We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and Natural Language Processing algorithms. We use this dataset to study the...

We investigate the moderating effect of product attributes and review ratings on {views, conversion|views (conversion conditional on views), final conversion} of a purchase-based collaborative filtering recommender system on an e-commerce site. We run a randomized field experiment on a top...

Personalization is becoming ubiquitous on the World Wide Web. Such systems use statistical techniques to infer a customer’s preferences and recommend content best suited to him (e.g., “Customers who liked this also liked…”). A debate has emerged as to whether personalization has...

We investigate the impact of collaborative filtering recommender algorithms (e.g., Amazon’s “Customers who bought this item also bought”) commonly used in e-commerce on sales diversity. We use data from a randomized field experiment run on the website of a top retailer in North America...

We propose an interpretable deep survival model that can capture human-understandable nonlinear patterns from big data while handling censored observations and time-varying customer dynamics. To this end, we build WTTE-TCN (Weibull Time to Event Temporal Convolutional Networks) and apply...

We empirically investigate the demand for tied goods sold through competing retail channels. Tied good pricing strategies commonly involve a low price on the initial purchase (i.e. the primary good) to drive adoption, and a substantial markup on aftermarket goods to capture value. However, if...