marketing-measure-learn — score, then steer

Measure and learn always run as a pair: first get the numbers (a deterministic API + math step), then reflect on them (agent judgement). Do them in order.

Step 1 — MEASURE (deterministic)

studio marketing measure --channel <name> --comments-n 60

Fetches views/likes/comments (+ retention & subs gained if the analytics scope is granted), computes a virality composite (log-damped view-velocity + retention + engagement + sub-conversion), ranks every video into a percentile within this channel's own portfolio, and tags each win (≥P75) / loss (≤P25) / neutral / cold-start. Writes back to the journal and drops 08_stats.json + 08_comments.json into each run dir.

Watch for:

• Wait for watch time — measuring same-day gives noise. 48–72h+ minimum.
• Cold-start (<10 deployed): percentiles are meaningless; every outcome is cold-start.
• Retention/subs need one extra OAuth scope; fetched best-effort, the loop runs fine

without. See ../marketing-guru/references/analytics.md.

• Scoring weights (0.5 velocity / 0.2 retention / 0.2 engagement / 0.1 subs) are slated to

be re-tuned to a retention-first order per research finding F-SI9 — see ../marketing-guru/references/scoring.md and docs/20-research/self-improving-loop.md.

Step 1.5 — SNAPSHOT + SLICE (deterministic analysis)

Before changing strategy, collect age-bucket snapshots and ask the CLI for the hidden-relation pack. This is what lets the agent compare effects/cost/theme/music/sfx/animation at consistent ages instead of mixing a 1-day video with a 30-day video.

studio marketing due-snapshots --channel <name>
studio marketing snapshots     --channel <name> --buckets 1,3,7,14,30
studio marketing insights      --channel <name> --json

Use focused slices/comparisons when a pattern looks interesting:

studio marketing slice --channel <name> --bucket 7d \
  --group-by theme,effects,animators,music_provider,sfx_provider --metric virality

studio marketing compare --channel <name> effects=glitch --bucket 14d --metric virality
studio marketing compare --channel <name> animators=parallax --bucket 7d --metric retention
studio marketing compare --channel <name> music_provider=synth --bucket 3d --metric virality_per_dollar

Interpret these as associations, not causation. Always check n, best/worst examples, and confounders such as topic quality, publish timing, spend, and whether the video is still too young.

Step 2 — LEARN (agent-driven reflection)

This is where the loop self-improves. YOU reflect (assumption testing is judgement, not a formula); the CLI just persists what you conclude.

1. Read the measured portfolio (best→worst) + relevant episodes:

   studio marketing journal --channel <name>
   studio marketing recall "<theme or direction under review>" --channel <name>
   studio marketing insights --channel <name> --json

1. Reflect — for each measured bet compare its pre-stated `assumption` against the

measured virality/percentile/outcome, age-bucket snapshots, slice results, and top audience comments. Was it held or refuted? Then across the portfolio extract:

• winning_patterns — traits of the ≥P75 bets,
• losing_patterns — traits of the ≤P25 bets,
• production correlations — effects/animation/music/sfx/cost formats that look promising or weak,
• current_direction — a one-paragraph thesis for what to make next,
• next_seeds — 3–5 concrete idea seeds.

Be honest when an assumption was refuted — that's the signal that improves the next bet.

1. Persist (no LLM, just I/O):

   studio marketing strategy --channel <name> \
     --direction "<thesis paragraph>" \
     --winning "trait a;trait b" --losing "trait c" \
     --seeds "seed 1;seed 2;seed 3" \
     --note j0007=cosmic-scale shock hooks beat soft intros

--winning/--losing/--seeds are ;-separated; --note ENTRY_ID=text files a per-bet learning. Repeat --note for several bets.

The strategy + seeds you write here are exactly what marketing-ideate reads next — the cycle closes.

Scripted fallback (non-agent)

For a quick non-agent reflection: studio marketing learn --provider <llm> runs the built-in LLM reflection and writes the strategy. (measure is already a deterministic script — no fallback needed.)

Memory model (journal / strategy / recall, who writes what): docs/50-marketing/memory.md.