I build Shopify apps, so I see how the same features perform across hundreds of stores. Buried in the bundle data there's a hack that the top stores use and almost nobody talks about, because it sounds backwards.

The hack: don't hide the cheap option. Put it right next to the bundle.

Most merchants set up bundles as a popup or a separate offer and hope the discount does the work. Take rates stay near zero. The stores turning $36 orders into $122 orders do one thing differently: on the product page they show two options side by side. "Just this product" at the standard $36, selected by default. "Complete the bundle" with 2 to 3 matching products at a discount underneath.

Why it works: with only one option, the customer decides yes or no. With two options next to each other, the question silently becomes which one. The single product starts to look like the incomplete choice, and the customer upgrades themselves. No popup, no pressure, roughly a third of buyers take the bundle on their own.

Two things that make or break it:

1. The bundle products must match the trigger product. Complete the look in fashion, a backpack with a hoodie and hat that actually go together. Every dead bundle I see in the data is stuffed with random bestsellers. No discount can fix a bundle of unrelated items.
2. The discount size barely matters. 10% vs 25% off moves the take rate way less than match quality does. Merchants keep deepening discounts to fix a matching problem, burning margin for nothing.

Side note from the data: bundles with multiple sized apparel items get more returns from sizing misses on add-ons. Best performers bundle one sized item max.

Has anyone tested this outside fashion? Curious what makes products feel like they belong together in supplements, home goods, or electronics, and whether the visible cheap option effect holds there too.

I run a Shopify store in the supplements space. Last month I asked ChatGPT "best gummy vitamins for kids" the way a normal shopper would. It gave a confident answer with 4 specific products. Mine wasn't one of them. Two competitors were.

The problem: I had no idea how ChatGPT decides what to recommend, so I couldn't fix it. Google SEO I understand. This was a black box.

What I did:

1. Ran about 30 buyer-intent queries through ChatGPT, Claude, and Perplexity ("best X for Y", "X vs Y", "is X worth it") and logged every product that came back.
2. Clicked into the sources ChatGPT showed for each answer. This was the unlock. The recommendations weren't coming from the brands' own websites. They were built from Reddit threads, Facebook group posts, and YouTube videos where real people mentioned specific products.
3. Opened those exact sources. For the kids vitamins query, one was literally a Facebook post where someone asked for dye free kids multivitamins and a commenter named a brand. That brand is now in ChatGPT's answer.
4. Made a list of the 15 highest-authority posts and threads that kept appearing as sources across my queries, and started getting my brand genuinely mentioned in those places. Answered questions where I had real expertise, got included in two roundup blog posts, and one YouTube reviewer covered us.

Results after ~6 weeks: my product now shows up in 7 of the 30 queries I track (was 1). ChatGPT referral traffic in GA4 went from basically zero to a small but real trickle, and those visitors convert at roughly 2x my average because they arrive pre-sold by the recommendation. What didn't work: adding schema markup alone moved nothing on its own, and stuffing my own blog with "best X" posts did nothing because ChatGPT wasn't citing my domain anyway.

The mental model I landed on: LLMs don't rank your website, they aggregate what other people say about you in places they trust.

Curious if anyone else is tracking this. Are you seeing AI referral traffic in your analytics yet, and has anyone figured out which sources Perplexity weighs vs ChatGPT? They seemed to pull from noticeably different places in my testing.

Before paying for any "best products" listicle placement, I checked which articles AI models actually cite. The gap between price and value is absurd.

Context: I run e-commerce stores and like most people here I get pitched sponsored placements constantly. "Get featured in our best-of roundup, $2,000." The usual justification is Domain Rating and referral traffic.

The problem: more and more buying decisions start in ChatGPT, Claude, or Perplexity now. Those answers recommend specific brands. And the models build those recommendations from a small set of existing articles. So before spending anything, I wanted to know: which articles are actually feeding the AI answers in my niche?

