synthetic-demand

name

synthetic-demand

description

Synthetic Demand Validation (SDV) — predict purchase intent, price tolerance, and objections for ad creatives, offers, and landing copy by eliciting free-text reactions from a synthetic persona panel and mapping them to Likert distributions. Comparative ranking is the headline signal. Use for "will this convert", "which variant wins", "is $X the right price", "pre-test this creative/offer/copy before spend".

Synthetic Demand Validation (SDV)

SDV is the demand-side pre-flight for the GTM Command Center. Your existing neuro-testing (Tribe v2) predicts whether the brain will notice, feel, and remember a creative. SDV predicts the question neuro-testing cannot answer: "would this persona actually buy it, at this price — and which variant wins?"

It turns a project's audience into a pollable synthetic consumer panel and measures reaction to a concept (ad creative, offer, or landing copy), returning a forecast you can act on before a dollar is spent.

The governing law (state it once, obey it everywhere)

Rank, don't score. Never ask a synthetic respondent for a number.

Both halves are load-bearing and both are enforced in code (scripts/sdv-score.py), not just prose:

• Never ask for a number. Asking an LLM "rate your purchase intent 1–5" collapses to a safe "3/4" and recovers only ~26% of human reliability. Instead, elicit a brief free-text reaction in character, then map it to a Likert distribution in a separate step. The scorer rejects any reading whose pmf is near one-hot — the signature of a number that was elicited and softmaxed.
• Rank, don't score. The reliable result is a ranking (the paper's ρ≈90% is a ranking metric); absolute scores are anchor-dependent and only trustworthy once locally calibrated. So comparative duels are the headline, and the absolute demand_score can only ever make a verdict more conservative, never mint a DEPLOY.

Provenance (and its limits)

Built on, and deliberately extending past, Maier et al. 2025 (PyMC Labs × Colgate-Palmolive), "LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings." Their finding: direct number-asking recovers ~26% of human test–retest reliability; free-text → Likert mapping recovers ~85% (FLR) to ~90% (SSR).

Out-of-distribution caveat. Those figures were established on the paper's stimuli and constructs (personal-care surveys). GTM surfaces — ad creatives, offers, LATAM pricing — are out-of-distribution until locally confirmed at F3 calibration. The ~90% requires the full protocol (two-step elicitation, ensembled anchors, comparative ranking); it does not transfer to shortcuts or new domains for free. Cite the figure as "reported on the paper's domain," never as a guarantee for GTM.

The Core Loop

1. Detect capabilities → declare a fidelity tier (F0–F3). See rules/fidelity-tiers.md.
2. Assemble the panel — read .gtm/personas/ if present; consume an external srd/personas.yml if found; else generate a lightweight panel (note it as generated).
3. Assemble the stimulus — one or more variants of the concept, per surface. See rules/stimulus-design.md (Phase 3+).
4. Elicit — each persona gives a free-text reaction, mapped to a pmf (FLR by default; SSR when embeddings are detected, via scripts/ssr_embed.py). See rules/elicitation-methods.md.
5. Rank comparatively — best-worst / pairwise duels are the PRIMARY signal. See rules/comparative-scaling.md.
6. Score the battery — the measured construct subset → composite demand_score. See rules/construct-battery.md.
7. Sweep price (offers only, Phase 3) — Van Westendorp + Gabor-Granger. See rules/price-sensitivity.md.
8. Calibrate — at F3, fit synthetic→actual against real Meta/Stripe/PostHog conversion. See rules/calibration.md.
9. Mine objections — structured reasons-not-to-buy → B0/B1/B2 blockers with revenue at risk. See rules/objection-mining.md.
10. Decide + write artifacts — run scripts/sdv-score.py, write the forecast to .gtm/sdv/forecasts/.

Steps 4–6, 9, and 10 ship in v1.8.0 (Phases 0–1). Step 7 (price) ships in Phase 3; step 8 outcome-calibration deepens through Phases 6–7.

