Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Their ability to generate human-like text has opened new possibilities for market research, particularly in conjoint analysis, where understanding consumer preferences is essential but often resource-intensive. Traditional survey-based methods face limitations in scalability and cost, making LLM-generated data a promising alternative. However, while LLMs have the potential to simulate real consumer behavior, recent studies highlight a significant gap between LLM-generated and human data, with biases introduced when substituting between the two. In this paper, we address this gap by proposing a novel statistical data augmentation approach that efficiently integrates LLM-generated data with real data in conjoint analysis. Our method leverages transfer learning principles to debias the LLM-generated data using a small amount of human data. This results in statistically robust estimators with consistent and asymptotically normal properties, in contrast to naive approaches that simply substitute human data with LLM-generated data, which can exacerbate bias. We validate our framework through an empirical study on COVID-19 vaccine preferences, demonstrating its superior ability to reduce estimation error and save data and costs by 24.9% to 79.8%. In contrast, naive approaches fail to save data due to the inherent biases in LLM-generated data compared to human data. Another empirical study on sports car choices validates the robustness of our results. Our findings suggest that while LLM-generated data is not a direct substitute for human responses, it can serve as a valuable complement when used within a robust statistical framework.

Artificial intelligence (AI) is having a profound impact on all aspects of our lives, and marketing is no exception. With the rapid growth of data and the increasing sophistication of AI technologies, marketing is undergoing a transformation. In the past, marketing was all about creating campaigns that would reach the widest possible audience. But with AI, marketers can now create personalized campaigns that are targeted to each individual. AI is also changing the way that market research is conducted.

Check out all the on-demand sessions from the Intelligent Security Summit here. For brands hunting for customer insights to drive decision-making, improve customer experience (CX), and ultimately spur growth, market research has long been part of the toolkit. Whether it's actually helpful or not is another question. In a typical market research project, brands invest (often heavily) in conducting research that amounts to a one-time snapshot of existing customer sentiment and, perhaps, competitors' prevailing differentiators. While this research can yield useful insights, it usually fails to recognize the wants of potential customers, or adequately correlate data that reveals exactly why customers are with competitors.

Computer vision is critical in the applications of facial recognition. It enables computers to match the identities of their faces. The algorithms detect facial features with face profiles in databases.

Artificial Intelligence (or AI) is being used increasingly in many critical business operations in all sectors. While you may think of AI being most effective at technical work, such as manufacturing or computing, it is also becoming proficient at understanding subjective human drives and desires. You can see this in the algorithms on sites and apps like TikTok, YouTube, and Spotify, which analyse your activity in order to create personalised recommendations tailored to your personality. It is no wonder, then, that AI is being used more and more for marketing and advertising purposes. AI-based tools can help generate ideas for ads in all media formats, find the right audience to target with ads and marketing campaigns, and generate specific campaigns for each individual based on their data.

In this experiment, I used the results of the market research of my thesis made with real people to alter the kernel of a CNN. The idea was to add human rationality to solve the MNIST task using a…

Even pre-pandemic, Amazon was an ecommerce giant like no other. You could find practically anything on the platform and have it delivered straight to your door. Cut to a nearly-post-pandemic reality, and Amazon has become bigger and more competitive than ever. FBA sellers are rapidly multiplying, and as the competition gets fiercer, standing out becomes far more difficult. In the world of marketing and advertising, artificial intelligence has been thriving as well.

Artificial Intelligence (AI) in Fintech Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis. It also provides market information in terms of development and its capacities.

Artificial Intelligence in Video Games Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis. It also provides market information in terms of development and its capacities.

Whether you are just overwhelmed with data or just curious about what you will learn, you may be feeling the impulse to jump on the artificial intelligence (AI) bandwagon. Before you go too far down the road, please consider this Top 10 list of the most common mistakes mangers make when building an AI project. This comes from long, hard lessons learned across multiple missions and IT clients over the years. Mission owners have a lot to do. It is usually the most annoying or time-intensive tasks they want to automate the most. I never begrudge someone who is trying to better optimize the cognitive talent of their team.