u/Inner-Sink8420

A client came to me after an agency quoted them to rebuild on WooCommerce. The agency's position was that Shopify couldn't handle their setup, and they'd charged around €1,300 just for the B2B login-pricing piece.

The requirement: one store selling both wholesale and retail, across multiple European markets, in multiple languages, where logged-in B2B customers see their own negotiated prices instead of retail. The claim was that this needs to leave Shopify entirely.

I didn't think that was true, so I took the project to test it. Here's what it actually took:

1. Markets. Multiple markets handled the per-region setup, with localized languages running off one catalog instead of duplicating the store per country. This was less painful than expected once the market structure was mapped out first.
2. Language. Translation handled the storefront-facing content per market. The gotcha was making sure pricing and language were tied to the right market context, not set globally.
3. B2B login pricing. This was the part the agency said was impossible. The approach: assign business customers to groups, attach a price list to each group, and gate the wholesale prices behind login so retail visitors never see them. The customer logs in, the correct price shows automatically, and there's no discount code anywhere to leak or get shared.
4. One catalog, not two. The biggest time save was refusing to run a separate wholesale store. Retail and wholesale pulling from the same product catalog meant no duplicate inventory, no double maintenance.

What worked: keeping everything on one platform was dramatically cheaper to maintain than the two-store / second-platform approach the agency proposed. The B2B login pricing was the genuinely tricky part, but it was a "hard" problem, not an "impossible" one.

What didn't work cleanly: the market-plus-language-plus-pricing interaction needed careful testing. It's easy to get a config where a customer sees the right language but the wrong market's price. Test every market with a real logged-in B2B account before going live.

The takeaway I keep running into: "you can't do that on Shopify" usually means the agency hasn't done it, not that the platform can't.

Curious if others here have hit the same wall. For those running B2B on Shopify, what's the part of wholesale pricing that's still the most painful to set up?

I've been auditing Shopify stores for a while and keep seeing the same gap.

You pay $30-50 in ad spend to land a visitor on a product page. They scroll, read, maybe add to cart. Then they leave.

The block right under "Add to Cart" is empty. Or it's a generic "customers also viewed" carousel with 12 random products.

That spot is your highest-intent surface in the entire store. The visitor has already evaluated one product. Their wallet is half out. The cognitive cost of adding one more item is basically zero — but only if you make it easy.

What actually works there:

1. 2-3 hand-picked complementary products (not algorithmic guesses)
2. Variant already selected — no dropdown decisions
3. One-tap add — no navigating to a new page
4. Visible price + savings on the upsell

What doesn't work:

• "You might also like" carousels with 8+ products (paralysis)
• Cross-category recommendations (coffee + camo hat — why?)
• Recommendations that require leaving the current page to add

The stores I see doing this well are pulling 15-25% AOV lifts from this one block. The ones leaving it empty are basically lighting their ad spend on fire.

Curious what others have tested here. What's been your biggest lift on the PDP between Add to Cart and the fold?

Spent the last few weeks building this and it just went live on the Shopify App Store.

The problem I kept running into as someone who builds upsell apps: every post-purchase upsell setup is static. One offer, one product, served on every single order regardless of what was in the cart. A $25 order and a $250 order see the same block. And every one of them needs the merchant to sit and configure offers, set rules, pick discounts, exclude SKUs.

The build:

1. Merchant installs and enables. That is the entire setup.
2. When a customer completes checkout, Claude reads the order, looks at the merchant's catalog, picks one product to offer, and decides the discount.
3. Customer accepts with one click. No second checkout, native to Shopify Checkout Extensibility.
4. Dashboard shows accept and decline rates per product so the merchant can see what the model is doing.

Stack: Remix on Shopify Checkout Extensibility, Claude API for the per-order decision and offer creation, standard Shopify Polaris for the admin dashboard.

What I am genuinely curious about from anyone running anything similar:

1. How are you measuring AI-selected offers against a static control fairly? Accept rate misleads because the model picks lower-AOV items more often. Net revenue per checkout has high variance at low order volumes.
2. Anyone running per-order LLM decisioning in production, what is your latency budget? I am at sub-second but the post-purchase page is forgiving.
3. Zero-config post-purchase is a strong promise. The thing I am wrestling with is whether merchants actually want zero control, or whether they want the option to override Claude on specific SKUs. Curious what others have found.

App is free during the early phase while I gather real-world data: apps.shopify.com/post_purchase_remix

Happy to answer build questions.

Shopify stores frequently pay significant amounts in user-generated content (UGC) creators, yet many overlook the importance of displaying these valuable videos on their product pages for building trust. This oversight represents a substantial missed opportunity.

The Libautech UGC app provides a solution by allowing stores to effortlessly incorporate these videos into their product pages.

