Market Entry War Room

Market-entry advice often arrives as a city name backed by a polished scorecard. The recommendation looks objective even when its result depends on unspoken judgments about who the customer is, how much they can pay, whether scale or growth matters more, and which costs the business can tolerate.

The War Room starts one level earlier. A user describes the business and target customer, then inspects the structured assumptions the parser produced before the model runs. That separation matters: AI helps interpret the description, while public data and deterministic scoring decide how each metro ranks.

The decision is not whether one metropolitan area is universally best. It is whether a candidate is attractive for this business under these priorities, why it reached that position, and whether the position survives a reasonable change in the assumptions.

The running system uses the US Census American Community Survey at the metropolitan level. Its current configuration compares the 2024 ACS 1-year vintage with 2019, producing observations for market size, affluence, growth, customer fit, and a residential-rent proxy for cost.

Raw metrics are converted to percentiles across the metros in the run. Metrics whose lower values are preferable are inverted, metrics roll into factor scores, and the final attractiveness score is a weighted sum. The requested weights must sum to one; when a factor is unavailable, its weight is redistributed proportionally and the interface states that explicitly.

Sensitivity analysis perturbs the effective weights within a configured band, renormalizes them, and recomputes the ranking across repeated runs. This turns the finding from a single rank into a distribution: which markets stay near the top when the user's priorities move?

The system is deployed and the full path from business description to assumptions, ranking, comparison, and sensitivity can be inspected. A built-in premium-fitness example currently scores all 393 loaded metros and exposes every factor contribution and raw observation behind the ordering.

That example is evidence that the workflow and arithmetic run, not a universal market conclusion. Changing the business, customer, or weights can change the ordering, which is the behavior the product is built to reveal rather than suppress.

The strongest current evidence is the sourced metro dataset, the deterministic score calculation, and the sensitivity mechanism. Evidence about category-level competition, actual commercial occupancy costs, or realized market-entry outcomes remains outside the MVP.

The most useful output is not 'Market X ranks first.' It is 'Market X ranks first under these assumptions, for these reasons, and remains near the top across this range of plausible assumption changes.'

That framing changes the conversation from defending one answer to examining what drives it. A weight that flips the ranking is not a nuisance; it identifies the strategic judgment the team actually needs to debate.

Competition is unmeasured in the current MVP because no public source covers every business category consistently. The factor is dropped and its weight redistributed rather than scored as zero.

Median residential gross rent is used as a proxy for operating cost. It is not commercial lease data and should not be presented as such.

Growth is a multi-year historical delta between configured Census vintages, not a predictive market forecast. A high rank establishes fit under the model, not the probability of a successful launch.

Add a bounded, category-specific competition source for one business type and test whether its inclusion materially changes the top tier without making coverage inconsistent across markets.

Run structured decision reviews with strategy practitioners: ask them to state a recommendation before and after sensitivity analysis, then record which assumption changes their conclusion and why.