Multi-Surface Behavioral Retention Telemetry — Cross-Surface Return Mechanics

Why we want this done

This project derives from three converging axioms: brand-kernel A-4 (utility on installable surfaces beats brand separation; users make install-or-neither decisions based on aggregate utility — therefore the relevant signal is whether they come back, not whether they install); productai-kernel A-4 (adjudication ends searches; the user hires Product.ai to judge, so the right retention signal is "did they return for a second judgment" not "time-on-page"); and revenue-kernel A-7 (transaction-based economics enable the confident no — better personalization produces faster closure produces higher conversion produces more revenue, and the closure signal is behavioral). The brand-and-growth State of Practice §4 makes this concrete: classical engagement metrics (time-on-page, share rate, bounce rate) are now structural noise because AI-generated content production has shifted the baseline; what survives as signal is behavioral trust (depth-of-evidence-engagement, kill-shot-acceptance, P-Axiom elicitation completion). The PERSONALIZE outcome's flywheel (P-Axiom corpus depth → personalization → conversion → P-Axiom depth) only compounds if return-visit behavior is measurable.

Scope

Surfaces: productai-web (primary), productai-extension (Research Mode initiative — active, Bri-owned), external (the ChatGPT App side; the cross-surface identity bridge that respects the Apple/OpenAI privacy boundaries). Repos: productai-web, productai-extension. Coordinates with: Bri Stanback (MULTI-SURFACE measurer + post-Jonah Extension owner), Phil Larsen (Mobile App next-zone — the project surfaces whether to prioritize it), the future Brand Director (downstream beneficiary of behavioral-trust telemetry). The operator owns: the identity bridge (privacy-respecting), the behavioral-signal taxonomy, the cross-surface event substrate, the dashboard. Does NOT own: surface UI design (each surface team), brand voice (out of scope per recruiting-driver §5.1).

What success looks like

A working solution looks like this: a privacy-respecting cross-surface identity mechanism (no third-party cookies, no PII leakage to merchants, fully consistent with Revenue kernel A-7 closed-loop architecture) joins user sessions across web, ChatGPT App, and Extension. Each session emits the behavioral-trust signal taxonomy: did the user scroll into the evidence trace, did they accept or reject the kill-shot, did they complete a P-Axiom elicitation prompt. The dashboard the operator ships shows return-rate per surface, cross-surface continuity, and the behavioral-trust signals over time. Bri Stanback (MULTI-SURFACE measurer) and the team can read at trial end: "users who enter via ChatGPT App return at X%; users who enter via Extension return at Y%; cross-surface continuity is Z%; behavioral-trust signals show acceptance is highest on web verdicts and lowest on ChatGPT App." That diagnostic determines the next surface investment. The Test: a stranger to the team, 60 days after the framework ships, can read the dashboard and answer "is multi-surface compounding or fragmenting retention" with statistical confidence.

References

Constraints

• 2 weeks default.
• Claude Code or Co-work primary substrate.
• No aios-methods access (trial contractor scope).
• Identity bridge MUST respect Revenue kernel A-7 closed-loop architecture. No third-party data sharing. No mechanism for export.
• Behavioral signals must trace to kernel physics, not generic engagement vocabulary.
• Coordinate with Bri Stanback on Extension scope (post-Jonah-departure ownership transition).
• Pre-launch sample-size constraint must be explicitly handled in the protocol (low-power readings bracketed; not silently averaged).

```

---

##