Product Positioning: Understanding and Analyzing Brand Perceptions and Preferences, Study Guides, Projects, Research of Design

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Outline

▪ The concept of product positioning

▪ Conducting a positioning study

▪ Perceptual mapping using principal

component analysis

▪ Incorporating preferences into perceptual

maps

STP – Segmentation, Targeting,

Positioning

Product Price Communication Distribution All consumers in the market Target market segment(s) Marketing mix Marketing strategies of competitors Target marketing and positioning

▪ A brand’s positioning should tell customers □ what the brand is – what category need it satisfies (brand- market positioning), □ who the brand is for – what the intended target audience is (brand-user positioning), and □ what the brand offers – what benefits it provides (brand- benefit positioning) ▪ The selection of benefits to emphasize should be based on □ importance (relevance of the benefit to target customers’ purchase motives in the category), □ delivery (the brand’s ability to provide the benefit), and □ uniqueness (differential delivery of the benefit)

Central questions in positioning

(based on Rossiter and Percy)

Positioning statement ▪ To [the target audience] ▪ ________ is the (central or differentiated) brand of [category need] ▪ that offers [brand benefit(s)]. The positioning for this brand □ should emphasize [benefit(s) uniquely delivered], □ must mention [benefit(s) that are important “entry tickets”], □ and will omit or trade off [inferior-delivery benefits].

Illustrative positioning statement for

Volvo automobiles

▪ To upper-income car buyers ▪ Volvo is a (differentiated) brand of prestige automobile ▪ that offers safety, performance, and prestige. The positioning for this brand □ should emphasize safety and performance, □ must mention prestige as an entry ticket to the category, □ and will downplay the previous family-car association.

▪ What are the central dimensions that underlie customers’ perceptions of brands in the product class? ▪ How do customers view our brand on these dimensions? ▪ How do customers view our competitors? ▪ Are we satisfied with the way our customers view our brand relative to the competition, or are changes required? Are there opportunities for new product introductions? ▪ How do perceptions relate to preferences? ▪ How can we improve our competitive position (market share) given the distribution of preferences in the market?

Issues to consider when

thinking about positioning

A simple positioning example:

Perceptions of PA beers

(Otto’s) Apricot Wheat 2 2 (Victory) Hop Devil 7 8 Penn Pilsner 4 5 (Happy Valley) Stratus 5 4 Troegenator 8 4 Yuengling Lager 3 3

▪ Assumes that maltiness and bitterness are the two relevant attributes in this market ▪ Tells us how customers perceive the various brands on the two dimensions ▪ Brands that are close on the map compete for the same customers ▪ If we want to differentiate a brand, the map tells us which attributes to focus on ▪ The map suggests where there is white space and what we have to do to fill the white space (assuming there is demand for this type of product) Implications

▪ Perpendicular axes assume that the two attributes are uncorrelated □ Attributes could be positively correlated □ Attributes could be negatively correlated ▪ If there are more than two attributes, we cannot (easily) plot the raw data Solution: use a data reduction method (e.g., principal components/factor analysis) ▪ Perceptions are useful, but we also have to incorporate customers’ preferences Solution: joint perceptual/preference mapping Positioning in practice

▪ PCA as a data reduction technique : what are the

central dimensions underlying customers’

perceptions of brands on more specific

attributes?

▪ Questions in PCA : □ How many dimensions should be retained? □ How should the dimensions be interpreted? □ How can both brands and attributes be represented in the reduced perceptual space?

How to construct a perceptual map:

Principal component analysis (PCA)

▪ Assume we have n brands that are rated on p attributes by a sample of representative respondents. ▪ The original data are usually averaged across respondents, so the input data consist of average perceptions of the brands on the attributes of interest. ▪ It is difficult to map the brands in p dimensions, but if the attributes are correlated, we might be able to summarize the essential information contained in the original data in a space of reduced dimensionality.

How to construct a perceptual map:

PCA (cont’d)

▪ The basic idea is to redefine the dimensions of the space such that the first dimension (component, factor) captures as much variation in the ratings as possible, the second dimension is perpendicular to the first dimension and captures as much of the remaining variation in the ratings as possible, etc. ▪ If a few of the new dimensions capture a large amount of the total variation in the data, the analysis can be simplified by retaining only 2 or 3 dimensions. ▪ Look at the amount of variance explained by each factor – hopefully, a few factors will capture a substantial portion of the total variance. ▪ If we can summarize the data with a few dimensions, we can graph the data in a low-dimensional space even if there are many different attributes.

How to construct a perceptual map:

PCA (cont’d)

Summarizing attribute information

using components/factors