{
  "id": 7802012,
  "title": "NPS vs CSAT: Which Survey Metric Should You Actually Use?",
  "url": "https://urgent.news/2026/09/16/nps-vs-csat-which-survey-metric-should-you-actually-use",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-16T14:15:42.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/more10/nps-vs-csat-which-survey-metric-should-you-actually-use-3e07"
  },
  "original_language": "en",
  "account": "Many people mistakenly use Net Promoter Score (NPS) and Customer Satisfaction (CSAT) interchangeably, without realizing that these two metrics serve distinct purposes. NPS focuses on overall loyalty, while CSAT evaluates a single interaction.\n\nNPS is derived from a single question asking respondents how likely they are to recommend a company to a friend on a 0-10 scale. Promoters (9-10) score highly, while detractors (0-6) are less favorable. The NPS score is calculated by subtracting the percentage of detractors from promoters, resulting in a range between -100 and +100.\n\nOn the other hand, CSAT asks about a specific experience, such as a support chat or checkout process, using a 1-5 star rating. CSAT is deliberately narrow and provides insights into how well a particular moment went. It's typically reported as the percentage of respondents who gave the top rating.\n\nThe key takeaway is that NPS measures loyalty over time, making it useful for tracking changes in overall satisfaction and detecting potential churn. CSAT, in contrast, is ideal for assessing the effectiveness of a specific interaction and can provide quicker feedback, often within days rather than waiting for quarterly reports. Most teams that prioritize customer feedback run both metrics to gain a comprehensive understanding of their customers' experiences.",
  "summary": "Quick confession: for about a year I used NPS and CSAT interchangeably in my head. Same thing, I (wrongfully) thought. Ask people how happy they are, get a number, feel good or bad about it. Then I actually built the scoring logic for both into a survey product and realized they do genuinely different jobs, and mixing them up means acting on the wrong signal. Here's the short version. Perhaps…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}