Why Your Product Descriptions Don't Sound Like Your Brand (And What To Do About It)

You’ve spent years building a distinct point of view. Your buyers know your market. Your social media sounds like you. Your email newsletters sound like you. Then a customer lands on a product page and reads copy that sounds like it was written by the brand’s PR team in 2019. Because it was.

This is the brand voice gap. Every multi-brand retailer has it, and most have quietly accepted it as an unsolvable cost of doing business. It is not.

Where the problem actually comes from

When you stock 30 brands, you receive product descriptions from 30 different marketing departments. Each one has written copy optimised for their own brand, their own audience, and their own editorial calendar. Not for your store, your customer, or your voice.

The practical result looks like this:

One brand sends you 400-word essays about craftsmanship and heritage
Another sends you bullet-pointed spec lists
A third sends you nothing at all, or a generic “product from [Brand Name], available in three colours”
And a fourth writes copy so sales-y it reads like a marketplace listing

All of it ends up on your site. Some of it is published directly. Some of it gets a light edit from whoever has ten minutes that week. Almost none of it sounds like your brand.

For independent retailers, this is a particularly acute problem. Your curation is your value. Customers shop at your store because they trust your taste and your voice. When the product description sounds like the brand’s wholesale catalogue, you’ve handed that trust back to the supplier.

Why this matters more than you might think

The most visible consequence is brand inconsistency. A customer who reads five product pages in a session will notice (subconsciously, if not consciously) that each one sounds different. The cumulative effect is that your store feels like an aggregator, not a curator.

But there are two less obvious consequences that compound over time.

SEO fragmentation. Supplier descriptions are sent to every retailer stocking that product. If you publish them unchanged, you have duplicate content across every site carrying the same brand. Google’s systems will consolidate ranking authority around one version, and it is unlikely to be yours. Your product pages compete against the brand’s own site, ASOS, and any other major retailer stocking the same items, all with identical copy. Unique descriptions, written in your store’s voice, are the only way to own your product page rankings long-term.

Conversion friction. A product description exists to help someone decide whether to buy. Supplier copy is written to present the product in the best possible generic light. Your copy should be written for your specific customer, in the register and vocabulary they respond to. “Crafted from premium materials for the discerning wearer” converts differently to “Clean cut. Wears well from the office to the pub. Buy two.” The latter sounds like advice from someone whose opinion you trust. The former sounds like a label.

The manual fix and why it doesn’t scale

The obvious solution is to rewrite supplier copy in your own voice. A good editor can do this. The problem is throughput.

A skilled editor can rewrite perhaps 30 to 50 product descriptions in a day, with quality. An independent retailer with 3,000 SKUs who adds 200 new products each season is looking at a backlog that grows faster than it can be cleared. Hire one copywriter and the maths still don’t work: at 40 descriptions per day, a 3,000-product catalogue takes 75 working days to complete, by which point 200 more products have arrived.

The manual fix is not a fix. It is a treadmill.

Most retailers settle into one of two failure modes: they rewrite the hero products (the bestsellers, the new arrivals) and let the long-tail languish, or they accept a blanket standard of mediocre supplier copy across everything. Neither position is defensible as a brand strategy.

What good looks like: three stores, three voices

Consider what voice-consistent product copy actually looks like in practice.

A store like Goodhood would describe a Carhartt WIP jacket as: “A workwear silhouette that’s moved from the job site to the street. Duck canvas shell, blanket lining, the kind of thing that gets better with wear. Size up if you’re layering.” That is specific, confident, and opinionated: the voice of a curator, not a brand press release.

The same jacket described in supplier copy might read: “The Carhartt WIP Michigan Coat is a heritage workwear style crafted from durable duck canvas with a warm blanket lining, finished with a corduroy collar. Available in multiple colourways.” Accurate. Flat. Forgettable.

The difference is not the information. Both descriptions contain broadly the same facts. The difference is the voice. One sounds like a person who has thought about this jacket and has a view. The other sounds like a form that was filled in.

Scaling the first version is the problem AI product content solves.

How AI learns and applies a store’s voice

The capability that makes AI-generated product content useful for independent retailers is not generic text generation. It is configurable, context-grounded generation that works within a specific editorial framework.

That means the AI is given your brand guidelines, your vocabulary preferences, your prohibited phrases, your structural preferences (opener length, whether you use bullet points or flowing prose, how you handle technical specifications), and examples of descriptions you consider exemplary. It applies all of that consistently, across every product in every batch.

The practical outcome: a product arrives from a supplier with a spec sheet and brand copy. The AI rewrites it in your voice, referencing your store’s tone-of-voice parameters, producing a description that sounds like a member of your team wrote it. At scale. For every product. Without the backlog.

Writing Knowledge and advanced writing assets in merchi.ai are the specific features that implement this. You define the voice parameters once; they apply to every subsequent product run.

The Grosvenor Flooring proof point

This is exactly how merchi.ai worked for Grosvenor Flooring. Flooring is not streetwear, but the content problem is structurally identical: a large catalogue, multiple supplier data sources, inconsistent incoming content, and a small team with no capacity to rewrite thousands of descriptions manually.

merchi.ai cleared a backlog of thousands of products and produced consistent, on-brand descriptions across the entire range. The downstream result was 976% online revenue growth, driven significantly by organic search traffic that incomplete and inconsistent content had previously failed to capture.

The voice fidelity challenge at a fashion or lifestyle retailer is, if anything, more acute than in flooring. But the approach is the same: configure the AI to understand the store’s editorial framework, then let it run at catalogue scale.

The practical question

If you manage product content for a multi-brand retailer and you are reading this recognising the problem, the practical question is: what would it cost to fix it, and what would it cost to leave it as it is?

The leaving-it cost is harder to see, because it is distributed across pages you have never measured individually. Every product page with supplier copy is capturing less organic traffic than it should, converting at a lower rate than it could, and subtly diluting your brand with every visit.

The fix cost, with AI-powered content generation, is a fraction of what a manual content operation would require. We built merchi.ai specifically to solve this for retailers who cannot resource a content team at catalogue scale.

Try it on your own catalogue

The best way to evaluate this is to see what the output looks like on your own products. Book a demo and we will run merchi.ai on a selection of your catalogue so you can see exactly what voice-consistent, SEO-optimised product descriptions look like for your store.

Or start a 30-day free trial and run it yourself.

For a broader look at how AI handles product content at scale, see our posts on AI product descriptions for retailers and product content at scale in retail.