Because even the most basic medical devices and pharmaceutical products have a myriad of attributes, features and interactions, CNB offers what are known as Product Feature Value Models (PFVM) and Competitive Market Positioning Maps (CMPM). These tools are designed to take the guesswork out and simplify your marketing decisions.

A PFVM culls information from a qualitative study pertaining to the value of each of your productıs perceived attributes (usually obtained from large-scale quantitative studies), and provides you with precise, measurable data on the value and importance of each of your productıs features. Data is collected on all major brands in a product category, including the "ideal" product. Data can also be collected on different "versions" of a proposed brand (e.g., best and worst case scenarios) to determine feature sensitivities.

Similar to a multivariate perceptual map, a CMPM displays multiple dimensions and complex structure for a specific product market. Usually this information is provided through a highly controlled quantitative study, carefully designed by CNB. The overall purpose is to graphically depict a spatial representation of how products compete with each other in terms of their profiles associated with different factors.

PFVM Features Include:

  • Enhance information from qualitative research
  • Specific measurement data for each product feature, including "ideal product"
  • Information to gauge market potential
  • Can be graphically presented for ease of communication and understanding
  • Valuable aid in decision making, specifically for new products as feature levels change, e.g., less efficacy, more side effects
  • CNB Research exclusive tool


 

Data is collected using a structured survey during the qualitative research process.

  1. The customer provides an estimate of importance according to different pre-determined attributes in a particular product category (constant-sum scale is a useful technique).
  2. The customer provides product performance ratings on major brands, ideal product, and client's product(s).
  3. These two sets of data, importance and performance, are combined to provide summary measures that compare brands, ideal product and clients product(s) on a "performance index".
  4. The customer provides an estimate of "likelihood of trial" based on the different scenarios of the client's product(s) (e.g., best and worst case scenarios).
  5. The "likelihood of trial" and "performance index" data are combined to provide a Customer Acceptance Summary.

PFVM Limitations:

While PFVM offers a unique, qualitative value by providing hard data for decision-making, it is not a substitute for quantitative research. The value of PFVM is its use as a complement to quantitative studies. Due to potential variation in the way samples are chosen and their limited size, results can be interpreted as directional only. Therefore, a minimum sample size needs to be determined before implementation.

CMPM Features Include:

  • Information is gathered by CNB Research throughout the qualitative research process
  • Highlights the strengths and weaknesses that delineate a particular product's relative market position
  • Useful in assessing alternative product positioning
  • Visually displaying products based on customer's perception of product features
  • Graphic representation of products' placement in the marketplace


 

  1. Study participants complete a quantitative questionnaire that assesses various product brands on several factors
  2. Resulting responses (either ratings, CuSum or rankings) are then compiled into a single database
  3. Data is analyzed utilizing one of a number of methods (canonical correlation, multi-dimensional scaling, etc.) producing a measure of association that satisfies the "best fit" criteria for the chosen methodology
  4. Association measures are used to produce plot coordinates to display the results in graphical format
  5. A graph is produced which includes all Brands, and Factors, in their resultive positioning based on the association, and selective distance measures, such as Euclidean distances

CMPM Limitations:

  • A minimum sample size needs to be determined before using CMPM.
  • Results are limited to association, thus causation cannot be inferred.
  • Although all factors will be included, the magnitude that each factor contributes to explaining variability is not identified, (i.e., removal of a single factor may cause
  • drastic changes, or no change).
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