The target company name and selected details have been generalized to preserve confidentiality.
The situation
A conventional list of competitors was not enough to support strategic judgment. The real question was how different companies overlapped with the target business across multiple dimensions and where that overlap was most strategically meaningful.
The approach
I designed an ontology-based model that translated qualitative market research on 17 external companies into explicit enumerated attributes. The model then calculated category-level similarity scores and a configurable composite score.
What I built
The workbook evaluates each company across six similarity dimensions:
- target audience
- core value proposition
- product features
- potential gaps in offering
- unique selling points
- competitive advantage
The ontology table defines the available qualitative attribute values for each category. The calculation layer converts selected attributes into comparable similarity scores and supports adjustable weighting for the composite score.
Why it matters
The model makes strategic reasoning inspectable. It replaces an informal competitor list with a repeatable framework for comparing business models, testing assumptions, and identifying meaningful overlap. It also makes the inputs visible, so the analysis can be challenged and recalibrated rather than treated as a black-box ranking.
Result
The model became the team’s core artifact for reasoning about which companies were and were not the closest comparables. That question surfaced repeatedly during fundraising, and the workbook gave the team a consistent framework for discussing the answer internally and explaining the logic behind it.
What I learned
A quantitative score is most valuable when its qualitative assumptions remain visible. Formalizing the ontology improved the analysis because it made disagreement more useful: individual attributes and weights could be challenged directly instead of debating an opaque overall ranking.