Automatic Associations

Some scientists estimate that up to 95% of human thought occurs below conscious awareness, and that most human behavior is driven by our automatic processes including gut feelings and visceral reactions such as emotions (Chartrand & Bargh, 1999; Haidt, 2001). Yet, the vast majority of traditional market research methods attempt to predict consumer behavior based on self-reported data arising from consumer conscious recall. This flies in the face of the fact that consumers are often unaware of the true drivers of their actions and in many cases people will attempt to cover their motives in order to be seen as more politically correct (Zaltman, 2003). This indicates that understanding the nonconscious mind is critical for discovering the true drivers of consumer behavior.

Automatic Associations measure top-of-mind associations with your brand. This methods determines which associations are most strongly associated with your brand equity and does not rely on self-reported data to deliver the insight. A response time based methodology taps the consumer non-conscious to measure the strength of associations with your brand. Both emotional and rational associations are measured, showing the power of specific emotional associations (both positive and negative) with your brand. The method easily captures associations that consumers are reluctant to admit or aren’t aware of, such as associations of “sexy” and “cool” approach related emotions or “fear” and politically incorrect (but imminently present) avoidance related emotions. The power of assessing non-conscious associations with your brand will separate you in the marketplace.

Examples of non-conscious drivers of consumer behavior abound. Consumers will consciously tell you that there is no difference between the effectiveness of a generic and a brand name drug, yet are significantly more likely to buy the brand name drug when they are really sick. Consumers are much more likely to buy a product priced at $9.99 than $10.00, yet fail to admit that the penny difference had anything to do with their decision. Consumers choose more expensive hotels if they have read words like “luxurious” and “deserving” before making their decision, yet remain unaware of why they made their choice (Reid et al., 2003). In one study of purchase behavior, only 12 percent of consumers who said they were likely or very likely to buy a kitchen appliance actually followed through with the purchase (Zaltman, 2003). These examples illustrate the paucity of explanatory power in some self-reported data. Measuring the consumer nonconscious mind provides a more precise picture of the factors that drive behavior.

If self-reported data is this woeful, the market is in desperate need of research tools that do a better job of predicting behavior. We are in the business of behavioral insight. Therefore the success of our business is dependent on developing and using research tools that give better prediction of behaviors of interest. Automatic Associations are now one of those tools.