Marketer Localize Campaign

Localize campaign

Take one finished campaign and adapt it to N markets — translated copy, culturally appropriate visuals, right-to-left handling for AR/HE, and text-expansion-safe layouts. Built for consistency (same campaign identity per market) and speed (one batch per locale).

When to Use

• User has a shipped campaign in one market and needs to launch across 3+ additional locales.
• "Localize across DE, FR, JP, BR, ES" / "translate + adapt creatives per market" / "RTL version for AR".
• Master campaign has on-image text, people, or cultural motifs that need swapping per locale.
• Do NOT use for pure text translation (use an LLM directly) or for a fresh campaign (use marketer-campaign-kit first, then this skill).

Prerequisites

Ask up front if the brief doesn't cover it (combine into one message):

1. Master campaign assets — paths/URLs to the approved creative. What's already finalized (hero, ad variants, email, LP)?
2. Target locales — language + region (e.g. de-DE, fr-FR, ja-JP, ar-SA, pt-BR). Region matters for cultural appropriateness.
3. Translated copy — is it done? If yes, paste it. If no, note which copywriter/vendor owns it (don't auto-translate ad copy — compliance risk).
4. Cultural swaps needed — people (face/ethnicity), settings (interior style, streetscape), props (food, gestures)? If the hero has a specific person, does that persist or get replaced per locale?
5. RTL locales in scope — Arabic, Hebrew, Farsi? Layout needs to mirror, text extends right-to-left.
6. Deliverable naming — ad-platform uploaders expect {campaign}_{locale}_{size}.webp.

Never auto-translate ad copy without a reviewer — legal/compliance owns sign-off.

How to Run

1. Inventory the master assets. List every creative that needs localizing (hero, 6 ad variants, email, LP hero = 9 per locale × 5 locales = 45 assets).
2. Collect translated copy per locale BEFORE generating. Don't batch-run with English placeholders — you'll re-render everything.
3. Choose adaptation strategy per asset:
   • Text-only change (headline on a clean bg): use ideogram-v3 with the translated copy and same visual.
   • In-place motif swap (person, setting): use an edit model (qwen-image-edit-plus, flux-kontext-pro) with the original image + a locale-specific prompt.
   • Full regeneration (complex cultural swap): generate fresh from the original prompt + locale guidance, referencing the original as image for composition.
4. Write per-locale manifests. One manifest per locale keeps naming clean and lets you parallelize across locales without collision.
5. Estimate per locale.

   gen-ai batch run localize-de.json --dry-run
   

6. Run locales in sequence, not in parallel. Review one locale's output end-to-end before kicking off the next — catching a cultural mismatch in DE means fixing the prompt before JP, FR, and BR inherit it.

   gen-ai batch run localize-de.json -c 4 -o ./out/de-DE
   

7. Review with a native speaker per locale. Every locale. No exceptions. Especially for JP (text rendering), AR (RTL + text), and any market with a dedicated country manager.
8. Package and hand off. Folder per locale, filename includes locale code, results.json archived for audit.

Quick Reference

One manifest per locale keeps things tidy. {locale} and {name} placeholders support cross-locale reuse.

{
  "defaults": {
    "model": "qwen-image-edit-plus",
    "imageUrls": ["https://cdn-pipeline-output.picsart.com/.../master-hero.webp"],
    "negativePrompt": "low quality, watermark, culturally inappropriate"
  },
  "jobs": [
    { "id": "hero_de-DE",        "prompt": "adapt hero for German market — preserve composition, swap setting to central-European urban, replace overlay text with: \"Jetzt entdecken\"",                     "aspectRatio": "16:9" },
    { "id": "ad_9x16_de-DE",     "prompt": "adapt ad for German market — keep product/subject, swap scene to Berlin street, overlay headline: \"Jetzt entdecken — 20% Rabatt\"",                          "aspectRatio": "9:16" },
    { "id": "ad_1x1_de-DE",      "prompt": "adapt ad for German market — clean background, German headline: \"Jetzt entdecken\", CTA: \"Mehr erfahren\"",                                                  "aspectRatio": "1:1"  },
    { "id": "email_de-DE",       "prompt": "email header for German launch — German copy on-canvas: \"Jetzt entdecken\"",                                                                                   "aspectRatio": "3:1"  },
    { "id": "lp_hero_de-DE",     "prompt": "LP hero for German market — same product, German subtitle: \"Entdecken Sie die neue Kollektion\"",                                                              "aspectRatio": "16:9" }
  ]
}