Surfaces (and what NOT to compare)

Surface	What it tests	Construct subset (see rules/construct-battery.md)
ad_creative	a rendered creative + hook	purchase_intent, appeal, comprehension, differentiation, share_intent
offer	a full offer stack + visible price	the full battery incl. willingness_to_pay via a price test
copy	a headline / landing block	purchase_intent, comprehension, believability, appeal

A demand_score of 70 on the 4-construct copy subset is not the same evidentiary thing as 70 on the 7-construct offer battery. The scorer raises if you try to rank across surfaces. feature exists in the construct table as a future seam but is not a wired surface.

Fidelity tiers (the forecast's honesty header)

Tier	Auto-detected trigger	What it may claim
F0 Cold-start	nothing detected	Ranking only; absolute is "directional"
F1 Persona-grounded	.gtm/personas/ (or srd/personas.yml)	ranking + relative; absolute still soft
F2 Data-anchored	customer language / analytics text	ranking + better-grounded absolute
F3 Outcome-calibrated	real conversion data (Meta CVR / Stripe / PostHog)	absolute go/no-go enabled (with the evidence floor in rules/calibration.md)
+SSR	embeddings provider/key	swaps FLR mapping for true SSR at any tier

F = Fidelity (renamed from the source's T0–T3 so it never collides with HVA's Read-Ladder rungs). Objection severity uses a separate B-axis (B0/B1/B2), so the two never clash. A forecast below F3 that presents a confident absolute verdict is invalid — lead with the ranking.

Confidence-interval width follows: CI_width ∝ tier_factor (F0 widest → F3 tightest) × (1 − familiarity) ÷ √(effective_samples).

The deterministic scorer

All decision math lives in scripts/sdv-score.py (stdlib-only, MIT, no Tribe v2 dependency), pinned by scripts/tests/test_sdv_score.py. The scorer never polls a model and never acts — it only decides. The demand-forecaster agent elicits reactions and acts on the decision. Verdicts use one vocabulary across SDV and the (Phase 2) unified gate: DEPLOY / TEST / ITERATE / SKIP.

Quality standards (every forecast must pass)

1. Each construct distribution is a valid pmf (length 5, sums to ~1.0) — the scorer enforces it.
2. Each reading records its elicitation provenance (elicitation_method, react_call_saw_scale: false, map_call_separate: true); number-elicited (one-hot) pmfs are rejected.
3. Comparative ranking is reported with a rank_stability flag computed from ≥5 resamples; an unstable top is a tie, never a winner.
4. demand_score is reconstructable from construct means × the recorded weights.
5. No absolute go/no-go below F3; the absolute field is labeled demand_score_relative until then.
6. Objections are structured (segment + severity + frequency), not prose; B0 items carry revenue at risk.
7. Anti-positivity defenses (skeptic stance, budget salience, do-nothing option, comparative mode) are recorded; missing ones derate confidence.
8. The forecast states its own caveats and its fidelity tier — never present a directional read as a verdict.

Rules reference

• rules/elicitation-methods.md — FLR (default, prompt-only) · SSR (optional, embeddings) · the 3-step protocol · anti-positivity stack
• rules/comparative-scaling.md — best-worst & pairwise duels · aggregation · rank stability (the accuracy spine)
• rules/anchor-sets.md — 5-statement anchors · auto-generation · ensembling · data-derivation
• rules/construct-battery.md — the 8 constructs · per-surface weights · composite Demand Score
• rules/fidelity-tiers.md — capability detection · F0–F3 · familiarity · CI sizing
• rules/calibration.md — intent→behavior discount · the F3 evidence floor · synthetic→actual map
• rules/objection-mining.md — structured objections/drivers · B0/B1/B2 · revenue at risk
• rules/srd-interop.md — optional SRD bridge · read-only detection · inverted coupling (GTM never depends on SRD)

Relationship to neuro-testing and HVA

SDV is the demand half of a two-stage pre-flight. The unified gate (/gtm-validate, Phase 2) composes the outputs of neuro-score.py (attention/memory — CC-BY-NC) and sdv-score.py (intent/price — MIT) without ever importing neuro code, so SDV runs fully standalone when Tribe v2 is absent. Where HVA's CLEAR governs spend after launch on real money, SDV governs which concept earns the launch on synthetic demand. See skills/neuro-testing/ and skills/high-velocity-advertising/.