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Data analytics case study · SEO growth
The largest market was not the strongest first move.
An SEO market opportunity analysis of 213 keywords and 125 competitor ranking rows for a new organic bus travel vertical across Spain, France and the United Kingdom.
Launch decision map
Decision ready
Raw scale versus accessible demand
The background bar shows total monthly searches. The mint overlay shows demand not tied to a named operator or brand.
ES
53.2%
FR
69.7%
UK
40.4%
Recommended first marketFrance
Best balance of scale, accessible demand and launch potential.
Total demand analysed2.15M
Monthly searches across the three markets.
Non-brand demand1.11M
Demand available beyond named operators.
France accessible share69.7%
The strongest non-brand ratio in the comparison.
Market evidence
Raw demand tells only half the story.
The United Kingdom leads on raw search volume, with almost 959,000 monthly searches. Change the metric to see how the market ranking shifts.
Select a metric and then a market. The written recommendation remains based on the combined evidence, not a single headline number.
View the comparison as a table
| Market | Total searches | Non-brand searches | Non-brand share |
|---|---|---|---|
| Spain | 653,900 | 348,100 | 53.2% |
| France | 534,500 | 372,300 | 69.7% |
| United Kingdom | 958,700 | 387,000 | 40.4% |
Analysis lenses
What makes the opportunity usable?
Transactional demand leads in every market. France has the clearest commercial profile relative to its total market size.
France search intent
Commercially useful demand dominates the market, while navigational dependence remains lower than in Spain or the United Kingdom.
Values come from the cleaned portfolio dataset. Market labels and written interpretation update with the selected lens.
FranceMonthly searches
01Transactional365,100
02Navigational147,500
03Informational21,900
Method and data quality
Comparable markets require comparable data.
Raw keyword and ranking exports do not arrive ready for a launch decision. The workflow protected totals from duplicate rows, separated operator demand from open demand and retained match status when a ranking market had to be inferred.
Protect the denominator
Flag duplicate keyword and market combinations before calculating totals.
Define accessibility
Separate brand and operator queries from non-brand demand.
Map the SERP
Classify visible competitors as operators, aggregators, OTAs or marketplaces.
Join and test
Build one clean crosswalk with QA checks, summaries and reproducible logic.
Keyword table213 clean rows
Competitor table125 ranking rows
Cross-market scope3 markets
Project evidenceDownload the cleaned workbook ↘
SQL logicInspect a reproducible market summary
SELECT
market,
SUM(avg_monthly_searches) AS non_brand_search_volume,
COUNT(DISTINCT keyword_norm) AS non_brand_keywords
FROM keyword_clean
WHERE brand_flag = FALSE
AND dup_keyword_market_flag = FALSE
GROUP BY market
ORDER BY non_brand_search_volume DESC;Decision logic
Scale shows demand. Structure shows opportunity.
The recommendation changes when headline volume is read together with brand dependence, scalable query types and the visible competitor mix.
Signal
Biggest is not automatically best.
Implication
The United Kingdom contributes 958,700 monthly searches, but 571,700 are tied to brands or operators. Broad entry would begin against the strongest incumbent concentration in the sample.
Signal
Accessibility changes the ranking.
Implication
France reaches 372,300 non-brand searches and a 69.7% accessible share. It combines commercial demand with a less brand-dependent query mix.
Signal
Routes create the scalable asset.
Implication
City-to-city searches contribute 688,300 monthly searches across the three markets, with demand distributed almost evenly between Spain, France and the United Kingdom.
Recommended rollout
France first. Spain second. The UK selectively.
The sequence balances addressable demand, intent and competitive structure. It keeps the first implementation focused enough to learn before scaling.
France
Launch the core architecture where non-brand demand has the highest share.
- Commercially strong query mix
- Lower operator dependence
- Best first learning market
Spain
Extend the tested model into strong route and schedule-led demand.
- Strong route demand
- Useful second-stage scale
- Localise the proven template
United Kingdom
Pilot selected routes and airport-transfer clusters instead of a broad rollout.
- Largest raw market
- Highest incumbent concentration
- Selective entry over blanket coverage
Organic architectureRoute-page-led, not blog-first
FoundationCountry and city hubs
Core assetCity-to-city route pages
Demand supportPrice, schedule and transfer modules
Scope and interpretation
Strong evidence needs clear limits.
The analysis supports a launch sequence, not a revenue forecast. Clear limitations are part of the recommendation rather than a footnote after it.
It indicates demand direction but does not predict traffic, conversion or revenue.
The 125 ranking rows describe visible patterns rather than the complete SERP landscape.
QA fields remain in place so the analytical logic can still be inspected.
A live decision should validate ranking difficulty, route margin and conversion potential.
Project evidence
Inspect the analysis behind the recommendation.
The public files separate the concise hiring-level narrative from the detailed, auditable analytical workbook.
XLSX · Analytical workbook
Cleaned data, QA checks and reproducible logic.
Source structure, normalised fields, duplicate controls, summary tables, data dictionary and SQL examples.
PDF · Executive case summaryA concise decision memo for employers and stakeholders.
Business question, method, findings, recommendation and limitations in a focused document.
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