The Future of Generative AI in Market Research

Excitement for Generative Artificial Intelligence (GenAI)—the branch of artificial intelligence that can generate images, videos, audio, text, and 3D models by studying existing data and using its findings to produce new, original data—is palpable across sectors. Already, C-Suite leaders are leveraging GenAI’s ability to generate highly realistic and complex content that can mimic human thought, creativity, and speech for countless use cases.

From helping engineers develop 3-D designs to assisting data scientists in writing code and even drafting and reviewing legal documents in the legal world, there’s no shortage of evidence that GenAI could be a panacea for streamlining processes and eliminating mundane, tedious work in every industry.

This is especially true of the market research and intelligence industry, where a number of companies across sectors are wielding GenAI for similar purposes. But even more promising is this technology’s capability to help companies develop growth opportunities in new markets, overlooked consumers, and aiding in strategy building. This technology has led analysts and experts to forecast GenAI playing an ever-increasing role in market research departments over future YoYs due to its efficiency, effectiveness, and simplicity.

Below, we dive into how GenAI and its abilities are being applied to current market research efforts, as well as the pros and cons of leveraging this technology. 

Gen AI Application in Market Research

The question on top of research professionals minds is simple: how can ChatGPT and other iterations of generative artificial intelligence simplify the way we conduct and evaluate market research? 

To start, GenAI’s algorithm infrastructure is not just used to generate content. Rather, the capabilities used for generating output are harnessed and applied to other processes. These contributions streamline the process of collecting and analyzing data results, which take form in real-time insights, predictive modeling, and pattern detection. 

Below, we dig into what each of these entails and how they benefit research and, ultimately, decision-making:

• Predictive modeling is a process that uses historical logs of data to forecast future outcomes. From projecting customer demand to stock prices, leadership is leveraging predictive modeling to inform buying and selling decisions, analyze competition, and improve product performance.
  
• Pattern detection is a capability of GenAI that can find patterns in data sets, and decipher hidden connections between different variables . With pattern detection, researchers can explore linkages between product performance, consumer attitudes, and behaviors that would otherwise be ambiguous. 
• Real-time insights are insights based on live data, and are used best for metrics that are constantly changing, (i.e., social media, website traffic, etc.). Real-time insights are beneficial to understanding how competitors are approaching a market, product performance, and corresponding consumer attitudes. 

The Pros of GenAI

While being touted as a tool that simplifies tedious and time-consuming work, automating tasks, and allowing teams to focus on more complex, thoughtful labor, GenAI also possesses the capability to generate new market growth opportunities. 

Because GenAI can easily collect data from far-reaching or remote locations, this technology can provide insights into new or neglected markets that, by traditional methods, were too costly to research manually. Likewise, Generative AI can also identify potential target markets by analyzing existing customer data. Using this intel, GenAI then generates synthetic customer data to produce a more accurate picture of what the target markets look like and how likely they are to respond to a product or service—mitigating the risk of a business venture.

More precisely, GenAI is providing a bigger picture of who, or what, are potential customers and how market researchers can reach and entice them. Rather than relying on basic surveys and focus groups for understanding consumer likes and dislikes, GenAI generates specific customer preferences, detailed profiles, and preferences, allowing for a more effective assessment of a target market.

In the grander scheme, GenAI can also produce customized research plans based on the aforementioned data, that detail how to better reach these specific markets. And with GenAI’s ability to automate the data analysis process, market researchers are given time back that was otherwise spent on the tedious analysis and structuring of a research plan.

But what most market researchers are focused on is GenAI’s capability to quickly generate data—quicker than surveys, focus groups, and experiments take to produce results, making it the ideal methodology for quickly collecting accurate data.

And while questions surrounding what data sets GenAI pulls from to generate its results, it can ensure greater accuracy in market research data by accounting for things like changing demographics or regional preferences. Further, it prevents and removes biases from market research gatherings by using synthetically generated data and algorithms to assess the results—which are then based on collected data, without any preconceived notions about demographics or markets.

The Cons of GenAI

• Scalability: While faster than conducting surveys and peer studies, generating new data can be a time-consuming process, which could lead to data that isn’t available for research teams when needed. With rushed data, there is the potential for lower levels of accuracy and, consequently, research delays. 
• Accuracy: GenAI-generated data is historically reliable, but may not be as accurate as data collected through empirical research. As such, researchers should be considerate as to how they are using this data to reach target audiences. The core of an unbiased algorithm starts with the content sets it’s based on. Vetting data sources that serve as the foundation for a GenAI system’s knowledge becomes critical to ensuring reliable results or decisions.
• Interpretability: The way data is transformed from raw to usable state is not entirely transparent with generative AI—where is this data being pulled from and by what means? Without this exact knowledge, market researchers will have limited insight into why their GenAI data is the way it is, weakening its credibility.
• Reliability: Although GenAI can write impressive texts that mimic human writing, the results can be inaccurate when based on incomplete, outdated or unverified information sources. For those wielding GenAI, be cautious of the fact that most byproducts of this technology leverage unverified or unreliable sets.
  

A More Reliable GenAI for the Future

Unlike other generative AI tools that are focused on consumer users and trained on publicly available content across the web, AlphaSense takes an entirely different approach. As a platform purpose-built to drive the world’s biggest business and financial decisions, our first iteration of Gen AI technology—Smart Summaries—leverages our 10+ years of AI tech development and draws from a curated collection of high-quality business content.

Read the full announcement here about our Smart Summaries and start your free trial of AlphaSense today.