Regional Market
Expansion.
Drove 342% DAU growth and 7.4× ROI at ProBit Global, by building a weighted prioritization framework that selected 10 markets from 40 candidates and tiered budget allocation by performance.
Problem
ProBit Global operated in 150+ countries with a finite team and budget. Marketing in the category was generally spread thin: small efforts everywhere, dedicated investment nowhere. The pattern produced flat traction across markets that looked similar on paper but performed wildly differently in practice.
The team needed to pick where to concentrate. The hard part was that market potential was multidimensional, the team's instinct gravitated toward big-name markets, and the data lived across three different systems with no shared methodology for comparing them.
Approach
I built the prioritization in three layers, each one designed to be defensible in front of leadership.
1. Weighted ranking framework. Three signals, three weights. Trading volume (60%) measured conversion. Signups (20%) measured top-of-funnel traction. Centralized service value from Chainalysis (20%) measured user adoption depth. I ranked 40 candidate countries on each criterion, applied the weights, and produced priority scores. Lower total scores meant higher priority.
| Region | Trading volume (60%) | Signups (20%) | Service value (20%) | Total score |
|---|---|---|---|---|
| United States | 0.6 | 0.2 | 0.6 | 1.4 |
| India | 1.2 | 0.4 | 0.2 | 1.8 |
| China | 2.4 | 1.0 | 0.4 | 3.8 |
| Indonesia | 1.8 | 0.6 | 3.2 | 5.6 |
| Russia | 3.0 | 1.4 | 1.6 | 6.0 |
| Turkey | 3.6 | 1.2 | 1.8 | 6.6 |
| Vietnam | 4.8 | 1.6 | 1.0 | 7.4 |
| Brazil | 6.0 | 0.8 | 1.4 | 8.2 |
| Colombia | 5.4 | 1.8 | 4.6 | 11.8 |
| Ukraine | 9.0 | 2.4 | 1.2 | 12.6 |
Qualitative factors, such as product-fit and regulatory matters, were considered for the final list and budget distribution.
2. Qualitative adjustments after the math. The data ranking surfaced markets the team would have skipped (Vietnam, Turkey) and downranked markets the team gravitated toward (China, US). I documented two qualitative adjustments on top of the data: excluded China for regulatory risk at the time, deprioritized the US for licensing complexity. I also prioritized markets with local payment availability (Pix in Brazil, UPI in India, local bank transfers).
3. Tiered budget allocation. The prioritized 10 became three tiers.
- Tier 1 (Vietnam, Turkey, Brazil) got 50% of budget, the highest-ROI markets.
- Tier 2 (Indonesia, Russia, India) got 30%, scaling phase.
- Tier 3 (Colombia, Ukraine) got 20%, testing phase. Quarterly reallocation was based on LTV/CAC and DAU performance, so Vietnam exceeded targets and got an additional 30% the following quarter.
Deliverable
The work shipped as four artifacts.
- Weighted ranking framework with the three-signal methodology and the 40-country ranking.
- Localized GTM playbooks for each Tier 1 market (Turkey, Brazil, Vietnam) including messaging, channel mix, payment integration.
- Tiered budget allocation model with quarterly reallocation triggers based on LTV/CAC and DAU.
- Repeatable playbook for future market entries beyond Tier 4.
Outcomes: 342% DAU increase over two years (53% within the first 6 months). 7.4× average ROI across 10 regional campaigns. Vietnam became the #2 market by volume. Turkey and Brazil entered the top 5 within 12 months.
What I would do differently
The qualitative adjustments after the data ranking should have been weighted criteria from the start. Excluding China for regulatory risk and deprioritizing the US for licensing complexity were the right calls, but doing them as overrides made the framework look softer than it was. I would have built "regulatory stability" and "licensing path complexity" as scored inputs, not adjustments.
I would also have tested smaller in tier 3 markets before committing to full localization. The framework was good at identifying priorities. It was less rigorous about staging investment within priority markets.