Company Context

FreshCart is a regional grocery delivery marketplace operating in 14 U.S. metro areas with $180M in annual gross merchandise value (GMV). The company has strong internal data on orders, customer cohorts, merchant performance, and marketing spend, and it recently completed an initial analysis to identify where to expand next and how to improve local go-to-market efficiency. The CEO has asked the strategy team a follow-up question: if you had two more weeks to work on this dataset, what additional external data sources would you integrate?

Strategic Situation

FreshCart must decide which 3 of 8 candidate metro areas to prioritize for expansion over the next 12 months and how to tailor its launch playbook by city. The current analysis relies mostly on internal operating data from existing markets and a high-level population view of the target cities. Management is concerned that the recommendation may miss critical external signals on demand density, competitive intensity, merchant supply, and local economics. You are the strategy manager preparing a recommendation on which external data would most improve decision quality within a short two-week window.

Current Facts

• FreshCart generated $180M GMV last year on 9.6M orders, with an average order value of $18.75 and take rate of 18%.
• Existing mature markets average 6.8 orders per monthly active customer, while newer markets average 3.9 orders.
• Launching a new metro requires roughly $2.5M upfront investment and management can fund only 3 launches this year.
• The 8 candidate metros range from 1.2M to 5.8M population and estimated first-year GMV potential of $12M-$38M based on internal analogs.
• In current markets, contribution margin turns positive when order density exceeds 1,100 orders per square mile per month.

Candidate Metro Snapshot

Your Task

You are advising the COO.

1. Identify the most valuable external data sources you would add in the next two weeks.
2. Explain how each source would improve market sizing, competitive analysis, or go-to-market decisions.
3. Prioritize the sources based on decision impact vs. feasibility.
4. Show how the added data could change which metros FreshCart should launch first.
5. Recommend a practical two-week data integration plan under tight budget and time constraints.

Constraints

• Only two additional weeks are available before the board meeting.
• External data budget is capped at $150,000.
• The analytics team has capacity for only 3 major new data integrations.
• Recommendation must be actionable for this year's launch decisions, not a long-term research program.