Martin Wetzels: “In Marketing research, analysis of images and video represents the next frontier for deeper insights”

In which direction is the marketing research field evolving?

Martin Wetzels: Marketing research is undergoing a profound transformation, fueled by the rapid growth of unstructured data sources. Today, researchers look far beyond traditional structured data, such as surveys, experiments, and secondary datasets, and increasingly focus on the vast, varied world of unstructured information. This includes text (e.g., social media posts and online reviews), images, video, voice, facial expressions, and even neurological signals like for instance EEG, MRI, and GSR (2).

Text analytics has already become a staple in marketing research, helping researchers sift through and interpret massive volumes of consumer feedback and online content. Building on this foundation, analysis of images and video represents the next frontier for deeper insights. By examining visual and audio data, researchers gain a more nuanced understanding of consumer behavior, emotional responses, and decision-making processes, insights that are typically difficult to capture with traditional surveys or experiments.

From a practical standpoint, incorporating unstructured data analysis into marketing not only allows businesses to refine their messaging but also helps them create more relevant products, services, and customer experiences. For example, analyzing customer facial expressions and voice intonations during product testing can reveal subconscious preferences and pain points. Similarly, neuro data from EEG and GSR can uncover hidden emotional triggers that inform product design and advertising strategies.

Recognizing this enormous potential, EDHEC Business School has established Sigma Lab, an advanced research hub dedicated to harnessing these emerging data analytics techniques. Through Sigma Lab, marketing scholars and industry partners can collaborate on cutting-edge projects that leverage machine learning, computer vision, natural language processing, and neuro-analytics.

What is your latest paper titled “The Role of a Smile in Customer–Employee Interactions: Primitive Emotional Contagion and Its Boundary Conditions” (1) about?

Emotions are central to marketing, particularly in service contexts where employees act as a critical touchpoint in the customer journey. Through a process called emotional contagion, employee emotions can directly influence how customers feel, often via subtle nonverbal cues like for example facial expressions and body language. Research has shown that nonverbal communication can be more influential than what people actually say, prompting scholars to seek objective methods—beyond self-report surveys—to measure these emotional exchanges directly and unobtrusively.

In our recently published study (1), we investigate how primitive (or unconscious) emotional contagion affects customer satisfaction, drawing on data from an experimental laboratory study and two field studies conducted in hospitality and retail settings. We employed the Emotional Facial Action Coding System (EMFACS), developed by Ekman and colleagues, to capture both employee and customer emotions based on their facial expressions.

Our findings underscore that employees’ nonverbal emotional signals can significantly shape customers’ emotional states, which in turn influences their satisfaction with the service. These insights highlight the importance of training service staff to maintain positive emotional displays, as even unconscious emotional cues can play a decisive role in enhancing customer experiences.

What can managers in service organizations take away from your research?

Our findings show that nonverbal communication, particularly facial expressions like a smile, can significantly boost customer satisfaction. Consequently, service employees at various touchpoints in the customer journey can positively shape the overall customer experience. To capitalize on these insights, managers should offer training programs that emphasize emotional awareness and incorporate emotional intelligence into their hiring processes, selecting candidates who naturally display positive emotions. Leading companies such as Apple, Disney, and IKEA already invest heavily in such training to promote positive emotional exchanges during service interactions.

In our study, we employed the EMFACS methodology to analyze facial expressions. Looking ahead, advances in machine learning and artificial intelligence could further augment these techniques for detecting and interpreting emotional cues, whether by human service employees or AI-enabled systems (3).

References

(1) Stockburger-Sauer, N., Hofmann, V. & Wetzels, M. (2024), The Role of a Smile in Customer–Employee Interactions: Primitive Emotional Contagion and Its Boundary Conditions, Psychology & Marketing, 41(10), 2181-2196 - https://doi.org/10.1002/mar.22047

(2) Balducci, B., & Marinova, D. (2018). Unstructured data in marketing. Journal of the Academy of Marketing Science, 46(4), 557–590 - https://doi.org/10.1007/s11747-018-0581-x

(3) Huang, M. H., & Rust, R. T. (2024). The caring machine: Feeling AI for customer care. Journal of Marketing, 88(5), 1-23 - https://doi.org/10.1177/00222429231224748