Explore the app for free at https://apps.shopify.com/libautech-shoppable-ugc

Spent the last few weeks digging into how ChatGPT's product catalog actually surfaces Shopify products. Sharing what I found because I think most merchants are missing something obvious.

The setup

When someone asks ChatGPT something like "ceremonial grade matcha that ships to USA" or "non-greasy moisturizer with hydrating effect", it doesn't just match keywords in your title and description. It filters by structured product attributes. The same ones Shopify already added to your admin under "Category metafields."

Open any product in your Shopify admin. Scroll past the description. There's a section called Category metafields with fields like Product form, Skin care effect, Cosmetic function, Ingredient origin, Material, Age group, Target gender, etc. Shopify generates these automatically based on the product category you set.

What I checked

I went through about 40 stores across beauty, food, apparel, and home goods. Rough breakdown of how many had category metafields filled:

1. Around 70% had zero fields filled. Completely empty.
2. About 20% had one or two fields filled, usually Material or Product form.
3. Less than 10% had most of the relevant fields filled.

The stores in that bottom 10% were almost all bigger DTC brands with dedicated ops teams. Everyone else, blank.

Why this matters

Three surfaces use these exact metafields:

1. ChatGPT's product catalog when it filters product results
2. Shop App search and recommendations
3. Shopify's own AI-powered search inside your storefront

Fill them once, get visibility on all three. Leave them blank, you're invisible to filter-based queries no matter how good your title SEO is.

The interesting part: this isn't a ranking factor yet. It's a visibility factor. If your product doesn't have "Skin care effect: Hydrating" filled in and someone asks for "hydrating moisturizer", you're not in the candidate pool. Doesn't matter how good your title is.

What I built

This is the actual reason I'm posting. I run a Shopify app called Shoptank that handles AI visibility stuff (llms.txt, schema, AI catalog). Just shipped a feature that reads each product and auto-fills the standard Shopify category metafields with AI. Takes a store from 0% filled to ~80%+ filled in one pass.

Not pitching it here, just being transparent about why I noticed this gap in the first place. You can do the same thing manually — go product by product, fill in the fields. It's tedious but free. The Shopify admin literally has the boxes waiting.

Open question

For anyone here who's already filled their category metafields manually or via app — have you noticed any traffic shift from Shop App or AI surfaces since? Curious whether the impact is already measurable or whether it's still early.

Also curious if anyone has a different read on which AI shopping layers actually use these metafields vs. just title/description matching.

I've been looking at a lot of Shopify 7-figure DTC product pages lately and noticed a pattern.

The stores that actually move AOV don't use pop-up upsells. They don't use slide-ins. They don't use post-purchase funnels as the main play.

They all use one slot: a small card directly under the Add to Cart button.

Summer Fridays calls it "Pairs Well With." Glossier uses the same slot. Lululemon calls it "Complete The Look."

Same spot on every page. Same width as the main CTA. Same button style. One related product.

Why it converts better than any pop-up I've tested:

1. The customer already decided to buy. Decision fatigue is at zero at that exact moment
2. One related product shown, not five. No paradox of choice
3. The Add to Cart button on the pair uses the exact same style as the main cart button. One tap to accept
4. The framing is "pairs with" or "completes" — not "customers also bought" or "you might like." The word does the psychological work

Every pop-up upsell app I've seen tries to interrupt the decision. This slot does the opposite. It extends the decision the customer already made.

The reason most stores skip it: setup is a pain. You have to pair every trigger product with a recommended one, write copy, match the theme, and maintain it as your catalog changes.

But the stores that push through the setup work see AOV lifts in the 30-60% range on the products where the pair actually makes sense.

Anyone else running this slot? Curious whether you're pairing manually or using an Libautech Bundles & Upsell AI recommender, and how you're picking the pair product (bestseller, margin, category complement).

I've been testing ChatGPT's shopping experience for the last few weeks — asking it things like "best minimalist wallet under $80" or "where can I buy a stainless steel water bottle in the US." When it recommends stores, it pulls from a product catalog with specific filters: shipping availability, stock status, price, return policy, and a few other signals.

Out of ~50 Shopify stores I tested across different categories, only 6 showed up in ChatGPT's recommendations. The other 44 were invisible — even established brands with decent SEO and active traffic.

What's going on:

1. ChatGPT reads structured product data differently than Google. Schema markup, product feed quality, and llms.txt matter more than traditional SEO signals.
2. Stores missing return policies, shipping info, or with stale product feeds get filtered out.
3. Even if your product ranks on Google, it can be completely invisible to ChatGPT — they're different systems.

Why this matters: Gartner projects 25% of search traffic will move to AI assistants by 2026. If your store isn't visible to ChatGPT now, you're already losing buyers who are asking AI instead of Googling.

