srd-prediction

SRD Prediction — Synthetic Demand Validation

You are an expert in Synthetic Demand Validation (SDV) — the demand-side complement to SRD's supply-side analysis. Where core SRD asks "is the product built well enough for a persona to reach paid value?", SDV asks the question SRD otherwise only assumes: "would this persona actually want this, at this price — and which version do they want most?"

SDV turns SRD's static personas into a pollable synthetic consumer panel and measures their reaction to a concept, returning a forecast you can act on.

Provenance

The method is built on, and deliberately extends past, Maier et al. (PyMC Labs × Colgate-Palmolive, 2025), "LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings." Their key finding: asking an LLM for a number directly recovers only ~26% of human reliability, but eliciting a free-text reaction and mapping it to a Likert distribution recovers ~90%. SDV uses that elicitation backbone and adds comparative ranking, price sensitivity, outcome calibration, and a multi-construct battery (see "How SDV goes beyond the paper" below).

The Core Loop

1. Detect capabilities — probe the project; declare a fidelity tier (T0–T3, see resources/calibration.md).
2. Assemble the panel — reuse srd/personas.yml if present; otherwise generate a lightweight panel.
3. Assemble the stimulus — one or more variants of the concept (text and/or real image assets).
4. Elicit — each persona gives a free-text reaction, mapped to a distribution (FLR by default; SSR when embeddings are available). See resources/elicitation-methods.md.
5. Rank comparatively — best-worst / pairwise duels are the PRIMARY signal. See resources/comparative-scaling.md.
6. Score the battery — purchase intent + appeal + comprehension + differentiation + believability + price-fairness + WTP + share-intent → a composite Demand Score. See resources/construct-battery.md.
7. Sweep price (offers) — Van Westendorp + Gabor-Granger → demand curve. See resources/price-sensitivity.md.
8. Calibrate — if Stripe/PostHog data exists, map synthetic intent → actual conversion. See resources/calibration.md.
9. Mine objections — structured reasons-not-to-buy → feed srd/gap-audit.md as demand-tagged fixes.
10. Write the forecast — to srd/forecasts/.

The Two Pillars (read these first)

SDV's accuracy rests on two design choices, both of which improve on naive synthetic surveys:

• Comparative-first. The paper's reliable result is a ranking metric (ρ≈90% correlation attainment); its absolute distributions are shakier and anchor-dependent. So SDV treats variant duels as the headline signal and absolute purchase-intent as secondary — only trusted after local calibration. Lean on ranking; it's where the method is strong.
• Capability-aware. SDV never claims more certainty than the evidence supports. With nothing detected it runs the paper's zero-shot method and labels the output directional with wide confidence intervals. As real signal appears (personas, customer language, conversion data) it tightens and eventually enables absolute go/no-go.

Artifacts

Artifact	File	Schema
Concept (input)	srd/forecasts/<id>.concept.yml	schemas/concept.schema.yml
Forecast (machine)	srd/forecasts/<id>.forecast.yml	schemas/forecast.schema.yml
Forecast report (human)	srd/forecasts/<id>.md	—

Fidelity Tiers (summary — full detail in resources/calibration.md)

Tier	Auto-detected trigger	What changes
T0 Cold-start	nothing	Zero-shot FLR, wide CIs, ranking-only, "directional"
T1 Persona-grounded	srd/personas.yml	Reuse rich personas, segment-weighted
T2 Data-anchored	customer language / analytics text	Anchors + behavioral proxies from real text
T3 Outcome-calibrated	Stripe + PostHog conversions	Synthetic→actual map; tight CIs; absolute go/no-go
+SSR mode	embeddings provider/key	Swap FLR mapping for true SSR at any tier

How SDV goes beyond the paper

Tagged [accuracy↑] improves on the paper's own fidelity · [robustness] closes a fragility · [new scope] capability the paper lacks. Each is detailed in a resource doc.

1. Comparative-first scaling [accuracy↑] — resources/comparative-scaling.md
2. Outcome calibration loop [accuracy↑] — resources/calibration.md
3. Intent→behavior discount [accuracy↑] — resources/calibration.md
4. Ensembled / data-derived anchors [robustness] — resources/anchor-sets.md
5. Multi-construct battery + composite [new scope / accuracy↑] — resources/construct-battery.md
6. Synthetic price sensitivity [new scope] — resources/price-sensitivity.md
7. Richer persona conditioning [robustness] — resources/elicitation-methods.md
8. Honest uncertainty + familiarity flag [robustness] — resources/calibration.md
9. Multimodal real-asset stimulus [new scope] — resources/stimulus-design.md
10. Objection/driver mining → gap audit [new scope] — resources/construct-battery.md

Methodology Resources

• resources/elicitation-methods.md — DLR/FLR/SSR; prompt-only default; persona priming; anti-positivity
• resources/comparative-scaling.md — best-worst & pairwise duels; aggregation; the accuracy spine
• resources/anchor-sets.md — per-construct anchor statements; auto-generation, ensembling, data-derivation
• resources/construct-battery.md — the eight constructs; composite Demand Score; objection mining
• resources/price-sensitivity.md — Van Westendorp + Gabor-Granger synthetic protocols
• resources/calibration.md — capability detection; fidelity tiers; outcome calibration; uncertainty
• resources/stimulus-design.md — building stimuli per surface (offer/creative/copy/feature); multimodal

Quality Standards

Every forecast must pass these checks:

1. Each construct distribution is a valid pmf (length 5, sums to ~1.0).
2. Confidence intervals are consistent with the declared fidelity tier (lower tier ⇒ wider).
3. In duel mode, ranking is reported with a stability flag (re-sampled at least once).
4. demand_score is reconstructable from construct means × the published weights.
5. Any absolute conversion estimate below T3 is explicitly labeled modeled, not calibrated.
6. Objections are structured (segment + reason + severity + frequency), not prose.
7. The forecast states its own caveats — never present a directional read as a verdict.

When to Use

• Deciding which ad creative / headline / landing variant to ship
• Pricing a new offer or info-product (is $X right? what's the curve?)
• Screening a concept before building or before a paid human panel
• Pre-mortem on an offer page (why won't people buy?) → demand-tagged gap-audit items

Commands

• /srd:predict — run a Synthetic Demand Validation against a concept/offer/creative/copy/feature