Phos Labs Behavioral Science API

Why aren't users converting? Send your funnel or journey data and get a behavioral root-cause analysis with friction points ranked by impact, plus prioritized fixes with expected lift percentages.

What kind of user is this? Send behavioral signals (clicks, time spent, comparisons, purchases) and get a persona classification: Maximizer, Satisficer, Loss-Averse Protector, Status Seeker, Value Optimizer, or Mission-Driven. Includes recommended approach and what to avoid.

What does this user actually want? Get adaptive trade-off questions (conjoint analysis) that reveal preference weights across attributes like price, quality, speed, brand. Returns questions to ask plus scoring methodology.

How should I talk to this user? Send a message draft and get it rewritten using behavioral science — framing, social proof, loss aversion, identity framing. Returns optimized copy with principles cited and expected impact.

Is this nudge or recommendation ethical? Detects dark patterns, manipulation, and autonomy violations. Returns an ethics assessment with specific issues and fixes. Uses Thaler & Sunstein's nudge-vs-manipulation distinction.

Help me change this behavior. Full intervention design: COM-B diagnosis (why aren't they doing it?), EAST-scored nudge design, implementation plan, commitment mechanisms, and success metrics. Evidence-based with effect sizes.

Is this user about to leave? Send behavioral signals and get a churn risk score with behavioral diagnosis (why they're disengaging) plus ranked retention interventions. Uses loss aversion, commitment devices, and re-engagement strategies.

What price or offer should I present? Get a behaviorally-optimized pricing strategy using anchoring, decoy effect, loss framing, scarcity, and price partitioning. Returns tier structure, presentation recommendations, and expected impact.

Which interventions should I do first? Send a list of candidate interventions and get them ICE-scored with behavioral bias adjustments (planning fallacy, overconfidence, status quo bias). Returns a ranked list categorized as Quick Win, Big Bet, Maybe, or Time Sink.