Market Research

This policy defines what OSBR does before it takes a single requirement from a client. We proactively study the client's industry, its market structure, and the actual work people do inside it — so that when we finally sit down to gather requirements, we already speak the field's language and understand the business the software has to serve. It sits at the front of the Development Guide's Planning & Shaping work: this policy is about understanding the problem, so that everything downstream is about building the right solution.

We do not treat the client's stated request as the specification. A stated request is a symptom and a hypothesis — evidence about a deeper business need, not the need itself. Serving the client well is Be Nice and Be Kind: we serve the real need, even when it differs from the literal ask. Doing that takes Be Strong — the discipline to research before building, and the honesty to tell a client that their stated solution is not the one their business needs.

How to read this policy

• Requirement levels follow RFC 2119. MUST / MUST NOT are absolute. SHOULD / SHOULD NOT state a strong default overridable only with a documented reason. MAY marks a free choice.
• Named practice. Where a rule adopts an industry practice, the practice is named inline and cited under References. We adopt the criteria of each practice and right-size it for an SME engagement — we do not import the headcount or ceremony of the reference material.

• How to read this policy

• 1. Goal

• 2. Responsibility

• 3. Practices

  • 3-1. Desk research first — industry and market structure
  • 3-2. Study the real work, in context
  • 3-3. Frame needs as jobs, not features
  • 3-4. Harvest the field's language
  • 3-5. Run a structured discovery, not an ad-hoc chat
  • 3-6. Feed research into requirements, then keep it honest

• References

1. Goal

The goal of market research at OSBR is to enter every engagement already fluent in the client's world, so that requirements-gathering refines a shared understanding instead of starting from zero. Concretely, before requirements are taken we aim to know:

• The industry — how this sector makes money, who the players are, and what forces shape it. We use Porter's Five Forces and the value chain as the lens for market structure: suppliers, buyers, substitutes, new entrants, rivalry, and where value is actually added along the chain. Software that ignores this optimises the wrong link.
• The real workflows and pain points — what people actually do all day, in their real context, versus the idealised process on the org chart. This is grounded in contextual inquiry and the broader UX-research practice.
• The job to be done — the progress the client and their users are trying to make, independent of any particular solution. People don't want a quarter-inch drill; they want a quarter-inch hole.
• The field's own language — the exact terms, units, artefacts, and edge-cases practitioners use, captured verbatim as the raw material for the project's domain terminology.

The output of this phase is not a signed spec. It is a shared, evidence-based understanding of the client's business that de-risks everything downstream — because most expensive project failures are failures of understanding the problem, not of building the solution.

2. Responsibility

Whoever leads an engagement owns this research. It is not optional homework, and it is not the client's job to spoon-feed us.

• The engagement lead MUST complete a market-and-workflow survey before the first formal requirements session, and record it where the whole team — human and AI — can read it.
• We MUST NOT take a stated request at face value as the specification. Every stated request is logged together with the underlying job it is trying to satisfy, and the two are tracked separately.
• We MUST bring the field's own terminology back into the project as candidate terms for its domain terminology — not paraphrased into our own words, which quietly discards meaning.
• We MUST be honest and kind when research contradicts the client's stated solution: surface the gap early, with evidence, framed as service to their business — not as being clever at their expense. This is Be Nice and Be Kind expressed as professional courage (Be Strong), not as quiet compliance.
• This work is a human ⇄ AI collaboration. AI agents fan out the desk research, structure findings, and draft the domain glossary at speed; humans own the field contact, judgement, and the relationship with the client. Neither half is optional.

3. Practices

3-1. Desk research first — industry and market structure

Before talking to anyone, build a map of the sector.

• MUST analyse market structure with Porter's Five Forces and the value chain: who the client's suppliers, buyers, substitutes, and rivals are, and where in the value chain the client actually captures value.
• SHOULD identify the incumbents, their products, and the standard tools practitioners already use — the software we build competes with, or must interoperate with, these.
• SHOULD collect the industry's regulations, standards bodies, and compliance constraints as first-class inputs, not late surprises.

3-2. Study the real work, in context

Idealised process diagrams lie; watch the actual work.

