The field of marketing has benefited enormously from breakthroughs in behavioral science – a fusion of social sciences, psychology and anthropology. Marketers leverage insights from customer behavior and buying habits to design targeted campaigns.
The implicit association test is a popular behavioral science tool used in marketing. Imagined in 1995, the psychological test was used for the first time in 1998.
Academics and businesses use the Implicit Association Test to uncover unconscious attitudes and beliefs prevalent in the marketplace. However, experts caution against confusing the test’s popularity with its accuracy.
IAT and consumer behavior
Behavioral sciences have major applications in marketing. Uncovering insights into customer behavior, including their implicit attitudes, has helped organizations understand the impact of factors such as social image, ethnocentrism, celebrity endorsement, and more. in decision making.
Studying customers’ implicit behavior offers advanced insights into their decision-making without asking direct questions. Most of these techniques study aspects like body language, hand gestures, voice, facial expressions to understand preferences and emotions.
With the Implicit Association Test, researchers can dig deeper into implicit cognitive processes such as attitude formation, response to advertising, and connections between brands and consumer self-concept.
Consumer behavior, more often than not, takes shape in the absence of conscious deliberation. While much of the marketing world believes in the rational decision-making process dependent on analytical reasoning and strongly driven by data, I believe that attitudes, interests, lifestyle, deliberation and intuition shape also customer behavior. And techniques like IAT offer a good chance to assess these aspects.
Industries like telecommunications and media rely heavily on the ethnocentrism of customers. This makes the analysis of attitudes implicit in consumer behavior towards ethnocentrism more relevant in these areas.
For other data-intensive fields like BFSI and retail, data science and analytics are the most powerful tools. With more customers purchasing products and services online, a huge amount of transactional data is available. Organizations can benefit from a data-driven approach that leverages behavioral science to improve business results.
Many consumer processes in these sectors are digitized. The user experience of their websites can impact consumer behavior. Therefore, companies pay great attention to self-reports generated by these websites for data analysis. Although self-reports are useful tools, these methods may lack inferences about implicit attitudes. Few studies have shown that the IAT can complement and in some cases even replace these self-reports for better results.
The challenges of IAT
Although the IAT has been dubbed the next big thing in behavioral science, the test is not without controversy. Journalist Jesse Singal called out IAT developers Mahzarin Banaji and Anthony Greenworld in a scathing article. He accused the developers of the IAT and its defenders of making “a series of outrageous claims”, particularly with regard to racism and inequality. He attributed the immense popularity of the IAT to such unsubstantiated claims.
Critics of the IAT have pointed out that test results can be compromised and manipulated. In many cases, participants may not follow instructions correctly or may become distracted during critical blocks. To change the scores, participants can deliberately make mistakes or change the response time. Something as trivial as changing the position of the hands can influence the outcome.
Interestingly, machine learning may offer a solution to address the shortcomings of IAT. Many researchers have shown that advanced AI techniques can help improve predictions through image classification algorithms. Additionally, an advanced data-driven approach could improve the accuracy of IAT test results.
Lately, researchers and experts have mobilized to incorporate data science techniques into behavioral science tools to refine the results.
This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives from the data science and analytics industry. To check if you are eligible for membership, please complete the form here.
