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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Healthcare market segmentation and data mining: A systematic review.

Eric R Swenson1, Nathaniel D Bastian2, Harriet B Nembhard3

  • 1a Pennsylvania State University, University Park , Pennsylvania , USA.

Health Marketing Quarterly
|November 25, 2018
PubMed
Summary

Understanding healthcare consumer behavior is key for patient-centered care. Advanced data mining can improve health marketing research by leveraging patient data beyond traditional survey methods.

Keywords:
Healthcare market segmentationdata miningsystematic review

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Area of Science:

  • Health Marketing Research
  • Consumer Behavior Analysis
  • Data Mining Applications

Background:

  • Healthcare delivery is shifting towards patient-centered models.
  • Understanding consumer attitudes and behaviors is crucial for this transition.
  • Effective health marketing strategies require deep consumer insights.

Purpose of the Study:

  • To systematically review the literature on healthcare market segmentation and data mining.
  • To identify gaps and opportunities in current health marketing research.
  • To inform future research directions in leveraging patient data.

Main Methods:

  • Systematic literature review.
  • Analysis of common themes in healthcare market segmentation studies.
  • Identification of data mining techniques used in health marketing.

Main Results:

  • Current research often relies on survey data and basic clustering methods.
  • Limited use of advanced classification modeling post-clustering was observed.
  • Demographic analysis of clusters is common, but patient-level data is underutilized.
  • Opportunities exist for advanced data mining with patient-level data.

Conclusions:

  • There is a need to move beyond traditional survey data in health marketing.
  • Advanced data mining techniques offer significant potential for deeper consumer insights.
  • Future research should focus on leveraging patient-level data for more sophisticated segmentation and marketing strategies.