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Reducing Data Complexity

In: Python for Marketing Research and Analytics

Author

Listed:
  • Jason S. Schwarz

    (Google)

  • Chris Chapman

    (Google)

  • Elea McDonnell Feit

    (Drexel University)

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.

Suggested Citation

  • Jason S. Schwarz & Chris Chapman & Elea McDonnell Feit, 2020. "Reducing Data Complexity," Springer Books, in: Python for Marketing Research and Analytics, chapter 0, pages 195-222, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-49720-0_9
    DOI: 10.1007/978-3-030-49720-0_9
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