Product Research

The operational layer of continuous product discovery: choosing a method that actually answers the question asked, recruiting the right people without poisoning the sample, running interviews that surface behaviour rather than opinion, and converting a pile of session notes into insights with an honest confidence attached.

When to use this skill

• A team is about to build something and the evidence behind it is three sales anecdotes and a strongly held opinion
• Choosing a method — someone has asked for "a survey" or "some user interviews" before anyone has written down the question
• Designing a screener for a study where recruiting the wrong participants would be worse than not running it
• Writing an interview guide that has to be run consistently by several people across a dozen sessions
• Synthesising evidence into insights after a round of discovery, with a defensible confidence level on each claim
• Standing up a continuous discovery cadence — a repeatable weekly rhythm rather than one-off project research

Inputs the skill expects

• The decision the research feeds, and who makes it
• The question in interrogative form — what you do not know, not what you want confirmed
• Decision reversibility — can this be undone in a sprint, or is it a one-way door
• Timeline and budget for the study
• Access to participants: existing customers, prospects, panel, or none
• Existing evidence already on hand (tickets, session recordings, sales calls)

Clarify First

Before generating, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• The decision this research informs, and its reversibility — a one-way door justifies weeks of evidence; a reversible change is often better answered by shipping an experiment
• The question in interrogative form — "do users want X" and "how do users currently accomplish X" call for completely different methods
• Participant access — whether you can reach real users determines whether the plan is feasible at all, and it is the constraint teams discover last
• Timeline — a two-day answer and a three-week answer are different studies, not the same study rushed

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Workflows

Workflow 1 — Pick the method before anyone books a session

1. Write the question in interrogative form. If it starts with "should we," it is a decision, not a research question — rewrite it as what you would need to know to decide.
2. Classify the question: generative (what is going on), evaluative (does this work), or descriptive (how many, how often).
3. Rate decision reversibility and state the timeline and participant access.
4. Run the recommender. It returns a primary method, a cheaper fallback, the minimum sample, and the methods it explicitly ruled out with reasons.
5. If the recommendation is "ship an experiment instead," take that seriously. For reversible decisions, an experiment usually beats a study on both speed and evidence quality.

python3 research-ops/product-research/scripts/method_recommender.py \
  --input research-ops/product-research/assets/sample_research_question.json \
  --format text

Workflow 2 — Validate the screener before recruiting opens

1. Draft the screener: qualifying criteria, disqualifying criteria, and the items that test each.
2. Run the validator. It checks for transparent qualifying answers, missing disqualification logic, professional-respondent exposure, quota coverage, and criteria that no item actually tests.
3. Fix every fail. A screener defect costs you the whole study — you find out only during the sessions, by which point the incentives are spent.

python3 research-ops/product-research/scripts/screener_validator.py \
  --input research-ops/product-research/assets/sample_screener.json \
  --format text

Workflow 3 — Score insight confidence during synthesis

1. Draft each candidate insight as a claim, and attach the evidence items that support it — each with its source type, participant, and whether it is observed behaviour or reported opinion.
2. Run the scorer. It weights observed evidence above reported evidence, rewards source and participant diversity, and penalises claims resting on a single session or a single channel.
3. Ship only the insights scoring moderate or above as decision inputs. Everything below that is a hypothesis and must be labelled as one.

python3 research-ops/product-research/scripts/insight_confidence_scorer.py \
  --input research-ops/product-research/assets/sample_evidence.json \
  --format text

Decision frameworks

Method by question type

Question type	Example	Primary method	Minimum sample
Generative — what is going on	"How do support agents currently triage tickets?"	[PROVEN] Contextual inquiry or semi-structured interview	6-8
Evaluative — does this work	"Can users complete onboarding unaided?"	[PROVEN] Moderated usability test	5-8
Comparative — which is better	"Which of two flows converts?"	[PROVEN] A/B experiment	Powered by traffic
Descriptive — how many, how often	"What share of accounts hit this limit?"	[PROVEN] Instrumentation or log analysis	Full population
Prioritisation — which matters most	"Which of five problems is most acute?"	[RECOMMENDED] Survey with forced trade-offs	100+
Desirability — would people want this	"Would customers use X?"	[RECOMMENDED] Painted-door or pre-commitment test	Traffic-dependent
Diagnostic — why did this drop	"Why did activation fall 12%?"	[RECOMMENDED] Funnel analysis first, then targeted interviews	5-6 after analysis

The pattern worth internalising: quantitative methods tell you what and how many; qualitative methods tell you why and how. Reaching for interviews to answer a "how many" question, or for a survey to answer a "why" question, is the most common and most expensive method error in product research.

