| name | srd-prediction |
| description | Synthetic Demand Validation (SDV) engine for SRD. Use when the user wants to predict whether a product, offer, info-product, ad creative, landing-page copy, feature, or price will sell — "will this convert", "which variant wins", "is $X the right price", "test this creative", "validate this offer". Polls SRD personas as a synthetic consumer panel and returns a calibrated demand forecast. Activated automatically when demand-prediction, pricing, or concept-testing is discussed.
|
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
- Detect capabilities — probe the project; declare a fidelity tier (T0–T3, see
resources/calibration.md).
- Assemble the panel — reuse
srd/personas.yml if present; otherwise generate a lightweight panel.
- Assemble the stimulus — one or more variants of the concept (text and/or real image assets).
- 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.
- Rank comparatively — best-worst / pairwise duels are the PRIMARY signal. See
resources/comparative-scaling.md.
- Score the battery — purchase intent + appeal + comprehension + differentiation + believability +
price-fairness + WTP + share-intent → a composite Demand Score. See
resources/construct-battery.md.
- Sweep price (offers) — Van Westendorp + Gabor-Granger → demand curve. See
resources/price-sensitivity.md.
- Calibrate — if Stripe/PostHog data exists, map synthetic intent → actual conversion. See
resources/calibration.md.
- Mine objections — structured reasons-not-to-buy → feed
srd/gap-audit.md as demand-tagged fixes.
- 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.
- Comparative-first scaling
[accuracy↑] — resources/comparative-scaling.md
- Outcome calibration loop
[accuracy↑] — resources/calibration.md
- Intent→behavior discount
[accuracy↑] — resources/calibration.md
- Ensembled / data-derived anchors
[robustness] — resources/anchor-sets.md
- Multi-construct battery + composite
[new scope / accuracy↑] — resources/construct-battery.md
- Synthetic price sensitivity
[new scope] — resources/price-sensitivity.md
- Richer persona conditioning
[robustness] — resources/elicitation-methods.md
- Honest uncertainty + familiarity flag
[robustness] — resources/calibration.md
- Multimodal real-asset stimulus
[new scope] — resources/stimulus-design.md
- 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-positivityresources/comparative-scaling.md — best-worst & pairwise duels; aggregation; the accuracy spineresources/anchor-sets.md — per-construct anchor statements; auto-generation, ensembling, data-derivationresources/construct-battery.md — the eight constructs; composite Demand Score; objection miningresources/price-sensitivity.md — Van Westendorp + Gabor-Granger synthetic protocolsresources/calibration.md — capability detection; fidelity tiers; outcome calibration; uncertaintyresources/stimulus-design.md — building stimuli per surface (offer/creative/copy/feature); multimodal
Quality Standards
Every forecast must pass these checks:
- Each construct distribution is a valid pmf (length 5, sums to ~1.0).
- Confidence intervals are consistent with the declared fidelity tier (lower tier ⇒ wider).
- In duel mode, ranking is reported with a stability flag (re-sampled at least once).
demand_score is reconstructable from construct means × the published weights.- Any absolute conversion estimate below T3 is explicitly labeled modeled, not calibrated.
- Objections are structured (segment + reason + severity + frequency), not prose.
- 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
