Batz Hungary — How a €40M Central European DTC footwear brand became AI's default comfort-shoe recommendation in 25 days — ahead of Birkenstock and Skechers.
- +25% revenue
- 25 days
- €40M DTC
- vs Birkenstock, Skechers
Batz is the largest comfort footwear DTC brand in Central Europe, doing meaningful nine-figure-forint revenue, with strong organic brand equity in its home market. But the moment a buyer asked an AI assistant 'best comfort shoes for standing all day' — even in Hungarian — the answers came back with global incumbents: Birkenstock, Skechers, Scholl, New Balance. The brand AI defaulted to was rarely the brand the local market actually trusted.
This is the silent tax of being a strong regional brand in the AI era. Your real-world demand is fine. The next generation of buyers, who start with a chat prompt rather than a Google search, will never hear about you unless the models do.
The brief was tight: 25 days to shift category-defining queries in target languages and prove revenue impact in the same window.
- 01
Mapped the comfort-shoe query graph in Hungarian, Slovak and English — the three languages Batz's buyers actually use. Identified the 40 prompts where competitors were named and Batz was not.
- 02
Rebuilt product entity profiles: schema, multilingual content, third-party signals (review sites, fashion editorial, comparison content) so models had a coherent answer to 'who makes the best [category]' in each language.
- 03
Pushed targeted comparison content head-to-head against the named incumbents. Not attack content — earnest, sourced comparisons that gave models something concrete to cite.
- 04
Coordinated with the existing paid program so the moment AI-warmed traffic landed, the on-site experience matched the recommendation. Friction between AI promise and site reality kills conversion.
- 05
Daily citation tracking across ChatGPT, Perplexity and Gemini, with weekly reports showing rank shift per query cluster. Exact internal team allocations across the programPending client approval
We don't publish what we can't verify with the client. This will fill in when they sign off.
Strong regional brands are uniquely exposed in the AI era: the model does not know they are strong, only that the global names have more content addressed to them. The fix is not more advertising — it is making the model's source data match the market's lived truth. When you reconcile that gap with multilingual entity coherence and earnest comparison content, the model's default answer flips. Revenue follows because you are now being recommended to the next buyer before they have even decided to look for you.
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