Quick check anyone can do right now: open ChatGPT, ask "where can I buy [your product category]" and see if your store gets mentioned. If not, here are the 4 things I'd audit first:

• Is your product schema complete? (Product, Offer, AggregateRating)
• Do you have a shipping/returns page ChatGPT can crawl?
• Are your product titles written for humans, not keyword-stuffed?
• Do you have an llms.txt file telling AI crawlers what your site is about?

Curious — has anyone else tested their own store? What did ChatGPT return?

Quick context. MrBeast's store is essentially a POD-adjacent merch operation. Tees, hoodies, jackets, caps. Same product categories most people here are selling. So the patterns on his product pages are directly applicable to a POD storefront, not just DTC brands with custom manufacturing.

What caught my attention

On a tee product page, there's a "Pairs well with" block at the bottom. Three addons. A jacket at $89.99, a tee at $29.99, a hoodie at $49.99. Every single one has a "LIMITED RELEASE" badge on it.

No discount. No "10% off when bought together." No bundle price. Just a badge.

For POD specifically this matters because our margins are already thinner than custom manufacturing. Every point of discount on a $29 tee from Printful or Printify is a meaningful chunk of profit gone. A badge costs zero margin.

Why most POD stores are doing this backwards

The default upsell block for a POD store on Shopify is "save 10% when you add this." Two problems with that:

1. You've trained the buyer to expect a discount on every future addon. Next time they see an upsell without one, they feel like they're losing something and skip.
2. You've told them the addon isn't worth full price on its own. On POD products where perceived value is already harder to build than on premium manufactured goods, this is the opposite of what you want.

For a POD store running on tight Printful or Printify margins, this is a double hit. You're paying for the conversion in price today and in future expectation forever.

What badges do instead

A badge doesn't touch the price. It changes how the buyer reads the price.

The five that actually work on a POD product page upsell:

1. Most popular. Social proof. Buyers pick the addon other buyers already picked. Works especially well when the main product is a design and the addon is a different product with the same design.
2. Limited release. Scarcity without a countdown timer (which POD stores shouldn't use anyway because nothing is actually limited).
3. Staff pick. Works well for design-led POD stores. Signals taste over algorithm.
4. Best value. Reframes a higher-priced addon like a hoodie as the smart pick versus a second tee.
5. Fan favorite. POD-specific. Community-led stores use this effectively because your buyers identify as fans first.

Same addon. Same price. Different read. Margin stays intact.

What I'm testing on our own test store

Swapping a "10% off when bought together" block for a "Most Popular" badge on the highest-margin addon. Rest of the block identical. Running two weeks. My hypothesis is AOV holds and margin per upsell click goes up meaningfully.

Full disclosure on the app side

I build Libautech Bundles & Upsell, a Shopify app. Badges on addon cards is something we added directly into the app, so you can set up that exact "Pairs well with" style block with any badge you want on each addon without touching theme code. If anyone wants to see it, it's on the Shopify app store as Libautech Bundles & Upsell. Not gating anything behind a DM.

Question for the sub

For POD operators specifically: are you still running discount-based upsells, or has anyone here tested a pure badge/framing play? And if you've done both, which carried more of the lift. Curious whether the POD category responds differently than general DTC, because margin dynamics are so different.

I build Shopify apps. A merchant came to us a few months ago - good store, running ads, but zero sales coming from AI. ChatGPT, Perplexity, Gemini. Nothing.

They kept searching for their own products on ChatGPT. Competitors showed up every time. They never did.

We structured their store data for AI in one session. Under 10 minutes of work.

They've now made over $10,000 in orders directly from ChatGPT referrals.

Here is what was actually happening.

When a shopper asks ChatGPT for a product recommendation, AI doesn't pick randomly. It recommends the stores it understands best. The ones where it knows exactly what they sell, who they ship to, what their policies are, and why customers trust them over competitors.

Most Shopify stores give AI almost nothing to work with. The data is there — but it's not structured in a way AI can read and use. So AI skips them and recommends whoever did the work.

That's the entire gap. Not better products. Not lower prices. Just structured data that AI can actually read.

We structured this merchant's store - their product catalog, brand story, shipping info, FAQs, policies - in a format every major AI assistant understands. ChatGPT could now read exactly what they sell and why a shopper should choose them over a competitor.

First order came in 2 weeks later. $78. Source: chatgpt.com.

$10,000 followed after that. Same store. Same products. Same prices. Just visible to AI now.

The app to structure Shopify store data for AI visibility is Shoptank. It structures your store data for AI automatically - no technical knowledge needed. Takes about 10 minutes to set up. Starts at $14.99 a month, 7-day free trial at shoptank.io.

How to setup video - https://youtu.be/yStVdmzAj98

(Screenshot of the latest order attached — orderValue: 708, source: chatgpt.com)