Multi-Language Paid Search:
Managing Spanish & Catalan Intent

Catalonia has a population of roughly 7.7 million people where Catalan is co-official. Substantial shares of the population—especially outside Barcelona in cities like Girona, Lleida, and Tarragona—prefer Catalan daily, meaning language segmentation drives regional trust, distinct search behavior, and lower competition.

Query volume asymmetry means some categories see heavy Catalan search volume (local services, cultural events, public administration), while others see almost none (highly technical B2B terms or global brand searches). Blind translation wastes budget on dead keywords while missing unique local phrasing.

Transactional modifiers differ significantly, such as “el millor” versus “el mejor”, “preu” versus “precio”, or “a prop meu” versus “cerca de mí”. These carry different search volumes, match behaviors in Google’s systems, and distinct commercial intent signals.

Code-switching occurs when a single user searches in Catalan for informational queries, then switches to Spanish for transactional or brand queries later in the same session. This means Spanish and Catalan campaigns are overlapping customer journeys rather than independent silos.

Option 1 (Full campaign-level separation by language under the same account) is generally recommended. It features Campaign A for Spanish (National + Regional) and Campaign B for Catalan (Catalonia, Balearic Islands, Valencia), allowing independent bids, Quality Scores, and ad testing.

Option 2 structures campaigns by geography (e.g., a Catalonia campaign with separate Spanish and Catalan ad groups). This fits retail or local service businesses with physical store footprints tied to regional budgets, though isolated language reporting becomes less prominent.

Google Ads location targeting relies on physical location rather than language preference. Target Catalonia, Balearic Islands, and Valencia broadly for both campaigns, letting the keyword and ad copy language do the intent-matching work.

Google’s broad and phrase match algorithms bleed across languages on brand terms, numbers, or borrowed English words. Adding the entire Spanish keyword list as exact and phrase negatives into the Catalan campaign (and vice versa) prevents internal cannibalization and muddy data.

Go beyond translated seed lists by using native-language autocomplete data, mining live search term reports, consulting native speakers for official Generalitat terminology, and checking for dialectal variations like Valencian or Balearic phrasing.

Machine-translated Catalan ad copy is often detectable to native speakers, especially around idioms, CTAs, and regional preferences. Poor translation undermines the local trust signal that makes investing in Catalan campaigns worthwhile.

Landing pages require full language parity rather than small toggles to protect Quality Score and conversion rates. Correct hreflang implementation, structured data, and localized ad extensions (sitelinks, callouts) ensure site-wide linguistic consistency.

Start with manual or Target CPA bidding during the learning phase because lower search volumes can trap automated smart bidding models. Once a stable threshold of conversions is reached, automated bidding becomes viable.

Avoid population-proportional splits or underfunding due to lower absolute volumes. Insufficient spend creates a self-fulfilling data problem; give the Catalan campaign a genuine testing budget for at least one full quarter before reallocating.

Use consistent campaign naming prefixes like [CAT] and [ES], segment conversion tracking by campaign, monitor cross-campaign path reports for multi-touch attribution, and benchmark Quality Score components independently.

Common mistakes include treating Catalan as a minor add-on, relying entirely on machine translation, failing to build cross-language negative keyword lists, applying a uniform national budget, and neglecting landing page localization.