Daily deals sites such as Groupon offer deeply discounted goods and services to tens of millions of customers through geographically targeted daily e-mail marketing campaigns. In our prior work we observed that a negative side effect for merchants selling Groupons is that, on average, their Yelp ratings decline significantly. However, this previous work was primarily observational, rather than explanatory. In this work, we rigorously consider and evaluate various hypotheses about underlying consumer and merchant behavior in order to understand this phenomenon, which we dub the Groupon effect. We use statistical analysis and mathematical modeling, leveraging a dataset we collected spanning tens of thousands of daily deals and over 7 million Yelp reviews. We investigate hypotheses such as whether Groupon subscribers are more critical than their peers, whether Groupon users are experimenting with services and merchants outside their usual sphere, or whether some fraction of Groupon merchants provide significantly worse service to customers using Groupons. We suggest an additional novel hypothesis: reviews from Groupon users are lower on average because such reviews correspond to real, unbiased customers, while the body of reviews on Yelp contain some fraction of reviews from biased or even potentially fake sources. Although our focus is quite specific, our work provides broader insights into both consumer and merchant behavior within the daily deals marketplace.

VGAM package and some other documentation. This document is current for prerelease version VGAM-0.8-1. The theory 1 underlying the software can be found in Yee