The seven domains assessment model for entrepreneurs
How can the seven domains assessment model for entrepreneurs support strategic choice or positioning?
Contents
Many entrepreneurs launch their business ideas without a great deal of careful analysis.
Entrepreneurs often act before testing the assumptions behind an idea. The seven-domains model provides a disciplined pre-launch review of market, industry and team conditions so a founder can expose fatal weaknesses early.
When to use it
- Assess an opportunity before committing heavily to launch.
- Find the most important gaps in a venture concept or plan.
- Analyse why one new-business idea may be viable while another is not.
Origins
John Mullins of London Business School introduced the framework in his 2003 book The New Business Road Test. It integrates established market, industry, resource and team perspectives rather than claiming a wholly new theory. The output is a feasibility diagnosis, not a full business plan. A promising idea may proceed to a funding plan or a lean experiment designed to earn evidence and revenue early.
What it is
The framework examines seven domains:
- Macro-market attractiveness: Is the overall market large, growing and supported by favourable trends?
- Micro-market attractiveness: Is there a specific group with an urgent need and willingness to buy now?
- Industry attractiveness: Can participants retain attractive economics given rivalry, entry, substitutes, buyers and suppliers?
- Sustainable advantage: Can the venture defend customer value against imitation and response?
- Mission, aspiration and risk fit: Does the opportunity suit the founders’ purpose, ambition and tolerance for risk?
- Execution capability: Does the team possess or have access to the critical skills required?
- Value-chain connectedness: Can relationships open access to suppliers, channels, partners, talent and capital?
How to use it
Treat the domains as a checklist and evidence file. Rate the strength of each conclusion, identify the assumptions that could invalidate it and design research or experiments for the most consequential uncertainty.
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