growth-engine

Automating A/B testing and multivariate experiments for marketing growth requires orchestrating hypothesis-driven tests, logging data points, and scoring results to promote winners to a living playbook. You define hypotheses, variables, and variants, then log data points to begin automatic statistical scoring.

Growth experimentation analysis uses bootstrap confidence intervals and Mann-Whitney U tests to evaluate results. These statistics, powered by numpy and scipy, compute experiment scores to determine winning variants and promote them to a living playbook for evidence-based decision making.

Batch mode growth experiments support up to 10 variants with automatic playbook updates. You define multiple variants for a single hypothesis and variable, log data points across variants, and the system scores each variant and automatically promotes winners to the living playbook.

Weekly scorecards and pacing alerts are generated automatically across marketing channels by logging experiment data points. The system creates cross-channel summaries, monitors pacing against targets, and produces next-step suggestions to guide evidence-based growth decisions.

Numpy and scipy are required dependencies to run statistical analysis on growth experiments. These libraries provide the computational foundation for bootstrap confidence intervals and Mann-Whitney U tests used to score experiments and evaluate statistical significance.

A living playbook for growth experimentation is an automatically updated record of winning test variants. When experiments score successfully through statistical analysis, winners are auto-promoted to the playbook, creating an evolving knowledge base of evidence-based marketing strategies.