Consumer brands need to drop long used old-fashioned focus groups, interviews and surveys to gauge what consumers want and use Artificial Intelligence (AI) and Machine Learning (ML) to improve the process, say researchers.
AL and ML can better identify customer needs, while reducing research time substantially, helping consumer marketing brands avoid delays in bringing products to the market, said researchers from the Massachusetts Institute of Technology (MIT).
Machine learning can help analyse user-generated content (UGC), which involves the collection of data from online reviews, social media, and blogs that provide insights on consumer needs, preferences and attitudes.
"As more and more people turn to the digital marketplace to research products, share their opinions and exchange product experiences, large amounts of UGC data is available quickly and at a low incremental cost to companies," said Artem Timoshenko from MIT.
Despite the potential for better information, marketers have raised concerns over the value of UGC data because the sheer scale and quality of UGC makes it difficult to process.
Timoshenko and John R. Hauser of MIT decided to tackle this problem through research designed to examine the challenge of how to most efficiently use UGC to identify customer needs in ways that are more cost-efficient and accurate.
They developed and evaluated a machine-learning hybrid approach to identify customer needs from UGC.
First, they used machine learning to identify relevant content and remove redundancies.
The processed data was then analysed by human beings to formulate customer needs from selected content.
"We found that UGC does at least as well as traditional methods based on a representative set of customers," said Hauser in a paper that appeared in the INFORMS journal Marketing Science.
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