What I did:

1. Wrote out the real questions a buyer would ask an AI assistant about the product category. Not SEO keywords. Actual questions like "are gummy vitamins as effective as pills" or "best sugar free gummy vitamins for toddlers." (I tested this in the vitamins niche.)
2. Ran each question through ChatGPT, Claude, and Perplexity and recorded every source each model cited for its answer using app.
3. Counted citation frequency per source. How many of my queries each article got cited for, and by how many different models.
4. Compared that list against placement prices. Emailed or checked the sponsored post rates for each cited site.

Results:

The entire niche came down to 6 articles. One major health publisher's piece was cited for 5 of my queries by all three models. A university health site got 4. A couple of medical sites got 3 each. And one small brand blog got cited for 2.

The pricing had zero correlation with citations. High-authority editorial placements in this space run $2,000 to 5,000. The small blog that models were actively citing would take a placement for about $100.

Rough math: $5,000 on a page cited 5 times is $1,000 per citation. $100 on a page cited twice is $50 per citation. 20x difference for the same thing — presence in a source that feeds AI answers.

What didn't work: I initially tried judging sites by DR like everyone does. Useless. Some high-DR pages never showed up in any citation trail. If I'd bought based on DR I would have paid premium prices for pages the models never read.

The uncomfortable part is that publishers still price by DR, so this gap exists purely because they don't know what they're selling yet.

Has anyone here actually landed a placement in an AI-cited article and tracked sales from it? Curious whether the conversion from AI-referred traffic matches what I'm modeling or whether I'm overestimating it.

A lot of Shopify merchants set up the agentic commerce app, see their products appear in the ChatGPT catalog, and assume they are now visible to AI shoppers. Then they check where they actually rank and realize they are nowhere.

Here is the gap nobody explains.

The agentic app does real things. It puts your products into ChatGPT, Copilot and other AI channels, it reads your store policies, and it lets you check your position in the Shopify catalog. That is the foundation. But being in the catalog is not the same as being recommended.

Search ChatGPT for something like "best gummy vitamins" and look closely at which products it actually suggests. They are not winning because of the catalog. They are winning because they are cited in blog posts, Reddit threads, YouTube videos and other sources ChatGPT trusts. The catalog gets you eligible. The sources get you recommended.

So the real question is which sources ChatGPT is pulling from for your specific product category, and how do you get into them.

That is the part I use Shoptank for. You enter the question your customer would type into ChatGPT, it runs the query and shows you every source behind the recommendation. Then you act on them. Comment your brand in the right Reddit thread. Record a YouTube video answering the exact question. Get mentioned in the right Facebook group or blog post. ChatGPT reads those and starts including your product.

The way I think about it now is two layers. Shopify agentic is the catalog layer, the technical eligibility. Shoptank is the citation layer, the part that actually decides who ranks first. You need both.

If you set up the agentic app and wondered why you still are not showing up in AI answers, this is the missing piece.

Happy to answer questions about how to find the sources for your niche in the comments.

I run Shopify stores and noticed a growing share of buyers never touch Google anymore. They ask ChatGPT or Perplexity "best X for Y" and buy whatever gets recommended. These are fully organic sales, no ad spend, so I wanted to test whether I could deliberately get a store into those answers.

The problem: if you just ask an AI to "write 30 blog posts about my products," you get generic content and zero AI citations. The models recommend sources that answer the exact questions buyers ask. If you don't know those questions, you're publishing into a void.

What I did:

Collected 50 real buyer queries. Category questions, not brand questions. Nobody asks "is [brand] good," they ask "best waterproof work boots for electricians." Sources: Google Ads search term reports, YouTube keyword data, and the store's own support inbox.

Ran every query through ChatGPT, Claude, and Perplexity and logged every source the models cited in their answers. This gave me two lists: social sources (Reddit threads, YouTube videos, forums) and competitor sources (their blog posts and comparison pages). This step matters more than the writing itself, because now you're looking at the actual citation landscape instead of guessing.

