An exact pricing algorithm for revenue maximization under the logit demand function
Moddassir Khan Nayeem, Omar Abbaas, Suzan Alaswad, Sinan Salman

TL;DR
This paper introduces an exact, closed-form pricing algorithm for revenue maximization under the logit demand function, improving over heuristic methods and demonstrating significant revenue gains.
Contribution
It develops a novel analytical solution using the Lambert W function to determine the optimal price under the logit demand model, correcting common heuristic practices.
Findings
Optimal price is consistently lower than the inflection point.
Average 20% price reduction leads to 15% revenue increase.
Numerical experiments validate the effectiveness of the proposed algorithm.
Abstract
Determining the optimal selling price is a challenge in revenue management, especially in markets characterized by nonlinear and price-sensitive demand. While traditional models, such as linear, power, and exponential demand functions, offer analytical convenience, they often fail to capture realistic purchase dynamics, leading to suboptimal pricing. The logit demand function addresses these limitations through its bounded, S-shaped curve, offering a more realistic representation of consumer behavior. Despite its advantages, most existing literature relies on heuristic approaches, such as pricing at the inflection point, which prioritizes maximum price sensitivity but does not guarantee maximum revenue. This study proposes a novel, exact pricing algorithm that analytically derives the revenue-maximizing price under the logit demand function using the Lambert W function. By providing a…
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Taxonomy
TopicsConsumer Market Behavior and Pricing · Supply Chain and Inventory Management · Economics of Agriculture and Food Markets
