In brief
- SEO copy on the product page does not guarantee citations in ChatGPT, Perplexity, or AI Overviews.
- AI search reads attributes, Schema.org, FAQ, and clear entities better.
- Start not with the whole catalog, but with 5–10
SKUwith demand and margin. - For Prom.ua, clean import, feed, and product fields without duplication are critical.
- For Horoshop and Shopify, modifications, variants, and stable attributes are critical.
- Check the result through citations, AI answers, and changes in visibility after 2–3 weeks.
- GEO should not be launched where product pages are empty, messy, or there is no real demand.
Definition
GEO is the optimization of a store’s content and data so that AI search can correctly mention, compare, and cite products.
AI-citation is a mention of a store, category, or specific product in a response from ChatGPT, Perplexity, Gemini, or AI Overviews.
Schema.org is structured page markup that tells search engines the type of entity: product, FAQ, how-to, or product variant.
FAQPage is a markup type for questions and answers that helps AI pull short, verifiable snippets from a product page or category page.
HowTo is markup for step-by-step instructions, suitable for products where the buyer asks about installation, care, selection, or use.
Entity penetration is the закрепление of a brand, category, or product group in external sources and knowledge graphs, including Wikidata.
What GEO is for a product catalog and why product page SEO is not enough here
What is GEO for a product catalog, and why product page SEO is not enough here
GEO for a product catalog is data preparation so that a store can appear in an AI search answer: ChatGPT, Perplexity, Google AI Overviews, and other systems that assemble answers from sources rather than simply showing a list of links. The goal here is not to “move a product page up 3 positions.” The goal is to make the product clear, verified, and structured so AI has a reason to mention it in the answer.
A classic SEO product page usually relies on title, description, H1, category copy, internal links, and commercial keywords. All of that is still needed. Google Merchant Center requires high-quality, accurate, and up-to-date product data, including title, description, price, availability, link, and images: Google Merchant Center Help. But that is not enough for AI-citation.
An AI system does not need a “pretty” paragraph about how a backpack “is perfect for an active lifestyle.” It needs facts it can identify, compare, and tie to an entity:
- attributes:
brand, model, material, size, color, weight; - characteristics: volume, compatibility, seasonality, warranty;
- entities:
brand, category, series, manufacturer, country; - answers to common questions: who it is for, how to choose a size, how it differs from another model;
Product, Offer, AggregateRating, FAQPage markup, and for variants — a correct ProductGroup / Product structure, which Google describes in the product variant structured data documentation: Google Search Central.
For a store on Prom.ua, Horoshop or Shopify, the problem is usually not that the descriptions are “not SEO enough.” The problem is that the catalog does not have a single data logic. Some characteristics live in the description, some in the feed, some in variants, and some in an import table filled by different managers over different years.
In UPLIFY’s practice, we see this all the time: the owner has already invested in SEO copy, categories, and meta tags, but AI search does not cite the store because the product page does not provide a stable structure. For GEO, what matters is not nice wording, but repeatable attributes, consistent field names, proper markup, and answers to real buyer questions. It is less flashy than “rewrite the whole catalog for AI.” But that is exactly where visibility in AI search answers starts.
SEO copy can help Google understand the page. GEO should help AI understand the product as an object: what it is, who it suits, how it differs, what parameters it has, and what questions it answers. For a catalog with 1000+ SKU, this is no longer copywriting. It is data normalization.
So GEO does not replace SEO. It covers a different layer of work. title and description remain the foundation for search, advertising, and Merchant Center. But AI-citation more often appears where the catalog has a clean structure, stable entities, and machine-readable answers. This week, do not rewrite the whole catalog. Take 5–10 products, align attributes, FAQ, and markup, then check whether ChatGPT or Perplexity start pulling those pages into answers.
How AI search reads product pages on Prom.ua, Horoshop, and Shopify
AI search does not read a product page like a buyer does. It pulls out entities: product, brand, category, characteristics, price, availability, variants, rating, and questions and answers. If this data exists in structured fields or Schema.org, the model can use the page in the answer more easily. If everything is hidden in a 900-character paragraph, the chance of citation is lower.
A classic SEO product page is usually built for indexing: keyword in the title, keyword in the first paragraph, a few more mentions in the copy. For GEO, that is not enough. ChatGPT, Perplexity, and AI Overviews more easily use a page where the buyer’s query is already broken down into fields:
brand: who the manufacturer is;category: whichcategorythe product belongs to;- material, size, color, weight: characteristics without promotional noise;
priceandavailability: current commercial information;- aggregateRating or reviews: a trust signal, if they are real;
- FAQPage: short answers to typical buyer questions;
- HowTo: instructions for selection, installation, use, or care.
