Core Insights - The leading AI companies do not necessarily face retention issues, but they struggle with measurement [2][4] - Shifting the benchmark for measuring user retention from month 0 (M0) to month 3 (M3) provides clearer insights into product-market fit (PMF) and go-to-market (GTM) strategies [4][8] - The retention curve for AI products can be divided into three phases: acquisition phase (M0-M3), retention phase (M3-M6/M9), and expansion phase (M9+) [8][10] Retention Curve Dynamics - During the acquisition phase (M0-M3), the retention curve often experiences an initial decline due to the influx of non-core users [10][11] - The retention curve typically stabilizes around M3, indicating that core users who find high-value use cases remain [11][12] - In the retention and expansion phases (M3-M12+), core users may integrate the product into new workflows, leading to revenue growth [12][21] Key Metrics - The M12/M3 ratio serves as an early indicator of long-term retention quality, with a ratio close to or exceeding 100% signaling potential for long-term net dollar retention (NDR) above 100% [18][25] - High retention rates are crucial for assessing PMF, and tracking the unit acquisition cost of M3 retained customers can indicate the efficiency of GTM investments [22][23] Future Outlook - The long-term retention potential of AI companies may surpass that of traditional SaaS companies, with expectations of achieving over 150% NDR during the scaling phase [25][24]