Why AI Isn’t Enough for Translation: How a Shoe Web Shop Lost Time, Money

Cheap Translation Turned Into a Costly Mistake

In early 2024, a mid-sized Slovenian shoe retailer decided to expand into Croatia, Germany, and the UK. With over 600 SKUs and three markets to launch into, they chose an AI translation plugin to localize their web shop, believing it would be faster and cheaper than hiring a translation partner.

It wasn’t.

In 4 weeks, they spent €5,000 on AI and localization SaaS subscriptions. What they got: translated brand names, incorrect product descriptions, entire blocks left in Slovenian. And a 3-week go-live delay. Estimated revenue loss from the delay alone: €18,000–€22,000, based on their average cart value and seasonal campaigns.

In this article, we break down:

  • What exactly went wrong

  • How you can avoid the same trap using a Human+AI strategy

  • Three proven localization strategies we use at Starling to turn language into sales, not friction

If you’re a web shop owner, developer, or localization manager expanding into new markets, this will help you reduce risk and increase conversion without breaking your budget.

The Real Story: When AI Doesn’t Understand Footwear

The company plugged in a popular AI-based translation plugin (Weglot + DeepL Pro API credits). It translated:

  • “Žiga SoftStep” (a registered brand name) into “Soft Step by Žiga” in English and “Weicher Schritt von Žiga” in German

  • “čevlji za gleženj” into “ankle medical boots” instead of “ankle boots”

  • 73 product descriptions were partially left in Slovenian due to dynamic CMS fields

  • Meta titles, collection names, and CTAs were literal or nonsensical

  • Several product filters didn’t work due to untranslated back-end tags

Customer complaints started appearing within hours of pre-launch testing: one user asked if they were shipping orthopedic shoes; another said the site looked “dodgy and half-translated.”

They brought in our team at Starling to clean up the mess. It took us 54 working hours across 3 linguists, 1 reviewer, and 1 project manager to correct the errors, establish terminology consistency, and rebuild language assets. Most of this could have been avoided with a basic glossary and a QA layer.

Why AI-Only Localization Falls Short

Modern MT engines like DeepL and Google Translate are incredibly fast. But they still:

  • Translate what shouldn’t be touched (brand names, SKU codes, SEO terms)

  • Struggle with context (a “heel” for a shoe vs. a body part)

  • Can’t distinguish between front-end and back-end content

  • Have zero awareness of tone, campaign style, or local SEO

In e-commerce, where language sells, this becomes a liability.

Research backs this:

  • 76% of consumers prefer to buy from websites in their native language (CSA Research)

  • 40% say they never buy from sites not in their language (CSA Research)

  • Quality translations can increase conversion rates by 14% (Unbabel, 2023)

The Better Approach: Human+AI Strategy

At Starling, we don’t reject AI. We refine it.

Our hybrid strategy combines machine speed with human precision to deliver fast, accurate, and locally resonant content. This workflow gives you:

  • Faster go-live (60–70% faster than human-only)

  • Lower costs (up to 50% cheaper than full human workflows)

  • Brand-safe, conversion-optimized text

And most importantly: customer trust.

Three Smart Localization Strategies to Prevent Disaster

We use the following three strategies with every e-commerce client entering CEE or DACH markets:

1️⃣ BrandGuard: Lock Your Identity Before You Translate

Goal: Prevent translation of protected or brand-sensitive terms

How it works:

  • We build aproject glossary before translation starts (via Excel or tools like Phrase or Lokalise)

  • Key elements, such as brand names, category titles, SEO terms, size guides, are marked “Do Not Translate”

  • These are uploaded to CAT tools and synced with AI engines (e.g., via DeepL glossary imports)

Result:

  • No brand damage

  • No duplicated terminology or SEO dilution

  • Product filtering and metadata remain intact

In the shoe retailer’s case, BrandGuard would have saved 2,000+ glossary-related edits post-translation.

 

2️⃣ SenseCheck: AI-Train and Human-Review Smartly

Goal: Use AI, but QA it like a pro.

How it works:

  • Train AI on existing bilingual product data (we use custom TMX/TBX datasets)

  • Run initial MT draft

  • Human linguists do bilingual segment-level review in tools like memoQ or Trados

QA checklists verify consistency, correct length, product logic, and regional language norms

Tools we use:

  • QA automation: Xbench, Verifika

  • CAT tools: memoQ, Phrase, Lokalise

  • MT: DeepL Pro + custom glossary injection

Result:

  • 40–60% faster delivery compared to human-only

  • Maintained tone of voice across 3 language versions

  • Clean, error-free content ready for CMS import

In our case study, the initial €5,000 AI investment could’ve delivered value if SenseCheck had been applied. Instead, the fix cost them €3,800 extra.

3️⃣ MarketFit™: Don’t Just Translate, Localize the Experience

Goal: Adapt content, UX, and structure to each market’s buying behavior

How it works:

  • Use localized sizing, currency, and delivery options

  • Adjust CTA phrasing to regional style (e.g., “Kupi sada” in Croatian vs. “Jetzt kaufen” in German)

  • Translate structured data (filters, breadcrumbs, menus) with backend IDs preserved

  • Check SEO meta descriptions, titles, and slugs per language

Results from past projects:

  • +28% average cart completion rate (localized checkout page copy)

  • -35% bounce rate in German site vs English fallback

  • 2x increase in session duration in Croatian site with adapted UI labels

In our shoe retailer case, MarketFit adjustments helped recover 12% in lost conversions after the re-launch.

 

What It Really Costs: Human+AI vs AI-Only

For our shoe client:

  • AI-only pre-launch: €5,000

  • Fixing and human revision: €3,800

  • Missed revenue: €18–22000

A properly scoped Human+AI process would have cost ~€4,000, avoided launch delays, and saved over €20,000.

 

Embedding QA and Process from Day One

Localization is not a task. It’s a process. Here’s how we build it with clients:

  • Glossary Building – Define brand terms and upload into all CAT/MT tools

  • Test Run – Translate 5–10 SKUs first, validate output and format

  • Workflow Automation – Set up auto-export/import from CMS or PIM (e.g. Shopify)

  • Bilingual QA – Implement review using checklists, check regex and tags

  • Post-Go-Live Review – 72-hour sanity check post-launch to catch last-mile issues

At Starling, we’ve used this structure with both high-SKU Shopify web shops and complex Magento stores entering CEE countries such as Slovenia, Croatia, or Hungary.

 

Localization is a Revenue Tool, Not a Cost Center

What looks like a cheap shortcut, such as AI translation without control, can turn into a brand liability, delay, and lost sales.

But with a smart strategy combining automation and expertise, localization becomes a revenue multiplier.

Our Human+AI workflows, combined with BrandGuard, SenseCheck, and MarketFit, are already supporting web shops in sportswear, fashion, and health products across Central and Eastern Europe.

Want to Localize Your Webshop Without the Headaches?

Starling supports e-commerce brands in reaching German, English, or CEE countries with precision and scale through:

  • Proven Human+AI workflows

  • Localized for tone, trust, and conversion

  • Ready for Shopify, Magento, WooCommerce, or custom CMS

Book a free 20-minute consultation and get an audit of your existing language setup.

Let’s turn your translations into conversions.