Google explains that structured Product data can pass price, availability, ratings, product variants, and other properties for search. For products with variants, Google separately recommends ProductGroup and Product markup so the variants are connected as one product group rather than being duplicated (Google Search Central: Product structured data, Google Search Central: Product variants).
The platform affects where the structure breaks. On Prom.ua, a store often works through bulk import and feed, so the weak point is filling in characteristics in the right columns, not the nice description. Prom.ua separately describes product import and feed export for Google Merchant Center, so part of the structure is already tied to tabular data rather than page copy (Prom.ua: product import, Prom.ua: feed for Merchant Center).
Horoshop is more convenient for working with the catalog, variants, and templates, but discipline is still needed there. Attributes must be normalized, not entered in different formats for every SKU. Shopify gives more technical freedom through themes, metafields, apps, and CSV import. That is a plus as long as there is one attribute model. Without it, freedom quickly turns into data chaos.
In UPLIFY’s practice, we do not start GEO by rewriting every product description in the catalog. First, we take 5-10 SKU, clean up the attributes, Product schema, FAQPage or HowTo, and only then see whether the pages appear in AI answers. In our approach, the first AI-citation should appear within 2-3 weeks after structuring the pages and working on entity presence. If that does not happen, scaling changes to 1000+ SKU is too early.
AI search does not reward long descriptions without structure. It prefers a page where the product can be clearly identified, compared, and explained to the buyer in one paragraph of the answer. That is bad news for catalogs that have lived for years on SEO copy. But it is good news for stores ready to bring order to the data this week, at least in a pilot SKU group.
Catalog diagnosis: why AI does not cite even a strong SEO store
A strong SEO store may have good titles, meta descriptions, category copy, and stable organic traffic. But for AI search, that is not enough. If product characteristics live only in the description paragraph and not in fields, the feed, or markup, the model sees the page but does not get a clear enough signal about what can be cited.
The audit should not start with rewriting 1000 product pages. Start with the question: does the catalog have a machine-readable structure? Google’s product data specification describes required and recommended attributes for products because search engines work not only with page copy but also with product data: price, availability, brand, identifiers, link, image, condition, and category [1].
Check 20–30 product pages from different categories and note where the product facts exist only in the description:
- material;
- size, weight, volume;
- compatibility with models or series;
- purpose;
- warranty;
- package contents;
- manufacturer,
brand, series, model; - type of product;
- country of manufacture, if it affects the choice.
If this data exists in the text but is missing as attributes, filters, parameters, Product schema, or feed fields, that is a weak signal for AI. This is especially true on Prom.ua and Horoshop: some data may be entered in the admin panel but not pass correctly into the export, page template, or microdata.
The second diagnosis block is entities. AI search cites pages better when it is clear that the product belongs to a specific brand, category, series, model, and type. If one page says “pump,” another says “water pump,” and a third says “water unit,” while the attributes have no stable product type, you are blurring the entity signal yourself.
The technical side is no less important. Check whether the pilot pages are indexed, not blocked in robots.txt, do not have a conflicting canonical, are not duplicated through URL parameters, and do not give different information to the user and the crawler. For products with variants, separately check whether sizes, colors, or modifications are collapsed into one page without a clear structure. Google separately describes ProductGroup and Product markup for product variants, because different modifications must be connected but not mixed into one page without logic [3].
In our practice, the best start is a controlled pilot, not a big catalog redesign. We take 5–10 SKU, clean the attributes, align the entity names, add proper microdata, check indexing, and only then see whether mentions appear in ChatGPT, Perplexity, or AI Overviews. If the pilot does not show a signal, scaling only multiplies the mistake.
How to restructure 5–10 pilot SKU for AI-citation
Do not start with the whole catalog. For AI search, that is almost always a weak start: the team spends weeks rewriting 1000+ SKU in bulk, but then does not understand which changes affected citations. Better to take 5–10 products and make them a test group. Each product page should have clear attributes, useful answers, and context that AI systems can read without guessing.
Pilot SKU should not just be “popular.” Choose products with demand, margin, and a real decision scenario. Not “any kitchen chair,” but a model people compare by material, height, load capacity, compatibility with the table, or interior style. AI is more likely to pick up pages with concrete facts for an answer than the same SEO copy repeated in 2000 characters.