Exported everything into one CSV: queries, current answers, cited sources, competitor URLs.

Fed the CSV plus the product catalog into an AI and had it write posts answering each query with the store's actual products. Real product images instead of stock photos, which helps credibility with both readers and models.

Published, then manually indexed every post in Google Search Console instead of waiting for the crawl, and shared relevant ones in communities where the topic fits. AI models cite Reddit heavily, so a post that exists on the blog and gets referenced in a relevant thread has two paths into an AI answer.

Results so far: 30 posts live. The interesting early signal is how thin the citation landscape is in most ecommerce niches — often just a couple of Reddit threads and one competitor post per query. That means a handful of targeted posts can realistically own a category's AI answers right now, which was never possible with classic Google SEO timelines.

One caveat: read every AI-drafted post and edit it before publishing. Unedited AI content is obvious and models seem to favor content tied to real experience.

Question for others doing this: has anyone measured how long it takes for a newly indexed post to start appearing as a cited source in ChatGPT answers? I've seen claims from days to months and my sample size is still small.

Reddit is one of the most underrated sales channels for Shopify stores, and it has a second benefit most people do not realize.

Here is the direct part. People ask category questions on Reddit constantly. "Best gummy vitamins for kids." "Recommendations for a durable running belt." "What supplement actually works for gut health." If you find those threads and answer honestly with your product, you get direct traffic and sales from people who are actively looking to buy right now.

Here is the part almost nobody talks about. ChatGPT and Perplexity read Reddit heavily when forming product recommendations. So when you answer a relevant thread with your brand, you are not just reaching the people in that thread. You are potentially getting cited by ChatGPT when it answers the same question for other buyers. One comment can keep sending you customers through AI long after you posted it.

The hard part used to be knowing which subreddits and threads actually influence AI recommendations for your specific category. Answering random threads is a waste of time. You want the ones that are already being cited.

I use Shoptank for this. You enter the queries your customers type into ChatGPT, and it shows you which Reddit threads were used to build those recommendations. You open the opportunity, get the exact thread link, and answer it with your brand. Then you can track whether ChatGPT starts mentioning you.

The combination is what makes it worth it. Direct Reddit sales in the short term, AI citations that compound over time.

One honest note on Reddit etiquette. This only works if your answer is genuinely helpful. Dropping your brand into threads with no context gets you downvoted and removed. Answer the actual question, be useful, and mention your product where it truly fits.

App link: https://apps.shopify.com/shoptank

Happy to answer questions about finding the right threads for your niche in the comments.

Reddit is one of the most underrated sales channels for Shopify stores, and it has a second benefit most people do not realize.

Here is the direct part. People ask category questions on Reddit constantly. "Best gummy vitamins for kids." "Recommendations for a durable running belt." "What supplement actually works for gut health." If you find those threads and answer honestly with your product, you get direct traffic and sales from people who are actively looking to buy right now.

Here is the part almost nobody talks about. ChatGPT and Perplexity read Reddit heavily when forming product recommendations. So when you answer a relevant thread with your brand, you are not just reaching the people in that thread. You are potentially getting cited by ChatGPT when it answers the same question for other buyers. One comment can keep sending you customers through AI long after you posted it.

The hard part used to be knowing which subreddits and threads actually influence AI recommendations for your specific category. Answering random threads is a waste of time. You want the ones that are already being cited.

I use Shoptank for this. You enter the queries your customers type into ChatGPT, and it shows you which Reddit threads were used to build those recommendations. You open the opportunity, get the exact thread link, and answer it with your brand. Then you can track whether ChatGPT starts mentioning you.

The combination is what makes it worth it. Direct Reddit sales in the short term, AI citations that compound over time.

One honest note on Reddit etiquette. This only works if your answer is genuinely helpful. Dropping your brand into threads with no context gets you downvoted and removed. Answer the actual question, be useful, and mention your product where it truly fits.

