Abstract
Marketing data sets often have many variables—many dimensions—and it is advantageous to reduce these to smaller sets of variables to consider. For instance, we might have many items on a consumer survey that reflect a smaller number of underlying concepts such as customer satisfaction with a service, category leadership for a brand, or luxury for a product. If we can reduce the data to its underlying dimensions, we can more clearly identify the relationships among concepts.
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Chapman, C., Feit, E.M. (2015). Reducing Data Complexity. In: R for Marketing Research and Analytics. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-14436-8_8
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DOI: https://doi.org/10.1007/978-3-319-14436-8_8
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