Product Research

# product-research Product / user research as an operational discipline: choosing the right method, sizing it honestly, and synthesizing findings into governed insights. The core rule: **method must match the goal**, and **an insight requires recurrence across independent participants** — a single quote is an anecdote. ## Purpose Product researchers, ResearchOps teams, and PMs running discovery need method rigor and an insight repository they can trust. This skill structures three decisions: Three deterministic tools: 1. `study_designer.py` — Maps (research goal × product stage) to an appropriate method and emits a method-matched plan skeleton (objective, participant criteria, guide structure, success criteria). Redirects live A/B to `product-team/experiment-designer`. 2. `saturation_planner.py` — Method-based sample guidance with an explicit **confidence label**: Nielsen problem-discovery (5/segment), Guest et al. thematic saturation (~12), and evaluative coverage. Never claims a prevalence rate from a small-n usability test. 3. `insight_synthesizer.py` — Clusters coded observations by tag, counts distinct participants, ranks by cross-participant recurrence, and flags any candidate below the source threshold as an **ANECDOTE**, never promoting it to an insight. ## When to use Invoke this skill when: - You are planning a study and need the method to match the goal (generative vs evaluative vs validation). - You need a defensible sample size / saturation rationale with a stated confidence. - You have raw coded observations and need to synthesize insights without over-claiming. - You are setting up or auditing a research repository and need the insight-vs-observation discipline. **Do NOT use this skill to**: generate personas / journey maps (use `product-team/ux-researcher-designer`), plan a discovery sprint or validate an opportunity (use `product-team/product-discovery`), design or analyze a live product A/B experiment (use `product-team/experiment-designer`), or do market sizing / surveys (use the `market-research` sibling). ## Workflow 1. **Frame the study** — Fill `assets/research_plan_template.md` (research questions, method rationale, participant criteria, analysis plan, repository tagging scheme). 2. **Pick the method** — Run `study_designer.py --goal {discovery|evaluative|validation} --stage {concept|prototype|beta|live} --profile {b2b-saas|consumer-app|enterprise|marketplace|hardware|platform}`. Honor the redirect if it routes to experiment-designer. 3. **Size it** — Run `saturation_planner.py --method {usability|thematic|evaluative-coverage} --segments N`. Record the confidence label and limits. 4. **Synthesize** — After fielding, code observations and run `insight_synthesizer.py --input observations.json --min-sources 3`. Treat ANECDOTE-flagged clusters as signals to probe, not findings to ship. 5. **File in the repository** — Tag insights to the atomic schema at synthesis time, with their evidence and confidence. ## Scripts | Script | Purpose | Profiles | |---|---|---| | `scripts/study_designer.py` | (goal × stage) → method + plan skeleton | b2b-saas, consumer-app, enterprise, marketplace, hardware, platform | | `scripts/saturation_planner.py` | Method-based sample guidance + confidence | n/a (method-driven) | | `scripts/insight_synthesizer.py` | Cluster observations, flag anecdotes | n/a (evidence-driven) | All three: stdlib-only, `--help`, `--sample`, `--output {human,json}`. ## Onboarding & customization Run the onboarding questionnaire **once before you start** — it captures your defaults so every tool in this skill is pre-configured. Customization is the point: the answers actually change tool behavior (e.g. the insight source-threshold). ```bash python3 scripts/onboard.py # interactive (also: --defaults, --set key=value, --reset) python3 scripts/onboard.py --show # see the questions + current effective config ``` Answers are saved to `~/.config/research-ops/product-research.json` (global) or `./.research-ops/product-research.json` (`--scope project`) and are read automatically by `config_loader.py`. They set the default product **profile**, the **insight source-threshold** (how many independent participants make a finding an insight, not an anecdote), the default **saturation method**, and the **high-stakes** flag. CLI flags always override saved config; `RESEARCH_OPS_NO_CONFIG=1` ignores it. **The four questions:** product profile · insight source-threshold · saturation method · high-stakes flag. ## Optimize with autoresearch (opt-in) This skill ships an **isolated, opt-in** bridge to `engineering/autoresearch-agent`. Only when you ask to "optimize the synthesis" / "run a loop" does an autoresearch experiment iteratively refine the coding/clustering of a fixed evidence set so more cross-participant patterns surface. `scripts/ar_evaluator.py` is