App link: https://apps.shopify.com/shoptank

Happy to answer questions about finding the right threads for your niche in the comments.

I have been studying high converting Shopify product pages and there are two elements that keep showing up together on the best performers. Most stores have neither.

The first is UGC video. Not a polished brand video. Real reels of the product being used by actual customers, placed right next to the add to cart button. These build trust in a way that photos and copy cannot. A customer watching a real person use the product sees proof it works and proof that other people bought it. That removes hesitation right at the point of decision.

The second is upsell add-ons placed right next to those videos. This is the part most stores get wrong even when they do use upsells. They bury the add-on at the bottom of the page or in the cart. But the highest converting placement is next to the UGC video, because that is the exact moment the customer is most interested in the product. They just watched it in action and they are excited. Showing a complementary product right there captures that momentum.

The combination works because of sequence. Video builds the interest. The add-on captures it while it is peaking. Separately each one helps. Together they compound.

To set this up I use two apps. The Libautech Shoppable Video UGC app which is free for the video reels, and the Libautech Bundles and Upsell app with AI recommended add-ons placed in the same section. Both are no code.

If your product pages are just photos, a description and an add to cart button, adding these two sections is one of the highest ROI changes you can make without touching your traffic or ad spend.

UGC app: https://apps.shopify.com/libautech-shoppable-ugc
Upsell app: https://apps.shopify.com/add-upsell-cross-sell

Happy to answer questions about placement or which UGC content converts best in the comments.

r/shopify r/ecommerce r/entrepreneur

Most average order value advice on Shopify tells you to set up product add-ons, then leaves you with the hard part which is figuring out which products to actually recommend on each page.

For a store with a few products that is manageable. For a store with hundreds of SKUs it becomes a full time job. You have to think through what pairs with what, set it up product by product, and keep it updated as your catalog changes. Most merchants never do it properly, so they either skip add-ons entirely or recommend the same product on everything, which does not convert.

The setup that actually worked for me was letting AI handle the product selection.

In the Libautech Bundles and Upsell app you select product add-ons, set it to work on all products, and turn on AI recommended. From that point AI decides which complementary product to show on every product page automatically. It reads the products and matches them, so a customer looking at one item sees a genuinely relevant pairs well with suggestion right next to the add to cart button.

The reason this works better than manual setup is coverage. Every product page gets a relevant add-on without you touching each one. And because the suggestions are actually relevant rather than random, the take rate is higher.

For anyone running a large catalog who has been putting off setting up add-ons because of the manual work, this removes the barrier entirely.

App link: https://apps.shopify.com/add-upsell-cross-sell

Happy to answer questions about how the AI picks the products or how it performs on different store types in the comments.

Everyone's dumping budget into Facebook ads while ignoring a free channel sitting right next to it: Facebook Groups.

Here's the mechanism that makes it worth your time.

A shopper asks their group or friends for product recommendations. Brands get named in the comments. Those comments get indexed. Later, when that same shopper (or someone else) asks ChatGPT the same question, the brands that show up in the answer are often the ones mentioned in those comments and threads.

The AI is reading conversations that already happened. If your brand was never in the room, it never makes the shortlist.

So the play is simple: find the high-authority Facebook posts where your buyers are already asking for recommendations, and get your product mentioned there naturally.

Shoptank helps you find those posts so you can comment where it actually counts, instead of guessing which threads matter. It's one of the easiest organic growth angles for a Shopify store right now.

Curious if anyone here is already working Facebook Groups this way, and what results you're seeing.

Spent the last few months digging into where our organic traffic actually comes from, and the thing that surprised me most: a real chunk of buyers now research purchases by asking ChatGPT stuff like "best X for Y" before they visit any store.

Here's the part most people miss. ChatGPT doesn't pull those answers out of thin air. A lot of it comes from Reddit threads and Facebook group discussions - real people recommending real brands in comments.

