The Human Touch in the Age of AI: How Transformers are Reshaping Marketing and Consumer Behavior
I still remember the first time I used a chatbot on an airline company's website years ago. Let me just say, it was not a fun experience. The bot didn’t seem to follow any of my questions, and I found myself repeating the same information over and over again. It almost felt like I was shouting my complaints at a wall with no response.
Luckily, it’s now fair to say that those days are far behind us in the rear-view mirror. The AI chatbots of the present are way better than the chatbots of the past. They can write code, tell jokes, and even help with customer service needs, such as the one I once had. Although I (thankfully) haven’t had to use one on an air travel website yet, it’s clearly not an unresponsive wall anymore. A good chatbot seems more like an actual human being—or even an expert in their respective field.
But unlike humans who learn from a variety of sources—including role models like parents and teachers, materials like books and educational websites, and especially their own personal experiences—many chatbots learn from one source alone: the internet. This approach to training chatbots is what’s known as transformer architecture, or, more simply, a “transformer.” Like anyone else conducting machine learning research in the computer science domain, I could have never imagined a world where AI would become as sophisticated as it is today. Thanks to transformers, we are now in an era where AI and humans feel remarkably similar, at least as far as understanding context is concerned.
To no one’s surprise, marketing is taking advantage of this technology. It has traditionally been very difficult to extract signals from large volumes of text or images since this data is so context-dependent—meaning information can make big differences for one user but not for another. Transformer architectures, however, can compress such knowledge and unpack understanding across all data in a way that makes such differences apparent for marketing. Now, experts can easily identify elements that influence the likelihood of consumers paying attention to certain details.
In this article, we shall discuss how transformers are making waves in the world of marketing and consumer behavior, exploring the behavioral economics behind how to approach ethical dilemmas with best practices.
The Transformer: AI Inspired By How Humans Understand Context
Before we dive into the connection between marketing and consumer behavior, let's better understand how transformers function first.
Imagine someone playing an action-adventure video game. As the player navigates the world trying to complete their quest, they must use key pieces of information in their environment—like hidden tools or conversations with NPCs—to help them along their way. The player can temporarily enjoy the scenery, but ultimately, they must focus on the quest at hand and complete the game without getting too distracted.
This is how a transformer operates. It’s basically a gamer who’s determined to complete their task (such as generating text) while being super-aware of their surroundings (that is, the context from previous sentences or words). In this sense, the main idea behind a transformer is its ability to pay attention to different parts of the input data efficiently while generating text, much like how a gamer would track multiple elements while playing and ultimately completing their quest.
The transformer methodology was initially introduced by Ashish Vaswani and colleagues in their 2017 paper, “Attention is all you need,” with the title alone already hinting at the mechanisms behind these algorithms.1 Drawing inspiration from how humans focus attention on a task while leveraging the information in the background (without getting distracted by it), transformers can move from the beginning to the end of pretty much any given problem, appreciating and leveraging its context to reach a solution.
Since attention is so critical to transformers, why isn’t “attention” in the name? Well, it actually almost was. The original term was meant to be “Attention Net,” but the authors felt it wasn’t exciting enough. Since these models transform representations, they went with the fitting title of “transformer,” and the rest is history.
What this technology means for behavioral economics and marketing, however, remains to be seen. One great advantage about a significantly improved ability to understand context is that transformers open new doors for companies and other organizations to better understand their customers than ever before. For example, transformers could help consumers navigate the shopping experience by putting itself in their shoes. In this way, we can think of transformers as being an “online shopping assistant” who’s determined to help shoppers check out without abandoning their cart while being super-aware of the context from shopping advice it gave to consumers in the past, along with how they initially responded.
References
1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems.https://user.phil.hhu.de/~cwurm/wp-content/uploads/2020/01/7181-attention-is-all-you-need.pdf
2. Jones, E. R. (2023). Deep Learning. The Decision Lab. https://thedecisionlab.com/reference-guide/computer-science/deep-learning
3. Johnson, E.J., Shu, S.B., Dellaert, B.G., Fox, C., Goldstein, D.G., Häubl, G., Larrick, R.P., Payne, J.W., Peters, E., Schkade, D. and Wansink, B., 2012. Beyond nudges: Tools of a choice architecture. Marketing Letters, 23, pp.487-504.
4. Martin, G. (2011). The Importance Of Marketing Segmentation. American Journal of Business Education, 4(6), 15-18.
5. Thaler, Richard H., and Cass R. Sunstein. Nudge: The final edition. Yale University Press, 2021.
6. Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
7. Edelman, David C. and Mark Abraham, Customer Experience in the Age of AI. Harvard Business Review.
8. Grigoraș, A., & Leon, F. (2023). Transformer-based model for predicting customers’ next purchase day in e-commerce. Computation, 11(11), 210.
9. Mena, G., Coussement, K., De Bock, K.W., De Caigny, A. and Lessmann, S., 2023. Exploiting time-varying RFM measures for customer churn prediction with deep neural networks. Annals of Operations Research, pp.1-23.
10. Bashiri, H., & Naderi, H. (2024). Comprehensive review and comparative analysis of transformer models in sentiment analysis. Knowledge and Information Systems, 1-57.
11. Awad, Edmond, Sydney Levine, Michael Anderson, Susan Leigh Anderson, Vincent Conitzer, M. J. Crockett, Jim A.C. Everett, Theodoros Evgeniou, Alison Gopnik, Julian C. Jamison, Tae Wan Kim, S. Matthew Liao, Michelle N. Meyer, John Mikhail, Kweku Opoku-Agyemang, Jana Schaich Borg, Juliana Schroeder, Walter Sinnott-Armstrong, Marija Slavkovik, and Josh B. Tenenbaum (2022). “Computational Ethics”, Trends in Cognitive Sciences, 26(5), 388-405. https://www.sciencedirect.com/science/article/pii/S1364661322000456#:~:text=Computational%20ethics%20shows%20how%20machine,characterize%20ethics%20in%20algorithmic%20terms.
12. Opoku-Agyemang, Kweku (2023). Generalized Transformer. Machine Learning X Doing Research Paper Class 7. https://machinelearningxdoing.com/generalized-transformers/
About the Author
Kweku Opoku-Agyemang
Kweku Opoku-Agyemang is the founder of Machine Learning X Doing, an AI research company that focuses on solving what it means to be human. He is also the founder of Development Economics X, which focuses on empowering the next generation. Kweku was previously an economics postdoctoral fellow at the University of California, Berkeley, a computer science postdoctoral research associate at Cornell Tech, and a visiting scholar in mechanical engineering at UC Berkeley. Kweku also taught multiple highly-rated undergraduate courses at Berkeley, and presented his research to government officials from more than 10 countries on all continents and independently advised research scientists from various big tech companies. He is a co-author of Encountering Poverty: Thinking and Acting in an Unequal World, published by the University of California Press.
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