Predictive LTV (pLTV)

Predictive LTV (pLTV) is a data-driven estimate of the total revenue a customer will generate over their entire relationship with a brand, calculated before that full lifetime has occurred. Unlike historical LTV which looks backward at completed customer journeys, pLTV uses statistical models and machine learning to project future value based on early signals.

Modern pLTV systems use a tiered approach based on available data. At the simplest level, order history alone (purchase frequency, recency, average order value) can produce a baseline estimate. Adding subscription data improves accuracy by incorporating renewal patterns and churn signals. First-party behavioral data (site visits, email engagement, session frequency) provides further refinement. The most advanced tier uses ML-trained churn models for the highest confidence predictions.

pLTV is critical for e-commerce brands because it enables forward-looking decisions: setting acquisition bids based on expected customer value rather than first-order revenue, prioritizing retention efforts on high-potential customers, and projecting revenue from young cohorts before they mature.