Reversibility gate

Decision type	Evidence bar	Typical spend
Reversible in a sprint	Ship it behind a flag and measure	Hours. Research here is usually waste.
Reversible in a quarter	5-6 interviews or one experiment	Days
Costly to reverse — pricing, data model, public API	Mixed methods; qual for the why, quant for the size	1-3 weeks
One-way door — platform, contract, market entry	Triangulated across 3+ independent sources	Weeks, and worth it

[PROVEN] Match evidence spend to reversibility, not to how interesting the question is. The most common research-ops failure is not too little research — it is expensive research on reversible decisions while one-way doors get decided on intuition.

Saturation — when to stop interviewing

Track new themes per session. Stop when two consecutive sessions produce no new theme.

Sessions run	Typical state
1-3	Every session is new. Do not synthesise yet — you are pattern-matching on noise.
4-6	Themes start repeating. First real patterns appear.
7-9	Saturation for a homogeneous segment. Diminishing returns set in hard.
10-12	Needed only when covering 2+ distinct segments — treat each segment as its own count.
15+	Almost always over-research, unless the segments are genuinely many

The count that matters is per segment, not in total. Eight sessions spread across four segments is two per segment, which is anecdote.

Anti-Patterns

The Confirmation Study

Mistake: Running research after the decision is made, with a question phrased to validate it — "we want to check users like the new dashboard." Why it happens: The team needs air cover for a choice already funded, and nobody wants to be the person whose study kills the roadmap item. Instead: Write down, before recruiting, what result would cause you to change course. If no such result exists, cancel the study and save the money — you are buying decoration, not evidence. Getting that sentence written is also the fastest way to discover the decision was never really open.

Asking Users to Design

Mistake: "What features would you like to see?" and treating the answers as a roadmap. Why it happens: It feels maximally user-centred, and it produces concrete output quickly. Instead: Ask about the last time they hit the problem — what they were doing, what they tried, what it cost them. People are reliable reporters of their own experience and unreliable designers of solutions. Extract the problem from the story; the solution is your job.

Sample of Convenience

Mistake: Interviewing whoever answers the recruiting email — usually your most engaged power users — and generalising to the whole base. Why it happens: They respond fastest, they are pleasant to talk to, and the sessions feel productive. Instead: Recruit against a quota that includes the segments you most need to hear from — churned users, low-engagement accounts, people who evaluated you and chose a competitor. Those are harder to reach and worth several times more per session. If you can only get power users, say so explicitly in the writeup and scope the conclusion to them.

Synthesis by Highlight Reel

Mistake: Building the findings deck from the most quotable moments across sessions. Why it happens: Vivid quotes are persuasive and memorable, and a striking quote from one participant carries more weight in a readout than a pattern across six. Instead: Count first, quote second. Establish how many participants exhibited each theme, then select a quote to illustrate a theme you have already quantified. A quote is an illustration of evidence, never the evidence itself.

Research Theatre on a Reversible Decision

Mistake: A three-week study to decide something that could be shipped behind a flag on Tuesday and measured by Friday. Why it happens: A research process exists, so it gets applied uniformly regardless of what is at stake. Instead: Run the reversibility gate first. If the decision is reversible in a sprint, ship the experiment — it produces better evidence (observed behaviour at real scale) faster and cheaper than any study. Reserve the research capacity for the one-way doors that are currently being decided on nothing at all.

Files

File	Purpose
scripts/method_recommender.py	Recommends a research method from question type, reversibility, timeline, and access
scripts/screener_validator.py	Checks a screener for transparency, missing disqualification logic, and quota coverage
scripts/insight_confidence_scorer.py	Scores insight confidence from evidence count, type, and source diversity
references/method-selection-guide.md	Every method with cost, sample, output, and the questions it cannot answer
references/interview-craft.md	Guide construction, probing technique, moderator failure modes, synthesis mechanics
assets/interview-guide-template.md	The structure a semi-structured discovery guide ships in
assets/sample_research_question.json	Runnable input for the method recommender
assets/sample_screener.json	Runnable input for the screener validator
assets/sample_evidence.json	Runnable input for the insight confidence scorer