Consumer research costs companies billions annually yet suffers from panel biases and limited scale. Large language models (LLMs) offer an alternative by simulating synthetic consumers, but produce unrealistic response distributions when asked directly for numerical ratings. We present semantic similarity rating (SSR), a method that elicits textual responses from LLMs and maps these to Likert distributions using embedding similarity to reference statements. Testing on an extensive dataset comprising 57 personal care product surveys conducted by a leading corporation in that market (9,300 human responses), SSR achieves 90% of human test-retest reliability while maintaining realistic response distributions (KS similarity > 0.85). Additionally, these synthetic respondents provide rich qualitative feedback explaining their ratings. This framework enables scalable consumer research simulations while preserving traditional survey metrics and interpretability.

ABSTRACT This report analyzes the potential for large language models (LLMs) to expedite accurate replication of published message effects studies. We tested LLM-powered participants (personas) by replicating 133 experimental findings from 14 papers containing 45 recent studies in the Journal of Marketing (January 2023-May 2024). We used a new software tool, Viewpoints AI (https://viewpoints.ai/), that takes study designs, stimuli, and measures as input, automatically generates prompts for LLMs to act as a specified sample of unique personas, and collects their responses to produce a final output in the form of a complete dataset and statistical analysis. The underlying LLM used was Anthropic's Claude Sonnet 3.5. We generated 19,447 AI personas to replicate these studies with the exact same sample attributes, study designs, stimuli, and measures reported in the original human research. Our LLM replications successfully reproduced 76% of the original main effects (84 out of 111), demonstrating strong potential for AI-assisted replication of studies in which people respond to media stimuli. When including interaction effects, the overall replication rate was 68% (90 out of 133). The use of LLMs to replicate and accelerate marketing research on media effects is discussed with respect to the replication crisis in social science, potential solutions to generalizability problems in sampling subjects and experimental conditions, and the ability to rapidly test consumer responses to various media stimuli. We also address the limitations of this approach, particularly in replicating complex interaction effects in media response studies, and suggest areas for future research and improvement in AI-assisted experimental replication of media effects. STUDY OVERVIEW AND RELATED WORK Research about the effectiveness of media messages is increasingly difficult, attributable to both administrative challenges (e.g., stimulus acquisition and creation, data management demands of digital trace data, acquisition of participants and especially those in special groups like children, minorities and international groups), as well as requirements to deal with new and critical challenges to the very nature of social research, as exemplified by existential issues of replication and reproducibility, and the ability to generalize findings across people, media stimuli and experimental contexts. We briefly review these issues with an eye toward our current test of whether new LLM tools may help solve the problems mentioned, and with significant advantages in cost, time, and research personnel.

Loneliness is a distressing personal experience and a growing social issue. Social robots could alleviate the pain of loneliness, particularly for those who lack in-person interaction. This paper investigated how the effect of loneliness on the anthropomorphism of social robots differs by robot appearance, and how it influences purchase intention. Participants viewed a video of one of the three robots (machine-like, animal-like, and human-like) moving and interacting with a human counterpart. Bootstrapped multiple regression results revealed that although the unique effect of animal-likeness on anthropomorphism compared to human-likeness was higher, lonely individuals' tendency to anthropomorphize the animal-like robot was lower than that of the human-like robot. This moderating effect remained significant after covariates were included. Bootstrapped mediation analysis showed that anthropomorphism had both a positive direct effect on purchase intent and a positive indirect effect mediated by likability. Our results suggest that lonely individuals' tendency of anthropomorphizing social robots should not be summarized into one unified inclination. Moreover, by extending the effect of loneliness on anthropomorphism to likability and purchase intent, this current study explored the potential of social robots to be adopted as companions of lonely individuals in their real life. Lastly, we discuss the practical implications of the current study for designing social robots.

