E-commerce Product Content Add-On
The paragraph on your product page arrived with the photograph, from the same supplier, on every competing store. Ivan Sp writes long-form product content at catalogue scale, priced per page. The add-on works alongside any package or as a standalone service, and it suits stores that cannot rank on the manufacturer descriptions every competitor also publishes.
What Each Product Page Receives
Every page in the add-on carries six elements:
- A product description of 1,000 words or more, covering features, benefits, usage guidance, and customer questions
- A FAQ section answering what real buyers ask before purchase
- Full schema markup, covering Product, Review, and FAQ types
- An optimized title tag and meta description written for buyer intent
- Internal linking recommendations connecting the page to related products and category pages
- A readability pass confirming answer engines can summarize the page accurately
Handling Different Product Categories
Raw product data varies enormously between categories, and the writing has to adapt. Cosmetics and personal care products need ingredient-aware descriptions. Food products arrive with technical composition data, and each figure has to become a reason a customer would buy. Home goods need dimensions, materials, and gifting context.
One past engagement produced title tags and meta descriptions per individual SKU across three separate sub-brands under a single parent company.
Why Product Pages Need Original Content
Manufacturer descriptions appear identically across every retailer selling the same item. A store publishing only manufacturer copy offers search engines nothing to distinguish it from dozens of competitors.
Original descriptions also answer the questions that decide a purchase. Sizing, compatibility, materials, care, and returns are the sub-questions behind most product searches, and manufacturer copy addresses almost none of them.
Generation at Catalogue Scale
Large catalogues run through a programmatic system rather than page by page. The system generates from the store’s own product data, applies the template, and publishes with schema and internal links already in place.
The full method sits on the page covering programmatic SEO systems built at scale.
Pricing
The add-on is priced per page, and a quote depends on product volume, data quality, and category complexity. A store supplying complete structured product data costs less per page than one supplying a partial spreadsheet.
What This Looks Like for a Retailer
Consider a store owner who has spent two evenings a week rewriting product descriptions since March. She is on page four hundred of three thousand.
She wants the catalogue finished more than she wants any single page to be excellent. Every week she does not finish is another week her store reads like everybody else’s.
I open her product feed and find nine attribute fields nobody has ever published. The compatibility data alone gives every page something to say that no competitor’s page does.
Where the Standard Product Content Advice Breaks Down
How many of your product pages carry copy your supplier also gave your competitors?
- The usual advice
- Rewrite the descriptions in-house, since nobody knows the products better than your own team.
- What holds up in practice
- Product knowledge is genuinely better in-house, and available hours are not. An in-house rewrite of a large catalogue stops partway through, which leaves an inconsistent catalogue and the highest-intent category pages still untouched.
Picture a team committing to rewrite 3,000 product descriptions between other duties. The knowledge is real and the intention is sound.
Four hundred get rewritten over eight months. The rest keep supplier copy, the catalogue reads inconsistently, and nobody has time to finish.
Ivan Sp generates from the attribute data the business already holds, then has the team review the categories rather than the individual products.
A half-finished catalogue rewrite spends the effort and leaves the inconsistency. Finishing matters more than the first four hundred pages being excellent.
If your page and four competitors carry the identical paragraph, price is the only thing a shopper has left to compare.
Four Things Commonly Assumed About Product Content
Which of these describes your catalogue?
I meet these four assumptions on most catalogue conversations.
| Commonly assumed | What actually holds |
|---|---|
| Supplier copy is fine because it is accurate. | Accuracy is not the problem. Identical copy across every retailer leaves price as the only visible difference. |
| Rewriting in-house is cheaper. | In-house rewriting is cheaper per page and rarely finishes. An unfinished catalogue is worse than a consistent generated one. |
| Generated copy is thin by nature. | A page built on real specifications can be more useful than a hand-written one. Thinness comes from having nothing to say. |
| Unique photography solves it. | Photos help conversion and change nothing a search engine reads. The paragraph is still shared. |
| Every product needs the same treatment. | Categories differ in which attributes matter. One generation rule across a whole catalogue produces copy matched to nothing. |
The information that would differentiate your product pages is already in your own attribute data. Nobody has published it yet.
