12 Real Product Descriptions, Torn Apart: The Exact Sentence Doing the Work

Every product description quoted in this post was pulled from the store's own live product feed on July 30, 2026, and the URL of that feed is printed next to it so you can re-check the quote yourself. That sounds like a small procedural note. It is actually the whole point, and here is why.

We took the four brands quoted by the article currently ranking first for this keyword, re-fetched their live product data on July 30, 2026, and found that three of the four quoted descriptions no longer exist as written. The article is six months old.

Why every example here carries a date

TL;DR We re-checked the four brands quoted by the top-ranking article on this keyword against their live product feeds. Three of four quotes no longer match. The copy is not wrong, it is expired, and no reader could tell.

Search “product description examples” and you get a lot of copy in quotation marks. Almost none of it tells you when that copy was read. It matters more than it sounds, because product descriptions are one of the most frequently rewritten assets in a store. Brands reposition, they consolidate SKUs, they hire a new copywriter, they run a rebrand, they retire a bundle. The sentence you are being told to imitate may have been replaced months ago by the very brand you were told to imitate.

So before writing a single teardown, we ran the test on the incumbent. The article ranking first on Google for this keyword is Shopify's own, published January 30, 2026 by Lizzie Davey (Shopify blog). To its credit, it quotes real, named brands rather than invented sample copy. We took four of the brands it quotes and re-fetched each one's live product data on July 30, 2026, using the store's own public product feed. Here is what came back.

Brand and product	The quoted copy	Status on July 30, 2026
Dr. Squatch, Pine Tar soap	“Made with real pine extract, this all-star bar is as tough as a freshly cut bat. A true MVP of the shower…”	Product live, description rewritten. The visible description is now a single sentence: “Rugged, woodsy, and strong, just like a Squatch man should be.” The old paragraph survives only inside a scent_story metafield in the page's JSON payload, not in the description field.
Beardbrand, Best Sellers Bundle	“A do-it-all beard wash… in a massive 14 oz. bottle… coconut-derived surfactants”	URL returns 404. The writing pattern survives in rewritten form on the Utility Beard Wash page: “no harsh sulfates, just coconut-derived cleansers that are gentle enough for your beard, hair, and face, but strong enough to wash where the sun doesn't shine.”
The Oodie, I Love Plants Oodie	“Can't stop buying plants? Unbeleafable.”	URL returns 404. Zero matches for the word “plants” across the 478 products in the store's live feed.
Casper, Snow mattress	“Snow Technology for 12+ hours of cooler sleep”	Partially live, moved. Both “Snow Technology” and “12+ hours of cooler sleep” still appear in the live feed, but on pillow products rather than the Snow mattress.

Method: each store's public /products.json feed plus a full rendered-HTML fetch of the quoted product URL, run July 30, 2026. Dr. Squatch was cross-checked on the rendered page: the old phrases appear exactly once, inside a JSON payload, while the current one-line description appears three times in visible markup. This is our own measurement, not a cited figure.

Three of four, six months after publication. And to be explicit about what this is not: it is not a criticism of the writer. Shopify's article does the harder and more honest thing by quoting real brands. The failure is structural. Once copy is quoted without a capture date, there is no way for a reader, or for a search engine, or for an AI assistant summarising the page, to know whether the example is current. The quote outlives the thing it quotes.

The fix is boring: date the capture, and publish the method so the reader can re-check. That is what the rest of this post does. Every sentence below was in the store's live product feed on July 30, 2026. Some of them will not be by the time you read this, and that is fine, because you now have a way to find out.

How we captured twelve live descriptions

TL;DR Every Shopify store publishes its catalogue at /products.json, including the raw body_html of each description. That endpoint is the reproducible source for all twelve examples, and you can run it against any store in about ten seconds.

Shopify stores expose a public JSON feed of their catalogue. Add /products.json to the store's domain and you get back a paginated list of products, each one carrying its title, its handle, its variants, and critically its body_html, which is the product description exactly as the theme renders it. Append .json to any individual product URL and you get the same thing for one product.

Two reasons this matters for an examples article. First, it removes the interpretation layer. You are not reading a screenshot of a description or a writer's paraphrase of one, you are reading the field itself, the same field the merchant edits in the admin. Second, the fetch is trivially repeatable, so any claim made about the copy is falsifiable by the reader in one browser tab.

The exact procedure

1. Pull the catalogue. Open https://example.com/products.json?limit=250. Add &page=2, &page=3 and so on until the products array comes back empty. That is the whole live catalogue.
2. Search the description field. Every product object has body_html. Search it for the phrase you want to verify. If the phrase is not there, the description has been rewritten since whoever quoted it last looked.
3. Confirm on the rendered page. The feed is the source of truth for the description field, but themes sometimes render extra copy from metafields that never appears in body_html, and occasionally hide parts of body_html behind tabs or accordions. Fetch the product page itself and confirm the sentence is actually visible to a shopper. This step is what caught the Dr. Squatch case above, where the old copy still exists in the page's data but is no longer the description.
4. Record the date. Not optional. Write down the date you fetched, because that is the only thing that makes the quote checkable later.

Three honest caveats about the method, because it is not magic.

The updated_at timestamp is not a copy-edit date. Every product in the feed carries published_at and updated_at. It is tempting to read updated_at as “when the description was last changed.” It is not. It reflects any change to the product record, including inventory movement and price changes, which for an active store means it is often today's date regardless of when the copy was written. All sixteen products we captured showed an updated_at of July 30, 2026, which tells you they are actively trading, not that they were all rewritten that morning. Use the field as a liveness signal, not as a copy history.

Some stores turn the endpoint off. It can be disabled, and headless builds may not expose it at the storefront domain. If the feed 404s, fall back to fetching the rendered product page and reading the description block directly.

This only sees what the store publishes publicly. Nothing here reveals traffic, conversion, or whether a given sentence sells. That limitation runs all the way through this post, and the last two sections are about facing it rather than papering over it with an invented lift percentage.

Using that procedure we captured sixteen live descriptions across ten brands on July 30, 2026. Twelve are torn apart in full below. The other four are listed in an appendix table at the end of the teardown sections, because they demonstrate the same patterns without needing another thousand words.

The four jobs a description has to do

TL;DR Across sixteen live descriptions, the sentence doing the work was always doing one of four jobs. Everything else, tone, length, structure, formatting, varied wildly and correlated with nothing.

