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Article

A Quantitative Analysis on E-Books Sampling Optimization

Fayetteville State University, School of Business and Economics, Department of Management, Marketing and Entrepreneurship, Fayetteville, NC, USA
J. Theor. Appl. Electron. Commer. Res. 2017, 12(1), 68-76; https://doi.org/10.4067/S0718-18762017000100006
Submission received: 27 July 2016 / Revised: 27 July 2016 / Accepted: 25 August 2016 / Published: 1 January 2017

Abstract

E-book sales have seen a slow growth in recent years. A major issue is how to attract consumers and promote sales. Motivated by the marketing strategy of free sampling, retailers start to offer free partial sampling of e-books. We propose a quantitative model for retailers to optimize the selection of e-books for sampling in their promotion campaign with a limited budget. Simulation-based numerical analysis shows that our model increases retailers’ profit by around 8% when the free partial sampling strategy can double the e-book demand. We also develop a dynamic programming model to help retailers better utilize a budget in relatively long period of time. An experiment analysis reflects an improvement of retailer’s profit of around 7%. Our model can be extended to other information goods such as digital music, video, and newspaper.
Keywords: E-book; Sampling; Information goods; Optimization; Dynamic programming E-book; Sampling; Information goods; Optimization; Dynamic programming

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MDPI and ACS Style

Chen, L. A Quantitative Analysis on E-Books Sampling Optimization. J. Theor. Appl. Electron. Commer. Res. 2017, 12, 68-76. https://doi.org/10.4067/S0718-18762017000100006

AMA Style

Chen L. A Quantitative Analysis on E-Books Sampling Optimization. Journal of Theoretical and Applied Electronic Commerce Research. 2017; 12(1):68-76. https://doi.org/10.4067/S0718-18762017000100006

Chicago/Turabian Style

Chen, Li. 2017. "A Quantitative Analysis on E-Books Sampling Optimization" Journal of Theoretical and Applied Electronic Commerce Research 12, no. 1: 68-76. https://doi.org/10.4067/S0718-18762017000100006

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