A Worked Example: What Attribute Data Produces
Consider one catalogue item described two ways. The comparison below is illustrative, written to show what generation reaches. No client engagement is described here.
| Property | Supplier copy | Generated from attributes |
|---|---|---|
| Also on competitor sites | Yes, verbatim | No |
| Specifications stated in prose | 2 | 9 |
| Compatibility information | Absent | Generated from the compatibility field |
| Dimensions and materials | In a table only | In the prose and the table |
| Words unique to this product | 0 | About 180 |
| Schema properties the page supports | 3 | 8 |
| Pages completed across the catalogue | All, identically | All, differently |
I use data the retailer already holds. Every row on the right came from the existing product feed rather than from new research.
Publishing supplier copy is putting the manufacturer’s leaflet in your own window. Professional, accurate, and identical to the shop next door.
The compatibility row is usually the strongest. Shoppers search for what a part fits, and that field sits unpublished in most catalogues.
How the Add-On Runs
I start from the attribute data rather than from the existing descriptions.
- Measure current duplication by searching exact sentences from a sample of product pages.
- Audit which attributes exist in the product data and which genuinely vary between items.
- Group products by category, since categories need different fields emphasised.
- Write a generation rule per category rather than one rule for the catalogue.
- Generate descriptions from real attributes, including compatibility, dimensions, and materials.
- Prohibit generating any specification, rating, or review the source does not hold.
- Generate titles and descriptions from each product’s own fields.
- Check uniqueness across the whole generated set rather than within a category.
- Generate Product schema conditionally, so no property claims something the page lacks.
- Publish in batches and check the index rate before releasing the next.
How This Engagement Is Scoped
How This Add-On Is Priced and Scoped
- Pricing basis
- Per product page, since catalogue sizes vary enormously.
- Minimum viable data
- Enough varying attributes per product to support genuinely different copy.
- What the client supplies
- The product attribute data and platform access.
- Included
- Duplication audit, generation rules per category, copy, metadata, and schema.
- Not included
- Product photography, and enriching attribute data that does not exist.
- What stops the work
- A catalogue whose attributes barely differ between products.
- Where it attaches
- As an add-on to any package, or standalone at catalogue scale.
- How it is quoted
- After an attribute audit, since the data decides what is possible.
Tools and Primary Sources
The references below cover the markup and the policy. Each opens in a new tab.
- Product structured data documents the required and recommended properties for a product page.
- Google ecommerce SEO documentation covers product pages, duplication, and out-of-stock handling.
- Google spam policies defines scaled content abuse, which generated catalogue copy must stay clear of.
- Google Merchant Center documents the feed specification the on-page data has to agree with.
Measure Your Own Duplication
I suggest a check that settles the question in five minutes.
- Open one product page and copy a full sentence from the description.
- Search Google for that sentence inside quotation marks.
- Count how many other retailers return the identical sentence.
- Repeat on two more products from different categories.
A dozen retailers returning your exact sentence means that page offers nothing of its own. Your attribute data almost certainly holds what would differentiate it.
Common Questions
Is there a minimum order?
No fixed minimum applies, though per-page cost falls as volume rises. Very small orders are usually better handled inside a Premium package.
Do you need our product data in a specific format?
A spreadsheet or a database export both work. Complete data produces better content and a lower per-page cost, so gaps get identified before a quote is issued.
Can this run on Shopify or BigCommerce?
Shopify implementation is supported directly. Other platforms receive the content plus a documented import process for your developer to run.
How many products make this worthwhile?
Pricing is per page, so there is no minimum. What matters is whether your attribute data holds enough genuinely varying fields to support different copy, which is what the audit checks first.
Next Step
The first call is a free 30-minute consultation with no pitch attached. You receive an honest assessment of what is holding the site back and what can realistically be done about it.