The paper by Manfred Bruhn, Andrea Gröppel-Klein, and Manfred Kirchgeorg examines the myths and misconceptions in both managerial and behavioral marketing.
Over time, marketing has developed a series of principles that have led to simplifications and generalizations, many of which have turned into myths. These myths are explored in two key areas: managerial marketing (which focuses on the supplier perspective) and behavioral marketing (which explores consumer behavior).
Key Points:
- Managerial Marketing Myths:
- Primacy of Marketing: The myth that marketing should lead all corporate functions is questioned, as marketing is not the only critical element in a company’s success.
- Competitive Advantage: Marketing’s ability to generate competitive advantages is often overstated. Cross-functional collaboration is more critical.
- Innovation: The myth that marketing alone drives successful innovation is challenged. Innovation is typically a cross-functional process, often driven by external factors.
- Brand Management: The idea that marketing solely “makes” brands is considered a misconception. Brand development involves cross-functional efforts, not just marketing.
- Sustainability: Marketing’s role in fostering sustainability is critiqued, with recognition that broader regulatory and external forces are necessary for sustainable practices to take hold.
- Behavioral Marketing Myths:
- Unconscious Motives: Many assumptions about consumers’ unconscious motives, especially claims like those of Ernest Dichter, are critiqued. While some unconscious influences on behavior exist, many of Dichter’s claims are considered exaggerated or methodologically unsound.
- Subliminal Advertising: The paper debunks the myth that subliminal stimuli (such as Vicary’s claims about popcorn sales) can lead to significant behavior changes. However, subliminal priming may influence behavior when aligned with existing motivations.
- Consumer Awareness and Decision-Making: It critiques the assumption that consumer decisions are always conscious and intentional, noting the significant role of unconscious processes and stimuli in shaping consumer behavior.
- Perception-Behavior Link: The idea that perceptions directly control behavior is examined, with some studies supporting this while others fail to replicate findings, suggesting that this “perception-behavior expressway” may not be as influential as once thought.
The paper concludes by advocating for a more critical and scientifically rigorous approach to marketing, calling for demystification of long-held assumptions and more reliance on empirical evidence. It also stresses the importance of understanding marketing’s broader role, especially in the context of sustainability and interdisciplinary collaboration.
Citation
Bruhn, Manfred, Andrea Gröppel-Klein, and Manfred Kirchgeorg. “Managerial marketing and behavioral marketing: when myths about marketing management and consumer behavior lead to a misconception of the discipline.” Journal of Business Economics 93.6 (2023): 1055-1088.
The paper “How can machine learning aid behavioral marketing research?” by Linda Hagen et al. (2020) explores the potential of integrating machine learning (ML) techniques into behavioral marketing research.
It offers a primer on various ML methods that could benefit behavioral scientists, with practical applications for improving insights into consumer behavior.
Key Points:
- Overview of Machine Learning in Behavioral Research:
- Machine Learning is a computer science field focused on creating algorithms that learn from data, often utilizing big data to make predictions. In marketing research, ML is increasingly used to analyze consumer behavior patterns.
- Behavioral Science studies human behavior and is widely applied in fields like business, health, and education. The paper discusses how big data from sources like online experiments, customer reviews, and sensor data (e.g., fMRI, spatial patterns) can be analyzed using ML techniques.
- Machine Learning Methods: The paper classifies ML methods into three categories:
- Supervised Learning: This involves training a model with labeled data (predictors and outcomes) to make predictions.
- Unsupervised Learning: In this approach, only predictors are used, and the goal is to group data based on similarity.
- Semi-Supervised Learning: Combines labeled and unlabeled data. This method is useful in situations where some data is missing, such as incomplete customer surveys combined with web behavior data.
- Applications in Behavioral Research:
- Heterogeneous Treatment Effects: ML can help estimate the varying effects of treatments on different individuals, especially in experiments involving social comparisons or consumer decision-making.
- Stimulus Sampling: ML techniques like clustering and autoencoders can help select experimental stimuli (e.g., visual ads) by grouping similar types of stimuli.
- Dynamic Phenomena: Semi-supervised learning can track changes in consumer behavior over time, such as how goal-setting behaviors evolve during a health program.
- Ethical and Practical Considerations:
- Explainability: ML models can sometimes be opaque (“black boxes”), making it difficult to interpret why they produce specific predictions. The authors call for more work on explainable ML to clarify the decision-making process behind ML predictions.
Citation
Hagen, L., Uetake, K., Yang, N., et al. (2020). How can machine learning aid behavioral marketing research? Marketing Letters, 2020.
