109 Kellogg Insight Using Natural Language and Video to Glean Customer Insights People communicate mostly through natural language—not equations or Excel spreadsheets or structured daily reports. This has historically posed a challenge for marketers, because while the words we use are full of con - sumer insights, they have been generally inscrutable to analytical tools. This is, of course, changing. With natural-language processing and machine-learning software, companies can now scrape vast libraries of text to generate insights on almost any subject. It has now become com - monplace for companies to analyze, say, customers’ Facebook comments to design more effective digital-marketing strategies. But organizations are becoming more creative and ambitious in thinking about how to apply insights from unstructured data, according to Pah. Consider a cell-phone manufacturer launching a new phone. Because these manufacturers make most of their sales through service providers such as ATT or T-Mobile, they are not directly connected to their customers. This means that post-launch customer feedback has his- torically been pulled from either customer returns or the manual moni - toring of social media and tech websites by a small group of employees. Pah worked with a manufacturer to create a different process. “We wrote a program that crawled the Internet for mentions on Twitter and Facebook along with about 30 sites where people review and talk about cell phones,” he says. “We then built a machine-learning solution that would automatically identify when the company’s product was being Based on insights from Adam Pah