Relevant space

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 [http://www.directionsresearch.com Directions Research, Inc.] (Dennis Q. Murphy and Robert Kushner) in June 2005, the approach is founded atop [http://www.ericmarder.com Eric Marder’s] pioneering Brand Choice modeling work and integrates the philosophies of [http://www.gsbapps.stanford.edu/facultybios/biomain.asp?id=09939760 Seenu Srinivasan’s] Adaptive Self Explicated choice model and Kevin Keller’s work around 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.