User:Ekaphan Suchintabhundid/sandbox

QuadChain Segmentation

QuadChain framework is a framework for segmenting, classifying, and partitioning consumer data into similar groups. The use of QuadChain Segmentation is widely applied in consumer research and is gradually taking role in Big Data Analytic

The QuadChain is coined after the fact that the model explicitly partitions segmentation variables into 4 distinctive sets; starting at (O) Outcomes (e.g. noticable consumer choices and preferences), (D) Determinants (e.g. Needs or Drives underlying the outcomes), (I) Influences (e.g. Internal and External Influences that support the formation of determinants) and finally, Characters (e.g. Consumer Demographic or typologies that are the basic foundation of each of the preceding O.D.I). each of which is sequentially chained into a relationship pattern (O-D-I-C) and treated in a cause and effect manner.

The key differences between QuadChain Principle and other widely used frameworks reside on the fact that QuadChain Segmentation is based on sequential cause and effect relationship of the 4 variable sets while others usually treat all of these different variables horizontally at the same level. Each segment formed under the QuadChain Principle is termed a "Chain DNA", each of the Chain DNA will accommodate unique O.D.I.C relationship

QuadChain Segmentation is a proprietary framework of Kenetixs Consulting's