With the acceleration of social media, consumers are a never-ending fountain of complaints, praises, recommendations, and questions—and are more empowered than ever to tell manufactures exactly what they think. Gone are the days when a customer might tell one friend they didn’t like the taste of a product; today they’re more likely to go on to a brand’s Facebook page and tell thousands of people. Social media has truly shifted power from the hands of the manufacturer to the customer.
In a resource-constrained environment, manufactures can’t afford to use human employees to sort through a growing database of tens of millions of consumer contacts looking for valuable insights, whether they be problems or opportunities. That is, until machine learning entered the picture. Machine learning has tremendous potential to extend capabilities and empower organizations to gather insights from all the data being generated directly by consumers.
JoLynn Lavin explores the tremendous opportunities and challenges manufactures face when trying to use consumer contact data (both structured and unstructured) to identify consumer trends and influence product development. Along the way, JoLynn shares real-world examples of how General Mills uses machine learning to catch product issues early and bring new products to market—simply by listening to consumers.
JoLynn Lavin is a manager of decision sciences and analytics at General Mills, where she leads a team of analysts focused on unleashing the power of data to drive consumer-led decision making. Previously, JoLynn was a loyalty marketing consultant helping clients acquire, retain, and build profitable relationships with their customers across virtually every industry. JoLynn holds a master’s degree in agricultural and consumer economics from the University of Illinois at Champaign-Urbana.
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