu
Published works
Published works
- Laurino dos Santos, Henrique and Jonah Berger (2022), “The Speed of Stories: Semantic Progression and Narrative Success.” Journal of Experimental Psychology: General, 151(8), 1833–1842. https://doi.org/10.1037/xge0001171
In this article we use sentence-level embeddings to plot "narrative curves" for a large number of movie scripts. We then relate those to audience popularity and find common shapes of storytelling that are correlated with high scores.
Working papers (available by request)
Working papers (available by request)
- Laurino dos Santos, Henrique. “Analyzing Privacy-Restricted Clickstream Data Using Hawkes Processes.”
My job market paper. I propose a methodology for modelling clickstream data with Hawkes processes. Although Hawkes-type models are uncommon in the Marketing literature, they nest many usual alternatives and have a variety of advantages over them when datapoints are both sparse and clumpy. I add to that by solving some estimation issues when privacy-restrictions both truncate and censor datapoints.
- Laurino dos Santos, Henrique and Noah Gans. “Reducing Ad Waste: Optimal Ad Scheduling with Point Processes.”
Starting from the methodology presented in my job market paper, we solve a variety of optimal dynamic advertising problems. We consider optimal policies for advertisers subject to different legal restrictions and with varying goals and planning horizons. We find that a variety of incentives, both structural and idiosyncratic, may lead to overadvertising practices.
- Laurino dos Santos, Henrique and Jehoshua Eliashberg. “Forecasting Endogenous Patterns in the Theatrical Performance of New Movies.”
Adding to the long tradition of forecasting how new movies will perform in theaters, we present a novel model that captures the endogenous loop between tickets sold, volume of user reviews posted, and score distribution among those reviews. We combine Bayesian hierarchical models and posterior distributions approximated by deep learning to devise forecast updating methods that achieve great performance with as few as three days of post-release data available.
Some other research in progress
Some other research in progress
- “Network Customer Lifetime Value,” with Patrick Zhang
- “Cross-Feature Spillovers in Super Apps,” with Raghuram Iyengar, Ashis Mishra
- “Strategic Media Bingeing,” with Rachele Ciulli, Cait Lamberton, Annie Wilson, Nathan Warren
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