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An Empirical Comparison of Variable Standardization Methods in Cluster Analysis.

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    Standardizing marketing data columns before clustering can be problematic, especially with similar units. However, results remain robust for background data profiling regardless of the standardization method used.

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    Area of Science:

    • Marketing Research
    • Data Science
    • Statistical Analysis

    Background:

    • Standardizing data columns (mean zero, unit standard deviation) is common in marketing research before clustering entities.
    • This practice persists even when variables use similar units, like 7-point scales.

    Purpose of the Study:

    • To examine six data column standardization methods against no standardization.
    • To compare these methods using derived importances from conjoint analysis data.

    Main Methods:

    • Comparative analysis of six standardization techniques and a null case.
    • Replication across ten large-scale datasets of conjoint-derived attribute importances.

    Main Results:

    • Prevailing column standardization practices may negatively impact segmentation results for certain marketing data.
    • Data profiling outcomes demonstrate reasonable robustness across different column standardization methods.

    Conclusions:

    • The common practice of column standardization in marketing research requires careful consideration, as it can affect segmentation accuracy.
    • While segmentation may be sensitive, background data profiling appears unaffected by the choice of standardization method.