A common assumption underlying the analysis of consumers' choices among optional tariffs is that consumers choose the tariff that maximizes their surplus and, thus, the tariff that leads to the lowest billing rate for a given amount of usage. Yet there is evidence that many users prefer a flat rate even though their billing rate would be lower with a pay-per-use tariff (flat-rate bias), and some users prefer a pay-per-use tariff even though they would save money with a flat rate (pay-per-use bias). The authors conduct four empirical analyses based on three different data sets. They show that the flat-rate bias is more important and has a greater regularity and time persistence than the pay-per-use bias. They classify potential causes of the flat-rate bias as "insurance effect," "taxi meter effect," "convenience effect," and "overestimation effect" and show that the insurance, the taxi meter, and the overestimation effects lead to a flat-rate bias. They provide evidence that underestimation of usage is a major cause of the pay-per-use bias. They show that the flat-rate bias does not significantly increase customer churn and thus results in a short-and long-term profit increase. In contrast, the pay-per-use bias largely increases churn so that in the long run, the additional short-term profit is offset by higher churn.

We explore data from a field test of how an algorithm delivered ads promoting job opportunities in the Science, Technology, Engineering and Math (STEM) fields. This ad was explicitly intended to be gender-neutral in its delivery. Empirically, however, fewer women saw the ad than men. This happened because younger women are a prized demographic and are more expensive to show ads to. An algorithm which simply optimizes cost-effectiveness in ad delivery will deliver ads that were intended to be gender-neutral in an apparently discriminatory way, due to crowding out. We show that this empirical regularity extends to other major digital platforms.

In communication, information, and other industries, three-part tariffs are increasingly popular. A three-part tariff is defined by an access price, an allowance, and a marginal price for any usage in excess of the allowance. Empirical nonlinear pricing studies have focused on consumer choice under two-part tariffs. We show that consumer behavior differs under three-part tariffs and assess how consumer demand uncertainty impacts tariff choice. We develop a discrete/continuous model of choice among three-part tariffs and estimate it using consumer-level data on Internet usage. Our model extends prior work in accommodating consumer switching to competitors, thereby capturing behavior in competitive industries more accurately. Our empirical work shows that demand uncertainty is a key driver of choice among three-part tariffs. Consumers' expected bill increases with the variation in their usage, steering them toward tariffs with high allowances. Consequently, demand uncertainty decreases consumer surplus and increases provider revenue. A further analysis of consumers' responsiveness to the different elements of a three-part tariff under the provider's current pricing structure reveals that prices affect a consumer's tariff choice more than her usage quantity and that the allowance plays a strong role in consumer tariff choice. Based on our results, we derive implications for pricing with three-part tariffs.pricing, nonlinear pricing, discrete/continuous choice model, Internet access, three-part tariffs, uncertainty, choice

Firms can now serve personalized recommendations to consumers who return to their website, based on their earlier browsing history on that website. At the same time, online advertising has greatly advanced in its use of external browsing data across the web to target internet ads. 'Dynamic Retargeting' integrates these two advances, by using information from earlier browsing on the firm's website to improve internet advertising content on external websites. Consumers who previously visited the firm's website when surfing the wider web, are shown ads that contain images of products they have looked at before on the firm's own website. To examine whether this is more effective than simply showing generic brand ads, we use data from a field experiment conducted by an online travel firm. We find, surprisingly, that dynamic retargeted ads are on average less effective than their generic equivalent. However, when consumers exhibit browsing behavior such as visiting review websites that suggests their product preferences have evolved, dynamic retargeted ads no longer underperform.