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...

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 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...

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 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...

Observed contracts in the real-world are often very simple, partly reflecting the constraints faced by contracting firms in making the contracts more complex. We focus on one such rigidity, the constraints faced by firms in fine-tuning contracts to the full distribution of heterogeneity of its...

This paper reports on the development and implementation of a large-scale, marketing analytics framework for improving the segmentation, targeting and optimization of a consumer-facing firm's marketing activities. The framework leverages detailed transaction data of the type increasingly...

We present a framework to measure empirically the size of indirect network effects in high-technology markets with competing incompatible technology standards. These indirect network effects arise due to inter-dependence in demand for hardware and compatible software. By modeling the joint...

Discrete choice models of aggregate demand, such as the random coefficients logit, can handle large differentiated products categories parsimoniously while still providing flexible substitution patterns. However, the discrete choice assumption may not be appropriate for many categories in which...

Firms in durable good product markets face incentives to intertemporally price discriminate, by setting high initial prices to sell to consumers with the highest willingness to pay, and cutting prices thereafter to appeal to those with lower willingness to pay. A critical determinant of the...