Local Market Opportunity Index 2026: Open Model
Market selection is often reduced to list size: choose the country with the most businesses and start outreach. That ignores data completeness, verification cost and commercial fit. We built an open “observed gap score” from 3,147 sampled local-business profiles to demonstrate a more transparent approach.
The country opportunity index publishes every component. The downloadable market prioritization template adds a blank commercial-fit input and a spreadsheet formula. You can replace our weights rather than accepting a black-box rank.
This model extends the evidence in our Google Maps profile completeness score, but changes the unit from one profile to an aggregated country sample.
Highest observed gap scores
| Country | Sample | Website gap | Phone gap | Zero reviews | Score |
|---|---|---|---|---|---|
| Mexico | 143 | 68.5% | 23.8% | 11.2% | 40.4 |
| Argentina | 155 | 41.3% | 16.8% | 45.2% | 34.4 |
| South Africa | 300 | 58.0% | 6.7% | 23.0% | 33.8 |
| India | 150 | 56.0% | 16.0% | 4.7% | 31.1 |
| Türkiye | 196 | 39.8% | 7.1% | 13.3% | 23.0 |
These are not the “best countries for sales.” They are the highest scores under one formula applied to one uneven convenience sample. A market can show many observable gaps and still be commercially unsuitable because of language, regulation, pricing, competition or service fit.
Formula
The observed gap score is:
website gap × 0.45 + phone gap × 0.15 + both missing × 0.15 + zero reviews × 0.25
Each input is a percentage, so the result remains on a 0–100 scale. Website gaps receive the largest weight because the initial research question focused on digital-presence services. Zero reviews receive 25% as a separate profile-maturity signal. Phone and dual-contact gaps represent reachability and verification friction.
There is no universal correct weighting. A phone-sales team could invert the priorities. A reputation agency might replace zero reviews with median review volume. A data provider may subtract points for duplicate rates. Open components make those choices inspectable.
Add commercial fit before choosing a market
The template uses a second score supplied by your team: commercial fit from 0 to 100. Its example formula combines 70% observed gap score with 30% commercial fit. That weighting is only a starting point.
Commercial fit can include:
- language and support capability;
- legal basis and outreach rules;
- average contract value;
- payment and currency constraints;
- density of the exact target category;
- ability to deliver the promised service;
- competitive intensity;
- evidence from previous campaigns.
Document how each input was scored. Otherwise a transparent formula merely hides subjective guesses in a new column.
From country to market cell
A country is too broad for execution. After selecting two or three candidates, create market cells such as “independent dental clinics in City X” or “family restaurants in Region Y.” Collect a fresh batch for each cell and recalculate the same components.
This second pass answers whether the national sample survives contact with the actual segment. If it does not, trust the segment data. The country index is a routing device, not a substitute for local evidence.
Validation gates
Before committing budget, require four gates:
- Coverage gate: enough records exist in the target category and geography.
- Quality gate: a manual sample confirms identity, links and contact fields.
- Compliance gate: the collection and outreach workflow is reviewed for the relevant jurisdiction and channel.
- Commercial gate: a small campaign produces qualified conversations at an acceptable total cost.
Google's Business Profile information guidance is useful for understanding which public fields owners can maintain. It does not turn profile gaps into permission to contact or into proof of need; those questions require separate review.
Why raw rank is dangerous
Mexico's 40.4 score is driven heavily by its observed website and phone gaps. Argentina's score includes a much larger zero-review share. The same total can therefore represent different underlying situations. Read the components before designing an offer.
Sampling bias is equally important. The dataset covers 20 countries and 58 cities but is dominated by restaurant and dental records. It is neither random nor population-weighted. Scores may move when category mix, city coverage or collection time changes.
How to rerun the model
- Download the component CSV.
- Filter to markets with an adequate sample.
- Replace weights to match your use case.
- Add commercial-fit scores with written evidence.
- Select a small number of market cells.
- Collect current cell-level data.
- Run a limited, compliant test and record outcomes.
- Update the model instead of defending the original rank.
Bottom line
An opportunity index is useful when it exposes its assumptions. In this sample, observed profile gaps differ across countries, but those gaps are only one part of market attractiveness. Use the open score to structure a decision, add commercial reality, validate at category-and-city level and keep the model easy to revise.