Ssr market research

SSR Market Research

Estimate how a target market will react to product concepts by simulating consumers with an LLM and scoring their reactions with Semantic Similarity Rating (SSR). SSR avoids the "everything is a 3" failure mode of asking an LLM for numeric ratings directly.

Why direct numeric ratings fail (and what SSR does instead)

If you ask an LLM "rate your purchase intent 1–5," it regresses to the center — low variance, unrealistic, ~50% accuracy. SSR fixes this with four design choices, all of which matter (dropping persona detail alone cut accuracy 92% → 50% in the paper):

1. Rich persona conditioning. Each synthetic respondent impersonates a specific person with demographics + backstory + context, not a generic "user."
2. Free-text elicitation first. NEVER ask for a number. Ask an open question ("How likely would you be to adopt/pay for this, and why?") and capture the natural-language answer. The variance lives in the words.
3. Embedding → Likert mapping. Embed the free-text reply and 5 anchor statements (one per Likert point). Cosine-similarity, subtract the minimum, normalize → a probability distribution over 1–5.
4. Average over multiple anchor sets + many respondents. 6 stylistic anchor sets capture different ways people express intent; many respondents give a survey-level distribution.

The paper hit ~90% of human test–retest reliability this way, beating supervised models.

The pipeline

segments + personas  →  concepts to test  →  free-text reactions  →  ssr.py  →  intent distributions
   (who)                  (what)               (LLM impersonation)    (scoring)   (per concept × segment)

Step 1 — Design the study

• Segments & personas. Write ≥1 persona per target segment with real demographic + psychographic + situational detail (role, tools, pains, context). More personas per segment = tighter estimates. Store in personas.json.
• Concepts. One short, concrete description per feature/product idea you want to test. Store in concepts.json. Keep each concept self-contained — a respondent reads it cold.

Step 2 — Elicit free-text reactions (the impersonation step)

For every (persona × concept) pair, produce a 2–4 sentence first-person reaction answering "How likely would you be to adopt and pay for this, and why?" — in that persona's voice, with their specific concerns. Two ways to generate them:

• Inline: author them yourself, varying voice/skepticism/enthusiasm per persona. Fast, fine for a directional v1. Risk: responses homogenize within one context.
• Workflow (preferred for rigor): spawn one subagent per persona so each reacts in an independent context (true respondent independence). Use this when the user opts into multi-agent orchestration / a larger N. See references/workflow-example.md.

Store as responses.json: a list of {persona_id, segment, concept, response_text}.

Step 3 — Score with SSR

python3 scripts/ssr.py \
  --responses responses.json \
  --anchors scripts/anchors.json \
  --out results.json

Outputs per-concept and per-(concept × segment): the Likert PMF, mean intent, top-2-box % (mass on the top two points = strong intent), bottom-box %, and n. Prints a ranked table to stdout. Requires the venv from "Setup" below.

Step 4 — Interpret

• Rank concepts by top-2-box (share of strong adopters) and by mean.
• Read the segment split — a concept can score mid overall but dominate one segment (your wedge). Variance/polarization is signal, not noise.
• Treat absolute numbers as directional, not validated, unless N is large and personas are grounded in real data. Report it that way.

Setup (one-time, per machine)

python3 -m venv .venv && . .venv/bin/activate
pip install numpy model2vec openai

ssr.py defaults to OpenAI text-embedding-3-small (closest to the paper; sharpest distribution spread). It reads OPENAI_API_KEY from the environment or a local .env file (auto-loaded). If no key is found it falls back to local static embeddings (minishlab/potion-base-8M, no torch, no key, free) with a printed notice — good enough for ranking, just lower discrimination. Force local explicitly with --backend local.

Files

• scripts/ssr.py — the SSR scoring engine (embeddings → Likert PMFs → ranked report).
• scripts/anchors.json — 6 generic, domain-independent adoption/WTP anchor sets (1–5).
• references/methodology.md — the paper's method, math, and fidelity caveats in depth.
• references/workflow-example.md — how to generate reactions with parallel persona agents.

Caveats

• SSR validates at the aggregate level. A single response's Likert is noisy; trust distributions over individuals.
• Fidelity depends on persona realism. Garbage personas → confident garbage.
• The impersonating model carries its own biases; results lean toward "what an LLM thinks segment X would say," which is a prior, not ground truth. Use to prioritize and falsify hypotheses, then validate winners with real users.