Those who remember the “New Coke” debut in 1985 will understand just how useful sentiment analysis can be when a brand is considering a major change, such as introducing a new product. If the Coca-Cola Company had this tool back then, it might have determined that New Coke wasn’t such a wise move.

Today, companies have the benefit of data analysis to measure customer perception. That data comes in many forms, from social media chatter to formal instruments such as surveys. But sentiment analysis, a discipline of machine learning, is what makes that data so useful.

Sentiment analysis uses natural language processing to apply an algorithm to data sets. By searching for keywords or other markers, it reveals insights that organizations can incorporate into future planning. In essence, it delivers a baseline analysis for whatever an organization wants to measure and then helps to guide future decision-making or the implementation of necessary course corrections.

Retailers are using data, powered by artificial intelligence, to achieve an impressive list of objectives: loss prevention, operational efficiencies, customer insights and buying behavior enhancements.

One of retail’s most versatile data solutions is computer vision, a form of AI that enables computers to glean insights from the visual information flowing to digital cameras. Recommendation engines are also on the rise, letting retailers leverage data to deliver timely, customized experiences. These advances support easier, more personalized shopping for consumers — exactly what they’re looking for.

Until someone invents a crystal ball, business analytics tools are the best way to achieve a deeper understanding of business trends, such as emerging opportunities and changes in customer expectations and habits. The latest BA tools also enable organizations to unlock insights that otherwise would require hiring data scientists.