Relevant space

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Relevant Space refers to a market research methodology developed to overcome the shortcomings of traditional brand tracking and brand choice techniques. Crafted by researchers at Cisco Systems (Bryan Maach and Engeli Gagni) and Directions Research Inc. [1] (Dennis Q. Murphy and Robert Kushner) in June 2005, the approach is based on Eric Marder's[2] pioneering Brand Choice modeling work and integrates the concepts of Seenu Srinavasan's[3] Adaptive Self Explicated choice model and Kevin Keller’s work concerning points of parity and differentiation.

In the Relevant Space approach, respondents are asked to isolate, from an extensive list, the attributes most germane to their decision making within a given product category. Next, respondents are asked to identify up to five brands with which they are most familiar. The resulting matrix is a set of personalized questions fixed upon attributes that matter to them and brands that they know.

When stated importance ratings are combined with a constant sum choice exercise (i.e., the unity of both stated and derived importance), the method answers not only what attributes are important, but how they are important. Individual brands can then be plotted relative to their competitive standing, yielding actionable insight for brand managers and product developers.

Though relatively new, the Relevant Space methodology has proven to be a useful tool in understanding how customers view and make decisions in a competitive context. Like any research design, the approach has constraints and delimiters that require careful fitting to the business questions at hand.