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Market Expansion Prioritizer
Change the criteria, compare scenarios, and see why the ranking moves.
Synthetic data. See limitations below.
AI can help estimate which markets look most promising, but those predictions are rarely certain enough to make the decision on their own. This tool lets you weigh demand, funding, competition, and feasibility based on what matters most to the business, then shows how those priorities change the final ranking.
Scenario presets
Adjust the weights
35
25
20
20
How close is the top call?
Close call, the top two markets are within 4.8% of each other. A small assumption change could flip the recommendation.
Ranking movement
Top markets, before this change versus now.
Ranked markets
| Rank | State | Score | Moved | |
|---|---|---|---|---|
| 1 | Arizona | 76.5 | — | |
| 2 | Ohio | 72.8 | — | |
| 3 | Tennessee | 72.3 | — | |
| 4 | Georgia | 71.5 | — | |
| 5 | North Carolina | 70.7 | — | |
| 6 | Virginia | 68.8 | — | |
| 7 | Texas | 68.0 | — | |
| 8 | Michigan | 67.1 | — | |
| 9 | Colorado | 63.1 | — | |
| 10 | Pennsylvania | 62.7 | — | |
| 11 | Florida | 62.4 | — | |
| 12 | Illinois | 59.7 | — | |
| 13 | Nevada | 59.1 | — |
What this shows
A transparent weighted model can turn uncertain market signals into a ranked decision. The recommendation changes with the priorities you set, so the result remains tied to business judgment rather than a single confidence score.
How it works
This mirrors what I actually did to evaluate 13 state markets for a civic education organization, scoring every market on demand, funding access, competition, and partnership feasibility, then ranking them. Move any weight and every market re-ranks live, with the math fully visible in the “Why?” drawer on each row.
Design decisions and guardrails
The ranking uses a transparent weighted model so the logic remains visible. In the original analysis, AI supported the research by organizing demand signals, funding information, and competitive context, while human judgment determined the criteria, weights, and final recommendation.
Limitations
The display uses synthetic data because the original dataset is not public, but it follows the same scoring method used in the analysis. I kept the formula visible because a market-expansion decision should be traceable to the assumptions and priorities behind it.
Production next steps
A more advanced version could retrieve current market evidence, preserve source citations, and ask the user to approve or revise each factor before recalculating the ranking.
Technology used
TypeScript recalculates the weighted scores directly in the browser whenever a user changes the criteria. Every number comes from the visible formula and selected weights, so the user can trace why each market moved.