Evaluation of subsidiary marketing performance: combining process and outcome performance metrics

Abstract Issues in evaluating marketing performance and devising appropriate metrics for measurement have taken center stage in marketing thought and practice in recent years. We propose an empirical model that enables a multinational enterprise (MNE) to assess the marketing performance of its subsidiaries, taking into explicit consideration the fact that tactical actions by subsidiaries contribute to the creation of assets that can be harnessed for marketing outcomes. Thus, our model captures the asset creation abilities of marketing expenditures and also takes in to account the environmental differences of the context in which each MNE subsidiary operates. We evaluate comparative, overall, and process-level (creation of market assets and market yield) marketing performance in the context of multi-country operations. This simultaneous examination of marketing process and marketing outcome performance enables a global corporation to gain strategic, operational, and diagnostic insights into the performance of its subsidiaries. Our approach is empirically illustrated with an evaluation of the marketing performance of subsidiaries of a large global corporation. Keywords Multinational performance evaluations . Marketing metrics . Outcome measures . Performance measures . Standardization There is now more pressure on marketing scholars and practitioners to demonstrate that the marketing function contributes to shareholder value for the firm (Doyle 2000; At the same time, the justification of marketing expenditures and the assessment of marketing performance is particularly complex for multinational enterprises (MNE). Although MNE performance assessment is clouded by various economic and accounting exposure risks, such as translation and transaction risk

Estimating models with binary dependent variables: Some theoretical and empirical observations

Many mathematically similar models are being used by business researchers to link binary dependent variables with a set of predictor variables. Typical research results indicate little difference between models in their ability to properly classify observations. But, there appear to be major differences in the interpretation of coefficients resulting from the calibration of these competing models. The empirical results in this article clearly show that when the assumptions underlying binary-dependent-variable techniques are violated, parameter estimates may be misleading. This can be true even when the goodness-of-fit statistics are not substantially affected

Choice Models and Customer Relationship Management

Customer relationship management (CRM) typically involves tracking individual customer behavior over time, and using this knowledge to configure solutions precisely tailored to the customers' and vendors' needs. In the context of choice, this implies designing longitudinal models of choice over the breadth of the firm's products and using them prescriptively to increase the revenues from customers over their lifecycle. Several factors have recently contributed to the rise in the use of CRM in the marketplacePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47023/1/11002_2005_Article_5892.pd

Issues in the estimation and application of latent structure models of choice

Our paper provides a brief review and summary of issues and advances in the use of latent structure and other finite mixture models in the analysis of choice data. Focus is directed to three primary areas: (1) estimation and computational issues, (2) specification and interpretation issues, and (3) future research issues. We comment on what latent structure models have promised, what has been, to date, delivered, and what we should look forward to in the future.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47160/1/11002_2005_Article_BF00999208.pd

Discrete and Continuous Representations of Unobserved Heterogeneity in Choice Modeling

We attempt to provide insights into how heterogeneity has been and can be addressed in choice modeling. In doing so, we deal with three topics: Models of heterogeneity, Methods of estimation and Substantive issues. In describing models we focus on discrete versus continuous representations of heterogeneity. With respect to estimation we contrast Markov Chain Monte Carlo methods and (simulated) likelihood methods. The substantive issues discussed deal with empirical tests of heterogeneity assumptions, the formation of empirical generalisations, the confounding of heterogeneity with state dependence and consideration sets, and normative segmentation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46977/1/11002_2004_Article_230988.pd