Author: Mustafif Khan Paper ID: MK2740US Date Added: 2025-05-22 19:13:39
In this paper, we present a pricing optimization framework for determining minimum set prices across multiple publication formats under royalty and affordability constraints. Motivated by the not-for-profit mission of the MoKa Reads Collective—"spend the least to support the most"—our goal is to align publication pricing with fair compensation for the author to support themselves while maintaining accessibility for readers. We begin by analyzing price and page count data for programming books using the Google Books API and establish price boundaries for different formats. We then formulate a nonlinear optimization problem where the objective is to maximize expected royalties while penalizing excessive pricing. Constraints enforce pricing monotonicity and reward platforms with higher royalty rates. Using methods such as Sequential Least Squares Programming (SLSQP) and Trust-Region Constrained optimization, we demonstrate how our model provides a rational and principled method for price-setting across digital and print platforms. Our results show consistency between solvers and allow transparent, data-driven pricing decision for our publications.
manuscript.pdf- Main manuscript filefigures/- Directory for figures and imagesdata/- Directory for datasets and supplementary datacode/- Directory for code and scripts
- Focus on ebook pricing firstly, with adding random variable of sales volume to adjust how much an impact a platform's probability to be purchased from affects the price
- Dynamic pricing based on sales volume and platform probability
- For paperback pricing consider printing costs
Optimizing Pricing Strategy with Royalty Constraints © 2025 by Mustafif Khan is licensed under CC BY-SA 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/
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