the ground-truth evaluator; it prints `validated_insights: <int>` (higher is better). It optimizes the **coding**, never fabricates evidence. ```bash /ar:setup --domain custom --name insight-synthesis \ --target observations.json \ --eval "python3 ar_evaluator.py --target observations.json" \ --metric validated_insights --direction higher /ar:loop custom/insight-synthesis ``` Isolated: no hard dependency — autoresearch runs only on demand, and the loop edits `observations.json`, never the evaluator. ## References - `references/research_methods_canon.md` — Portigal *Interviewing Users*; Christensen/Ulwick JTBD; Rohrer's UX-research methods landscape (NN/g); Sauro & Lewis *Quantifying the User Experience*; Goodman/Kuniavsky. - `references/sampling_and_saturation.md` — Nielsen "test with 5 users"; Guest, Bunce & Johnson saturation; Faulkner on more-than-5; Sauro usability sample size; Braun & Clarke thematic analysis. - `references/repository_and_synthesis.md` — ResearchOps / atomic research (Tomer Sharon "Polaris"); insight-vs-observation discipline; repository governance; affinity mapping; democratization guardrails. ## Assumptions - Method selection assumes you can name the goal honestly; if the goal is fuzzy, grill it first (the goal drives everything). - Saturation guidance is method-based, not a power calculation — usability tests find problems, not prevalence rates. - The synthesizer counts evidence you provide; coding quality is upstream of it. Garbage tags → garbage clusters. - The insight threshold (`--min-sources`) defaults to 3; raise it for high-stakes or heterogeneous populations. ## Anti-patterns - **Mismatching method to goal.** A usability test cannot discover unmet needs; an interview cannot measure task success. - **Reporting usability problems as percentages.** Small-n tests surface problems, not population rates. - **Promoting an anecdote to an insight.** One participant is a signal to probe, not a finding. - **Framing interview questions as feature reactions.** Probe the job-to-be-done and recent real behavior, not hypothetical opinions. - **Synthesizing without a repository scheme.** Tag at synthesis time, or insights rot unfindable. ## Distinct from | Neighbor | Scope | Difference | |---|---|---| | `product-team/ux-researcher-designer` | Personas, journey maps, usability frameworks tied to design output | That produces **artifacts**; this is **method + repository discipline** | | `product-team/product-discovery` | Opportunity validation, discovery-sprint planning | That plans **discovery sprints**; this designs and synthesizes the **research** | | `product-team/experiment-designer` | Live product A/B hypothesis + sample size | That runs **live experiments**; this runs **qualitative/evaluative research** | | `market-research` (sibling) | Market sizing, surveys, segmentation | That studies **the market**; this studies **users** | ## Quick examples ```bash python3 scripts/study_designer.py --sample python3 scripts/saturation_planner.py --method thematic --segments 3 python3 scripts/insight_synthesizer.py --sample --min-sources 3 ``` The synthesizer sample correctly promotes "import-confusion" (3 independent participants) to INSIGHT and flags "wants-slack" (1 participant) as an ANECDOTE. ## Forcing-question library (Matt Pocock grill discipline) Walked one at a time by `/cs:grill-research-ops` or the orchestrator. Recommended answer + canon citation per question. Never bundled. 1. **"Is this study generative (discover problems) or evaluative (test a solution)?"** Recommended: name it first — the method follows from the goal. Canon: Rohrer, *When to Use Which User-Experience Research Methods* (NN/g). 2. **"What's your sample size and saturation rationale — and at what confidence?"** Recommended: method-based n (5/segment usability; ~12 for thematic saturation), state the confidence. Canon: Nielsen; Guest, Bunce & Johnson (2006); Faulkner (2003). 3. **"How many independent participants support each insight — or is it a single-source anecdote?"** Recommended: require recurrence across ≥3 sources before calling it an insight; flag singletons. Canon: atomic research / ResearchOps; Braun & Clarke thematic analysis. 4. **"Are your interview / usability tasks framed as outcomes (jobs) or as feature reactions?"** Recommended: frame around the job-to-be-done and recent real behavior, not hypothetical opinion. Canon: Christensen/Ulwick Jobs-to-be-Done; Portigal *Interviewing Users*. 5. **"Where does this land in the repository, and how is it tagged for reuse?"** Recommended: tag to the atomic schema at synthesis time, not later. Canon: Tomer Sharon, *Polaris* / ResearchOps repository practice. Walk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke `study_designer.py` → `saturation_planner.py` → (after fielding) `insight_synthesizer.py`.