Customer loyalty metric measuring willingness to recommend your product on a -100 to +100 scale.
% Promoters (9-10) - % Detractors (0-6)
Higher is better · Unit: score
How to Improve
Implement a voice‑of‑customer program that systematically collects and acts on feedback. Invest in product quality and reliability as the foundation of customer satisfaction. Build self‑service resources that empower customers to solve problems independently. Create customer advisory boards that make top customers feel heard and valued. Launch surprise‑and‑delight programs to turn satisfied customers into promoters.
Ehsan's Analysis
AI tool NPS is artificially high right now because of novelty bias — users rate tools highly when they are excited about AI capabilities in general, not the specific product. ChatGPT's initial NPS was reportedly 70+, but that reflected "AI is amazing" more than "ChatGPT specifically is amazing." As the novelty wears off and alternatives proliferate, NPS will normalize to product-specific quality. The meaningful AI NPS question is not "would you recommend this?" but "would you notice if this disappeared from your workflow tomorrow?" The first measures enthusiasm; the second measures dependency. Grammarly scores 80+ on both. ChatGPT scores 70+ on the first but only 30-40 on the second — most users would simply switch to Claude, Gemini, or Copilot. AI companies should track the "dependency NPS" and treat it as the real number. When dependency NPS exceeds 50, you have genuine product-market fit, not just hype-market fit.
EJ
Ehsan Jahandarpour
AI Growth Strategist & Fractional CMO
Forbes Top 20 Growth Hacker · TEDx Speaker · 716 Academic Citations · Ex-Microsoft · CMO at FirstWave (ASX:FCT) · Forbes Communications Council
Frequently Asked Questions
What is a good Net Promoter Score (NPS) for AI/ML companies at Seed stage?
The median Net Promoter Score (NPS) for AI/ML companies at the Seed stage is 37.2 points. Top‑quartile companies (75th percentile) significantly outperform this baseline. The most important factor is consistent improvement over time rather than hitting any single target number.
How does Net Promoter Score (NPS) differ by company stage in AI/ML?
Net Promoter Score (NPS) typically improves as AI/ML companies mature from seed through growth stage. Earlier‑stage companies should benchmark against stage‑appropriate peers rather than comparing themselves to mature companies.
How often should AI/ML companies measure Net Promoter Score (NPS)?
AI/ML companies at the Seed stage should track Net Promoter Score (NPS) quarterly through systematic surveys and continuous monitoring. Set up automated dashboards and alerts for significant deviations from your baseline.
What factors most impact Net Promoter Score (NPS) in the AI/ML sector?
In AI/ML, the primary factors impacting Net Promoter Score (NPS) include product‑market fit maturity, competitive landscape intensity, customer segmentation strategy, pricing optimization, and operational efficiency. Seed‑stage companies should focus on the one or two highest‑leverage factors rather than trying to optimize everything simultaneously.
How does Net Promoter Score (NPS) for AI/ML compare to cross‑industry benchmarks?
AI/ML Net Promoter Score (NPS) benchmarks can differ significantly from cross‑industry averages due to factors specific to the AI/ML vertical including customer acquisition dynamics, competitive intensity, and typical deal sizes. Always compare against industry‑specific benchmarks rather than broad averages for meaningful insights.