Quirk’s Media: How AI will shape B2B market research in 2022

December 17, 2021

In the past decade, AI has become nearly omnipresent and has transformed how we conduct market research. Historically, companies trying to do B2B market research have faced the hurdle of contacting hard-to-reach professional audiences. Sascha Eder shares how AI can give researchers access to untapped audiences of professionals.

Originally published in Quirk’s Media on December 15, 2021

When we think about artificial intelligence, many of our minds turn to the exciting possibilities of self-driving vehicles or the dramatic excitement of sci-fi thrillers. Of course, AI has already changed our lives in major ways: matching algorithms have redefined dating; machine-curated playlists on Spotify shape how we discover and listen to music; and search engines have radically transformed how knowledge is transmitted.

AI has also had an impact on B2B market research. Back in 2018, a study from Qualtrics found that 93% of market research decision makers surveyed saw AI as a major opportunity for market research. Artificial intelligence can give researchers access to untapped audiences of professionals for niche research. As we live through a fourth industrial revolution, companies that don’t adopt AI-driven research methods may not be able to stay ahead.

AI in B2B research today: Custom recruiting 

Whether considering a new product launch or an acquisition, the decisions B2B research is meant to inform are often high stakes and carry an expensive downside. In a perfect world, respondents to B2B surveys, interviews, etc., would be reached in a matter of days at scale, with each offering their own unique, specific insights. In practice, though, meeting timing and respondent-targeting criteria is a challenge – particularly in niche or highly technical industries.

To solve this problem, insights leaders are turning to AI-powered search engines that can search for professional knowledge. Whether you need to conduct a survey with 100 enterprise ITDMs, or conduct qualitative interviews with CFOs, algorithms can identify the best people for the job.

AI-driven algorithms can enable researchers to identify respondents from multiple databases. This ensures companies can get data and insights from the people who fit their needs, as opposed to the best from within a closed network.

AI, NLP and the future of B2B research

Though AI is already making strides in the world of B2B market research, what we’re seeing now is just the tip of the iceberg. Innovations in AI show no signs of slowing down. For example, one area that’s shown a great deal of growth in recent years – and one that offers a huge opportunity to B2B market research – is natural language processing.

Natural language processing is the branch of computer science and AI that’s concerned with how computers interact with and understand human language. We see natural language processing in use every time we talk to Alexa, Siri or Google Assistant – all tools that show how useful natural language processing can be for the average person.

For market researchers, however, natural language processing offers the possibility of revolution. Imagine a personal assistant-esque AI processing the content of hundreds of interviews to identify key trends and present actionable findings without requiring a human being to go over every interview by hand. Technologies like natural language processing can enable better decision-making and free up time for insights leaders so they can focus on the strategic tasks.

It’s important to remember, though, that AI isn’t just another tool. For now, many enterprise organizations are still getting by relying on older market research techniques. But keeping ahead of the competition is a necessity for business success, and automation and AI are the name of the game going forward.

Market research companies that fail to adapt will be unable to provide clients with the quality and quantity of data offered by their AI-adopting competitors. Market research companies that continue to depend on archaic research methods will stifle their own innovation and risk providing their clients with bad data that jeopardizes their decision-making.

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