Perceptual maps

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

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: http://www.foolonahill.com/mbabeer.html

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

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.

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. Precio KUS$ Image: Philip Kotler, Marketing Versión para Latinoamérica, 11ª ed. P. 221, Pearson

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

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

Regression in Excel Roomy

Coefficients Roomy

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.

SPSS data

Using SPSS to build a map

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

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

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

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.

Results – Plot

Results - Brands F1 F2 -,28304 1,04797 -,20413 ,74712 ,68422 1,00082 1,04171 ,03931 1,17881 -1,53324 -1,28946 -,38453 -1,12810 -,91744 SPSS calculates the factor score (coordinates) for each brand and are generated in SPSS as new variables

Full plot Toyota Hyundai Nissan Kia VW Mazda Suzuki

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 – 5 - 4 points 6 – 6 - 5 points 7 - 7 - 6 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

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), 241-253.

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), 79-94. Wilcox, R.T. (2003). Methods for Producing Perceptual Maps from Data. Darden Case No. UVA-M-0665. Available at SSRN: http://ssrn.com/abstract=910097