Autonomous growth science
Autonomous growth science is the practice of running the full scientific loop over a customer base without a human in every step: observe, hypothesise, decide, launch, measure causally, and update. Humans set objectives, constraints and guardrails; the system supplies throughput and evidence.
Why it matters for ARPU
It reframes growth from campaign execution to continuous investigation, which is what a large B2C base with millions of heterogeneous customers actually needs to lift revenue per user.
Related terms
Growth agentA growth agent is an autonomous worker that runs one stage of the growth-science loop without being prompted: reading signals, writing hypotheses, designing experiments, launching them through connected systems or reading out causal results.HypothesisA hypothesis is a falsifiable statement linking an action to a revenue outcome for a defined population: if we do X for segment Y, metric Z moves by roughly N because of mechanism M.ExperimentAn experiment is a controlled release of a candidate action against a randomised holdout, sized in advance so the result can distinguish a real effect from noise.Decision layerA decision layer is the system that sits between the data warehouse and the execution tools and decides, per customer, what deserves to happen next.