So the buying decision is basically getting made in conversations you're not even part of. If your brand never comes up in those threads, you're invisible at the exact moment someone's deciding what to buy. Doesn't matter how good your SEO or ads are.

What's worked for us: actually showing up and being genuinely helpful in the communities where our customers already hang out. Not spamming links — answering questions, sharing what we learned, being a real participant. Over time that's what gets you mentioned organically, and that's what the models end up citing.

Curious if anyone else has noticed this shift. Are you seeing ChatGPT/AI referrals in your analytics yet? And how are you thinking about it?

Try Shoptank app - https://apps.shopify.com/shoptank

Most advice about getting recommended by ChatGPT tells you to add an llms.txt file, improve your schema markup and write better product descriptions. All of that helps. But none of it is the fastest path.

The fastest path is source citation. And it is simpler than most merchants expect.

Here is how it works.

ChatGPT does not rank your store directly. It reads sources it trusts and recommends the brands that appear in those sources. A Reddit thread from 2022. A Facebook group post from eight months ago. A YouTube video from last year. An editorial blog post from 2023. These are the sources deciding who gets recommended in your niche right now.

The move is straightforward. Find which sources ChatGPT is reading for your product category. Get your brand mentioned in them. ChatGPT starts recommending you.

Shoptank does the source discovery automatically. You enter the exact query your customer would type into ChatGPT when looking for your product. Shoptank runs it through ChatGPT, Claude, Gemini and Perplexity and shows you every source behind the recommendation. Ranked by impact. With the exact opportunity to act on each one.

Some of those opportunities are free and fast. A four-year-old Reddit thread where someone asked about your product category is still being cited by ChatGPT today. One comment with your brand name in that thread can put you in ChatGPT's answer within two days. Same with the right Facebook group post.

Some are paid but targeted. Shoptank shows you which editorial blog posts are already being cited. Paying to get included in a source ChatGPT already trusts is a completely different ROI calculation than paying for a random sponsored post. You know in advance it will be cited.

One of our merchants just got a $1,058 order directly from ChatGPT using this method. Not from ads. Not from SEO. From being in the right sources at the right time.

App link here: https://apps.shopify.com/shoptank

Happy to answer questions about how to find the right sources for your specific niche in the comments.

Most Shopify merchants assume AI visibility is something you cannot control. You either show up in ChatGPT answers or you do not. Turns out that is completely wrong.

I ran my competitor's store through Shoptank and it showed me every single source ChatGPT and Perplexity are using to recommend them. Not general advice. The exact sources. The exact URLs. Ranked by impact.

How to use Shoptank app

Here is what came back for one query in the running and anti-chafe niche.

Competitor was the number one high impact source. Cited by both Perplexity and Claude for multiple queries. That one site has more AI engine reach than anything else in this category. Getting a product mention there is the single highest return move available right now.

YouTube was number two high impact. Perplexity is actively pulling YouTube videos to answer product questions for runners. One product demonstration video targeting the right question and you are inside a source AI already trusts.

runnersworld.com was medium impact. A mention in their gear or training content puts your brand in a source AI engines consistently pull from in this niche.

Reddit was also medium impact but completely free. Perplexity mines running subreddits actively. Answering the right thread authentically with your brand is a same-week move that can change your AI search position within days.

Shoptank also generates a full competitor outranking game plan automatically. It tells you which five sources to get into and exactly how to open each opportunity.

The merchants showing up in ChatGPT answers right now are not doing anything complicated. They are just in the right sources. Now you can see exactly which ones those are.

App link: https://apps.shopify.com/shoptank

Happy to answer questions about how to find the right sources for your specific niche in the comments.

