Social Listening Is Revolutionizing New Product Development

New techniques better capture consumer intelligence in real time.

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Every day, billions of people talk on social media about where they’ve been, what they’ve bought, and their feelings and opinions about products and services. This information is a gold mine for consumer-facing industries, including retail, consumer goods, retail banking, insurance, and health care. But few companies have acted on this valuable data.

Companies have traditionally relied on surveys, focus groups, and research reports to assess what consumers think of their products or services, but these traditional approaches have several shortcomings. Sample sizes are limited and subject to bias. Studies take time to organize, and results quickly become dated. Moreover, what people say often differs from what they do, like complaining about discount airlines but using them all the same.

Leaders who employ social listening — analyzing what consumers say on social media — can gain a competitive advantage by getting better insights that they can act on quickly, without incurring the higher cost of traditional approaches. This new approach, fueled by social platforms, increasingly informs the new product development, marketing, operations, and international expansion of consumer product companies.

Put another way, social listening can serve as the map for a treasure hunt. It is already overturning how consumer product companies develop, market, and package their products — and we are only beginning to discover the scope of possibilities that machine learning advances will catalyze.

How Machine Learning Is Refining Social Listening

Until now, social listening has mostly been limited to public opinion monitoring, such as counting the number of times a brand is mentioned (buzz) and whether the content is positive or negative (sentiment). But this data tends to feed into standardized or aggregated metrics, which are too generalized to use for making business decisions.

Recent advances in machine learning have enabled smart analysis of natural language content, as well as the monitoring of pictures and videos. This progress allows companies to investigate a wider range of customers’ feelings and opinions and to identify specific trigger points — such as the color of a product or location of a service — for those feelings. By analyzing customer data on social platforms, companies can now map and update customer preferences and monitor how they connect with and influence one another.

Machine learning can also help with the problem of unclean data.

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