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Marketing engineering


Marketing engineering is currently definded as "a systematic approach to harness data and knowledge to drive effective marketing decision making an implementation through a technology-enabled and model-supported decision process".

The term "marketing engineering" can be traced back to Lilien et al. "The Age of Marketing Engineering" published in 1998, in this article the authors define marketing engineering as the use of computer decision models for making marketing decisions. Marketing managers typically use "conceptual marketing", that is they develop a mental model of the decision situation based on past experience, intuition and reasoning. That approach has its limitations though: experience is unique to every individual, there is no objective way of choosing between the best judgments of multiple individuals in such a situation and furthermore judgment can be influenced by the person position in the firm's hierarchy. In same year Lilien G. L. and A. Rangaswamy published Marketing Engineering: Computer-Assisted Marketing Analysis and Planning, Fildes and Ventura praised the book in their review, while noting that a fuller discussion of market share models and econometric models would have made the book better for teaching and that "conceptual marketing" should not be discarded in the presence of marketing engineering, but that both approaches should be used together. Leeflang and Wittink (2000) have identified five era of model building in marketing:

How to build market models and how to developed a structured approach to marketing questions has been an issue of active discussion between researchers, L. Lilien and A. Rangaswamy (2001) have observed that while having data gives a competitive advantage, having too much data without the models and systems for working with it may turn out to be as bad as not having the data. Lodish (2001) has observed that the most complicated and elegant model will not necessarily be the one adopted in the firm, good models are the ones who capture the trade-offs of decision making, subjective estimates may be necessary to complete the model, risk needs to be taken into account, model complexity must be balanced versus ease of understanding, models should integrate tactical with strategic aspects. Migley (2002) identifies four purposes in codifying marketing knowledge:


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