The useful thing about reading sixteen descriptions in one sitting is what stops looking important. Length varied from one sentence to several hundred words. Some used bullet lists, some used none. Some were written in a heavily branded voice, some in plain retail English. If there is a formula in there, it is not about form.

What did repeat was function. In every case, one identifiable sentence was carrying the decision, and it was doing one of four jobs.

Job	The shopper question it answers	Failure mode when it is missing
Specificity swap	“Is that claim actually true, or is it marketing?”	The description reads as interchangeable with every competitor's. Nothing in it could be false, so nothing in it is informative.
Objection pre-empt	“What is the catch, and will this be right for me?”	The shopper leaves the page to find the answer, which is where they find a competitor. Or they buy on a guess and return it.
Sensory anchor	“What will this actually be like?”	The product stays abstract. For anything experiential, scent, texture, taste, fit, this is the whole purchase.
Scroll-proof opener	“Is any of this worth reading?”	The other three jobs are never reached, because the reading stops at line one.

Our own classification of sixteen live product descriptions captured July 30, 2026. It is a reading, not a measurement: no traffic or conversion data was available for any of these pages.

The fourth job deserves an evidence note, because it is the one most people underestimate. Nielsen Norman Group's analysis of 45,237 page views found that users have time to read at most 28% of the words during an average visit, and that 20% is the more likely figure. Users read half the information on a page only when that page runs to 111 words or less, against an average page length in that dataset of 593 words (Nielsen Norman Group). That research was published in 2008 on data collected in 2005, so treat it as an old primary rather than a current benchmark. It remains the most-cited measurement of the problem, and no more recent public dataset we could verify replaces it.

The practical reading is not “write 111 words.” It is that the order of your sentences is a bigger decision than the number of them. If the specificity swap is in paragraph four, for most visitors it does not exist.

Teardowns 1 to 4: the specificity swap

TL;DR Replace an adjective with a number, a source, or a named material. The strongest example in the set is a beverage brand that cites its market-research provider inside the product description.

The specificity swap is the least glamorous of the four jobs and the one most stores can do this week. It works by making a claim checkable. A checkable claim carries information whether or not the shopper checks it, because the willingness to be specific is itself a signal.

1. Liquid Death, Raspberry Rage: a product page that cites its data source

Source: liquiddeath.com/products/raspberry-rage, captured July 30, 2026.

The sentence: “75% LESS SUGAR (LIQUID DEATH HAS 4G SUGAR) THAN THE LEADING NON-DIET ICED TEA (17G-44G SUGAR) PER 12OZ LISTED IN CIRCANA TOTAL US MULO+ W/ CONVENIENCE”

The job: specificity, taken further than almost any consumer brand takes it. This is a footnote on a canned iced tea, and it does four things at once. It gives the comparative claim (75% less sugar). It gives its own absolute number (4g). It gives the comparison set's range rather than a single flattering competitor (17g to 44g). And it names the syndicated retail data source the comparison was measured against, Circana's Total US MULO+ with Convenience panel.

Most brands would have written “low sugar.” Some would have written “75% less sugar than the leading iced tea” and left it there, which is the version that quietly invites the shopper to wonder which leading iced tea. Publishing the range and the panel converts a marketing claim into an auditable one.

What to copy: not the all-caps voice, which is brand-specific. Copy the structure. If you make a comparative claim, publish your own number, the comparison set, and where the comparison came from. Most Shopify merchants do not have Circana data, and do not need it. “Tested against the three best-selling alternatives on Amazon in June 2026” does the same structural work.

2. Wild One, Lightweight Nylon Dog Leash: the number that answers the real worry

Source: wildone.com/products/lightweight-nylon-dog-leash, captured July 30, 2026.

The sentence: “Ultra-flexible yet super strong, this 6-foot leash comfortably handles 90 lbs of pulling.”

The job: specificity, aimed precisely at the anxiety that stops the sale. Notice how the sentence is built. It opens with the interchangeable part, “ultra-flexible yet super strong,” which is what every leash on the internet says. Then it does the swap: 6 feet, 90 lbs of pulling. The second half is the only part a shopper could not have predicted, and it is the only part that resolves the actual fear, which is that the leash fails while a large dog lunges.

What to copy: find the sentence in your description that is doing the “ultra-flexible yet super strong” job, and give it a number. The right number is usually not the most impressive spec, it is the one attached to the thing the buyer is quietly worried about.

3. Public Goods, Turkish Cotton Towels: material, origin and a spec in one line

Source: publicgoods.com/products/turkish-cotton-white-500-gsm-towels, captured July 30, 2026.

The sentence: “Woven from 100% Turkish cotton sourced from the Aegean region, these 500 GSM towels are exceptionally soft, absorbent, and built to last.”

The job: specificity via provenance plus a technical unit. GSM (grams per square metre) is the spec that separates a serious towel buyer from a browser, and putting it in the first line signals to the person who knows what it means that this store is worth their time. The Aegean sourcing detail does the same job for the person who does not know what GSM means but understands that a place name is harder to fake than an adjective.

The rest of the sentence is generic, “exceptionally soft, absorbent, and built to last.” That is not a criticism. Generic reassurance is fine as the tail of a sentence whose head is checkable. It is only a problem when it is the whole sentence.

What to copy: if your category has a unit, use it. Thread count, GSM, ply, denier, ABV, ohms, lumens, capacity in litres. If it does not have a unit, use origin, process, or a named material.

4. Tushy, Eau Smart Toilet: the boring precision that closes the objection

Source: hellotushy.com/products/eau-smart-toilet-with-bidet, captured July 30, 2026.

The sentence: “Soothing Heated Seat: 6 skin-safe thermal settings up to 108°F.”

The job: specificity on a feature where vagueness reads as risk. “Heated seat” on a device you sit on raises exactly one question, which is how hot. The answer, six settings and a stated ceiling of 108°F, closes the question inside eight words. The phrase “skin-safe” is the softer half and is doing reassurance work, but the number is what makes the reassurance credible.

This is also a good example of a highlight-style line, a bolded label followed by a colon and a specific. Baymard's usability research found that only 22% of ecommerce sites structure their descriptions by highlights, meaning 78% do not, and that highlights cause users to “slow down and investigate each feature in detail” (Baymard Institute). That study is dated April 24, 2018, so read the percentage as historical and the behavioural finding as the durable part.

What to copy: the label-colon-specific format. It survives skimming, it survives being read by a machine, and it forces you to be concrete because there is nowhere for an adjective to hide.

