Identifying ‘Master Brands’ and Consumer Perceived Categories

THE PROBLEM
Consumers see products and brands as belonging to categories based on their own perceptions of the market place. The structure of these categories can differ across consumers and context, such that it is important for marketers to identify groups of consumers that share similar category structures. Research tells us that categories are often organized in consumer minds around a prototypical product or brand. Examples include: Kleenex for tissue, Xerox for copiers, Coke for colas, Heinz for ketchup. These “master brands” can shape our thoughts and expectations about a category; they are frequently top of mind; and they provide a point of reference for competitors.

To understand such market complexity, Simon Blanchard and colleagues developed a modeling tool that uses individual consumer generated categories to compile segments of consumers who share category structures and master brands. As an illustration, they modeled consumer categorization of 21 major retailers. Analysis revealed three segments of consumers; segments differed in the quantity and composition of categories they used to represent the market. For each category, the model identified a master brand that was most representative of the category, and a of list brands that composed the category.

FINDINGS

The empirically identified category structure for Segment 1 indicates ten unobserved categories including: clothing stores 1, discount clothing 2, outdoor specialists 3, department stores 4, home improvement 5, and discounters 6. Segment 2 exhibited six categories including: discount clothing 1, department stores 2, home improvement 3, and discounters 4. Segment 3 organized retailers into eight categories including: less expensive department stores 1, more expensive department stores 2, discount-themed stores 3, home improvement 4, and discounters 5. The category structures adopted by different segments were alike in some aspects. For example, the three segments similarly organized the home improvement category (i.e. Lowes and Home Depot) and the discounter category (i.e. Walmart, Target, and Kmart). However, there were some significant differences, for example, in the categorization of clothing and department stores. See the figure for a partial list of brands that depicts similarities and differences in category structure between segments.

In a follow up to their modeling effort, Blanchard and colleagues asked consumers to do unaided recall of the retailers they considered in the initial categorization task. Retailers identified as master brands were recalled faster than others, validating the predictive power of the model and demonstrating the tangible marketing advantage held by master brands.

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IMPLICATIONS & CONCLUSIONS

Given this modeling data, marketing managers can take several steps to improve their brand positioning. Managers for less defined retailers, those that are categorized differently between segments, could find that building a defined brand image benefits the business. More generally, managers for brands that do not emerge as master brands should consider devoting their resources to overtaking the master of their category or toward establishing a novel retail category in which their brand is the master.

The ever growing internet marketplace is presenting consumers a vast new array of options. This growth offers a once in a generation opportunity for websites and businesses to position themselves in a diverse, unsettled environment. Given the underlying lack of structure on the Internet, strategists need to understand how consumers categorize websites and the websites they see as master brands. How do consumers categorize online retailers? Are Amazon and E-Bay seen as competitors or as categorically distinct by consumers? Master brand information would allow developers to create new websites that encompass the key attributes of a given category or help existing websites mimic the masters. While some master brands have been established online, notably Google for searching and Amazon for shopping, many categories remain without a clear leader, providing a window of opportunity for newcomers to master the category.

Simon Blanchard

Assistant Professor

Simon Blanchard is Assistant Professor of Marketing at the McDonough School of Business, Georgetown University. Dr. Blanchard holds a PhD degree in marketing from the Smeal College of Business at the Pennsylvania State University, a M.Sc. in Management Science (Data Mining) from the Université de Montréal – HEC Montréal, and a B.B.A. from the Université de Sherbrooke.

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