Next, rewrite the characteristics as facts. Not “high-quality material and modern design,” but “suitable for underfloor heating,” “compatible with X mounting,” “not recommended outdoors without cover,” “supports loads up to 120 kg.” Google requires accurate and complete product data in Merchant Center: price, availability, identifiers, and product properties. This is described in Google Merchant Center’s product data specification.
The FAQ block should not be written from scratch. Sources are nearby: questions from Prom.ua, manager chats, Instagram comments, search queries, objections on calls. For one SKU, 4–6 questions are enough. The wording should sound like the buyer’s question: “Will it fit the bathroom?”, “How is 20 mm different from 30 mm?”, “Can it be cut to size?”. AI can use such answers as a fragment for recommendation.
For products with a usage process, add HowTo: selection, installation, setup, or care. This is not needed for every T-shirt or mug. But for EVA mats, filters, building materials, furniture, equipment, tools, or spare parts, HowTo structure often gives AI more context than a classic description. Google Search Central describes Product structured data as a way to pass product data to search, including price, availability, ratings, and variants.
After that, the SEO copy should become shorter. You do not need five paragraphs about a “wide assortment” and “a great deal.” Keep 600–1000 characters, but make them useful: what the product is, who it is for, which scenarios it suits, and where the limits are. For AI-citation, one exact phrase about compatibility is more useful than ten sentences about “the perfect solution for home and business.”
In our GEO projects on Prom.ua and Horoshop, the best pace is this: one week to choose and restructure 5–10 SKU, the second week to check indexing, Schema.org, feed.xml, and answers in ChatGPT and Perplexity. If the test group does not show signs of citation, it is too early to scale changes across the whole catalog. First you need to understand what exactly AI cannot read: attributes, FAQ, brand entity, or the page itself.
How to set up Schema.org on Prom.ua, Horoshop, and Shopify without manual chaos
Schema.org for a catalog with 1000+ SKU should not live in the “Product description” field. That is the first sign of chaos: today one manager inserted FAQPage into 20 pages, tomorrow another manager changed the wording, and the day after that half the products still have an old block with outdated warranty terms. AI search reads such pages poorly, and the team quickly loses control of the data.
The correct logic is different: Product schema should pull from product attributes, variants, price, availability, brand, GTIN, MPN, photo, and URL. That way it can be maintained at catalog level rather than edited manually on every page. Google describes product markup as a way to pass structured product data: price, availability, ratings, variants, and other properties, rather than arbitrary SEO copy in HTML description: https://developers.google.com/search/docs/appearance/structured-data/product
For Horoshop and Shopify, the basic implementation should go through templates, metafields, and structured blocks. In Shopify, additional fields for material, size, compatibility, use_case, faq_question, and faq_answer are better kept separately and then assembled into JSON-LD in the product template. Shopify CSV import supports bulk product data updates, so these fields can be prepared in batches without opening each page manually: https://help.shopify.com/en/manual/products/import-export/using-csv
FAQPage is better generated from controlled blocks. Not from copywriter text in the description, but from separate fields or modules: question, answer, product group, language, publication status. That way you do not end up with 300 SKU all having the same question “How do I order?” while AI search is looking for an answer about compatibility, installation, size, material, or use case.
HowTo schema should not be forced onto the entire catalog. For a T-shirt, mug, or standard case, it is often unnecessary. For EVA mats, building mixtures, auto parts, filters, fasteners, equipment, or selection-based products, it is appropriate, because there is a process: how to choose thickness, check compatibility, prepare the surface, install the product, or care for it.
On Prom.ua, there is less freedom. You cannot always manage JSON-LD precisely at template level, so the focus should be on what the platform really lets you scale: correct characteristics, product grouping, FAQ in product pages, clean category logic, quality import, and external entity strengthening. If the brand, category, or product line does not have clear entity signals outside Prom.ua, one page markup will not save GEO.
In UPLIFY’s practice, we do not start with “add Schema.org everywhere.” That is a bad plan for a large catalog. First we normalize the attributes, then build Product, then add FAQPage for pilot SKU, and only at the end test HowTo. We leave manual edits in descriptions for exceptions. In a large catalog, exceptions do not scale. This week, it is enough to choose 5–10 SKU, build a field mapping table for them, and check whether JSON-LD is assembled from data rather than from manual description text.
