داده جدید اسکنر برای بازاریابان نام تجاری(برند) : چگونه علم اعصاب می تواند به درک بهتر تفاوت اولویت های نام تجاری(برند) کمک کند؟

A core goal for marketers is effective segmentation: partitioning a brand's or product's consumer base into distinct and meaningful groups with differing needs. Traditional segmentation data include factors like geographic location, demographics, and shopping history. Yet, research into the cognitive and affective processes underlying consumption decisions shows that these variables can improve the matching of consumers with products beyond traditional demographic and benefit approaches. We propose, using managing a brand as an example, that neuroscience provides a novel way to establish mappings between cognitive processes and traditional marketing data. An improved understanding of the neural mechanisms of decision making will enhance the ability of marketers to effectively market their products. Just as neuroscience can model potential influences on the decision process—including pricing, choice strategy, context, experience, and memory—it can also provide new insights into individual differences in consumption behavior and brand preferences. We outline such a research agenda for incorporating neuroscience data into future attempts to match consumers to brands.

How do companies determine which consumers are the “right” buyers for their products? And, how do they target those consumers through their marketing programs? Consumers’ preferences for products or brands arise from the combination of many different factors. Some factors come from features of the product itself (e.g., price, durability), while others are attributes of consumers themselves (e.g., goals, attitudes, discretionary income). Marketing researchers—and the brands they support—acquire information about consumer preferences to identify how individuals will differ in their choices. In differentiated markets, companies are incentivized to identify sets of consumers (“market segments”) who share characteristics and hence have similar preferences—, ones that might differ from those of other consumers. Such an approach allows the brand managers to focus their marketing efforts (e.g., product development, communication and branding efforts) to particular segments, thus becoming more efficient in their allocation of resources. Identifying the appropriate segmentation criteria presents challenges to marketing researchers and brand managers. Consider the example of New Coke, which was introduced in 1985 after the market share of Coca Cola had dropped significantly to stiff competition from the sweeter tasting Pepsi (Schindler, 1992). New Coke was introduced after comprehensive market research involving focus groups, field tests and surveys. Although the focus groups uncovered some negative responses to the change, those responses were attributed to peer pressure and the executives moved forward with the launch of the new formula. Yet, within three months, they were forced to reintroduce their original formulation as Classic Coke after public outrage emerged, particularly among the most loyal Coke drinkers. Termed as a major marketing research failure, the responses and tests had failed to capture the emotional intensity of the negative responses among a particular customer segment (i.e., the most loyal Coca Cola drinkers) that had felt alienated by the switch. Traditional market segmentation groups potential consumers based on demographic variables: geography (e.g. country, population density, climate), demographics (e.g., age, sex, gender, socioeconomic status), purchasing history (e.g., store scanner data), and even responses to marketing activities (e.g., commercials or focus groups) (Keller, 2008). Current approaches to segmentation also often include measures of consumers’ attitudes, based on variables like anticipated benefits, brand loyalty, usage frequency, and others ( Churchill and Iacobucci, 2005, Greenberg and McDonald, 1989 and Yankelovich and Meer, 2006). What largely remains absent, however, is reliable information about potential individual differences in the decision-making process itself, as could be obtained from measures of cognitive or emotional processes. While a cognitive segmentation approach could hold substantial promise, it faces a key obstacle: the difficulty of understanding the thought processes, both conscious and nonconscious, that consumers apply when making decisions. Recent neuroscience research provides a potential new tool to address the challenge of understanding consumer decision making. Neuroscience has generated significant advances in identifying the neural mechanisms underlying decision-making processes, commonly grouped under the term neuroeconomics ( Clithero et al., 2008 and Sanfey et al., 2006). In parallel, marketing research has indicated that consumer behavior can be predicted by determining the likely decision processes consumers will employ in a given context ( Simonson et al., 2001 and Weber and Johnson, 2009), such as how consumers will respond to various brands in a snack aisle as they walk through. These independent successes suggest that models that integrate cognitive processes, traditional marketing data, and various measures of benefit would improve market research and segmentation ( Ariely and Berns, 2010, Kenning and Plassmann, 2008 and Plassmann et al., in press). For instance, there exist functional magnetic resonance imaging (fMRI) studies that elucidate neural markers for preferences for food items, both experienced and in monetary value ( Knutson et al., 2007 and O'Doherty et al., 2006), as well as how those processes can potentially be affected by context ( De Martino et al., 2006, Hare et al., 2009 and Venkatraman et al., 2011). An understanding of the neural mechanisms underlying consumer decisions could both increase understanding of the cognitive processes that lead to individual variability in consumer behavior, and would create new approaches for marketing researchers to segment their target markets. Our goal is to provide a conceptual framework for bridging research in neuroscience and marketing, particularly in the realm of brand evaluation. Brands, their images and their logos are pervasive in the everyday environment. Recent estimates suggest the typical United States consumer is exposed to several thousand brands each day (Story, 2007). Firms spend millions of dollars to understand how specific brand exposures and associations will motivate different segments of their consumer base (Brasel and Gips, 2011 and Hang and Auty, 2011). Yet, little is known about the processes underlying consumers' evaluation of brands. We first introduce some key findings from neuroscience research on decision mechanisms, with a focus on processes related to consumer choice. Next, we consider the perspective of a brand manager (e.g., a manager faced with a task analogous to the development of New Coke) and how she might employ measures of choice and process into her brand-evaluation method. Though we use brand evaluation as a prototypic example, we believe that the concepts introduced in this paper extend to many other realms of marketing research and consumer behavior. We conclude with a discussion for how marketing research might employ new types of “scanner” data, obtained from neuroscience.

While many discussions involving marketing and neuroscience attempt to justify a general use of neural data for marketers, we prefer to provide market segmentation as a prime example of how neuroscience can aid marketing and consumer research. As a parallel, consider personalized medicine (Hamburg & Collins, 2010). A central goal of medicine is to provide proper treatment and care; matching patients to certain drugs is an immensely complicated process, and momentum is growing to individualize elaborate medical treatments. However, some drugs (e.g., Tylenol) may work more consistently than others (e.g., anti-depressants). Similarly, traditional methods used by marketing researchers reveal that some approaches work more consistently than others in shaping consumer choices. Neuroscience approaches will not replace the data and methods in current marketing practice, but can provide complementary information about choice processes and types of consumers. Doing so, we argue, may lead to better approaches for market segmentation and more effective marketing practices.