Teardowns 5 to 8: handling the objection inside the description

TL;DR The strongest objection-handling move in the set is a page that tells you to buy a different product. Others state a limitation before a benefit, or resolve a sizing worry in four words.

An objection the description does not answer becomes one of three things: a bounce, a support ticket, or a return. Salsify's 2026 consumer research puts the first and third at scale. In a survey of 2,712 shoppers fielded through SurveyMonkey in October 2025 across the US, Canada and the UK, with a stated margin of error of 3% or better, 34% said they abandoned a sale because of incomplete or poorly written titles or descriptions, 38% abandoned when product information was inconsistent across sites, and 45% said they had returned an online purchase because of incorrect or misleading information (Salsify blog, and the methodology in Salsify's press release). Salsify sells product information management software, so this is vendor-published research. It discloses its sample, its fielding window and its margin of error, which is more than most vendor research does, and it is survey self-report rather than observed behaviour. Weight it accordingly.

The returns side has a public price tag. The National Retail Federation's 2025 Retail Returns Landscape report puts total US returns at $849.9 billion and says 19.3% of online sales were returned in 2025 (NRF, released October 15, 2025). Not all of that is a description problem. But every sentence that prevents a wrong-fit purchase is working on a number that large.

5. Peak Design, Travel Backpack 20L: the page that recommends a different product

Source: peakdesign.com/products/travel-backpack-20l-stone, captured July 30, 2026.

The sentence: “For heavier photo gear carry, we recommend the original Travel Backpack 45L. For slightly more capacity, try the Travel Backpack 30L.”

The job: objection pre-empt, executed at the cost of the immediate sale. This sentence lives on a product page and its function is to redirect a subset of readers away from the product. Read purely as conversion-rate optimisation on that page, it is a loss. Read as revenue and returns across the catalogue, it is a wrong-purchase filter that also makes every other claim on the page more believable, because a page willing to tell you not to buy is a page you can trust when it says buy.

It is worth being precise about why this is hard to copy. It only works if you have adjacent products, and it only works if the routing is honest. A version of this sentence that routes everyone to the more expensive item is a sales tactic wearing the costume of advice, and shoppers detect the difference quickly.

What to copy: name the buyer this product is not for, and tell them where to go. If you sell one product, the destination can be “wait for the size guide” or “this is not right if X.”

6. Beardbrand, Temple Smoke Mustache Wax: the limitation, stated first

Source: beardbrand.com/products/temple-smoke-mustache-wax, captured July 30, 2026.

The sentence: “It's not a high-hold wax, so it won't give you a stiff 'stache.”

The job: objection pre-empt through pre-emptive disqualification. The sentence leads with what the product does not do. For a mustache wax, hold level is the single specification the buyer is choosing on, and getting it wrong produces exactly the review that kills a product page.

There is a structural elegance to putting the limitation before the benefit. It sets the credibility floor for everything that follows, and it filters the buyer who wanted a hard hold before they have committed anything more than five seconds. Compare that to discovering the same fact from a one-star review after purchase.

What to copy: pick the most common one-star review theme on your product. If the theme is a real property of the product rather than a defect, state it in the description, in plain language, without hedging. Your return rate is the metric to watch, not your conversion rate.

7. Thinx, All-Day Breathable Hiphugger: two objections, one paragraph

Source: thinx.com/products/period-all-day-breathable-hiphugger, captured July 30, 2026.

The sentences: “Go ahead, wear those yoga pants! This style is designed for no visible panty lines.” and, separately, “fits true to size.”

The job: two objection pre-empts, one social and one logistical. The first names the specific situation the shopper is picturing, wearing something tight, and resolves it. Naming the scenario is what makes it work. “Seamless design” states the same fact and answers nothing, because the shopper has to do the translation from spec to situation themselves.

The second, “fits true to size,” is four words doing more commercial work than most paragraphs. In any apparel category, size uncertainty is both the top abandonment cause and the top return cause. Four words on the product page addresses both. It has to be true, obviously, and if your reviews say otherwise then the honest version is “runs small, we recommend sizing up,” which converts slightly worse and returns dramatically better.

What to copy: write the sizing sentence. If you sell anything worn or fitted and your description does not contain an explicit fit statement, that is the highest-value missing sentence on the page. For the structural side of this, where the sizing information sits relative to the variant picker and the buy button, see our guide to Shopify product page optimization.

8. Dr. Squatch, Soap Minis: naming the purchase risk out loud

Source: drsquatch.com/products/minis-free-manteen-canteen-046-nco, captured July 30, 2026.

The sentence: “Find your scent without committing to a full-size guess.”

The job: objection pre-empt, aimed at the risk rather than the product. The word doing the work is “guess.” It names, in the shopper's own internal vocabulary, exactly what buying a full-size scented product online feels like. Then the product is positioned as the removal of that feeling.

This is the sampler-pack argument, and most stores make it as a feature list: “five 2oz bars, try our bestsellers.” The feature list describes the box. This sentence describes the shopper's problem. The difference in effort is one sentence.

What to copy: for any trial, sample, starter or bundle SKU, write the sentence that names the risk of buying the full-size version. That is the entire product rationale, and it is usually missing from the page.

Teardowns 9 to 12: writing for the shopper who will never scroll

TL;DR If readers finish only a fifth of the words, the first line is the whole description for most of them. These four earn the second line, three of them by making the product physical.

Return to the Nielsen Norman finding: at most 28% of words read in an average visit, 20% more likely, and half the information read only on pages of 111 words or less (NN/g, 2008, data from 2005). Whatever the exact current figure, the direction has never been seriously contested: the opening line of a description is read by a much larger population than the closing line.

Two moves earn the second line. Either the first line makes the product physical, so the reader is now imagining something, or it says something the reader did not expect, so the reader is now curious. The next four are split between them.

9. Beardbrand, Norse Winter Deodorant: a place instead of a note list

Source: beardbrand.com/products/norse-winter-aluminum-free-deodorant, captured July 30, 2026.

The sentence: “Norse Winter Utility Deodorant smells like December in Scandinavia, ancient evergreen forests and sacred resins burning in snow-covered wooden halls.”

The job: sensory anchor, solving the hardest problem in ecommerce copy, which is selling a smell through a screen. The standard solution is a fragrance-note list: top notes of bergamot, heart of cedar, base of amber. That list is accurate and communicates almost nothing to a non-specialist, because most people cannot convert “vetiver” into an experience.