Where GEO works, and where it is better not to spend budget
GEO for a catalog makes sense where the buyer does not take the first item off the shelf, but compares options. AI search works well with queries like “which EVA mat should I choose for a children’s room,” “which generator is suitable for a 120 m² house,” or “what is the difference between R9 and R11 porcelain stoneware.” In such scenarios, the model is not looking for a pretty title, but for clear characteristics, use context, limitations, variants, and proof that the store is actually related to the topic.
- The best GEO candidates are categories where attributes differ:
- equipment, tools, building materials, furniture, sports gear;
- products with variants: size, thickness, power, material, compatibility;
- products where the buyer asks clarifying questions before purchase;
- categories with frequent “what is better,” “how to choose,” “what is it suitable for” queries;
- products where Product, ProductGroup, Offer, FAQPage, and HowTo can describe the real difference between
SKU.
Google’s Product structured data documentation ties the markup to product pages, prices, availability, ratings, and variants, not to the SEO copy at the bottom of the page: https://developers.google.com/search/docs/appearance/structured-data/product . For variant products, the ProductGroup and Product logic is described separately, so grouping modifications must be machine-readable: https://developers.google.com/search/docs/appearance/structured-data/product-variants .
Catalogs with mass-produced, repetitive products usually perform weakly: souvenirs without characteristics, basic clothing without material and fit, cheap accessories without use scenarios, dropshipping with supplier descriptions duplicated. Those need data cleanup first. GEO will not save a page that has only a photo, a price, and a paragraph saying it is a “high-quality product for everyday use.”
In highly competitive niches, product pages alone are also not enough. If a store sells generators, auto parts, cosmetics, medical products, or electronics, AI must see entity signals outside the site. You need brand mentions, category guides, reviews, company profiles, correct data in directories, and where appropriate, a link to Wikidata. This is not magic. A model is more likely to cite a store that is tied to the topic in several independent places, not only in its own feed.xml.
In UPLIFY’s practice, we first look not at catalog size, but at data quality in the pilot SKU group. If attributes are empty, the FAQ looks invented, and variants are reduced to color in the title, GEO should not be launched. First the product pages, category structure, Merchant Center, and basic store trust. Only after that does AI-citation make sense.
GEO does not replace SEO, Shopping Ads, or Merchant Center. And it should not. SEO brings organic demand, Shopping Ads capture the buyer at the moment of choice, and Merchant Center keeps product data in the advertising ecosystem. GEO adds another layer: it helps the store appear in ChatGPT, Perplexity, and AI Overviews answers, where the classic SERP position is no longer the only entry point.
We would not sell GEO to a store that does not have a proper catalog, a clean Merchant Center, and a basic category structure. It is an expensive way to mask chaos. But for stores with 1000+ SKU, strong characteristics, and niches where buyers compare for a long time, GEO already makes commercial sense. Not as a “new SEO button,” but as work with data, entities, and catalog trust.
How to check the result: citations, visibility, and errors after launch
A GEO pilot cannot be evaluated by the feeling that “it got better.” Two to three weeks after changes to 5–10 SKU, look not at overall organic traffic, but at specific citation signals: does AI mention your product, category, brand, or page in the answer, and in what context.
The check starts with a query set. Do not use only the obvious “buy X.” AI search more often answers mixed queries where the user is still comparing, clarifying characteristics, or looking for selection criteria.
- Test at least 4 groups:
- informational queries: “how to choose a kids’ mat for sports,” “which EVA mat thickness is best for judo”;
- commercial queries: “where to buy EVA tatami 20 mm in Ukraine,” “best mats for a gym 1×1 m”;
- comparison queries: “Prom.ua or a separate store for buying tatami,” “EVA mat 20 mm or 30 mm”;
- entity queries:
brand+ product,category+ city, manufacturer + material.
Record not only the fact of citation. More important is the role the page got. AI may mention the store as a seller, a source of characteristics, an example in the category, or a random URL next to stronger competitors. These are different signals. A first mention in a recommendation list and a link at the bottom of an answer without context are not the same result.
A control group is also needed. Take 5–10 similar SKU where the product page structure has not changed: same categories, close price, similar demand. If the pilot SKU start appearing in AI answers and the control ones do not, that is a signal. Not proof that “GEO won,” but enough reason to scale carefully.
We do not recommend rolling GEO changes out across the whole catalog after one successful example. In our practice, you first need to understand what exactly produced the citation: attributes, Schema.org, FAQ block, entity links, or a combination of these elements. Otherwise the team does not scale a solution. It scales a guess.
We also check the technical side without romance. Google explains that Product structured data should pass specific product properties, and for variants you need a correct ProductGroup / Product model rather than chaotic duplication of one page under different URLs: Product structured data, Product Variant Structured Data. For a catalog, this is critical: AI reads pages poorly when color, size, material, and availability are in different places without a stable structure.
Also compare product page data with the feed. Google Merchant Center requires accurate and up-to-date product data, including price, availability, link, image, and identification attributes: product data specification. If feed.xml has one material, the page another, and Schema.org a third, AI will not guess which version is correct.
Scaling starts after two types of result: the pilot gave the first citation signals, or it clearly showed the bottleneck. For example, AI sees the category but not the specific SKU. Or it cites the blog but ignores the product pages. That is a normal pilot result.
A bad result is when the team cannot explain what exactly worked or broke, and still launches changes across the whole catalog. This week, it is enough to do one simple thing: build a verification table, run 4 query groups, and compare the pilot SKU with the control group.
UPLIFY’s view
We see one recurring mistake: the store rewrites title and description, but leaves attributes, variants, FAQ, and Schema.org in the state they were after import. In our practice on 40+ launched GEO projects on Prom.ua and Horoshop, the first AI-citation often appears 2–3 weeks after the product pages are structured correctly and Wikidata entity penetration is done.
Not after SEO copy for the sake of SEO copy. After AI can understand what exactly the store sells, how one SKU differs from the next one, and why this source can be trusted.
Action checklist
- Choose 5–10 pilot SKU.
- This lets you test the hypothesis without reworking the entire 1000+ product catalog.
- Remove duplicate names, colors, and sizes from the product pages.
- AI compares products poorly when one attribute is hidden in three different fields.
- Check the core attributes:
brand, model, material, size, color, purpose. - These are the fields that most often become the basis of an AI search answer.
- Check
GTIN,MPN, andidentifier_exists=false. - Identifier errors break not only
Merchant Center, but also trust in the product data. - Update short descriptions without promotional noise.
- You need facts: who it suits, what parameters matter, what limitations there are, how to choose.
- Add FAQ to the pilot SKU or categories.
- Buyer questions give AI ready-made fragments to cite.
- Set up Product, ProductGroup, FAQPage, and, where appropriate, HowTo.
- The markup must match the real content of the page, not exist separately.
- Check the import and feed:
feed.xml, CSV, or platform export. - If the data source is chaotic, markup will only highlight the chaos.
- Add trust signals next to the product.
- AI search evaluates not only product parameters, but also signs of store reliability.
- Test queries in ChatGPT, Perplexity, and Google AI Overviews.
- Check not positions, but whether the store is mentioned in the answer and which competitors it appears next to.
- Record the result after 2–3 weeks.
- GEO only makes sense to scale after citations appear or the reason for their absence is clear.
FAQ
Why does the store have SEO traffic, but AI does not cite it?
SEO traffic often comes from the classic search results, where title, categories, internal links, behavioral signals, and domain authority work. AI search reads the page differently. It needs to quickly understand the product entity, parameters, differences between SKU, purchase conditions, and the level of trust in the source.
If a page has a long description but no stable attributes, FAQ, correct Product schema, and clear variants, AI may take a competitor with worse SEO but cleaner data. That is why GEO starts not with rewriting text, but with diagnosing catalog structure. This week, it makes sense to take a few product pages and check whether a person without context can quickly understand the product from characteristics rather than from a promotional paragraph.
Do we need to rework the whole catalog at once?
No. For a store with 1000+ SKU, that is almost always unnecessary risk and unnecessary budget at the start. It is better to take 5–10 pilot products: profitable ones, with demand, a clear difference from competitors, and enough questions from buyers.
On these SKU you can test attributes, FAQ, Schema.org, feed, variants, and the first AI-citations. If movement is visible after 2–3 weeks, the templates can be scaled to a category or product group. If there is no movement, it is easier to find the reason on 10 SKU than in the chaos of 5000 pages. A practical step: choose a pilot group and immediately record which queries, answers, and citations you will check.
What matters more for GEO: description copy or characteristics?
For AI search, characteristics are usually more important. A description is needed, but it should not replace structured data. If size, material, compatibility, color, brand, and purpose are hidden in a paragraph, AI has to extract them on its own.
