Strategy

The telecommunications industry is a fragmented market, characterized by a tremendous amount of customer heterogeneity. This paper shows how such customer heterogeneity dramatically affects nonlinear pricing strategies: (i) First, if there are unbalanced calling patterns between different customer types, networks make larger profits on the least attractive customers. In addition, the nature of the calling pattern substantially affects how networks discriminate implicitly between different customer types.

This paper develops a stochastic general equilibrium model for an oligopoly, in which all inventory constraint parameters are endogenously determined. We propose several systems of demand processes whose distributions are dunctions of all retailers' prices and all retailers' service levels. We proceed with the investigation of the equilibrium behavior of infinite-horizon models for industries facing this type of generalized competition, under demand uncertainty.

Many Strategic Account Programs are disconnected from the firm's strategic objectives and market place realities. If the Strategic Account Program is not central to your company's strategy formulation and implementation processes, it will have difficulty securing active senior management sponsorship and support. The Strategic Account Program then degenerates into a sales program and salespeople have difficulty getting alignment and commitment from other company functions.

This paper analyzes the role of jumps in continuous-time short rate models. I first develop a test to detect jump-induced misspecification and, using Treasury bill rates, find evidence for the presence of jumps. Second, I specify and estimate a nonparametric jump-diffusion model. Results indicate that jumps play an important statistical role. Estimates of jump times and sizes indicate that unexpected news about the macroeconomy generates the jumps. Finally, I investigate the pricing implications of jumps.

This study examines whether the quality of online buying experience represents a competitive advantage for Internet firms focused on business to consumer e-commerce (“e-commerce” firms). Forrester Research, a consulting firm, estimates that revenues in the business to consumer segment will grow from $20 billion in 1999 to $184 billion by 2004. Such explosive growth is due, in part, to the superior shopping experiences that new e-commerce firms offer.

Companies are collecting increasing amounts of information about their customers. This effort is based on the assumption that more information is better and that this information can be leveraged to predict customers' behavior in a variety of situations and product categories. For example, information about a customer's purchase behavior in one category can be helpful in predicting his potential behavior in a related category, which in turn could help a firm in its cross-selling efforts.

We assemble bank-level and other data for Fed member banks to model determinants of bank failure. Fundamentals explain bank failure risk well. The first two Friedman-Schwartz crises are not associated with positive unexplained residual failure risk, or increased importance of bank illiquidity for forecasting failure. The third Friedman-Schwartz crisis is more ambiguous, but increased residual failure risk is small in the aggregate. The final crisis (early 1933) saw a large unexplained increase in bank failure risk.

The authors propose that the revenue premium a brand generates compared with that of a private label product is a simple, objective, and managerially useful product-market measure of brand equity. The authors provide the conceptual basis for the measure, compute it for brands in several packaged goods categories, and test its validity. The empirical analysis shows that the measure is reliable and reflects real changes in brand health over time.

This paper considers the problem of sequential parameter and state estimation in stochastic volatility jump diffusion models. We describe the existing methods, the particle and practical filter, and then develop algorithms to apply these methods to the case of stochastic volatility models with jumps. We analyze the performance of both approaches using both simulated and S and P 500 index return data. On simulated data, we find that the algorithms are both effective in estimating jumps, volatility, and parameters.