Almost all customer feedback systems include surveys with open-ended questions. Indeed, in the popular Net Promoter System (NPS), the survey can consist entirely of just two questions: a likelihood-to-recommend score, followed by an open-ended question explaining the score. As a result, as much as half of the data generated by customer feedback systems is verbatim, “unstructured” text.
Centriam’s Data Science team recently authored a new white paper on the massive opportunities for modern machine learning algorithms to improve customer experience. This paper argued that newer methodologies such as gradient boosting, deep learning, and multi-arm bandits dramatically outperform regression, text analytics, A/B testing, and other methods commonly used today. In line with Centriam’s findings, McKinsey recently reported improvements of up to 62% using deep learning and other modern methods compared to existing predictive analytic methods. Imagine if you could increase your save rate, upsell attachment, or survey completion rate by 62%!
Machine learning. Artificial intelligence. Big data. Deep learning. These terms have saturated the modern business lexicon and permeated the zeitgeist. What is your experience with these buzzwords? You’ve certainly read about them and likely talked about them, but have you implemented them? Are you leveraging them to improve your customer experience? Would you like to learn where to begin?