Riffs, Routes, and Revenue: Artificial Intelligence–Enabled, Authenticity-Constrained Tour Marketing—Insights from Rock, Hard Rock, and Heavy Metal

Abstract

Rock, Hard Rock, and Heavy Metal tours are experiential products where value is co-created symbolically and communally, not only through functional service delivery (Holbrook & Hirschman, 1982). Tour marketing and revenue capture are platformed: discovery happens in social feeds and search, ticketing runs through e-commerce marketplaces, and value extends via merchandise and community engagement. This conceptual paper develops an artificial intelligence (AI)–enabled, authenticity-constrained tour marketing framework that integrates AI and big data analysis into strategic planning and performance measurement. The framework argues that AI-supported scanning of geographic signals—streaming, search, social engagement, and prior sell-through—can improve routing by reducing information frictions (Cho et al., 2018). However, optimization can trigger authenticity backlash when monetization appears extractive, because brand community norms shape legitimacy and repeat behavior (Muñiz & O’Guinn, 2001; Schau et al., 2009). Ticketing and resale are treated as risk-management issues because secondary markets can divert surplus and weaken perceived fairness (Courty, 2003). Pricing tiers, upgrades, and access policies are modeled as both economic and communications decisions in a market shaped by superstar effects (Krueger, 2005). Testable propositions specify when AI-enabled planning improves time-totrend advantage and sell-through velocity versus when it induces strategic convergence and weaker brand distinctiveness. A measurement blueprint links key performance indicators— sell-through velocity, conversion rate, customer acquisition cost (CAC), and return on advertising spend (ROAS)—to authenticity perceptions, community advocacy, and long-run fan value. Although developed for authenticity-sensitive rock subgenres, the sense–shape– capture logic generalizes to other live music contexts.