Tutorial · Research Workflows
How to Use Public Web Data for Market Mapping
A category map where every placement has a source and a reason.
Written by Aaron Grainger
Independent Content Strategist & Product-Marketing Writer · Published Jun 11, 2025
- Primary audience
- Research, intelligence, and editorial teams
- Also useful for
- AI application developers
- Tone
- Educational
- Reading time
- 2 min
- Published
- Jun 11, 2025
Direct answer
Map a market from public sources by seeding from directories and known participants, expanding through explicit references between them, and profiling each entry using its own self-description. Cluster by the language participants use rather than by your model of the category, and record an inclusion reason and a source for every entry — plus an exclusion list, which is what actually defines the boundary.
In active development — preview and full outline below
Two columns that change what a category map is
Category maps circulate widely and are almost never sourced. Adding two columns — inclusion reason and source URL — turns the artefact from an opinion into something a colleague can argue with productively. The argument then happens over the boundary, which is where it belongs.
Definition: the boundary is the deliverable
A map of "web data tooling" is meaningless until you say whether it includes proxy networks, browser-automation frameworks, and in-house crawlers. Write the boundary as an inclusion rule and an exclusion rule before seeding, and publish the list of entries you deliberately left out. Readers trust a map with a visible edge more than one that appears to cover everything.
| Seed source | Bias it introduces | Counterweight |
|---|---|---|
| Search results | Favours marketing spend and SEO maturity | Add directory and association listings |
| Curated directories | Favours paid or long-established listings | Follow references between participants |
| A known participant's comparison page | Favours their framing of the category | Collect the same page from three participants and diff the sets |
| Community lists and awesome-repos | Favours open source and developer-facing tools | Add procurement-oriented directories |
Clustering: participant language versus analyst taxonomy
Cluster first on the words participants use about themselves, taken from their own headers and product pages. Then apply your taxonomy as a second layer and look at where the two disagree. The disagreements are the finding: they usually mark a category that is splitting, or a label that has been adopted faster than the capability behind it.
Full outline
Sections planned for this guide
01Seeding
- — Directories, associations, and known participants
- — Seed bias and how to counter it
- — Defining the category boundary up front
02Expansion
- — Following explicit references
- — Stopping conditions
- — Long-tail coverage limits
03Profiling
- — Self-description extraction
- — Product lists and claimed segments
- — Dormant sites and stale entries
04Clustering
- — Participant language versus analyst taxonomy
- — Overlapping clusters
- — Manual review step
05Publishing
- — Inclusion reasons and sources
- — The exclusion list
- — Dating and re-running the map
Practical takeaway
- Self-description evidences positioning, not capability.
- The exclusion list defines the map as much as the inclusion list.
- Clustering by your own taxonomy hides how participants see themselves.
- Maps decay quickly; date them and re-run.
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Version history
Current: 1.1 · In active development
- 1.0Jun 11, 2025First published.
- 1.1Jun 11, 2025Marked in active development; sections still being expanded.
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