Next Best Action Orchestration

Create an AI-derived attribute

Build a segment

Generate email content variants per recommended_action: (a) educate variant — short value tip matched to the user

Generate in-app message variants for the surface-feature and surface-recommendations branches. Surface-feature: contextual tooltip pointing to the feature, with a 1-line value prop and

Configure a recommendation surface

Build an adaptive journey wired to the NBA segment. At each gate (signup+7d, +14d, +30d, +60d, ongoing weekly), the journey reads next_best_action attribute and routes the user down the matching branch: educate → email variant a; nurture → email variant b; offer → email variant c with discount; surface-feature → in-app tooltip + email variant d; surface-recommendations → activate recommendation surface + email digest with recs; handoff-to-agent → trigger agent conversation; wait → skip touch, recompute next gate. Each branch

Compose an NBA orchestration dashboard: distribution of recommended_action across users (which actions does the model favor — sanity check on model balance), per-branch engagement rates (which actions actually convert), confidence-vs-conversion correlation (does higher-confidence routing actually predict higher conversion — model-quality signal), uplift vs control (a 5-10% holdout that gets fixed cadence — the proof-of-value chart), and per-segment NBA quality (model may serve some segments better than others — informs retraining priorities).