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...

We use variation in wind speeds at surfing locations in Switzerland as exogenous shifters of users' propensity to post content about their surfing activity onto an online social network. We exploit this variation to test whether users' online content generation activity has a causal effect on...

We measure the revenue and cost implications to supermarkets of changing their price positioning strategy in oligopolistic downstream retail markets. Our estimates have implications for long-run market structure in the supermarket industry, and for measuring the sources of price rigidity in the...

New empirical models of consumer demand that incorporate social preferences, observational learning, word-of-mouth or network effects have the feature that the adoption of others in the reference group - the "installed-base" - has a causal effect on current adoption behavior. Estimation of such...

Marketing researchers have used models of consumer demand to forecast future sales; to describe and test theories of consumer behavior; and to measure the response to marketing interventions. The basic framework typically starts from microfoundations of expected utility theory to obtain a...