Consumer brands have long used old-fashioned focus groups, interviews and surveys to best gauge consumer wants, desires and needs as part of processes that range from product development, to marketing and sales. As machine learning and artificial intelligence (AI) have emerged, there is an increasing interest in the ability to harness these solutions to save time and money, and to yield more reliable consumer insights.
Machine learning can help to analyze 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.
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. While the data is accessible, identifying consumer insights requires human beings to analyze the data, which is hard to do at scale.
A new study in the INFORMS journal Marketing Science tackles 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.
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