Generative AI has ushered in the ability to generate content that closely mimics human contributions, introducing an unprecedented threat: Deployed en masse, these models can be used to manipulate public opinion and distort perceptions, resulting in a decline in trust towards digital platforms. This study contributes to marketing literature and practice in three ways. First, it demonstrates the proficiency of AI in fabricating disinformative user-generated content (UGC) that mimics the form of authentic content. Second, it quantifies the disruptive impact of such UGC on marketing research, highlighting the susceptibility of analytics frameworks to even minimal levels of disinformation. Third, it proposes and evaluates advanced detection frameworks, revealing that standard techniques are insufficient for filtering out AI-generated disinformation. We advocate for a comprehensive approach to safeguarding marketing research that integrates advanced algorithmic solutions, enhanced human oversight, and a reevaluation of regulatory and ethical frameworks. Our study seeks to serve as a catalyst, providing a foundation for future research and policy-making aimed at navigating the intricate challenges at the nexus of technology, ethics, and marketing.

The digital revolution has led to the digitization of human behavior, creating unprecedented opportunities to understand observable actions on an unmatched scale. Emerging phenomena such as crowdfunding and crowdsourcing have further illuminated consumer behavior while also introducing new behavioral patterns. However, the sheer volume and complexity of this data present significant challenges for marketing researchers and practitioners. Traditional methods used to analyze consumer data fall short in handling the breadth, precision, and scale of emerging data sources. To address this, computational methods have been developed to manage the "big data" associated with consumer behavior, which typically includes structured data, textual data, audial data, and visual data. These methods, particularly machine learning, allow for effective parsing and processing of multi-faceted data. Given these recent developments, this review article seeks to familiarize researchers and practitioners with new data sources and analysis techniques for studying consumer behavior at scale. It serves as an introduction to the application of computational social science in understanding and leveraging publicly available consumer data.

Segmentation is one of the most frequently used words in marketing but actually refers to many things. The background photo above is one popular way of thinking about segmentation; this article looks at it from a somewhat different perspective. At its most fundamental level, it means categorizing objects. The "objects" are often people - customers, shoppers, general consumers - but not necessarily. We can segment products into categories, brands into sub-categories, and brands based on their users or image.

According to Josh Sutton, chief executive at Agorai: "Artificial intelligence (AI) solutions are producing insights in seconds that used to take teams of people days or even weeks to produce. Early adopters are seeing financial benefits already." The main metrics for success are a reduction in insight time, followed by decreases in labour demands and overall expense. Mr Sutton adds: "McKinsey recently produced a report which states that over the next decade, early adopters of AI will dramatically outperform followers and laggards." "We are now at a point in time that is reminiscent of the mid-1990s, when the early winners of the internet were those who identified opportunities and experimented to address business problems," says Chris Duffey, strategic development manager at Adobe and author of Superhuman Innovation.

"Model" means different things to different people and different things at different times. As I briefly explain in A Model's Many Faces, I often find it helpful to classify models as conceptual, operational or statistical. In this post we'll have a closer look at the last of these, statistical models. First, it's critical to understand that statistical models are simplified representations of reality and, to paraphrase the famous words of statistician George Box, they're all wrong but some of them are useful. So why do we use statistical models?

Statistics is a huge field and many disciplines such as biology, economics and psychology have made significant contributions to it. This link to journals published by the American Statistical Association and this link regarding the popular statistical software R demonstrate just how big a field it is. Statistics is not just point-and-click and its growing complexity makes simplifying it more difficult, not easier. Moreover, in his popular textbook Statistical Rethinking, Richard McElreath of the Max Planck Institute makes a very important observation: "...statisticians do not in general exactly agree on how to analyze anything but the simplest of problems. The fact that statistical inference uses mathematics does not imply that there is only one reasonable or useful way to conduct an analysis. Engineering uses math as well but there are many ways to build a bridge."

"What is truth?" and "What is a lie?" are questions that have drawn the attention of philosophers, theologians, legal scholars and intellectuals of many kinds for centuries. I am not a scholar or intellectual, merely a hardhat statistician working in marketing research and what is vaguely called data science. Regardless of what we do for a living, however, all of us are consumers of statistics at work and in our daily lives. "Statistics" can refer to figures or mathematical models, and either can be used to deceive us, are often misinterpreted or can be flat out wrong. Deception in various forms can be found in nature, and pet owners may have noticed that it is not exclusively a human trait.

In summer 1972 Anthony Hopkins was chosen to play a leading role in a film based on George Feifer's novel The Girl from Petrovka. Not having the book himself, he went to London to buy a copy but none of the main London bookstores had one. On his journey home, however, waiting for an underground train at Leicester Square station he saw a discarded book lying on the seat next to him. It was The Girl from Petrovka! The story gets even weirder.

Say, you're developing a new product. One thing you'll want to know is how important various features of a product or service of that type are to consumers. We often try to get at this by asking respondents directly in focus groups or quantitative surveys, but this may mislead us because many people have difficulty answering questions such as these. In surveys, for example, many will claim that just about everything about a product is important. Instead, what conjoint does is force respondents to make trade-offs.

Regression is arguably the workhorse of statistics. Despite its popularity, however, it may also be the most misunderstood. The answer might surprise you: There is no such thing as Regression. The Dependent Variable is something you want to predict or explain. In a Marketing Research context it might be Purchase Interest measured on a 0-10 rating scale.