• SHOULD run lightweight contextual inquiry — observe real users doing real tasks in their real environment, and let them teach you (the master/apprentice stance) rather than interviewing them in a meeting room.
• MUST capture the pain points, workarounds, and shadow processes — the private spreadsheets and side-channels people have built to survive the current process. These are the sharpest signal of unmet need.
• SHOULD choose research methods deliberately from the established UX-research menu (interviews, field studies, diary studies, surveys) — attitudinal versus behavioural, qualitative versus quantitative — matching the method to the question.

3-3. Frame needs as jobs, not features

• MUST express each finding as a Job-To-Be-Done: "When [situation], I want to [motivation], so I can [expected outcome]." This separates the durable need from the disposable solution the client happened to ask for.
• SHOULD trace every stated request down to the job beneath it, and validate that job with evidence from §3-1 and §3-2 before it becomes a requirement. A request is a hypothesis; the job is what we are actually serving.

3-4. Harvest the field's language

• MUST record domain terms verbatim, with the practitioner's own definition, units, and edge-cases — the seed of the project's domain terminology. One term, one meaning, used identically by code, docs, and client.
• SHOULD run collaborative discovery — Event Storming or example mapping — with the client's domain experts to surface the events, commands, and boundaries in their own words. This doubles as the first draft of the domain model.
• MUST treat conflicts and synonyms in the terminology as findings, not noise: two words for one thing (or one word for two) usually marks a boundary between sub-domains.

3-5. Run a structured discovery, not an ad-hoc chat

Use a named inception format so nothing is skipped and the client is a participant, not a spectator.

• SHOULD adopt one of the established discovery frameworks and right-size it:
  • Lean Inception — a week-shaped sequence for aligning on the product's vision, personas, and a lean MVP.
  • Design Sprint — a time-boxed map → sketch → decide → prototype → test loop when the problem space is genuinely uncertain.
  • Working Backwards (PR-FAQ) — draft the future press release and customer FAQ first, forcing clarity on the customer benefit before any building.
• MUST produce, from whichever format is used, an artefact that states the customer, the job, and the expected outcome in the client's own language — reviewable by the client and by AI agents downstream.

3-6. Feed research into requirements, then keep it honest

• MUST hand the survey, the JTBD list, and the draft domain glossary into the requirements phase as its starting inputs — requirements refine this understanding, they do not replace it. This is the understanding the Quality Gate later holds the delivered work against: a solution can only be judged fit if the problem it serves was understood first.
• SHOULD revisit the research when reality contradicts it; a survey that is never updated becomes a comfortable fiction. As in Verify Before Building, verify against ground truth — the field, the data, the client's actual numbers — not against our earlier assumptions.

The lazy version of this phase is to write down the client's request and start coding. It feels efficient and it is the most common way projects fail. Researching first, and telling a client the honest result even when it is not what they asked for, is Be Strong in service of Be Nice and Be Kind: we protect the client's business, not just our own comfort.

References

Named practice this policy draws on, chosen because each is publicly documented and adoptable by a small team.

Market structure

• Michael Porter, "How Competitive Forces Shape Strategy" (Five Forces, value chain), HBR — https://hbr.org/1979/03/how-competitive-forces-shape-strategy

Customer need & jobs

• Clayton Christensen et al., "Know Your Customers' Jobs to Be Done", HBR — https://hbr.org/2016/09/know-your-customers-jobs-to-be-done

User & market research

• Nielsen Norman Group, "Which UX Research Methods?" — https://www.nngroup.com/articles/which-ux-research-methods/
• Beyer & Holtzblatt, Contextual Design (contextual inquiry) — https://www.sciencedirect.com/book/9780123044518/contextual-design

Domain discovery & language

• Eric Evans, Domain-Driven Design Reference (ubiquitous language) — https://www.domainlanguage.com/ddd/reference/
• Alberto Brandolini, Event Storming — https://www.eventstorming.com/

Discovery & inception formats

• ThoughtWorks / Martin Fowler, "Lean Inception" — https://martinfowler.com/articles/lean-inception/
• Google Ventures, Design Sprint — https://www.gv.com/sprint/
• Amazon, "Working Backwards" (PR-FAQ) — https://www.aboutamazon.com/news/workplace/an-insiders-guide-to-working-backwards

Related OSBR standards

• Development Guide — the Planning & Shaping work this research feeds.
• Domain Terminology — where the field's harvested language becomes the project's ubiquitous language.
• Quality Gate — the gate that judges whether the delivered solution serves the understood problem.
• Verify Before Building — verifying against ground truth rather than earlier assumptions.