Optimal versioning in two-dimensional information product differentiation under different customer distributions

摘要

Versioning is a widely adopted differentiation strategy in information technology industry. This paper investigates the optimality of versioning strategies of information products in a monopolist market with two-dimensional product quality and various customer distributions. A niching steady-state genetic algorithm (Niching SSGA) is adopted to obtain numerical solutions for analytically intractable optimization problems. Our experiments show that the three-version scheme is more profitable than the one-version scheme when an information product is differentiated along two independent quality dimensions, while the one-version scheme is more profitable when only one quality dimension is considered. In addition, we study three types of customer distributions, namely, the uniform distribution, the exponential distribution, and the Gaussian distribution. Our investigation verifies that the customer distribution has a significant impact on the optimality of the versioning strategy of information products in a two-dimensional vertical differentiation model. This issue has not been investigated yet either analytically or numerically. Moreover, when the highest-quality version is exogenously priced higher than the optimal price in the one-version scheme, the multiple-version strategy is more profitable and has a greater market coverage as well.