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%!
Data-driven decision making. We all aspire to it. Whether it’s in Customer Experience management, targeted marketing, deciding which candidates to hire, choosing a new car, or picking the teams in your March Madness office pool.