The move here is to give a place and a season instead. “December in Scandinavia” is instantly reconstructable by someone who has never been to Scandinavia, because the reader assembles it from cold, pine, dark, quiet. The second half then adds materials, evergreen and resin, which are close to fragrance notes but expressed as objects in a scene rather than entries in a list.

What to copy: for anything experiential, write the scene, not the specification. Then keep the specification too, lower down, for the shopper who wants it.

10. Misen, Silicone Roasting Racks: the mechanism, then the joke

Source: misen.com/products/silicone-roasting-racks-4-pack-black, captured July 30, 2026.

The sentence: “so it crisps instead of steams, because no one's aiming for 'kinda roasted.'”

The job: sensory anchor plus mechanism. “Crisps instead of steams” is the physics of a roasting rack in four words, and it is stated as an outcome the reader can taste rather than as a feature (“elevated design promotes air circulation”). Then the second clause names the failure state, “kinda roasted,” which is a phrase a person would actually say about a disappointing tray of vegetables.

The structural lesson is the ordering. Mechanism first, so the claim is grounded, then the recognisable failure, so the reader has a reason to care about the mechanism. Reverse it and the joke arrives before the reader knows what the product does.

What to copy: state the mechanism as an outcome, in the shopper's words. Then name the thing that happens without your product, using the phrase they would use.

11. Liquid Death, Killer Classics Vol. 2: the unexpected first line

Source: liquiddeath.com/products/killer-classics-vol-2, captured July 30, 2026.

The sentence: “Some joke that most sparkling waters taste like someone whispered the name of a flavor in another room.”

The job: scroll-proof opener. It works by describing a shared experience the reader has definitely had but has probably never seen written down: the faint, almost-absent flavour of a mainstream sparkling water. Recognition is the hook, and it is stronger than novelty because it makes the reader feel observed rather than sold to.

It is also a category attack that never names a competitor, which keeps it usable in feeds that prohibit competitor comparisons. Google Merchant Center's product data specification explicitly disallows comparisons to other merchants' products in the description field, along with promotional text and all-caps writing (Google Merchant Center product data spec). “Most sparkling waters” is a category statement, not a competitor comparison.

What to copy: open with the frustration your category creates, described precisely enough that the reader recognises their own experience in it. Precision is what separates this from “tired of boring sparkling water?”

12. Dr. Squatch, Pine Tar: one sentence, and that is the entire description

Source: drsquatch.com/products/pine-tar, captured July 30, 2026.

The sentence: “Rugged, woodsy, and strong, just like a Squatch man should be.”

The job: this is the whole description, and it is the most instructive entry in the set precisely because it is the one we would not recommend copying wholesale.

What it does well: it is unmissable, it carries the brand's identity, and it is scroll-proof by construction, because there is nothing to scroll. On a page carrying strong imagery, reviews, and a well-known brand, one line can be enough, and Dr. Squatch is a brand that has earned the shorthand.

What it gives up is everything in the first eight teardowns. There is no specificity swap, no ingredient, no objection handled, no answer to “will this dry out my skin” or “what does pine tar actually smell like.” Baymard's usability testing on product descriptions found that 50% of users in their desktop study required information about ingredients, and that 10% of ecommerce sites have product descriptions insufficient for users' needs, against 90% of the top-60 sites maintaining a high standard (Baymard Institute, published March 9, 2021, so a dated figure).

And there is the decay story from the top of this post. This one sentence replaced the longer, more specific copy that Shopify's article quotes, which now survives only inside a scent_story metafield in the page's data rather than in the description a shopper reads. A brand with that much recognition can make that trade. Most Shopify stores cannot, because the description is doing work that brand recognition is not yet doing for them.

What to copy: the discipline of a single unmissable opening line. Not the decision to stop there.

Source	The sentence	Job
dieuxskin.com/products/air-angel	“clinically shown to hydrate skin for up to 12 hours after application, without clogging pores”	Specificity. Note this is the brand's own claim quoted as an example of specific writing. We have not seen the underlying study and are not restating it as fact.
beardbrand.com/products/short-game-mens-cologne	“Inspired by classic green colognes without approaching it-smells-like-your-grandpa territory.”	Objection pre-empt, aimed at the exact fear a classic fragrance triggers.
beardbrand.com/products/desert-road-mens-cologne	“The engine hum drowns out the rest of the world, and the air smells like motor oil, a worn leather seat, creosote, and a hint of last night.”	Sensory anchor, scene-based rather than note-based.
liquiddeath.com/products/sparkling-energy-vp4	“it's the one energy drink for days when you want more than one energy drink”	Scroll-proof opener built on a use-case nobody else states out loud.

Captured from the same product feeds on July 30, 2026, using the method above.

The patterns that repeat across all twelve

TL;DR Six reusable sentence shapes, each one directly traceable to a teardown above. None of them require a brand voice you do not have.

Reading sixteen descriptions with the same question in mind, certain sentence shapes recur. These are patterns observed in the captured set, not tested formulas, and that distinction matters: nobody has published an experiment showing that any of these shapes outperforms its absence.

Pattern 1: adjective, then number

“Ultra-flexible yet super strong, this 6-foot leash comfortably handles 90 lbs of pulling” (Wild One). The adjective earns attention, the number earns belief. Running the number alone reads like a spec sheet, running the adjective alone reads like everyone else.

Pattern 2: label, colon, specific

“Soothing Heated Seat: 6 skin-safe thermal settings up to 108°F” (Tushy). This is the highlight structure Baymard found on only 22% of sites in 2018. It survives skimming, and it makes vagueness structurally awkward, because a label followed by a colon demands something concrete on the other side.

Pattern 3: the limitation, stated before the benefit

“It's not a high-hold wax, so it won't give you a stiff 'stache” (Beardbrand). Leading with what the product does not do sets the credibility floor for every claim after it, and filters wrong-fit buyers before they pay.

Pattern 4: the redirect

“For heavier photo gear carry, we recommend the original Travel Backpack 45L” (Peak Design). Sends a slice of readers elsewhere in the catalogue. Costs you conversions on that page, buys you fewer returns and a page nobody has to distrust.

Pattern 5: the scene, not the note list

“Smells like December in Scandinavia” (Beardbrand) and “the air smells like motor oil, a worn leather seat, creosote” (Beardbrand). For anything experiential, a scene is reconstructable by a non-specialist in a way that an ingredient list is not.

Pattern 6: name the shopper's risk in their own words

“Find your scent without committing to a full-size guess” (Dr. Squatch) and “because no one's aiming for 'kinda roasted'” (Misen). The vocabulary is the point. “Guess” and “kinda roasted” are words a shopper thinks, not words a brand writes.

