Explanation typically requires greater effort than mere description, requiring an attempt to define cause and effect, often in modeled format, and quantifying the sensitivity of various effects to specific and combined causes. If a component is objectively described and its sensitivity to various circumstances established, the user might be able to evaluate how new or changing circumstances act upon it to change its behavior. In prediction it is also essential to define and evaluate differences between stable and volatile behavior. For example, a retail market at equilibrium, when customers are behaviorally stable and satisfied, may not behave in similar manner to a retail market in flux, where consumers seek out new offerings and opportunities. Prescription To fulfill its complete mission, analysis must prescribe.
Prescription implies making recommendations to achieve specific goals.
Most common is the use of algorithms employed to describe, explain and predict, all encased within optimization techniques or algorithms of rule-based scenarios prescribing treatments or circumstances for attaining desired levels of performance. In retailing, prescription may take the form of such techniques as location allocation, price volume optimization or network rationalization. No analysis project should begin until its objectives have been carefully defined and priorities placed upon description, explanation, prediction and prescription. The preference for these determines not only the techniques employed, but also the design of the sampling and data to be collected. Once the data has been acquired, based upon specific objectives, simply organizing it into packets of information may suffice.