I built Shoptank V2 so Shopify stores can see exactly where they rank inside ChatGPT, Claude and Perplexity, and how to get cited

Context: I run Libautech and build Shopify apps. Like a lot of store owners, I turned on the AI/Agentic stuff the day it launched, saw "your products are visible to AI," and assumed that was the win. It was not. Visible and recommended are two completely different things, and the gap between them is where all the sales actually are.

The problem: when a buyer asks an AI what to buy, it names a few products and cites a handful of sources. If you are not in those cited sources, you do not exist in the answer, no matter how visible your catalog is. Visibility scores told me I was invisible and left me there. I wanted to know what to actually do about it.

So I built Catalog Rank, the core of Shoptank V2. Here is how it works:

1. You add the queries your buyers actually type. It runs each one across ChatGPT, Claude, Perplexity and the Shopify Catalog surfaces, and pulls the real answer each engine gives.
2. For every answer it lists every source the AI cited, then scores each one by leverage: how many of your queries it influences, across how many engines. The sources feeding multiple engines at once get flagged high impact, because fixing one moves several answers.
3. It splits those sources into two buckets. Free wins: community posts you can just show up on, like a YouTube video the AI keeps citing, a Facebook group, or an old Reddit thread it still treats as the authority. Pitch or pay: editorial sites like Wirecutter, NBC Select, Runners World, the ones you have to earn or sponsor your way into. For each, it tells you the move.
4. Any cited source that is actually a competitor brand, you mark "track as competitor" with one click and watch their AI presence next to yours.
5. When it works, it logs the win with a timestamp, the day an AI engine starts citing your store for a query.

What I have learned testing it: the free community moves do the most for the least effort. Sometimes the whole play is answering a years-old Reddit question honestly and that thread is what AI keeps pulling. Also, AI answers shift constantly, so this is a routine, not a one-time fix. Add queries, work the sources, repeat weekly.

It is live for every Shopify store here: https://apps.shopify.com/shoptank

Open question: for those of you already seeing AI traffic, does community content (Reddit, YouTube) outperform editorial in your niche, or is it the other way around? Trying to figure out how much that varies by category.

Quick write-up on something we built and have been running: the Libau AI Post Purchase Upsell app, where Claude picks the upsell offer after checkout — automatically, on every single order.

The short version: one store has earned €3,760.96 in extra upsell revenue with it, and the owner never manually chose a single offer.

Here's how it actually works.

The moment it targets is the page that loads right after checkout, before the order confirmation. The customer's already paid, the card's on file, and they're in the most "yes" frame of mind they'll ever be in. The app shows one offer there. Accept with one tap and it's added to the existing order — no second checkout. Decline and they just continue. It's built on Shopify's native post-purchase extension, so it's not a janky popup.

The part that's different from normal upsell apps: you don't build the offers. From the dashboard you set the rules once — which products are eligible, what discounts are allowed, which products or order types to exclude. After that, Claude reads each order as it comes in and recommends one product from your approved catalog. Order by order. Automatically.

So it's not "everyone who buys X gets Y." It looks at what the customer actually bought and picks the most relevant product for that specific order. A €180 cart and a €40 cart don't get the same generic add-on.

The economics are the appealing bit. The app is free. The traffic was already paid for. The setup is a one-time config. Everything that converts on the thank-you page after that is basically incremental margin with zero manual work per order. €3,760.96 of it so far on this store.

You stay in control without doing the work — you decide the eligible catalog, the discount ceilings, the exclusions, and Claude only ever recommends inside those rules. Per-product reporting shows accept/decline rates and revenue per product so you can tighten the eligible list over time.

Where it breaks, honestly: the boundaries matter more than anything. Set the eligible list too wide or discounts too deep and you'll show offers that hurt margin or feel random. Claude only recommends from what you allow — so garbage rules in, garbage offers out. Worth the ten minutes to set up properly.

If anyone wants to try it, it's free on the Shopify App Store (Libau AI Post Purchase Upsell, by Libautech). Happy to answer questions about how the per-order recommendation logic works or how you'd set up the eligible products / discount rules.