What did not repeat is worth listing too, because it is what most advice fixates on. There was no consistent length. There was no consistent use of bullets versus prose. There was no consistent presence or absence of humour. Two of the strongest examples, Tushy and Public Goods, are written in a flat retail register with no brand voice to speak of, and they work fine, because specificity does not require personality.

What the top-ranking “examples” articles get wrong

TL;DR Not that they invent copy, most do not. The problems are undated quotes, stale pages, and an AI Overview whose two worked examples are both made up.

There is a tempting version of this section that says every ranking article invents its sample copy. We checked, and it is not true. Salsify, Wisepops and Shopify all quote real, named brands. Publishing the false version would have been a fabricated claim inside an article about not fabricating claims, so here is the accurate version instead.

Problem 1: real copy, no capture date

This is the big one, and the decay audit at the top of this post is the evidence. Shopify's article, the strongest of the incumbents, links to the live product pages it quotes. Linking is good practice. It is not sufficient, because a link resolves to whatever the page says today while the quote is frozen at whenever the writer read it, and the reader cannot see the gap. Three of four quotes we re-checked no longer match. Two of the linked product URLs return 404.

Problem 2: pages that have aged out

We pulled the live desktop US search results for this keyword on July 30, 2026 using DataForSEO's SERP API. The organic results in order were Salsify, ocula.tech (dated March 13, 2026), a Reddit r/shopify thread that the SERP timestamps as six years old, Jasper, Wisepops (dated April 13, 2026), the Japanese-locale Squarespace article (updated September 14, 2023), Square's UK guide (dated December 20, 2022), and CXL (dated August 30, 2019).

Three of the eight predate 2024. One of them predates the pandemic. On a query whose entire premise is “show me current examples,” a 2019 page cannot be showing you current examples, whatever it said when it was written.

Problem 3: the AI Overview's examples are invented

The same SERP pull returned an AI Overview above all organic results, citing fluentcart.com, goaland.com, copymonkey.ai, bluestonepim.com, Reddit, YouTube, Shopify, and the Japanese Squarespace page. Two of those citations supply the AI Overview's worked examples: FluentCart and Squarespace. Both use invented, hypothetical copy. Squarespace's article (updated September 14, 2023) offers lines like “Our hand lotion is the refreshing antidote to a long year of reapplying hand sanitizer,” which is a writer's demonstration, not a live product page.

So the answer shown at the top of the results page, to a shopper or a merchant asking to see real product descriptions, is currently assembled from made-up ones. That is the gap this post exists to fill, and it is also a useful thing to know about AI Overviews in general: they inherit whatever the underlying pages do, including their fabrications.

Problem 4: uncited statistics

Several of the ranking articles carry conversion statistics with no traceable source. In assembling this post we found and discarded four figures for exactly that reason: two survey percentages attributed to research firms whose own sites carried no such number that we could find, and two conversion-lift claims we could not trace to any underlying report. We are deliberately not restating them here, because a discarded number quoted even once tends to get quoted again. If a number in this post has no source next to it, it is because it is our own measurement, and we say so.

Writing a description an AI assistant can quote back to a shopper

TL;DR OpenAI publishes a product feed spec: description is required, plain text only, capped at 5,000 characters, with a q_and_a field marked recommended. Google Merchant Center caps at 5,000 too. That convergence is the only part of this you can act on with certainty.

Product copy is increasingly read by software before it is read by a person. The scale of the shift is measurable on the search side: DataForSEO's keyword data, pulled July 30, 2026, shows US monthly search volume for “google ai mode” going from 74,000 in July 2025 to 1,000,000 in June 2026, a yearly change of +1,920%. That measures interest in the feature, not shopping behaviour, and should not be stretched into a claim about purchases.

On the shopping side, the best-sourced numbers we could verify come from Salsify's 2026 research, again vendor-published: 22% of shoppers report using AI shopping tools, only 14% say they trust AI recommendations on their own, and 31% say they are convinced to purchase when detailed product descriptions accompany an AI recommendation. Salsify's summary of the finding is that detailed descriptions and clear specifications remain the top triggers of trust in AI-recommended products (Salsify). Survey self-report, from a company that sells product content software. Directionally useful, not a measurement of behaviour.

The part that is documented rather than inferred

Speculation about what a model “prefers” is unfalsifiable. Published feed specifications are not. Two are worth reading in full if you manage product data.

OpenAI's product feed spec lists description as a required field, capped at 5,000 characters, plain text only. title is required and capped at 150 characters. q_and_a is marked recommended, as is reviews. material is capped at 100 characters. dimensions requires units if provided. There is even an optional return_rate field taking a value from 0 to 100% (OpenAI commerce feed specification).

Google Merchant Center's product data spec defines the description attribute as 1 to 5,000 characters, and prohibits promotional text, writing in all caps, and comparisons to other merchants' products (Google Merchant Center).

Two independent specifications, written by different companies for different purposes, converge on the same 5,000-character ceiling for a product description. Worth noting explicitly that this is not a Shopify limit. Shopify's own help documentation does not state a description character cap on the pages we checked, so if you have seen “Shopify limits descriptions to 5,000 characters” somewhere, it is a feed constraint being misattributed to the platform.

What that implies for how you write

• Plain text carries, markup may not. OpenAI's spec says plain text only. If your key claim lives in a table, an image, a tab, or a metafield your theme renders but the description field does not contain, it may not travel with the product. The Dr. Squatch case is the concrete example: the detailed copy still exists on the page, but in a scent_story metafield rather than the description.
• Self-contained sentences survive extraction. “This 6-foot leash comfortably handles 90 lbs of pulling” means the same thing pulled out of context. “It handles up to 90 lbs” does not, because the antecedent is gone.
• Answer questions in the description, because there is a field for them. q_and_a being a recommended field in OpenAI's spec is a strong hint that question-and-answer content has a place in the pipeline. The objection-handling teardowns above are, structurally, answers to unasked questions.
• Fill the structured fields you have. Material, dimensions with units, and returns data are all specified fields. Writing a beautiful description while leaving material blank is optimising the part that gets summarised and skipping the part that gets filtered on.
• Do not write for extraction at the expense of the human. The shopper is still the one who pays. Every teardown above works on a person first.

The wider mechanics of how assistants ingest a catalogue, including feeds, structured data and crawler access, are covered separately in our guide to how AI assistants read your product data.

