Calculating Net Promoter Score (NPS)

Respondents score this on a scale of 0–10. Their responses are then grouped into three buckets: Promoters, Passives, and Detractors, and the NPS formula converts that into a single score that reflects overall customer sentiment.

NPS is popular in SaaS because it compresses complex customer emotions into a clear signal you can track over time.

Why NPS matters in SaaS

Retention is the backbone of SaaS growth. NPS offers a quick pulse check that helps you understand:

• How satisfied customers are with your product
• How likely they are to renew or expand
• Whether your onboarding and support are working
• What friction points are hurting user experience
• How sentiment shifts after product or pricing changes

It is, by no means, a perfect metric, but it’s a practical indicator of customer health that correlates strongly with churn and expansion behavior.

How to calculate Net Promoter Score

The formula is straightforward:

NPS = (% of Promoters) – (% of Detractors)

Where:

• Promoters (9–10): Loyal advocates who are likely to recommend you
• Passives (7–8): Satisfied but not enthusiastic; not included in the formula
• Detractors (0–6): Unhappy customers who may churn or discourage others

Example: If 60% of respondents are Promoters and 20% are Detractors, your NPS is 40.

The score ranges from –100 to +100. Anything above 0 is decent. Above 30 is strong. Above 50 is exceptional.

How SaaS teams usually run NPS

Timing matters

Most companies run NPS:

• 30–60 days after onboarding
• Every 6 or 12 months
• After major releases or support touchpoints

Segmentation is crucial

NPS becomes more meaningful when broken down by:

• Plan tier (SMB vs. enterprise)
• Role (admin vs. end user)
• Region
• Tenure
• Product module

The qualitative question matters more.

The follow-up question - “What is the primary reason for your score?” - is where the real insight lives. This is how you find friction, broken workflows, confusing features, or value gaps.

Common mistakes when calculating NPS

• Treating NPS as a vanity metric rather than a feedback loop
• Collecting NPS too early, before the value is realized
• Ignoring Passives even though they’re closest to churn
• Not closing the loop with customers who leave comments
• Mixing transactional NPS with relationship NPS
• Running NPS too frequently and fatiguing users

The biggest mistake one can make is blindly celebrating Promoters while ignoring Detractors.

How AI enhances NPS analysis

AI gives NPS far more depth than a simple calculation:

• Sentiment analysis converts open-ended comments into themes
• AI models detect patterns predicting churn or expansion
• Automated alerts surface low scores from high-value accounts
• Topic clustering shows which issues impact NPS the most
• Personalized follow-up suggestions help CSMs close the loop faster

AI can transform NPS from a static survey into a customer intelligence engine.

AI prompt to analyze NPS

What to provide the AI beforehand

• Raw NPS scores (0–10)
• Segments (plan tier, role, region)
• Qualitative comments
• Account value (MRR/ARR per customer)
• Customer tenure data
• Product usage or feature adoption metrics
• Historical NPS trends

Use this with a generative AI tool to turn NPS data into actionable insight: