Product Feed Optimisation for Global Discovery

What makes multilingual product feed optimisation so valuable?

A translated feed is not an optimised feed

Most global brands still treat their product feed as a back-end file: translate the titles, ship the descriptions, move on. That approach was never built for how people actually search and compare, market by market.

It’s even less suitable for how AI shops today.

Large language models now read product feeds directly and recommend products to shoppers who never land on your site. If your feed doesn’t speak the local market’s language, structure and intent, it doesn’t get chosen. Your feed is now a discovery channel, not a technical file.

We treat it that way. Our approach combines in-market linguistic expertise, structured data, and performance measurement, so every feed is built to be found, understood and acted on, in every language you sell in.

An abstract, high-contrast neon cyberpunk visual symbolizing data localization. The background is a deep Locaria Imperial (#221624). From the left, a stream of generic, uniform digital data blocks in flat Paris Daisy (#EEFF41) yellow enters a central, geometric crystal prism. The prism refracts the light into multiple multi-directional, complex holographic streams of Dodger Blue (#017FFF) and Cyan Glow (#42F2F7). These refracted streams contain stylized, non-readable glyphs representing various global scripts (e.g., stylized Japanese, Arabic, and Western script), illustrating the shift from simple translation to localized demand. The lighting is harsh and directional.

Feed Content, Written for Local Demand

Native multilingual specialists write, adapt and test your product titles, descriptions, attributes, custom labels and imagery in-market. This means your feed reflects how people in that market actually search and compare, not a direct translation of your English original.

Result: products that match local language and local search behaviour.

A detailed abstract neon cyberpunk visualization focusing on data enrichment for artificial intelligence. The scene features a floating, detailed 3D wireframe structure of a generalized product (like a futuristic sneaker) rendered in Dodger Blue (#017FFF) lines against a deep Locaria Imperial (#221624) background. Surrounding the wireframe is a network of floating nodes. These nodes are connected by pulsing light lines (in Cyan Glow #42F2F7 and Paris Daisy #EEFF41) to a central pulsating sphere of light (the AI core) glowing in Cyan Glow (#42F2F7) with the text "LLM/AI" glowing clearly within it. The peripheral nodes feature glowing yellow labels with text like: [USE CASE: HIKING], [MATERIAL: GORETEX], [INTENT: PURCHASE], and [GENDER: FEMALE], visualising the deep contextual data needed for AI discovery.

Structured for AI and GEO Discovery
Search used to mean short keywords. AI discovery means shoppers typing detailed prompts about use case, budget and constraints. We enrich your feed with the contextual data LLMs need: product intent, use cases, variants, materials, schema markup and market-specific taxonomy.

Result: your products get actively recommended by AI.

An abstract neon cyberpunk visual illustrating scalable, modular data infrastructure. The scene shows a vast grid system of interlocking, glowing hexagonal and cubic blocks made of alternating Dodger Blue (#017FFF) and Cyan Glow (#42F2F7) lines, representing modular feed infrastructure extending into the distance against a deep Locaria Imperial (#221624) background. In the foreground, one central block is slightly elevated, pulsing intensely with Paris Daisy (#EEFF41) yellow light, marked with a cog and sync icon. From this central block, glowing pulses of logic light travel instantly along connecting data pathways to the surrounding network, illustrating single-point updates. Hovering above the structure are translucent, rising holographic line graphs and subtle, non-readable currency symbols ($ and €) in neon colors, representing commercial data metrics and profitability. The perspective is slightly elevated, looking across the vast network.

Built on Data, Not Assumptions

We tie feed optimisation to commercial metrics, building custom reporting around profitability, return rates and market performance. To manage this at scale, we build modular feed infrastructure: shared logic and rules updated once, deployed across every market.

Result: decisions backed by data, and a feed that scales without duplicating the work.

18%

Increase in Click-Through Rate delivered through title optimisation for a global sportswear brand's feed.

25.6%

Increase in Clicks achieved through description optimisation on the same account.

40%

YoY revenue increase driven in part by a dynamic custom labelling strategy.

Your feed is already being read by AI. The question is what it’s telling shoppers.

Let’s make sure it’s recommending you.

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FAQs

Every beautiful relationship begins with a simple hello. So let’s chat. It might be the start of something.

Talk to the team

What is multilingual product feed optimisation?

It’s the process of adapting your product feed for each market you sell in, not just translating it. This covers titles, descriptions, attributes, custom labels and imagery, written and structured so local shoppers (and AI shopping tools) can find and understand your products.

Why isn't translating our feed enough?

A translated feed carries over your original market’s structure and search logic. It doesn’t reflect how people in a different market phrase searches, compare products, or what details they expect to see. That gap costs you visibility and clicks.

How does AI shopping change what we need from our feed?

Traditional search runs on short keywords. AI tools like ChatGPT respond to detailed prompts about use case, budget and constraints, then recommend products directly, sometimes without the shopper visiting your site. Your feed needs the contextual data (use cases, variants, materials, schema markup) to be picked up and recommended, not just indexed.

Can this scale across many markets without duplicating work?

Yes. We build modular feed infrastructure, so shared logic and rules are set once and deployed across every market, with native-market content layered on top. That keeps the work manageable as you add languages.

How do you measure whether feed optimisation is working?

Through custom reporting tied to commercial metrics, profitability, return rates and market-level performance, so decisions are based on what’s actually driving results, not assumptions.

Do you use native speakers for the feed content itself?

Yes. Native multilingual specialists write and test the product titles, descriptions and attributes in-market, so the feed reflects real local search behaviour rather than a direct translation.