QuadChain framework is a framework for segmenting, classifying, and partitioning any consumer data into similar groups or subgroups. The use of QuadChain Segmentation is widely applied in both consumer research and Big Data Analytic.
The QuadChain is coined after the fact that the model explicitly partitions segmentation variables into 4 distinctive sets; starting from (O) Outcomes (e.g. noticeable 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 to, (C) 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 segmentation 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 simultaneously at the same level. Each segment formed under the QuadChain Principle is termed a "Chain DNA", and each of the Chain DNA will accommodate unique O.D.I.C relationship
QuadChain Segmentation is a proprietary framework of Kenetixs Consulting's