The rewrite checklist for a Shopify product page

TL;DR Nine checks, in order, all derived from the teardowns above. Do them on one page first and time yourself, because the time cost is what decides whether this ever gets done across a catalogue.

1. Read your first line alone. Cover everything after it. If a shopper read only that line, do they know what this is and have they any reason to continue? If not, you have written a description that most visitors will never reach the good part of. See teardowns 11 and 12.
2. Find every adjective doing load-bearing work and give it a number. Strong, durable, soft, fast, long-lasting, premium. Each one is a place where a spec could go. Wild One's 90 lbs, Tushy's 108°F, Public Goods' 500 GSM.
3. Open your reviews and find the top one-star theme. If it is a real property of the product rather than a defect, put it in the description in plain words. Beardbrand's “it's not a high-hold wax” is the model.
4. Write the fit or sizing sentence if you sell anything worn or fitted. “Fits true to size” or “runs small, size up.” Four words, aimed at both the abandonment and the return.
5. Name the buyer this is not for, and route them. One sentence, honestly pointed at the right alternative in your own catalogue. Peak Design's redirect is the model.
6. Convert one feature into a scene. Only for products where experience is the purchase: fragrance, taste, texture, sound, fit. Keep the specification underneath for the shopper who wants it.
7. Restructure into label-colon-specific for the scannable part. Baymard found highlight structure on 22% of sites in 2018 and that it makes users slow down and read features individually. It also forces concreteness.
8. Check the claim is self-contained. Read each key sentence as if it had been lifted out of the page, because it may be. If it needs the sentence before it to make sense, rewrite it.
9. Fill the structured fields. Material, dimensions with units, care instructions. These are specified fields in both feed specs above and they are usually blank.

One thing deliberately absent from this checklist: a target word count. There is no sourced ideal description length. The only defensible reference points are Nielsen Norman's 111-word threshold for reading half the words, and the 5,000-character feed ceilings. Write to the job. If a shopper's objections are handled in 60 words, stop at 60. If your product genuinely needs 400, the fix is structure, not compression, and the related question of when a long description is actively hurting you is covered in shortening product descriptions without losing the sale.

Before you rewrite: how much traffic that page needs for the change to be measurable

TL;DR At a 2% conversion rate, proving a 20% relative lift needs about 42,000 sessions across both variants. At 200 sessions a month, even a 50% lift takes over three years to detect. Most product pages cannot test their own copy, and no other article on this query says so.

Everything above is a reasoned opinion about sentences. The honest next question is whether you can ever know if it worked. For most Shopify product pages, the answer is no, and the arithmetic is not close.

The formula is the standard sample size calculation for comparing two proportions, published by the NIST/SEMATECH e-Handbook of Statistical Methods, a US Department of Commerce reference (NIST/SEMATECH, section 7.2.4.2). We validated our implementation against NIST's own published worked example: baseline 10%, alternative 20%, one-sided, alpha 0.05, beta 0.10, giving N = 102. Our code returns 102, matching the handbook exactly.

The tables below use the more conservative pooled-variance form that mainstream A/B testing tools use, at 95% two-sided significance and 80% power. Both numbers are conventional rather than sacred; AB Tasty's calculator describes 95% as “an accepted standard” and also recommends running any test for a minimum of 14 days even when the estimated duration is shorter, to cover a full weekly cycle (AB Tasty, a vendor tool).

Baseline conversion rate	+10% relative	+20% relative	+35% relative	+50% relative
1.0%	163,095	42,693	14,879	7,750
1.5%	108,153	28,304	9,860	5,134
2.0%	80,682	21,109	7,351	3,826
3.0%	53,211	13,914	4,841	2,518
5.0%	31,234	8,158	2,834	1,471

Computed by us on July 30, 2026 using the NIST/SEMATECH two-proportion formula, pooled-variance form, two-sided alpha 0.05, power 0.80. Double these figures for the total across both variants.

Now convert that into time, which is the number that actually decides anything. Take a page converting at 2%, running a two-arm test.

Lift you want to detect	Total sessions needed	At 200/mo	At 1,000/mo	At 5,000/mo	At 20,000/mo
+10%	161,364	over 10 years	over 10 years	32.3 months	8.1 months
+20%	42,218	over 10 years	42.2 months	8.4 months	2.1 months
+35%	14,702	73.5 months	14.7 months	2.9 months	0.7 months
+50%	7,652	38.3 months	7.7 months	1.5 months	0.4 months

Same formula and assumptions as above; sessions split evenly between two variants. Our computation, July 30, 2026.

Read the bottom-left cell. A product page doing 200 sessions a month cannot detect even a +50% lift in under three years. Not “would struggle to.” Cannot. And a +50% lift from a description rewrite would be an extraordinary result by any published standard.

The disclosure that most calculators skip

Sample size is not one number, it is a number plus a set of choices, and different defensible choices produce materially different answers. Two examples, both from our own work on this post.

Pooled versus unpooled variance. The tables above use the pooled form. NIST's unpooled form, at the same 2% baseline and the same power and significance, gives 39,571 / 10,161 / 3,445 / 1,747 sessions per variant for the four lift columns. That is roughly half. Neither is wrong. The pooled form is more conservative and is what most mainstream tools ship, so it is the one we published, but a tool quoting you the unpooled number is not lying to you, it is making a different assumption. If you ever compare two calculators and they disagree by about 2x, this is usually why.

Calculators genuinely disagree. Evan Miller's widely used sample size calculator shows a worked example of a 10.2% baseline with a minimum detectable effect taking it to 13.2%, returning 2,545 subjects per variation (Evan Miller). We could not reproduce 2,545 with either the pooled or the unpooled form of the NIST formula; we get 857 unpooled and 1,801 pooled. We are not claiming his figure is wrong, only that we cannot reconstruct it from the published formula, and we are not going to present it as corroboration of our table. Reporting a number we cannot reproduce, without saying so, is exactly the habit this post is arguing against.

The practical upshot: pick one calculator, understand which variance form it uses, and stick with it for every test so your decisions are at least internally consistent. Ours is on the site and runs the pooled form printed above, so you can put your own numbers into the statistical significance calculator and it will tell you how far short of a verdict your page is before it shows you a p-value. We walk through the same arithmetic on a worked seasonal example in how much traffic a copy test needs, and you will want your baseline conversion rate before any of these tables mean anything for your store.

