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A product manager’s goal is to develop the right product, sell it at the right price, and entice consumers to choose it over that of competitors. While seemingly straightforward, it's not because consumers themselves often don't know what “right” looks like until it is in front of them. And by that time, it’s too late. This review illustrates from a managerial perspective the practical application of conjoint analysis in product development. In doing so, we describe the overall strengths of the approach, as well as cite specific examples of how the method was used to address different information needs.

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According to a McKinsey global survey among chief marketing officers (CMOs) and other senior executives, 6 in 10 C-level executives indicated their companies employed very minimal to no systematic process to review its marketing spend. Hence, it’s no surprise that marketers are feeling heightened pressure to validate their efforts while not knowing the criteria on which success is measured. Consequently, they are increasingly turning to algorithms and machine learning for answers. In this review, we define predictive analytics in the context of marketing management, as well as describe productive changes that companies can expect in this area as a result of furthering their analytics capabilities.
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