Market research surveys are a powerful methodology for understanding consumer perspectives at scale, but are limited by depth of understanding and insights. A virtual moderator can introduce elements of qualitative research into surveys, developing a rapport with survey participants and dynamically asking probing questions, ultimately to elicit more useful information for market researchers. In this work, we introduce ${\tt SmartProbe}$, an API which leverages the adaptive capabilities of large language models (LLMs), and incorporates domain knowledge from market research, in order to generate effective probing questions in any market research survey. We outline the modular processing flow of $\tt SmartProbe$, and evaluate the quality and effectiveness of its generated probing questions. We believe our efforts will inspire industry practitioners to build real-world applications based on the latest advances in LLMs. Our demo is publicly available at https://nexxt.in/smartprobe-demo

When we hear about Artificial Intelligence (AI), the very first thought comes into our mind as it's being our personal home, office, or driving assistant. Because of the existing media representation on AI and the current progress of AI in the technology market, it is undoubtedly obvious to have such expectations from it. The involvement of AI in the technology space has been driven to a certain extent since the proliferation, especially in data analytics, and in which market data analytics to be precise. Many market researchers and data analysts believe that AI is an essential factor driving better performance efficiency and customer satisfaction, which eventually helps companies get better sales and revenues. According to one market survey, around 93% of market researchers consider AI as an industry opportunity, and 80% agree on AI driving a positive impact on the market.

In 2020, there is no doubt that the Covid-19 pandemic has impacted the global economy as well as the pace of technology advances and new product development in the electronics industry. Despite these delays, there have been a number of new innovations across the component industry, driving technology trends across various technologies, including 5G, artificial intelligence (AI), extended reality (XR), internet of things (IoT), and automotive safety. TrendForce's technology forecast for 2021 indicates that new technology adoption continues to ramp up. Here are the market researcher's top 10 technology trends. TrendForce expects the three major DRAM suppliers – Samsung, SK Hynix, and Micron – to continue their transition towards the 1Z-nm and 1-alpha-nm process technologies, and officially introduce the EUV era, with Samsung leading the charge in 2021. DRAM suppliers will gradually replace their existing double patterning technologies in order to optimize their cost structure and manufacturing efficiency, said analysts.

Although Hollywood movies can lead you to believe that AI is an ominous thing, market researchers have nothing to fear and everything to gain from it. The new artificial intelligence technologies transform interaction and contribute to the hottest market research patterns, with everything from broad scale data processing to report production. Here are some ways in which AI will help market researchers achieve success in 2019 and beyond. Artificial intelligence is able to capture and interpret large quantities of data at a faster level than ever before and with greater precision. This helps market analysts to get a better view of their audience, not only what they want but why they like it.

"Any researcher worth their salt was acutely aware of sample design and delivery on that design back in the 1970s. We knew that it would make or break our study and our competitive advantage lay in our data quality." So says Butch Rice, a South African market research industry pioneer who started one of that country's most successful agencies, Research Surveys, before it was subsumed by TNS and later Kantar when I spoke to him about this article. Over time, sampling has become such a fundamental and pervasive part of our industry that it has become almost an after-thought for many researchers. However, our once-dogged focus on sampling is beginning to rear its head in new and novel ways.

Artificial intelligence (AI) has increasingly been in the news as a technology that will radically change the world around us. Autonomous vehicles, virtual assistants and medical diagnostic systems are just some AI-based services that will alter how we live. However, whether the impact of these changes will all be positive is an open question. To that point, some individuals focus on the efficiencies and insights that AI will produce, while others are more concerned with a loss of control, a vulnerability to meddling and the loss of jobs that might result. With the potential ramifications AI presents across all industries and fields, what are the implications for market research? Specifically, will AI serve as a competitor and undermine the need for specific research services and tasks?

Market research is a $44.5 B market and growing. Online research is among the fastest growing parts of the market thanks to the pervasiveness of the web and the ease with which we can now collect data. However, as the world conducts more and more survey research, the issues that we see elsewhere with big data are now affecting the survey research industry as well, specifically the issue of data quality. Thanks to the growth in online survey research, billions of survey responses are collected every year. But 1/4th of those responses are of poor quality[1]. In fact Quality of Data Insights generated is the most important criteria (59% voted as most important decision) for deciding the Market Research software or vendor[2].

Market researchers face pressure to produce better results at faster speeds. Can AI help them meet this challenge? We usually hail the near-instant availability of information as a good thing, but it has its drawbacks. People are used to having their needs fulfilled almost on command. In this environment, waiting for anything â€" even high-quality research â€" can feel like an imposition.

The push for operational analytics that seeks to eliminate the requirement for constantly moving data between storage and databases to support transaction and analytical workloads is fueling the growth of translytical data platforms. The framework uses a single data tier that can serve both transactional and analytical workloads. The requirement to reduce data movement between data silos and technology stacks along with rise of machine learning and streaming analytics has fueled the rise of a maturing translytical data platform sector. Market watcher Forrester released a market survey earlier this month that identifies and ranks a dozen key players in the nascent analytics sector. The market researcher also identified the top translytical database workloads, including: real-time applications; Internet of Things (IoT) analytics operational data; connected data apps; and continuous learning in which translytical databases are used to train and retrain machine learning models. "The translytical data platform market is growing because more enterprise architecture pros see translytical as critical for their enterprise data strategy," Forrester said.

Market researchers face pressure to produce better results at faster speeds. Can AI help them meet this challenge? We usually hail the near-instant availability of information as a good thing, but it has its drawbacks. People are used to having their needs fulfilled almost on command. In this environment, waiting for anything -- even high-quality research -- can feel like an imposition.