If this data is moved into attributes, the feed, and Product schema, the machine reads it more consistently. A good description explains the choice and limitations of the product. A bad description repeats “high-quality, reliable, convenient” and adds no fact. For GEO, such a description is almost useless. This week, it makes sense to remove empty adjectives from the pilot pages and move factual parameters into the characteristics fields.
Can GEO be done on Prom.ua without code access?
You can start, but with limitations. On Prom.ua, the main control often comes through the product card, attributes, import, characteristic groups, and the feed for Google Merchant Center. If these fields are filled in cleanly, the catalog already becomes clearer for AI and search.
Full control over custom Schema.org markup may be limited by the platform. So for Prom.ua, we first work with what can actually be changed: titles, characteristics, descriptions, FAQ blocks, categorization, the feed, and external brand entities. It is not ideal freedom, but it is a workable start. The first step is to export a few products and check whether the same characteristics are duplicated in different fields.
What is the difference between the approach for Horoshop and Shopify?
Horoshop’s strength is usually structured work with the catalog, variants, import, and product groups. There it is important not to break the variant logic and not to duplicate attributes in different fields. Shopify gives more freedom through themes, apps, metafields, CSV, and custom markup, but that same freedom makes it easier to create chaos.
For GEO, the difference is not which platform is “better.” The difference is access to fields, import stability, and the ability to scale Schema.org without manual editing of every page. In our practice, the winners are usually not the stores with the most flexible platform, but the ones where product data structure does not fall apart after every import. This week, it is worth checking one category: are attributes named consistently, are variants assembled correctly, and does part of the important data live only in the description.
How do you know GEO is already producing results?
The first signal is that the store starts appearing in AI answers for commercial or comparison queries: “which product should I choose,” “where to buy,” “model comparison,” “best option for...”. The second signal is that AI correctly names the category, product parameters, and advantages without invented details.
Another strong signal is when Perplexity or AI Overviews pull the page as a source. Do not evaluate GEO only through Google Search Console positions. This is a different visibility layer, so you need a separate table of queries, check dates, answers, and citations. A practical minimum: create a list of 20–30 queries for the pilot SKU and check them once a week in the same way.
When does GEO for a catalog not make sense?
GEO does not make sense if the store sells a random set of products without a clear category strategy, has empty product pages, unstable prices, duplicates, indexing problems, or weak commercial trust. AI will not save a catalog that even a buyer cannot read properly.
It also does not make sense to invest in GEO for SKU without demand, without margin, or without a difference from dozens of identical offers. In such cases, first fix the basics: feed, product pages, categories, store policies, delivery, payment, and technical indexing. This week, it is better not to write new descriptions, but to find the pages that fail the basic check: the product is clear, the price is stable, the purchase conditions are visible, and the characteristics are not duplicated.
Do we need to add Wikidata for every store?
Not for every store. Wikidata and external entities are appropriate when a brand, manufacturer, product line, or category has enough grounds for a separate entity. For a small store without a public history, it is better to start with a clean catalog, the About page, contacts, policies, Merchant Center, structured data, and mentions in relevant sources.
Entity penetration does not replace product page quality. It strengthens it when AI already has something to read on the site. In practical terms, that means a simple sequence: first get product data in order, then work with external entities. Otherwise Wikidata becomes an expensive layer on top of a weak catalog.
Sources
- [1][1][1][1]Product data specification — Google Merchant Center Help, tier 1
- [2][2][2][2]How to provide high-quality data — Google Merchant Center Help, tier 1
- [3][3][3][3]Introduction to Product structured data — Google Search Central, tier 1
- [4][4][4][4]Product Variant Structured Data — Google Search Central, tier 1
- [5][5][5][5]Using CSV files to import and export products — Shopify Help Center, tier 1
- [6][6][6][6]Import: how to import products and services — Prom.ua Support, tier 1
- [7][7][7][7]Exporting a feed for Google Merchant Center — Prom.ua Support, tier 1
- [8][8][8][8]What a product page should look like — Horoshop, tier 2
- [9][9][9][9]Product modification: what it is and why it is needed — Horoshop, tier 2
- [10][10][10][10]What is the initial product import and how it works — Horoshop, tier 2
Need help?
If you need more than “add SEO copy” and want to prepare the catalog for AI search, UPLIFY can take on the GEO audit, Horoshop improvements, and AI content for pilot SKU. We start with 5–10 products and scale only after checking citations.