Why this is getting harder, not easier

Contentsquare's 2026 Digital Experience Benchmark, built on 99 billion sessions across 6,500 or more websites in 9 industries, reports conversion rates down 5.1% year over year and engagement down 10%, comparing Q4 2024 to Q4 2025 (Contentsquare, vendor-published). A falling baseline conversion rate makes every one of the numbers in the tables above larger, because lower baselines need more traffic for the same relative detection. The window for testing your way to certainty is narrowing for most stores, not widening.

What to do when the page will never have enough traffic to test

TL;DR Five options, ranked. Test the template rather than the page, measure a leading indicator, use a returns-based verdict, run a pre-mortem on the change, or just ship it and say out loud that you are not measuring it.

Here is where most CRO advice quietly stops, and it is the section that decides whether the eleven above are useful to you. If your product page does 200 sessions a month, and most do, an A/B test on its description is not a slow answer, it is no answer. That leaves five real options.

Option 1: test the template, not the page

The strongest option, and the most underused. A single product page has 200 sessions. Forty similar product pages have 8,000. If the change you want to make is a pattern rather than a sentence, adding a fit statement to every apparel product, or converting every description opener to label-colon-specific, then the unit of the test is the pattern applied across a group.

Split the group, not the traffic: half the products get the new pattern, half keep the old one, randomised, and you compare group-level conversion over a period long enough to accumulate the sessions from the table above. This is a weaker design than a session-level split, because products are not identical and product-level variance is now inside your measurement, so pair or stratify by traffic volume and price band where you can. It is still enormously better than never knowing.

The practical constraint is that it only works for changes that generalise. “Rewrite the Pine Tar opener” does not. “Every product gets a specific number in its first two sentences” does.

Option 2: move to a leading indicator, and say that you did

Add-to-cart rate happens more often than purchase, so it needs fewer sessions to detect the same relative change. If your page converts at 2% but adds to cart at 8%, the 5.0% row of the sample size table is closer to your reality than the 2.0% row, and the test that was impossible becomes merely slow.

The cost is real and should be stated rather than hidden. Add-to-cart is not revenue. A description change can lift add-to-cart and lose money if it oversells, and you would not see it. Use the leading indicator to make a decision you could not otherwise make, then watch revenue and returns for the following quarter as your check. Never report the add-to-cart lift as if it were a conversion lift.

Option 3: judge the change on returns instead of conversions

This one is specific to objection-handling copy and it is underrated. If you add a sizing sentence, a limitation statement, or a redirect, the intended effect is partly to prevent wrong purchases. That shows up in return rate, which for a store with returns is often a less noisy signal than conversion rate because the base rate is far higher: the NRF puts 19.3% of online sales returned in 2025, against a typical conversion rate in low single digits (NRF). A higher base rate means fewer units needed to see a change in it.

The honest caveat: returns lag. You are waiting out a return window plus a decision delay, so this is a quarterly read, not a weekly one, and seasonality will contaminate a naive before-and-after comparison. Compare like periods.

Option 4: run a pre-mortem instead of a post-test

When you cannot measure the outcome, upgrade the quality of the decision. Before shipping a description rewrite, write down two things: what you expect to happen, and what would have to be true for the change to backfire. For a sizing sentence saying “runs small, size up,” the backfire is fewer purchases from people who now doubt the fit. For a redirect sentence, it is revenue moving to a cheaper SKU. Naming the downside in advance means you notice it if the aggregate numbers move the wrong way, instead of retro-fitting an explanation.

Then set a floor rather than a target: “we will keep this unless site-wide conversion drops more than X over the next quarter.” That is a weak instrument. It is not nothing, and it is honest about what it is.

Option 5: ship it and say plainly that you are not measuring it

The most common real-world answer, and there is no shame in it as long as it is stated. Some changes are worth making on judgement alone: filling in a blank material field, correcting a description that contradicts the photos, adding the sizing sentence. The failure is not shipping unmeasured changes, it is shipping unmeasured changes and then telling yourself, your client, or your investor that they lifted conversion by 12%. That number does not exist. Nobody measured it.

This is worth being blunt about, because we searched hard for the alternative. In assembling this post we looked for any credible, published, properly powered A/B test isolating a product description rewrite. We found none. Plenty of case studies claim a percentage. None that we could verify disclosed sample size, baseline, duration, and significance together. If a copywriting agency quotes you a lift number for description rewrites, ask for those four things, and note what happens next.

Which one to pick

If you have more than 5,000 sessions a month on the page, run a real test. If you have a catalogue of similar products, test the template. If your category has meaningful returns, watch returns. Otherwise use a leading indicator with a pre-mortem, and be explicit in your own reporting that the change was shipped on judgement.

Copy is also not usually the highest-leverage element on a product page to spend your limited testing capacity on. If you only have the traffic for one honest test this quarter, the hero image is typically the bigger and faster-moving variable, and we cover that separately in how to test your product page hero image. The broader question of how to run a testing programme on a store that does not have enterprise traffic is covered in A/B testing on a low-traffic Shopify store.

A disclosure, since this post has been holding other people's numbers to a standard. We are building StorePilot, an AI CRO tool for Shopify that watches shopper behaviour, proposes ranked experiments, and refuses to call a winner before the sample size arithmetic above says it can. It has not launched yet. That is the extent of the pitch, and the tables in this post work identically whether or not you ever use it.

Questions merchants keep asking

What makes a good product description?

In the twelve live descriptions we captured on July 30, 2026, the sentence doing the work was always doing one of four jobs: replacing a vague claim with a checkable fact, answering an objection before the shopper had to go looking, giving the product a sensory anchor, or earning the next line of reading. Nothing else was consistent. Length was not consistent, tone was not consistent, structure was not consistent.

How long should a product description be?

There is no sourced ideal length, and anyone quoting one is guessing. Two defensible reference points exist. Nielsen Norman Group's analysis of 45,237 page views found users read half the information only on pages of 111 words or less (published 2008, data from 2005). And both Google Merchant Center and OpenAI's product feed spec cap the description field at 5,000 characters. Between those two numbers, write to the job, not to a word count.

Where can I find real product description examples instead of made-up ones?

Any Shopify store publishes its own live product feed at /products.json. That endpoint returns each product's body_html, which is the description exactly as it currently exists, along with published_at and updated_at timestamps. Every quote in this post came from that endpoint on July 30, 2026 and can be re-checked the same way.

Do product descriptions actually affect sales?

