What is Net Promoter Score (NPS)?

Net Promoter Score (NPS), explained

NPS was introduced by Fred Reichheld of Bain & Company in a 2003 Harvard Business Review article, “The One Number You Need to Grow”, and is now used widely as a headline loyalty metric. Net Promoter and NPS are registered trademarks of Bain & Company, NICE Systems and Fred Reichheld.

The calculation ignores passives except in the total. If 50% of respondents are promoters, 30% passives and 20% detractors, NPS is 50 − 20 = 30. It is expressed as a number, not a percentage.

NPS reflects the whole relationship: product, price, delivery and brand, as well as support. That makes it a poor fit for judging individual conversations or agents. A customer can have an excellent support chat and still give a low score because the product broke.

Support teams get more from NPS by reading detractor comments that mention service, and by checking whether customers who contacted support recently score differently from those who did not.

How to measure it

Result ranges from −100 to +100. Passives (7–8) count in the total number of respondents but not in either percentage.

Common mistake

How SuperBot relates

SuperBot's built-in analytics cover resolution rate, CSAT, sentiment and knowledge gaps; NPS is not one of them, so run it in your survey tool and compare it with support trends.

• SuperBot features

Frequently asked questions

Related terms

• Customer satisfaction score (CSAT)Customer satisfaction score (CSAT) measures how satisfied customers are with a specific interaction, usually from a one-question survey such as “How satisfied were you with this conversation?” on a 1–5 scale. CSAT is the percentage of respondents who choose the top ratings, typically 4 or 5, out of everyone who responded.
• Customer effort score (CES)Customer effort score (CES) measures how easy it was for a customer to get an issue resolved or complete a task, usually by asking them to rate a statement such as “The company made it easy to handle my issue” on a numbered agreement scale. Lower effort is associated with stronger loyalty, which is why support teams track it.
• Sentiment analysisSentiment analysis is the automated detection of the emotional tone in text, typically classified as positive, neutral or negative. In customer support, it is applied to messages and conversations to spot frustrated customers, track how people feel about topics over time, and flag conversations for a human to review, without waiting for a survey response.

Sources

1. Bain & Company, “Measuring Your Net Promoter Score”
2. Frederick F. Reichheld, “The One Number You Need to Grow”, Harvard Business Review, December 2003