Perceptual maps are often created to help marketers and brand managers understand how their target consumers perceive the competitive landscape. In particular, perceptual maps are intended to show a correlation between individual products and the attributes that define them, allowing marketers to understand which products are most closely aligned with which product attributes. This knowledge can inform product positioning exercises, message creation, and new product development.
Perceptual maps can be simple or very complex depending on the number of attributes the researcher would like to correlate to the products on the market. In their simplest form, researchers can simply ask respondents to use a numerical scale to rate how much they agree that a particular product has a particular attribute. In simple models, a researcher can test just two attributes. The resulting data is plotted on a graph and a classic 2X2 matrix can be created (see below):
In this example, Kia and Honda occupy the coveted upper right quadrant from respondents perceiving them most highly associated with quality and affordability. Chrysler and Saab, conversely, are not perceived as sharing a strong correlation with either of these attributes.
Because the healthcare market is more complex, with different products having sometimes very different attributes, we recommend that clients undertake a more complex perceptual mapping exercise. Complex perceptual maps can accommodate correlation testing with multiple different attributes at once. Furthermore, these attributes can be tested for their overall influence on the prescribing or ordering decision and that information can be included on a perceptual map as well. By combining these elements, marketing and brand teams can use a perceptual map to see which brands align most closely with the attributes that most influence an ordering decision.
How it works:
Data for perceptual maps are best collected through a survey, and are driven by asking two question types: Attribute influence question: Please rank the following attributes in terms of how much influence they have on prescribing treatment for [disease]. The attribute with the most impact should be ranked number one. Product performance question (asked for each product being tested): How well does [Product] perform on each of the attributes listed below? Please use a scale from 1-7 where 1 = Performs poorly 7 = Performs extremely well.
The following perceptual maps depict correlation strengths between the product attributes influencing choice of treatment and the perceived performance of treatments on these same attributes. Distances between product and attribute points indicate the strengths of correlation: shorter distances are stronger associations while longer distances are weaker. The size of the data point is proportional to how important physicians believe each aspect to be in influencing their choice of therapy. The maps are constructed by taking multiple regressions on data collected in the attribute importance question and the questions mapping each product’s performance.
For example, in the following perceptual map, Drug A is perceived to most strongly associated with the physician’s previous clinical experience with the agent. Drug B and C, however, are perceived as being very strongly correlated to their impact on a patient’s quality of life, and slightly less strongly associated with an overall survival benefit and a positive response rate. These three attributes are very influential in the prescribing decision of physicians, and likely make these agents strong performers in the marketplace. Drug D does not have a strong correlation to any particular attribute, and those it is associated with are all of minor influence in prescribing, suggesting that this agent is perceived poorly overall by these physicians.
If your survey sample is large enough and well segmented, it is also possible to create perceptual maps to compare the perceptions of agents across different target audiences. In the perceptual map below, we illustrate how a different type of oncologists view these same four products in association with the same 12 attributes.
As you can see, the perception of products is very different among community oncologists versus the perception of academic oncologists (above). Drug D is perceived to be strongly correlated with an important attribute – response rate – by this segment. And while Drug B and Drug C were perceived very similarly by academic oncologists, community oncologists rate Drug C as more closely associated with overall survival and progression free survival than they do Drug D. Finally, while the impact on quality of life was a tightly correlated variable to two drugs in the perceptions of academic oncologists, this attribute has the weakest correlation to drugs of any attribute tested in community oncologists.
As these examples show, perceptual mapping can be a useful exercise for understanding how products are perceived by different segments of your target audience and the results can be vital inputs to product positioning, messaging, and new product development efforts.