Perceptual Maps Overview and Examples

Perceptual Brand Maps - Illustrate sales correlations between brands, where the distance between the plots shows how they relate to each other and the size of each plot represents the unit sales. - The maps provide insights into: Product development opportunities – identify areas where the product portfolio is well developed and areas where the portfolio is more sparse. Products that can be grouped together for marketing purposes. – Pricing implications can result. Further analysis is required for more highly defined pricing strategy, but dimensions of product exoticness and elasticity of demand may be revealed at this stage setting the ground work for well-defined, targeted price testing. Overview

Coffee Brand Map Coffees were sized by sales Demand appears to be higher in the lower left part of the map. Scales are interpretive and appear to be exoticness by complexity of flavor. See Demo of which coffee would be the best to recommend to a shopper, based on customer type and taste preference.Demo

Baseball Card Set Perceptual/Brand Map There were 16 card sets of players, great teams, or season sets Larger bubbles indicate demand is higher for team sets (left) than player sets (right). Products tended to form clusters by “needs base.” Price appears to go diagonally across the map.

Competitive Map Purpose: Illustrate how products correlate with each other on various dimensions. Can be used for competitive analysis or to identify cross-promotional opportunities. Types of Maps: In this simple example, a broad array of products were plotted by their similarity to each other on two dimensions; median age and gender. The size of each bubble represents the number of subscribers/sales. Bubbles can be created with inner circles representing revenue or profitability. This can be a plot of any group of products. More sophisticated plots, which require advanced statistical analysis, can be designed to show sales correlations, based on an analysis of past purchases.

Map of Predictive Strength of Data Purpose: Illustrate the predictive strength of data variables and their relevance. Method: Leading variables from a response model are shown. Variables at the top of the map have the most predictive power. Ones to the right are the most relevant. “Females” (bottom right corner) comprise a large portion of the mail file, but offer little predictive power. “Agents” in the top left part of the map are much less common, but are more predictive of response.

Map of Leading Variables Purpose: Illustrate how variables correlate with each other in terms of their predictive relationships and relevance. Method: Variables above the center line positively correlate with response. Variables to the right are the most relevant. “Females” on this map are positioned near the center of the Y-axis, since they neither positively or negatively predict response. “Agents” appear way at the bottom, because while they are highly predictive, they negatively correlate with response.

Map of Predictive Strength of Data Subsets Purpose: Illustrate the predictive strength of data within a specific set of variables and their relevance. Method: This map focuses on one set of variables; geographic regions. Notice that WNC and MOU regions in the top right corner of the map comprise a large portion of the mail file and are highly predictive. The remaining regions cluster in the bottom left corner of the map, indicating that they occur less commonly and are less predictive.

Map of Variables within a Class Purpose: Illustrate how data within a specific set variables correlate with each other in terms of their predictive relationships and relevance. Method: In this example, the acquisition regions were plotted by their similarity to each other. The eastern regions cluster in the top left box, indicating a positive correlation with response, but low relevance, since not many people mailed live there. WNC and MOU negatively correlate with response.

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