Using genetic algorithms to assess the impact of pricing activity timing

Current methods of assessing competitive structure from aggregate sales data, such as the estimating of marketing mix elasticities, offer insight into the magnitude and direction of competitive actions. However, this level of analysis offers no insight into the impact that the timing of such mix activity has on one's market share. In this research, I illustrate a method of assessing market structure by utilizing the timing of competitive mix activity. Specifically, using a genetic algorithm approach, I estimate marketing mix timing rules for a consumer product market. This research illustrates the ability of these marketing mix timing rules to assess the impact that the timing of competitive mix activity has on one's market share. In addition, I show that these marketing mix timing rules offer strategic insight that can complement existing methods of assessing competitors using aggregate sales data, such as elasticity analysis, thereby improving the brand manager's understanding of the competitive structure of the market.Market structure Marketing strategy Marketing mix timing Genetic algorithms

Abstract Interfaces with Other Disciplines Optimal new product positioning:

Identifying an optimal positioning strategy for new products is a critical and difficult strategic decision. In this research, we develop a genetic algorithm based procedure called GA SEARCH that identifies optimal new product positions. In two simulation comparisons and an empirical study, we compare the results from GA SEARCH to those obtained from the best currently available algorithm (PRODSRCH). We find that GA SEARCH performs better regardless of the number of ideal points, existing products, number of attributes or choice set size. Furthermore, GA SEARCH can account for choice set size heterogeneity. Results show that GA SEARCH outperformed the best current algorithm when choice set size varied at the individual level, an important source of consumer heterogeneity that has been ignored in current algorithms formulated to solve this optimization problem

Managerial assessment of potential entrants: Processes and pitfalls

While others have studied the awareness and action phases of incumbent response, there has been little research on the threat assessment phase. In this paper, we focus on the incumbent’s threat assessment decision process, i.e. how task characteristics can influence the evaluation of potential entrants. In an experiment using experienced marketing managers as subjects, we examine the influence of firm dependence, decision accountability and task complexity on their information acquisition behavior while assessing potential entrants. Our results provide important insights into how companies can an