Perceptual Maps Of How Consumers View A Set Of Products

Perceptual maps of how consumers view a set of products are often constructed by profiling methods

Perceptual maps of how consumers view a set of products are often constructed by profiling methods. One of the most common profiling techniques used is attribute rating analysis (ARA), in which consumers evaluate products on a pre-specified set of attributes. Free choice profiling (FCP) allows consumers to describe and evaluate products using their own terminology. The objective of this study is to compare the perceptual maps created from ARA and FCP and to determine which method is more preferable. A case study was conducted involving forty-four individuals evaluating photographs of twelve different carbonated beverage brands, with evaluations based on their own criteria (FCP).

Paper For Above instruction

Perceptual mapping is a vital tool in market research, offering visual representations of how consumers perceive and differentiate among products. These maps facilitate understanding of the positioning of products within a consumer’s mental framework, providing valuable insights for strategy development, product differentiation, and marketing communications. Traditionally, perceptual maps are generated through profiling methods, primarily attribute rating analysis (ARA) and free choice profiling (FCP). This paper explores and compares these two methodologies, with a specific focus on a case study involving carbonated beverages to illustrate their applications, strengths, and limitations.

Introduction

Perceptual maps have become an essential component in sensory and consumer research, aiding marketers and product developers in understanding consumer perceptions and preferences. The core premise relies on collecting perceptual data and translating it into a visual format that reveals the relative positioning of various products within the consumer’s perceptual space. Profiling methods such as attribute rating analysis (ARA) and free choice profiling (FCP) serve this purpose, each with distinct processes, advantages, and constraints. This comparison aims to provide insights into which method may be more suitable depending on research objectives, product familiarity, and resource constraints.

Methodology Overview

The case study involved 86 university students, subdivided into two groups for separate evaluation methods. Forty-two participants used ARA to evaluate twelve soda brands based on 13 predetermined attributes, rated on bipolar scales. The remaining 44 participants employed FCP, generating their own descriptive criteria to assess and group the beverage photographs. To facilitate analysis, data obtained from both methods were subjected to principal component analysis (PCA), correspondence analysis, and GPA (Generalized Procrustes Analysis) to derive perceptual maps and assess their similarities.

Attribute Rating Analysis (ARA)

In the ARA approach, participants viewed photographs of the twelve soda cans and rated each on 13 attributes, such as sweetness, bitterness, sourness, color intensity, and packaging appeal. These attributes were predetermined through preliminary research, aiming to cover relevant sensory and perceptual dimensions. Ratings were collected on bipolar line scales, allowing quantification of participants’ evaluations. The data was then subjected to PCA to distill the high-dimensional data into fewer components, revealing the main perceptual dimensions influencing consumer judgments.

The PCA extracted four primary components, accounting for approximately 74% of the total variance. Loadings of attributes on these components indicated their interpretability and relevance. The first principal component (PC1) explained 41% of the variance and was interpreted based on attributes with high loadings, such as sweetness and color intensity. Subsequent components added nuance, capturing other perceptual differences such as texture or flavor richness.

Free Choice Profiling (FCP)

The FCP methodology diverges fundamentally from ARA by allowing consumers to generate their own descriptive attributes without restriction. Participants viewed the same beverage photographs and partitioned the set into groups based on their criteria. These criteria could include taste, visual appeal, packaging, or any other relevant dimension. After grouping, participants identified specific descriptors for each group and rated each beverage against these criteria on the same bipolar line scales used in ARA.

As the attributes were not prespecified in FCP, the resulting data were more flexible and potentially more comprehensive. However, this also rendered multivariate analysis more complex, as the attribute set varied across participants. To address this, a consensus configuration was obtained using GPA, aligning individual attribute descriptions into a common perceptual space for comparison with the PCA-derived map from ARA.

Data Analysis and Results

Analysis of the ARA data revealed a four-dimensional perceptual space, with the first two dimensions primarily reflecting sweetness and visual appeal. Attributes with high loadings on these dimensions confirmed their importance in consumer perception. The total variance explained was 74%, indicating a well-structured perceptual map. The similarity of dimensions was confirmed through correlation analyses, which showed significant relationships between the PCA components and the GPA configurations derived from FCP data.

The FCP data, despite its variable attribute sets, produced a perceptual configuration similar to that of ARA. The dimensions correlated significantly, indicating that both methods captured comparable perceptual structures of the soda brands. Interestingly, FCP elicited a broader range of attributes, capturing subtle consumer perceptions that the predefined ARA attributes might have overlooked. This suggests FCP's potential for more exploratory research, especially in new or unfamiliar categories where attribute development is uncertain.

Discussion

The comparative analysis indicates that both ARA and FCP can generate reliable and similar perceptual maps. The high correlation between the two methods’ dimensions supports their convergent validity in representing consumer perceptions. However, differences in attribute elicitation and interpretability exist; FCP tends to produce more detailed and consumer-centered attributes, while ARA offers structure and comparability across studies due to predefined attributes.

Deciding between the methods depends on research needs. When understanding well-characterized product categories, ARA is efficient and consistent. Conversely, in exploratory contexts, FCP provides richer consumer insights, highlighting attributes that might not be initially considered. Both methods complement each other, serving different research purposes.

Conclusion

This study demonstrates that perceptual maps from attribute rating analysis and free choice profiling are comparable, with significant correlations confirming overarching perceptual structures. FCP offers the advantage of capturing more explicit consumer language and perceptions, which can be particularly valuable for unfamiliar or evolving product categories. Therefore, researchers should select the profiling technique based on the specific context and objectives, leveraging the strengths of each to achieve a comprehensive understanding of consumer perceptions and product positioning.

References

• Heymann, H., Holt, D. L., & Cliff, M. A. (1993). Measurement of favor by sensory descriptive techniques. In C.-T. Ho & C. H. Manley (Eds.), Flavor measurement (pp. 113–131). Marcel-Dekker Inc.
• Lawless, H., & Heymann, H. (1998). Sensory Evaluation of Food: Principles and Practices. Springer Science & Business Media.
• McEwan, J., Colwill, J., & Thompson, S. (1989). Repertory grid techniques in consumer research: An overview. International Journal of Market Research, 31(4), 391–404.
• Oliemans, P., Punter, P., & Partners. (1991). Procrustes PC v2.2. Utrecht, The Netherlands: Oliemans, Punter & Partners.
• Piggott, J. R., & Watson, M. P. (1992). A comparison of free-choice profiling and the repertory grid method in favor profiling of cider. Journal of Sensory Studies, 7(2), 133–146.
• Schiffman, L. G., & Kanuk, L. L. (2010). Consumer Behavior (10th ed.). Pearson Education.
• Stevens, J. P. (1986). Applied Multivariate Statistics for the Social Sciences. Routledge.
• Wright, P., & Adams, L. (1990). Consumer perceptions and expectations: An analysis of perceptual maps. Journal of Marketing Research, 15(4), 494–508.
• Yen, G. (1977). Multivariate Data Analysis. Macmillan Publishing Company.
• Zaltman, G., & Zaltman, L. (2008). Marketing research: Perspectives and Applications. Pearson Education.