Over the last decade, Net Promoter Score® (NPS®) has become the standard for measuring customer satisfaction. For most enterprise businesses, the one-question survey is a good indicator of retention and other positive growth factors. On top of that, it is easy to understand and simple enough to implement.
So why are there so many naysayers?
In short, NPS’s opponents argue that it is not actionable, is flawed because the system throws out key feedback from a portion of respondents, and is not a true indicator of customer sentiment because most organizations don’t survey customers often enough. To date, there has been no clear solution that solves these issues…until now.
Natural language understanding (NLU), which is the application of artificial intelligence and machine learning to unstructured data, is quickly becoming a “need to have” for customer feedback teams. It is also the perfect complement to NPS. Supplementing structured feedback from NPS with text analytics is the most powerful and comprehensive way to measure customer feedback. Here’s how NLU solves each of the aforementioned issues in depth:
Answers The “Why” Behind an NPS Score
Many customers provide a response to the NPS follow-up question, “Care to tell us why you’ve given us this score?” Promoters, for example, want organizations to continue to do well, but they often ask for specific changes. This type of unstructured data has traditionally been difficult to act upon unless it’s on a 1:1 basis – and most organizations don’t have an account management system in place to handle that kind of volume. Natural language understanding analyzes these large-scale responses, surfaces key themes, and reveals what is truly significant. Organizations often have suspicions about why their NPS score is trending up or down. NLU helps you uncover hard evidence to support those suspicions and, in many cases, clear direction about what can be done to solve them.
Targets All Customers, Not Just the Most and Least Satisfied
To calculate the NPS score, organizations subtract the percentage of Detractors from the percentage of Promoters. This means ignoring the scores from Passives who are just shy of being Promoters and Detractors. This is risky because they could go in either direction at any time. With NLU, you can easily look at comments from all customers, not just those who are the most and least satisfied. It enables companies to determine patterns and trends among all customers, not just a portion of them.
Layers Real-Time Feedback with Annual Survey Data
Customer sentiment is typically only measured annually or semiannually with NPS, which makes it difficult for organizations to truly understand the ever-changing needs of customers. Surveys are just one source of data that can be analyzed with text analytics that provide insight into the customer experience. Additional
examples are online reviews, chat transcripts, online blogs and forums, social media, and other channels. Layering survey data with real-time feedback from these channels allows companies to get a steady pulse on customer satisfaction and seize opportunities as they arise. Importantly, in times of crises, companies have a better chance of catching early indicators of how customers are reacting and can work to prevent defection before it’s too late.In today’s world, an NPS score alone cannot provide the full picture of the customer experience. Natural language understanding is a critical component to customer feedback mechanisms like NPS. It enables organizations to utilize large amounts of feedback real-time to develop a complete understanding of customer’s needs.
