1. Perceptual maps

2. Content Learning objectives Description of perceptual maps
Benefits and limitations
Constructing a perceptual map from data
Conclusions

3. Learning objectives Understand the concept of a perceptual map
Understand the benefits and limitations of the tool
Learn a method to build a map out of data
Image:

4. Description of a perceptual map
Ferrell, O.C. & Hartline, M. (2011). Marketing strategy (5th Ed.). Ohio: Cengage Learning.

5. Benefits Clear to understand
Provides insights into the market structure for a defined set of competing alternatives
Suggests which attributes of a product the firm should modify to effect a desired change in the position of the product
Indicates market gaps that could offer business opportunities to fill in with new brands or products
Lilien, G. & Rangaswamy, A. (2004). Marketing engineering (2nd Ed.). Canada: Trafford Publishing.

6. Limitations
Maps do not offer indication of size of segments, only preferences and positioning of brands.
Field surveys and management judgment are needed to properly build maps.
Lilien, G. & Rangaswamy, A. (2004). Marketing engineering (2nd Ed.). Canada: Trafford Publishing.
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Image: Philip Kotler, Marketing Versión para Latinoamérica, 11ª ed. P. 221, Pearson

7. Constructing a perceptual map from data
Likert scale
Strongly disagree
Disagree
Neutral
Agree
Strongly agree 1 2 3 4 5

8. Xi Y Data from respondents
Roomy
Overall preferencein = α + β1 Attribute 1in + β2 Attribute 2in + … + βM Attribute Min + εin

9. Regression in Excel
Roomy

10. Coefficients
Roomy

11. Factor analysis
Data reduction method that we can utilize in mapping. Identifies a reduced number of factors that represent the relationships in the larger set of attributes. We would aim for 2 factors to capture a high percentage of the variance in the data.

12. SPSS data

13. Using SPSS to build a map

14. Options to select Descriptives Initial Solution
(Correlation) Coefficients
Extraction
Correlation Matrix
Unrotated Factor Solution
Extract – Fixed Number of Factors – 2
Rotation
Select None
Choose “Loading Plots”
Scores
Display Factor Score Coefficient Matrix

15. Results – Total Variance Explained
We will keep 2 factors, which explain 86.38% of the variance.

16. Results – Factor Loadings
This table shows the coordinates for the attributes plot in the factor space

17. Results – Commonalities
The data is the proportion of variance in each attribute accounted for by the 2-factor solution. It is the sum of the squared loadings for each attribute across the 2 factors. It represents the length of the vector.

18. Results – Plot

19. Results - Brands
F F2
-, ,04797
-,20413 ,74712
, ,00082
1,04171 ,03931
1, ,53324
-1, ,38453
-1, ,91744
SPSS calculates the factor score (coordinates) for each brand and are generated in SPSS as new variables

20. Full plot
Toyota
Hyundai
Nissan
Kia VW
Mazda
Suzuki

21. Now do it yourselves Needs improvement Complies with criteria Exceeds
Prepare the perceptual map for your own project using regression. Find below the assessment criteria.
Needs
improvement
Complies with criteria
Exceeds
criteria
5 – points
6 – points
points
Written report
Insufficient elaboration
Sufficient elaboration
Sufficient elaboration & relevant recommendations
Map
Sufficient elaboration & interpretation
Procedures
Does not follow procedures
Follows procedures
Follows procedures & suggests improvements
Total 14 17 20

22. References
Ferrell, O.C. & Hartline, M. (2011). Marketing strategy (5th Ed.). Ohio: Cengage Learning.
Harman, H. H. (1976). Modern factor analysis. EEUU: University of Chicago Press.
Houghton, D. (1940). How marketing instruction can be improved. Journal of Marketing. Vol. 5 Issue 2, p124
Keller, K.L. (2013). Strategic brand management. England: Pearson.
Lilien, G. & Rangaswamy, A. (2004). Marketing engineering (2nd Ed.). Canada: Trafford Publishing.
Rekettye, G., & Liu, J. (2001). Segmenting the Hungarian automobile market brand using perceptual and value mapping. Journal of Targeting, Measurement and Analysis for Marketing, 9(3),

23. Tryfos, P. (1997). Chapter 14: Factor Analysis
Tryfos, P. (1997). Chapter 14: Factor Analysis. Methods for Business Analysis and Forecasting: Text and Case.
Yong, A. G., & Pearce, S. (2013). A beginner’s guide to factor analysis: Focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology, 9(2),
Wilcox, R.T. (2003). Methods for Producing Perceptual Maps from Data. Darden Case No. UVA-M Available at SSRN: