Surfly Pricing ~upd~ ★ (DELUXE)

The gap in literature is the convergence of surge timing with behavioral personalization—a gap this paper fills by defining Surfly Pricing as a distinct category. | Feature | Traditional Dynamic Pricing | Surfly Pricing | |---------|----------------------------|----------------| | Trigger | Aggregate demand (e.g., seats left, days to departure) | Individual behavior + device signals + real-time demand | | Update frequency | Daily or hourly | Sub-second (per click/refresh) | | Transparency | Fare rules published | Opaque; user cannot see why price changed | | Segmentation | Discrete fare classes (Y, B, M, etc.) | Continuous; each user sees a unique price | | Primary goal | Maximize load factor × yield | Maximize willingness-to-pay extraction per session |

Whereas classic dynamic pricing relies on predictable supply-demand curves (e.g., higher prices for last-minute bookings or peak holidays), Surfly Pricing introduces personalized temporal volatility . Prices change not only with aggregate demand but also with individual user attributes. This paper asks: (1) How does Surfly Pricing differ from traditional revenue management? (2) What technological infrastructure enables it? (3) What are the welfare and regulatory implications? 2.1 Traditional Airline Revenue Management Since the 1980s, airlines have used yield management to segment markets into fare classes (Belobaba, 1987). Prices vary by booking date, refundability, and Saturday night stay rules—but within a given class, all customers face the same price at the same time. This is intertemporal price discrimination , not personalized. 2.2 Behavioral Pricing With e-commerce, firms began testing personalized offers using clickstream data. Hannak et al. (2014) documented price steering on travel sites, where prices changed based on operating system (Mac users quoted higher hotel rates). However, these changes were static per session. 2.3 Surge Pricing (Ride-hailing) Uber’s surge pricing adjusts prices in real-time based on local driver-to-rider ratios (Chen & Sheldon, 2016). Surfly Pricing borrows this real-time reactivity but applies it to individual digital footprints rather than public market conditions. surfly pricing

Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2020). Artificial intelligence, algorithmic pricing, and collusion. American Economic Review , 110(10), 3267–3297. The gap in literature is the convergence of

Hannak, A., Soeller, G., Lazer, D., Mislove, A., & Wilson, C. (2014). Measuring price discrimination and steering on e-commerce web sites. Proceedings of the 2014 Internet Measurement Conference , 305–318. This paper defines “Surfly Pricing” as a hypothetical but increasingly plausible evolution of existing practices. If you intended a different concept (e.g., “surf and fly” package pricing, or a specific company named Surfly), please clarify, and I will revise accordingly. This paper asks: (1) How does Surfly Pricing