If you’ve been following on LinkedIN, Generative AI is the RAGE right now. It is already transforming the way we live and work and will augment and enhance many industries including market research, enabling organizations to better understand the needs and desires of their target audiences. With the ability to generate high-quality data quickly and cost-effectively, generative AI is a powerful tool for uncovering insights that may otherwise be overlooked. This article provides an overview of the potential applications of generative AI in market research, focusing on its ability to generate predictive models, detect patterns, and provide real-time insights into consumer trends. By leveraging the power of generative AI, market researchers can gain a deeper understanding of their target audiences and make more informed decisions about their offerings.
The term generative AI refers to an artificial intelligence (AI) system that is capable of self-generation — creating new information and ideas without being programmed to do so. Companies like YouGov have developed software solutions that use machine learning and natural language processing to allow researchers to create new data without having to conduct additional empirical research. Market researchers can use generative AI to generate new data that can be used to generate insights about consumers, trends, and behaviors. For example, a researcher could use generative AI to generate new data related to consumers’ attitudes towards a product or service. This data could then be used to create a predictive model to understand more about trends in purchasing decisions.
Increased accuracy - Generative AI is able to generate more accurately than other data collection methods, like expensive social listening platform subscriptions, meaning it produces a more realistic view of target audiences. As such, it allows researchers to gain a deeper understanding of consumers and their attitudes and behaviors.
Rapid results - Generative AI can produce new data very quickly, meaning that researchers can start generating insights sooner. This speed is made possible by the use of artificial neural networks, which are capable of processing vast amounts of data and complex algorithms.
Cost-effectiveness - Generative AI allows researchers to create new data without having to conduct additional empirical research or spend $$ on expensive platforms. This means that organizations can save time and money, enabling them to increase their research budgets.
Convenience - Generative AI provides a user-friendly experience, meaning that anyone with access to the software like ChatGPT can generate new data. This is possible because of the algorithms used in the software, which are designed to be extremely user-friendly.
Predictive modeling - Predictive modeling is a process that uses historical data to forecast future outcomes. It can be used to forecast everything from customer demand to stock prices, and is a very powerful tool in market research. Predictive modeling can be used to understand more about a variety of topics, including buying and selling decisions, competition, and product performance.
Pattern detection - Pattern detection is based on the idea that there is a pattern in everything. It can be used to find hidden connections between different variables and gain a better understanding of consumer behavior. With pattern detection, researchers can explore consumer attitudes and behaviors, and find various connections between them. This can lead to fascinating discoveries that would otherwise remain hidden. Pattern detection can be applied to a variety of topics, such as behavior, preferences, and product performance.
Real-time insights - Real-time insights are based on live data, and are best applied to topics that are updated frequently, such as social media and website traffic. Real-time insights can be used to understand more about a variety of topics, including consumer behavior, product performance, and competition.
Scalability - Generating new data can be a very time-consuming process, meaning that the data may not be ready when the research team needs it. This can result in a lower level of accuracy and lead to research delays.
Accuracy - Generated data may not be as accurate as data that has been collected through empirical research. As such, researchers must be careful when using it to understand more about their target audiences.
Interpretability - The process by which data is converted from raw to usable form is not entirely transparent with generative AI, meaning that users have no way to understand how the data was generated. This means that researchers have limited insight into why data is the way it is, limiting its usefulness.
Dependability - The process of using generated data is not as reliable as using data that has been collected through empirical research. This means that researchers must be extra careful when interpreting and applying the data, and should be ready to question its accuracy.
While many are worrying that AI will replace the need for humans doing the work, I have a more optimistic view that adopting and harnessing AI will create new opportunities and jobs, allowing humans to focus on the higher level, more human tasks of market research. Marketers can use generative AI to gain a better understanding of their target audiences and make more informed decisions about their offerings. It’s important to embrace the potential of AI and use it to our advantage rather than fear it’s potential to disrupt the status quo.
Jay Tye - Chief Operations Officer
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