Reducing Data Complexity

Jason S. Schwarz, Chris Chapman and Elea Feit
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Jason S. Schwarz: Google
Chris Chapman: Google

Chapter Chapter 9 in Python for Marketing Research and Analytics, 2020, pp 195-222 from Springer

Abstract: Abstract Marketing datasets 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 questions (e.g. 9) on a consumer survey that reflect a smaller number (such as 3) 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 underlying relationships among concepts.

Date: 2020
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DOI: 10.1007/978-3-030-49720-0_9

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