Four considerations when using AI in market research:

While AI offers many benefits to market researchers, there are also important considerations for researchers to keep in mind.

1. Data Quality: AI is only as good as the data it has access to. This becomes a problem when AI is asked to interpret incorrect or incomplete data. For example, say you are drafting a report based on focus group learnings. Luckily, the groups were recorded, and you have automated transcripts for each group. You might think you can run these transcripts through an AI program to quickly extract themes, but doing so might hurt your report rather than help it. This is because transcribing programs often misinterpret foreign speakers, quiet speakers, regional accents, and slang. Relying too heavily on AI insights from transcripts like this could lead to misleading or incorrect learnings.
2. Minimal Contextual Understanding: AI also struggles to understand the context and nuances around language and behavior. Market research involves analyzing the things people say or do, whether in IDIs, focus groups, or open-ended survey questions. This can be especially challenging for AI models to interpret correctly because they do not understand things like emotion, sarcasm, cultural references, and subtle variations in language, which can lead to misinterpretations.
3. Lack of Creativity: AI shines when it comes to processing and analyzing enormous amounts of data, but it lacks the ability to produce creative ideas. Market research would be nothing without creatives generating innovative ideas, identifying unique strategies, and making intuitive connections based on experience and expertise. AI cannot replace the human element of market research in terms of producing out-of-the-box insights or making intuitive leaps. Lucky for us!
4. Ethical Considerations: More seriously, using AI in market research can raise ethical concerns related to data privacy, participant consent, and transparency. Market researchers should ensure that data collection, storage, and analysis align with legal agreements, relevant regulations, and ethical guidelines. Researchers should also practice transparency by communicating when AI is used.