The best-sourced evidence is survey evidence, not experimental. Salsify's 2026 consumer research (2,712 respondents, fielded via SurveyMonkey in October 2025, margin of error 3% or better) reports 34% of shoppers abandoning a sale because of incomplete or poorly written titles and descriptions, and 45% returning an online purchase because of incorrect or misleading information. Note that Salsify sells product information management software, so treat it as vendor-published. We found no credible published A/B test isolating a description rewrite, which is the honest gap this post ends on.

How much traffic does a product page need to A/B test its description?

More than most product pages have. At a 2% baseline conversion rate, 95% two-sided significance and 80% power, detecting a 20% relative lift needs roughly 42,000 total sessions across both variants. A page doing 200 sessions a month cannot detect even a 50% lift in under three years. The full table is in this post, computed with the NIST/SEMATECH formula.

What should I do if my product page does not have enough traffic to test?

Stop trying to test that page and start testing the template. Group similar products, change the description pattern across the whole group, and measure at the group level where the sessions actually add up. Or judge the change on a leading indicator you can move faster, such as add-to-cart rate, while accepting that the leading indicator is a weaker signal than revenue.

Do AI shopping assistants read my product description?

OpenAI's published product feed spec lists description as a required field with a 5,000-character cap and requires plain text. It also marks a q_and_a field as recommended. That is a public specification, not an inference. What an assistant does with the text is not disclosed, so treat structure and completeness as the controllable part.

Why do so many product description examples articles use fake copy?

Most do not. We checked. Salsify, Wisepops and Shopify all quote real named brands. Squarespace's article, updated September 14, 2023, uses invented sample copy, and so do the two worked examples inside the AI Overview Google showed for this query on July 30, 2026. The more common failure is different: real copy quoted without a capture date, which rots silently as brands rewrite their pages.

How do I check whether a quoted product description is still live?

Open the store's product feed URL, which is the product URL with .json appended, or the whole catalog at /products.json. Search the body_html field for the quoted phrase. If it is not there, the copy has been rewritten. We ran this against the four brands quoted by the top-ranking article on this keyword and three of the four quotes no longer matched the live description.

Sources

Every figure in this post traces to one of the sources below. Product-description quotes were captured from the stores' own live product feeds on July 30, 2026 and may have changed since; the feed URL on each one lets you re-check.

1. Jakob Nielsen, “How Little Do Users Read?”, Nielsen Norman Group. 28% and 20% word-reading figures, the 111-word threshold, and the 593-word average page, from 45,237 page views. Published 2008 on 2005 data. Retrieved July 30, 2026.
2. Google Merchant Center, product data specification. The description attribute at 1 to 5,000 characters, and the prohibitions on promotional text, all caps, and competitor comparisons. Retrieved July 30, 2026.
3. OpenAI, product feed specification. Description required, 5,000 characters, plain text only; title required at 150; q_and_a and reviews recommended; material 100; dimensions requiring units; optional return_rate. Retrieved July 30, 2026.
4. Salsify, 2026 Consumer Research summary. 34% abandonment on incomplete or poorly written titles and descriptions; 38% on inconsistent information. Vendor-published. Dated February 17, 2026. Retrieved July 30, 2026.
5. Salsify, 2026 Consumer Research press release. Methodology: 2,712 completed responses, US 908 / Canada 902 / UK 902, fielded via SurveyMonkey in October 2025, margin of error 3% or better; and the 45% returns figure. Vendor-published. Dated January 21, 2026. Retrieved July 30, 2026.
6. Salsify, AI trust gap research. 14% trust AI recommendations alone, 22% use AI shopping tools, 31% convinced to purchase when detailed descriptions accompany AI recommendations. Vendor-published, 2026. Retrieved July 30, 2026.
7. Baymard Institute, product description usability. 10% of ecommerce sites with insufficient descriptions, 90% of the top 60 maintaining high quality, 50% of desktop-study users needing ingredient information. Published March 9, 2021. Retrieved July 30, 2026.
8. Baymard Institute, structuring descriptions by highlights. 22% of sites using highlight structure, and the finding that highlights make users slow down and investigate features individually. Published April 24, 2018. Retrieved July 30, 2026.
9. Baymard Institute, current state of ecommerce product page UX. 155 or more benchmarked sites and 30,000 or more manually reviewed product page usability scores. Updated March 18, 2026. Retrieved July 30, 2026.
10. NIST/SEMATECH e-Handbook of Statistical Methods, section 7.2.4.2. The two-proportion sample size formula and the N = 102 worked example our implementation reproduces. US Department of Commerce. Retrieved July 30, 2026.
11. AB Tasty sample size calculator. The 95% significance convention and the 14-day minimum runtime recommendation. Vendor tool. Retrieved July 30, 2026.
12. Evan Miller, sample size calculator. The 10.2% baseline worked example returning 2,545 per variation, which we were unable to reproduce with either variance form. Retrieved July 30, 2026.
13. Contentsquare, 2026 Digital Experience Benchmark. 6,500 or more websites, 9 industries, 99 billion sessions; conversion down 5.1% and engagement down 10% year over year, Q4 2024 against Q4 2025. Vendor-published. Retrieved July 30, 2026.
14. National Retail Federation, 2025 Retail Returns Landscape. $849.9 billion in total US returns and 19.3% of online sales returned in 2025. Released October 15, 2025. Retrieved July 30, 2026.
15. Shopify blog, how to write product descriptions that sell, by Lizzie Davey. Published January 30, 2026. The source of the four quoted descriptions in our decay audit. Retrieved July 30, 2026.
16. Squarespace, how to write product descriptions. Updated September 14, 2023, originally November 8, 2021. The one ranking article we confirmed uses invented sample copy. Retrieved July 30, 2026.
17. DataForSEO Labs and SERP APIs, queried by us on July 30, 2026. Live desktop US search results at depth 20 for “product description examples”, including AI Overview citations and result dates; and keyword data for “google ai mode” showing 74,000 US monthly searches in July 2025 rising to 1,000,000 in June 2026, a yearly change of +1,920%.
18. Our own capture run, July 30, 2026. Sixteen live product descriptions read from the following public product feeds: liquiddeath.com, wildone.com, publicgoods.com, hellotushy.com, dieuxskin.com, peakdesign.com, beardbrand.com, thinx.com, drsquatch.com, misen.com. Plus re-checks of drsquatch.com, beardbrand.com, theoodie.com and casper.com against the four descriptions quoted in Shopify's article.
19. Our own computation, July 30, 2026. All sample size and test duration figures, calculated with the NIST/SEMATECH two-proportion formula at 95% two-sided significance and 80% power, validated against NIST's published worked example.