Lifesight Channel Deep Dive

Lifesight Channel Deep-Dive

Everything that matters about one channel: where it sits on its response curve, what the next dollar returns, and what a change would do. The whole game is marginal, not average — a channel with a great average ROAS can be a terrible place to add the next dollar.

Prerequisites (router handles): workspace calibrated, profile loaded. Operate under lifesight-core; present under lifesight-rendering. Load both.

Analyst guardrail. This spoke serves the heaviest-data persona, who is most exposed to the query_ad_data flood. Apply lifesight-core Rule 3 strictly: prefer ask_mia (it summarizes); if you must pull rows, scope hard (one channel, bounded window, aggregated). Never widen a deep-dive into a full-table dump.

Flow

1. Confirm the one channel (and the metric/question — saturation? marginal ROI? a specific what-if?). One channel at a time; if they name several, do the most important first and offer the rest.
2. One heavy call. Pull the channel's curve / marginal economics / what-if via ask_mia. Walk its gates if it asks (lifesight-core Rule 4).
3. Interpret against the curve, not in isolation.

Judgment checks (mandatory)

• Marginal, not average. Lead with the return on the next dollar and where the channel sits vs its saturation knee — not the blended ROAS.
• Platform vs causal. State whether the efficiency figure is platform-reported or causal iROAS. A "scale it" call on a sub-1.0 platform number needs the causal read.
• Saturation ≠ headroom for a weak channel. "Room in the curve" only matters if the channel is efficient there. A channel below the iroas_floor is not a growth opportunity just because it isn't saturated.
• What-ifs ride the curve. A "+20% spend" answer must reflect diminishing returns, not linear extrapolation. Respect profile guardrails (floor, caps, locked channels).

Output shape

1. Where it sits — current spend, saturation %, and the marginal return on the next dollar, in plain language.
2. The read — efficient with headroom / efficient but saturating / inefficient — and what that means.
3. The move — scale, hold, or cut, with the specific what-if if asked ("+20% → ~X incremental revenue at Y marginal ROAS").
4. Validation option — if it's a big bet, suggest a geo-lift test to confirm causally.

Clean channel name, causal language, signed/formatted numbers.

Next steps to offer

"Optimize across all channels" (→ budget-optimization) · "Compare to [another channel]" · "Run a geo-lift test to validate" · "Check what moved here recently" (→ anomaly-watch).

Red flags — STOP

• Leading with average ROAS instead of marginal return
• Calling a sub-floor channel "headroom / scale it"
• A what-if that extrapolates linearly instead of along the response curve
• Widening the deep-dive into a multi-channel raw data pull → scope it (Rule 3)
• Quoting a platform number as causal without naming the basis