So, Thomas, Seow, and Mues (2014) developed a logistic regression model for estimating the profitability of consumers’ use of credit cards using a transactor/revolver scorecard and explained how such a scorecard can be used as part of a risk assessment system. Building a scorecard that enables credit card issuers to classify an applicant as either good (low risk) or bad (high risk) has been the traditional approach to deciding whether or not to extend credit. According to the authors, credit card companies could better inform their decisions by also distinguishing between transactors and revolvers, or between those who pay off their balances to avoid interest charges and those who do not. The authors argued that while transactors are inherently good, they are also less profitable than revolvers and by scoring applicants along both good/bad and transactor/revolver dimensions can help credit card issuers make pricing decisions and more accurately estimate profits.
Ngo-Ye and Sinha (2014) developed a text regression model for predicting the helpfulness of 7,465 online restaurant reviews posted at Yelp.com and 584 book reviews posted at Amazon.com. According to the authors, most review opinion mining studies focused on sentiment rather than quality and ignored reviewer engagement characteristics. The authors argued that reviewer characteristics influence the perception of helpfulness and that decision makers at online organizations who rely on user-generated content to gain marketing advantage could better leverage that content if they understood what makes it helpful to others and if they could predict that helpfulness.