Net Promoter Score (NPS)
A single-question metric measuring customer loyalty: 'How likely are you to recommend this product to a friend?' Widely used in business but not a direct measure of user experience.
Definition: A single-question metric measuring customer loyalty: 'How likely are you to recommend this product to a friend?' Widely used in business but not a direct measure of user experience.
The Net Promoter Score (NPS), introduced by Frederick Reichheld in 2003, measures customer loyalty with a single question: "How likely are you to recommend this product/company to a friend or colleague?" on a scale of 0 to 10.
Calculating NPS
Respondents are grouped into three categories:
- Promoters (9-10): Loyal enthusiasts who will recommend
- Passives (7-8): Satisfied but unenthusiastic
- Detractors (0-6): Unhappy customers who may damage the brand
NPS = % Promoters − % Detractors
The score ranges from -100 to +100.
What NPS Measures (and Doesn't)
NPS measures overall customer loyalty and sentiment toward a brand or product. It is widely valued by business leaders because it is simple and correlates with growth.
However, NPS is not a UX metric. It does not diagnose specific usability issues and can be influenced by factors outside the product interface—price, brand perception, customer service, competitor behavior.
Best Uses
- Tracking trends within your organization over time
- Communicating with stakeholders in a language they understand
- Identifying overall sentiment at the customer relationship level
Always pair NPS with follow-up questions ("Why did you give that score?") to understand the reasons behind the number. The score is the "what"; the follow-up is the "why."
Related Terms
Customer Experience (CX)
The outermost layer of experience, encompassing every touchpoint a customer has with a company—from marketing and sales to product use and support. Broader than UX, which focuses on product interaction.
Survey
A Core Method of asking at scale using standardized questions. Enables data collection from larger samples but sacrifices the depth of interviews for breadth and standardization.
Mentions in the Knowledge Hub
This term is referenced in the following articles:
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