A perceptual map is a visual representation of how target customers view the competing alternatives in a Euclidean space which represents the market
The map has the following characteristics:
Pair-wise distances between product alternatives directly indicate how close or far apart the products are in the minds of customers
A vector on the map indicates both magnitude and direction in the Euclidean space. Vectors are usually used to geometrically denote attributes of the perceptual maps
The axes of the map are a special set of vectors suggesting the underlying dimensions that best characterize how customers differentiate between alternatives
Relevance means that the set of products chosen must be the set of competitive products that are relevant for managerial decision-making.
Two possible methodologies to collect information on consumer’s perception of products:
Method 1: Attribute based method (Factor Analysis).
Method 2: Similarity-Based method (Multi-Dimensional Scaling)
I am considering a similar example which was taught in the class today to explain the Factor Analysis method only,
Let us select a set of laptop computers of interest to be the target group including the new concept… (Say 4 products)
· Decide on the set of relevant attributes on which to capture consumer perceptions (6 attributes).
· Ensure that consumers are familiar with the laptops that are to be evaluated (e.g., through video presentation, or actual prototypes)
· Respondents (target customers) evaluate / rank or rate products.
Data Matrix = 4 (products) X 6 (attributes) X 300 (respondents).
Submit the data to factor analysis
Interpret the underlying key dimensions (factors) using the directions of the individual attributes
Explore the implications of how consumers’ view the competing products
DATA MATRIX à FACTOR ANALYSIS à PERCEPTUAL MAP
FACTOR ANALYSIS – Key Concepts
· It is difficult to get a clear picture of the market when dealing with so many attributes and products.
· All the data/dimensions might not be necessary to capture consumer perceptions.
· Highly correlated attributes
Create linear combination of the measures to get a single new dimension of the original attributes.
Factor analysis output:
Say 70% of the information contained in the original attributes can be represented by creating just 2 new dimensions. These dimensions are called factors.
The interpretations can be made based on the arrow direction towards an attribute, length of the line from the origin to the arrow which explains the importance of variance and so on.
· Researcher should be able to clearly conceptualize the attributes
· No perception gap between intended and actual perception of the attributes.
· Works well for hard or functional attributes, (price, product features).
Karthick Prakash Balakrishnan
Finance Group 6