As the competition among machine learning (ML) predictors is widespread in practice, it becomes increasingly important to understand the impact and biases arising from such competition. One critical aspect of ML competition is that ML predictors are constantly updated by acquiring additional data during the competition. Although this active data acquisition can largely affect the overall competition environment, it has not been well-studied before. In this paper, we study what happens when ML predictors can purchase additional data during the competition. We introduce a new environment in which ML predictors use active learning algorithms to effectively acquire labeled data within their budgets while competing against each other. We empirically show that the overall performance of an ML predictor improves when predictors can purchase additional labeled data. Surprisingly, however, the quality that users experience--i.e., the accuracy of the predictor selected by each user--can decrease even as the individual predictors get better. We demonstrate that this phenomenon naturally arises due to a trade-off whereby competition pushes each predictor to specialize in a subset of the population while data purchase has the effect of making predictors more uniform. With comprehensive experiments, we show that our findings are robust against different modeling assumptions.

The use of AI in marketing allows businesses to understand customer demand for their products and their willingness to purchase them. Predicting the purchase intent of anonymous visitors from various sources can be a daunting task for your business. Earlier, businesses would gauge customer interest in their products based on factors such as past demand, prices, and rival performance. Today, those factors are still relevant, in addition to a few more such as behavioral trends, reciprocity and product scarcity in the market. Such factors create huge reserves of customer data that need to be scanned and analyzed to get an idea about purchase intent.

In recent years, the efforts of digital marketing companies have taken a significant turn by using artificial intelligence technologies. One of the major problems that marketers are facing is how to personalize the content to users and generate better experience and results. Lately several startups developed AI technologies that aimed to help marketer solving this problem. The vast amount of information collected about users is used by advertising systems dominated by the big players such as: Google and Facebook. However, most businesses are having difficulties to use AI technologies to improve their digital marketing.The great challenge that any marketing manager faces is how to tailor the message to the customer in a personalized way that is fits to each user according to interests, purchase intent and the right timing.What is Artificial Intelligence Marketing?Artificial Intelligence Marketing (AI Marketing) allows you to leverage the data collected on users or customers to tailor the content or messages to the user profile so that the content presented to the user is tailored to their interests at the time relevant to the user and their purchase intent.

Marketers in 2019 will find it hard to be successful without understanding the cultural transformation that's happening in this country. Between 2012 and 2017, the US multicultural population – Hispanics, African Americans, and Asian Americans – grew to 11.7 million people. Notably, these groups are younger and growing at a faster rate than their White counterparts. This makes multicultural marketing an essential component of all advertising campaigns. Yet, even the most seasoned and "culturally woke" brands can have trouble navigating this cultural transformation and shifts in consumer behavior.

Businesses spend a significant chunk of their marketing budget in reaching out to new prospects and capturing their lead. Knowing what kind of customers one should be reaching out to, segmenting them based on purchase intent, navigating them through your sales funnel, and converting them can be incredibly complex and frustrating. Customer Relationship Management (CRM) tools have taken off in a big way over the past decade and a half. Some tools like TeamWave have enabled businesses to sync customer relationship management with other aspects of business like project management, time tracking and contact management. A number of other CRM tools have also enabled businesses to considerably automate the various components of relationship building. This includes tasks like identifying prospects, extracting their contact details, initiating outreach, and also ensuring periodic engagement.

AI is a regular feature of news stories across nearly every industry. But, says Stephen Upstone, the headlines hide the fact there's a big human role in making AI work effectively. From healthcare, to driverless cars, to cyber security, AI is increasingly seen as the Holy Grail for innovation, productivity and profitability. At the other end of the spectrum, nay-sayers preach the coming obsolescence of humans with the rise of the job-killing robots. But in reality, the use of AI – in marketing and advertising, at least – is not immediately as clear cut as the sensationalising headlines suggest.

Bayes Nets (or Bayesian Networks) give remarkable results in determining the effects of many variables on an outcome. They typically perform strongly even in cases when other methods falter or fail. These networks have had relatively little use with business-related problems, although they have worked successfully for years in fields such as scientific research, public safety, aircraft guidance systems and national defense. Importantly, they often outperform regression, particularly in determining variables' effects. Regression is one of the most august multivariate methods, and among the most studied and applied.