This paper proposes an easy-to-implement dynamic measure of market performance over time for various selling units (e.g., sales territories, sales regional offices, or the whole sales organization). It may be used as a diagnosis tool by comparing the market performances of various units, taking into account the conditions prevailing in the different markets (such as competition relative effectiveness, sales penetration, or local market fluctuations). Combining sales volume, market share, and profit variations data into an Index of Sales Unit Market Performance (ISUMP), provides managerial guidance for selling units' evaluation or resource allocations among units. This index may account for a firm's selected market strategy (market penetration, market skimming, etc.). Implementation in a large North American insurance company is reported.

This article explains how managers can use a new data-mining technique for solving problems related to individual risks of contracting nosocomial pneumonia. Using the genetic algorithm, a search technique provides practitioners with an optimal choice of parameters for Gini boosting type decision tree models. Thus, managers and technicians can choose better models. These new parameters are genetically controlled: number of trees, depth of trees, trimming factor, cross-validation (to avoid overfitting), proportion of the population used, and the minimum size to split a node. This technique has been satisfactorily tested on health data.