For RTL locales (AR, HE), mirror layouts explicitly in the prompt: "right-to-left layout, headline reads from right, mirror the hero composition".

For voiceover localization (video campaigns), pair with gen-ai generate -m eleven-multilingual-v2 per locale — keep voice ID consistent across locales for brand continuity.

Quick Reference

Confirm IDs with gen-ai models.

Procedure

• Text expansion is real. German runs 30% longer than English, French 20% longer, JP/ZH 30% shorter but character-dense. Leave 25-30% breathing room in text-safe zones before rendering.
• Never auto-translate ad copy. Compliance owns sign-off. Collect human-reviewed translations, then render.
• RTL locales mirror the layout. AR/HE need hero composition flipped and headline-reads-right. State it explicitly in the prompt.
• Culturally appropriate faces/settings. A Berlin scene feels wrong in Tokyo. If the master has a person, decide early: persist (identity consistency via Nano Banana Pro) or swap per market.
• ideogram-v3 is the only reliable option for on-image text in non-Latin scripts. Other models render JP/AR/KR as gibberish. Use it for any text-bearing asset.
• One locale at a time, reviewed end-to-end. Parallelizing 5 locales hides prompt bugs until you've spent 5x the credits.
• Native-speaker review is non-negotiable. LLMs miss cultural nuance. Every locale gets eyes on from someone in-market before shipping.
• Filename locale codes enable direct ad-platform upload. launch_de-DE_9x16.webp parses cleanly in Meta / Google / TikTok CSV importers.
• Keep voice ID consistent across multilingual VO. Same voice across markets = brand continuity. Set voice in defaults, not per-job.

Pitfalls

• Text overflowing the safe zone in DE / RU / FI. Copy expansion wasn't accounted for in the master layout — leave 25-30% margin before localizing.
• JP / AR / KR text renders as gibberish. Wrong model. Use ideogram-v3 for any non-Latin on-image text, not flux/recraft.
• RTL layout not mirrored. Arabic users get a headline that reads wrong direction + hero composition that feels wrong. Explicit mirror instruction fixes it.
• Culturally tone-deaf imagery. A Christmas-themed visual in a Muslim-majority market, a left-hand gesture in SEA — run cultural review per market.
• Translating in the prompt with the LLM. Ad copy goes through compliance; don't have the model "translate this headline to German" mid-prompt. Use human-reviewed translations.
• Parallelizing all 5 locales upfront. One prompt bug becomes 5 locales of wasted credits. Sequence them; review DE, then kick off FR.
• Inconsistent voice across locales in VO. Different voice IDs per locale breaks brand continuity — set one voice ID across all TTS jobs.
• Skipping native-speaker review. "The model did fine on German last time" is how you ship a mistranslation.

Verification

Run gen-ai whoami to confirm authentication, then re-run the failed command with --debug.

Cost & time

Rough estimate to localize a 9-asset campaign across 5 locales (45 total renders):

Full regeneration via flux-2-pro roughly doubles the image credits. Estimate explicitly with gen-ai batch run localize-{locale}.json --dry-run before spending.

See also

• gen-ai-use/SKILL.md — full CLI reference (flags, model catalog, auth, TTS models)
• gen-ai-workflows/SKILL.md — general multi-step patterns
• gen-ai-batch/SKILL.md — manifest shape, placeholders, concurrency
• workflows/marketer-campaign-kit/SKILL.md — run before this to produce the master campaign
• workflows/marketer-ad-variant-factory/SKILL.md — run winners through localization after A/B tests pick them