The Tip Desk

Strategic Queueing Rules Can Steal Market Share

New research shows that modifying service order can capture a majority of demand from competitors using standard first-come-first-served rules.

Service order is a competitive tool. When two queues have identical service rates, a provider can capture a strict majority of demand simply by abandoning the standard first-come-first-served (FCFS) rule in favor of a different service order. This shift changes the expected waiting time for arriving agents, pulling them away from the FCFS competitor.

This finding suggests that the ubiquitous FCFS model is an exploitable vulnerability in any market where customers choose a provider based on expected wait times. A provider that optimizes its service sequence can systematically divert traffic from a competitor who relies on the traditional chronological order of arrival.

In low-congestion environments, a specific variant of last-come-first-served (LCFS) emerges as a stable equilibrium when both providers can design their own rules. This implies that in certain high-efficiency or low-volume sectors, the most stable state is one where the newest arrivals are prioritized over those who have waited longer.

The ability to capture market share through queueing design depends heavily on commitment. Without a committed service rule, the system reverts to a state where both providers use FCFS and agents simply route to whichever queue is shorter at the moment of arrival.

Industries that manage high-volume stochastic arrivals—such as digital service platforms, logistics hubs, or automated ticketing systems—are exposed to this dynamic. If one operator implements a non-FCFS rule, they can theoretically establish an upper bound on the arrival share their FCFS competitor is capable of maintaining.

Market participants should monitor for shifts in how service providers sequence their workloads. A move away from chronological processing may not be a technical glitch or a random change, but a deliberate strategy to optimize demand capture.

Paper: https://arxiv.org/abs/2608.11710