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AI is perhaps the greatest success story of our generation. In the last few years, AI has made a giant leap from academic research to a household name. This astronomical change can be credited, in part, to the fantastic yarn spun around AI by Hollywood and the development of AI-based industry-changing technology. In a recent post, we discussed the in-depth applications of AI that have revolutionized the retail industry. This article aims to shed light on the dimensions of AI-driven transformation that has helped next-gen market researchers succeed.
Artificial Intelligence is a science that is truly industry agnostic. AI has improved industries that are completely unrelated to each other.
Almost 80 percent of Market researchers feel AI will harbor a positive impact on the current methods of market research. Let’s look at the reasons for this overwhelming optimism.
Let’s take a closer look at the basics of market research and how AI is helpful in it before diving into the magnificent world of AI-based market research.
Market researchers define their success based on 3 Es: Efficiency, Effectiveness, and Enhanced Results. AI has proven it’s worth by delivering on all these parameters.
Efficiency: AI-based market research is much more efficient than traditional market research. AI-based market research tools can deliver results in near real time, select the right target audience, and automatically classify and mine text for key insights, all in a matter of hours or, at most, a day.
Effectiveness: Research has established that there is a direct connection between survey design and the quality of data. AI-based market research has opened doors to interactive surveys. Such surveys change on the basis of the respondent’s answers. Such dynamic surveys are way more effective than traditional static surveys.
Enhancement: AI-based market research is an enhancement on the traditional methods. Real-time results have made insight discovery faster than ever before. AI-generated results are more accurate and free of any human-induced errors or biases.
Feedback analysis is at the very core of market research. A high percentage of users tend to leave feedback remarking on their experience and scope of improvement. Popular metrics are improved by pairing them with open-ended questions. Research has repeatedly shown that open-ended feedback is more detailed and can be very effective in understanding the user mindset. Improvement in speech recognition technology has saved everyone from the pain of typing long and tiring feedback. This, in turn, has increased the number of responses unstructured questions generate.
Acquiring open-ended feedback is important, but feedback is only as good as its analysis. This is the problem Natural Language Processing aims to solve. AI-based market research has contributed — and in many ways transformed — open-ended feedback analysis.
