Harnessing the Power of AI for Better Insights for Net Promoter Scores

Harnessing the Power of AI for Better Insights for Net Promoter Scores

With enormous amounts of customer data available online and a growing number of tools to analyze it, today's companies have become more focused on the customer experience than ever. One of the most important metrics they use to measure customer satisfaction is the Net Promoter Score (NPS), a simple survey that determines how likely customers are to recommend a brand's products or services.


According to Satmetrix, a company's NPS score determines 20 percent to 60 percent of its organic growth. NPS' value goes beyond just this organic growth when you can combine it with artificial intelligence-driven analytics. AI can help reveal the reasons customers like or dislike your products, giving you insights for how to improve customer satisfaction—and consequently, raise your NPS score.

The Power of NPS

NPS surveys typically ask customers how likely they are to recommend a company's product or service to someone else, on a scale of 1 to 10. Customers are then grouped into three categories: promoters, who will refer others; passives, who are satisfied but vulnerable to competitors; and detractors, who can damage a brand with negative comments and reviews. There's also an NPS survey for employees — eNPS — that measures how likely they are to recommend a company as a place to work.


Companies like using NPS because surveys are easy to conduct and response rates are high. After any customer interaction, nearly 68 percent of organizations send surveys including NPS questions. Among the largest companies, 75 percent use the metric, according to a report from Gartner.


NPS is important to a wide range of industries, including retailing, banking, transportation, hotels, auto insurance, and tech. Satmetrix publishes an annual Net Promoter Benchmarks survey that shows leading scores across industries, giving companies an idea of how they compare.


NPS scores correlate well to other measures of success. For example, Bain and Company found that in the retail industry, promoters spend 3.5 times more than detractors, while Airbnb found that higher scores correspond to more referrals and re-bookings.


The way companies use NPS to gauge organic growth is through not only understanding your promoters but knowing how many are willing to refer your company to others. These referred customers will lower the cost of growth, since companies don't have to spend marketing dollars trying to attract them.


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Businesses also reach out to high-spending promoters, encouraging them to make recommendations or sending special offers. Some companies even contact detractors to discuss what went wrong. One company, for example, developed an app to deliver answers to NPS questions in real time, then reached out to unsatisfied customers within an hour. Even if complaints couldn't be resolved right away, customers appreciated the attention and swift response, and the company lowered its churn rate.

AI: The NPS Game Changer

Despite its importance, NPS has two major flaws: It doesn't tell companies why customers are willing — or not — to recommend them and there is a selection bias. Reaching out to individuals to find out why they gave high or low scores is time-consuming and expensive.


Some companies include an open-ended question in surveys asking customers to explain their scores. But most organizations don't have the time or resources to read all the responses. Some just glance at them, while others only read those from the most and least satisfied customers, neglecting a large portion of their customer base.


A subset of AI called natural language processing (NLP) allows algorithms to understand human language to accurately categorize comments as positive, negative, or neutral. This capability within NLP is known as sentiment analysis and has the capability to provide insight on the actions that cause a customer to become dissatisfied.


AI works to analyze customer sentiment and customer feedback at a scale, precision, and speed humans could never achieve, providing companies with valuable real time insights they can't obtain from analyzing NPS metrics alone, a Gartner report notes.


Qualitative customer data is immensely valuable. While a large firm uses NPS surveys, they preferred qualitative feedback because it tells them which features customers like or dislike. However, like most, the company had no way of quantifying the open-ended responses, until they implemented an AI platform that automated analysis of customer comments. The company learned what customers were complaining about and went to work fixing areas of concern. As a result, the company's NPS score increased substantially.


These NLP tools can analyze other sources of NPS beyond just surveys. By looking at social media feeds, online reviews, forums, etc. they can provide the same level of insight on why your products or services have satisfied or dissatisfied your customers as an NPS survey could.


Not surprisingly, AI, and specifically NLP, are becoming important enterprise marketing tools. Formerly the province of trained data scientists, AI is becoming more automated and easy to use for business users with no expertise in data science. The market for text analytics is expected to have a compound annual growth rate of over 20 percent from 2019 to 2025, according to ResearchAndMarkets. And by 2021, over 50 percent of office workers will be using NLP to analyze text or voice recordings, Gartner predicts.


Armed with more insight about your customers, you can identify and fix problems more quickly, and react in real time. That will lead to improved customer experiences which will help your business thrive, and will, as a result, keep your NPS scores high.


To learn more about how an automated analytics software, like Ki, can help your NPS score, request a demo and see how your data can be turned into action.



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