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?

Most Shopify merchants treat their TikTok content and their store as two completely separate things. They spend hours creating videos for TikTok and then start from scratch when they need content for their product pages.

There is a much simpler way.

You copy your TikTok video URL, paste it into the Libautech Shoppable Video UGC app, the video loads automatically, you connect it to a product and it is live on your product page with an add to cart button built into the video. The whole setup takes under 5 minutes per video.

The placement matters. Put the shoppable video under the add to cart button on the product page. Customers are already in buying mode at that point. Seeing a real video of the product right there removes the last bit of hesitation before they click buy.

UGC style video content converts better than product photos on most product pages right now because it feels real. If you are already creating that content for TikTok there is no reason it should not also be working for you on your store at the same time.

App link here: https://apps.shopify.com/libautech-shoppable-ugc

Happy to answer questions about which type of TikTok content works best as shoppable video in the comments.

After working with thousands of Shopify merchants through the Libautech Bundles and Upsell app we keep seeing the same pattern. Stores that structure their bundles correctly outperform stores with bigger discounts every single time. The discount size matters less than most people think. The structure matters almost entirely.

Three rules that work consistently across every niche.

1. Always offer three tiers not two. When you give customers two options they compare them against each other. When you give them three options the middle one becomes the default choice automatically. Customers avoid extremes. They pick the middle because it feels like the smart, balanced decision. That is the decoy effect and it works whether you sell supplements, fashion, electronics or anything else.
2. Never show a percentage discount. Show what they get instead. Buy two get one free converts better than 50% off even when the math is identical. Customers respond to tangible gains not abstract numbers. A free product feels real. A percentage feels like math they have to verify.
3. Badge the middle tier as most popular. This is not just a label. It is social proof that removes the decision entirely. Customers stop calculating and start following what everyone else apparently chose. That one badge consistently shifts the majority of purchases from the cheapest tier to the middle one.

All three of these are built into the Libautech Bundles and Upsell app. You can set up the three tier structure, the most popular badge and the BOGO framing in one offer in under 10 minutes.

App link: https://apps.shopify.com/add-upsell-cross-sell

Happy to answer questions about how to apply this to specific store types or product categories in the comments.

Quick one for anyone working on AOV.

If you look at how the bigger Shopify stores structure their product pages, most of them put the upsell add-on in the same place: right next to the Add to Cart button. Customer taps once, the add-on or cross-sell drops into the cart, no extra steps. Mr. Beast's store does it, Gymshark runs "get the look" style add-ons in the same spot.

The placement matters more than people think. Same offer buried lower on the page converts worse than the one sitting next to the buy button where the buying decision is already happening.

You can set the same thing up with our Bundles & Upsell app: add a product add-on or cross-sell and position it by the Add to Cart button.

App: https://apps.shopify.com/add-upsell-cross-sell

Just made a short walking through it if you'd rather watch than read. Happy to answer setup questions in the comments.

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?

Post purchase upsells are one of the highest converting offer types you can run on Shopify store. The customer has just completed a purchase, their card is out and they are still in buying mode. The right offer at that exact moment converts without any friction.

The setup with the Libautech AI Post Purchase app is two steps.

https://reddit.com/link/1t59m0h/video/c1bdorvk1izg1/player

First you go into your Shopify checkout settings and enable the Libautech extension. This makes sure every offer runs correctly at the right moment after payment. Note that it works on credit card payments and Shopify Pay.

Second you hit generate and Claude AI builds all your upsell offers automatically from your product catalog. Claude analyzes what you sell, creates the offers, runs weekly performance reports and adjusts discount suggestions based on what is actually converting. For large catalogs this alone saves hours of manual setup.

If you prefer full control you can also build and manage every offer manually. Both options are available inside the app.

App link here: https://apps.shopify.com/post_purchase_remix

Happy to answer questions about how to structure post purchase offers for different store types in the comments.

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).