Gaining deeper insights through AI

Editor’s note: Rasto Ivanic is the CEO of Groupsolver, Adrian Del Bosque is the senior research team lead at GroupSolver and Sarah Parker is the marketing coordinator at GroupSolver. This is an edited version of a post that originally appeared under the title “The future of AI in market research” on Research World. 

Artificial intelligence is not just a buzzword. AI tools accelerate market research and transform online survey methodologies into forward-thinking insights. 

Recently the market research sector has seen an emergence of AI tools. These tools can change the market research game, but first, we need to understand how to use them. 

AI is the development of machines and computers that can perform tasks that typically require human knowledge. This includes the ability to understand the human language as it is spoken or written. AI and machine learning offer huge opportunities for automation, enhancing data quality and unlocking deeper findings in survey research. Although some companies have tapped into these possibilities, many are constrained by traditional market research approaches that leave little room for innovation. AI can alleviate some of the biggest obstacles in survey research. Specifically regarding how we handle text-based data. 

AI offers speed and efficiency

One of the most time-consuming aspects of survey research is analyzing free-text responses. Without some form of automation, organizing and deciphering answers to open-ended questions requires significant manual labor. However, these responses can also generate rich insights. 

In addition to an abundance of reading, researchers must filter and categorize free-text answers into meaningful themes. Humans are pretty good at doing this on a small scale. But when there are thousands of respondents in a survey, we need something more consistent and efficient. 

This is one of the many problems AI can solve.

AI tools, including natural language processing (NLP) algorithms, can automatically process and classify large quantities of unstructured language data. The use of NLP has become increasingly common in industries such as healthcare, finance, manufacturing, advertising and more. However, only a few companies have fully leveraged these tools in survey research. Aside from the slow pace of industry change, there are also technical obstacles to adopting AI. 

Building AI-powered survey technology in market research

For AI to do a good job in any unique application, industry-specific algorithms need to be trained to align with the context and purpose that they'll serve. In market research, AI technology needs to efficiently organize unstructured data without losing the specific, actionable insights researchers are looking for. The following are three considerations for AI-powered technology in market research

1.   Dirty data.

In most applications, language models are trained on sources of well-written formal text, but when it comes to survey responses, we know people don't typically write like that. They write all kinds of colloquialisms, abbreviations, emojis and sometimes they just write profanity. 

One of the primary challenges for AI in survey applications is adopting models to work on the dirty data: to recognize the difference between noise and signal, adequately filter out the nonsensical text and learn to categorize the remaining, clean information. 

2.   Flexibility.

Researchers ask new questions every day, and market trends and topics are ever-changing. Because research questions and answers are unpredictable, the models we create to manage them need to be flexible. The challenge is creating a model that can grab whatever respondents give them and reliably put their answers into logical groups that make sense to a human reviewer. 

3.   Tradeoffs between general and specific insights.

The ultimate balance that an AI survey solution needs to find is the sweet spot between categorizing and interpreting language data too broadly or too narrowly. If the algorithm combines individual answers too broadly, you can end up with vague insights. On the other hand, if the outputs are very detailed and specific, they might be too much to read and may not offer a comprehensive answer. 

Female researcher using laptop to analyze AI results through a futuristic virtual interface.

Three benefits of AI in market research

Despite the challenges of building flexible models to work with unpredictable language, the advantages of leveraging AI in market research are unparalleled. Let’s look at three of the top benefits we already see in the survey space. 

1.   Time.

AI-powered technology's biggest benefit is the time it saves. Automation in this field can reduce the length of a market research project from months to weeks or even days. This means more of the researcher's time can be spent evaluating the insights and the story behind the data rather than crunching numbers or trying to make sense of verbatims. 

2.   Increased data quality.

Another benefit is higher data quality, which solves a major concern for many researchers. AI and machine learning can drastically increase data quality by internally filtering the data, but perhaps more importantly, reducing the unconscious human bias that can skew our interpretation of qualitative data. 

3.   Deeper insights.

Higher data quality alone can help to improve the quality of your findings, but AI technology can also improve the depth of insights in other ways. Because AI tools make it easier to analyze qualitative data, researchers can collect more of it. Often, this free-text data contains the richest consumer insights. 

On top of that, AI technology can identify themes, correlations and subtle nuances between open-ended answers that we might otherwise miss, allowing us to get more out of our qualitative data. 

 AI-powered market research expectations 

AI tools offer many possibilities to accelerate and transform traditional market research processes, particularly survey methodology. Surveys are moving toward being more respondent-centric, and AI gives us powerful ways to further that goal. 

In the future, we can expect to see much shorter and more conversational surveys – with conversational being a tool that enables us to ask follow-up and probing questions in real-time. This gives us a more dynamic and authentic understanding of our audiences and markets. 

Survey research is already a foundational part of market research, but integrating AI tools makes it an even more efficient, trustworthy and profound source of market insights.