How to Find Under-the-Radar Markets—and Why They’re Often Better Bets

Who this is for

PM, PMM (Product Marketing Manager), and Growth leads exploring new countries for B2C/B2B2C EdTech.

Why look beyond Tier-1?

Hype vs. math. Teams chase US/UK/DE because that’s where press and investors point. But high CPM/CPC, payroll, legal friction, and entrenched competitors often erase the upside.

Execution speed. Underrated markets give quicker signal—less PR, more adoption. 1–3 lighthouse wins arrive sooner, feeding roadmap and case studies.

Portfolio logic. A few medium markets that convert reliably can beat a single Tier-1 moonshot in ROI and risk.

Reality check: a model that works in one European country rarely “just ports” to the US/UK—curriculum, payments, refunds, and channels reset your learning curve.

Tier-1 vs. Tier-2: myths vs. math

• Myth: “Tier-1 = biggest TAM, therefore best ROI.” Math: TAM ≠ reachable demand at your budget; CAC/LTV can be worse than Tier-2.
• Myth: “Tier-2 can’t spend.” Math: Many allocate a higher share of wallet to education; nominal spend is rising while ad costs remain sane.
• Myth: “Tier-2 won’t impress investors.” Math: Capital-efficient, repeatable wins with retention impress anyone.

Where to look first: a fast filter

Use this 8-point screen to shortlist 5–10 “quiet” markets in an afternoon; test the top 3 that pass ≥6/8:

• Language fit (serve in local language with modest effort)
• Payment rails (popular local methods within 1–2 sprints)
• Seasonality (align with school terms/exam cycles)
• Search interest (category volume in local phrasing)
• Ad costs (CPM/CPC in parenting/age segments are sane)
• Store signals (room to break into Education top charts)
• Socioeconomics (growing middle class, broadband, smartphones)
• Compliance friction (privacy/tax workable for MVP)

Measuring demand (tools & how to read them)

• Search volume (category, not just brand). Use SEMrush/Ahrefs/Keyword Planner with local phrasing (e.g., reforço escolar, zadania z matematyki). Look for head+mid-tail, steady seasonality, YoY growth.
• Google Trends. Compare local-language demand across candidates; watch exam-cycle seasonality.
• App/Play browse. Note ranking leaders, monetization style (IAP/sub), rating counts (scale proxy), and gaps between ranks (entry difficulty).
• Social discovery. YouTube/TikTok/IG: hooks that resonate with parents/teachers (proof, routine, exam-mapping).

Estimating ad costs & competitive pressure

Run $100 micro-tests (Meta/TikTok/Google) to pull live CPM/CPC/CTR—no model beats real auctions.

• Creative density: Meta Ads Library/TikTok Creative Center → lots of fresh creatives from the same brands = heated auctions.
• Store conversion proxy: Thousands of fresh ratings/month for leaders = strong conversion moats.

Back-of-napkin: CAC ≈ CPM × (1/CTR) × (1/CVR-to-lead) × (1/lead→trial) × (1/trial→paid). If CAC > 30–40% of projected 12-month LTV, danger zone.

Local data sources (socioeconomics & growth)

• National statistics offices (education spend, household budgets, urbanization, internet access)
• Education ministries/exam boards (cohorts, exam calendars, standards)
• Central bank/inflation data (pricing & refund expectations)
• Teacher/parent associations (channels and trust signals)

Summarize into a Market Basics card: population 6–16, smartphone %, payment rails, education spend patterns, refund norms.

Competition: direct & indirect

Map both, or you’ll misread demand and pricing:

• Direct: apps in your sub-category (phonics, math practice, exam prep). Note pricing, proof, onboarding friction.
• Indirect: real alternatives parents pay for—workbooks, private tutoring, after-school clubs, “productive screen time” substitutes (YouTube learning, WhatsApp homework groups).

Example lens (math app): if families buy printed workbooks every term, compete with that bundle (price, progress visibility, sibling plan), not just other apps.

Lightweight pilots (smoke tests)

Objective: learn real intent, price tolerance, and rails friction in 2–3 weeks—no full launch.

• Localized LP (headline, trust stack, price) + lead/waitlist
• Creative micro-tests per market (5–10 hooks × 2 visuals); optimize to CTR then lead quality
• Price & rails check (local prices, show common payment methods; capture intent)
• Draft App/Play listing (unlisted/test) to validate copy & screenshots with native reviewers

Stop/scale thresholds (examples): top creative CTR ≥ 1.5–2.0%; LP CVR-to-lead ≥ 8–12%; 30–50 qualified leads; predicted CAC ≤ 0.3–0.4 × 12-month LTV; no show-stoppers in payments/refunds/privacy.

A simple scoring model to pick your next market

Score each candidate 1–5 on: Demand, Ad costs, Competition, Payments, Socioeconomics, Seasonality fit, Compliance friction, Ops readiness.

Weight Demand & Ad costs double. Anything <3.5 avg is “later.” Highest score gets a 90-day country pilot with budget and owners.

Common traps (and how to avoid them)

• Translation-only launches. Localize the trust stack first; deepen content after early retention.
• English-keyword bias. Test local phrasing; false friends tank intent estimates.
• Seasonality whiplash. Don’t call “no demand” if the spike is two months later.
• Ignoring indirect rivals. If tutoring/workbooks dominate, build pricing & proof to beat them.
• Data without decisions. Define go/no-go thresholds before testing; avoid endless “more data” loops.

Appendix — 2-week Market Scan worksheet

Day 1–3: shortlist 5–10 via the 8-point filter; pick top 3.

Day 4–6: demand & ad-cost sizing (search, trends, store, $100 tests).

Day 7–9: competition map; Market Basics card; pricing hypothesis.

Day 10–14: run smoke tests; collect objections; compute CAC range; decide stop/scale.

Outputs: one-pager per market, metrics sheet, decision memo with next steps.