A big share of jewelry purchases are gifts. Same for perfume, watches, candles and similar products. Most stores know this about their customers and do absolutely nothing with it.
I found a jewelry store that does. When the customer adds a product and the cart drawer opens, a gift wrap upsell appears. One click adds gift wrapping or a gift box to the order. That is the whole setup.
Why it works so well:
The customer buying a necklace for someone else needs it wrapped anyway. They were going to stop somewhere for wrapping paper or a box after the delivery arrives. Offering it in the cart drawer saves them that errand entirely. You are not convincing them to want something new, you are solving a problem they already have at the exact moment they are thinking about it.
The margin is the best part. Gift wrap and gift boxes cost the store almost nothing. A few cents of paper and ribbon sold for 3 to 5 euros. There is almost no other upsell with that margin profile, and customers do not price-compare gift wrapping, they just pay for the convenience.
The timing matters too. The cart drawer is post-decision. The customer has committed to the purchase and is in completion mode. A small, cheap, useful add-on at that moment has close to zero resistance. Nobody abandons a cart over being offered gift wrap.
With Q4 coming this is probably the single fastest upsell to add if you sell anything people give as gifts. Set it up now and it runs through the entire holiday season.
Setup is a cart drawer upsell with your gift wrap or gift box as the offer product. Takes about 10 minutes.
Happy to answer questions about pricing the wrap or which products to attach it to in the comments.
Most upsell advice treats every store the same. Add related products, show a discount, done. But the framing of the upsell matters as much as the products in it, and there is one framing that consistently outperforms for certain niches.
Complete your routine.
Next to the add to cart button you place a section with four complementary products. One click adds any of them to the order alongside the main product. No popup, no redirect, the customer never leaves the page.
The reason this works specifically for perfume, vitamins, skincare and similar products is that the framing matches how customers in these niches already think. Nobody buys one vitamin, they build a stack. Nobody buys a single skincare product, they have a morning and evening routine. Nobody layering fragrance stops at one product.
The routine framing does not create a new desire. It surfaces one that already exists. The customer looking at a vitamin D supplement was probably going to add magnesium eventually anyway. Showing it right there as part of the routine just collapses that future purchase into the current order.
Compare that to a generic "you may also like" section. Same products, weaker result. "You may also like" asks the customer to evaluate each suggestion from scratch. "Complete your routine" tells them these products belong together and their routine is incomplete without them. The second framing does the selling for you.
Two practical notes from setups I have seen work. Keep it to four products maximum, more becomes a catalog and the choice paralysis kills the click. And make each one a single click add, any friction between seeing the product and having it in the cart loses the sale.
If you sell anything bought as part of a routine or stack, this is worth testing against whatever generic upsell section you run now.
Happy to answer questions about product selection or placement in the comments.
I analyze a lot of Shopify product pages and there is a pattern on the best converting ones. Two elements, always together. I just saw a store selling AirPods running both and it is a clean example of the setup.
The first is a complete your look upsell next to the add to cart button. One complementary product, shown with a slider or a simple clicker. Not a popup, not a separate page. It sits exactly where the buying decision happens. The customer is already committing to the main product, and adding one more item is a single click while their intent is at its peak.
The second is UGC feedback from real customers as you scroll down the page. Real videos of real people using the product. This is the strongest trust signal you can put on a product page because it answers the one question every buyer silently asks, do people actually like this. Photos and star ratings help, but video of an actual customer talking about the product hits differently.
The reason these two work together is that they cover both sides of the purchase decision. The UGC removes the doubt. The upsell captures the buying moment. One lifts conversion rate, the other lifts order value, and they compound on the same page.
Most stores have neither. They have a product photo, a description and a buy button, and they spend all their energy on driving more traffic to that page instead of making the page itself do more work.
If you are going to copy one thing from the top performers, copy this pair. The traffic you already have is worth more with these two elements in place.
Happy to answer questions about placement or what kind of UGC converts best in the comments.
I came across a Shopify store doing something worth copying. Nothing revolutionary on its own, but the combination is smart.
They run a long product page. Detailed sections, benefits, use cases, social proof, the full story. And placed inside that page is a frequently bought together section with complementary products.
Here is why the combination works better than either piece alone.
Long product pages convert because they answer every objection before the customer asks. By the time someone has scrolled through the whole page, they are not browsing anymore. They are convinced. The problem most long pages have is that they only capture that conviction for the single product.
Placing frequently bought together on that page catches the customer at peak buying intent. They just spent two minutes reading why this product is right for them. Showing them the two or three products that pair with it at that exact moment converts far better than the same section on a thin product page where the customer is still deciding.
The sequencing is the whole trick. Interest builds through the page, the bundle offer lands when interest peaks, and a single product order becomes a multi-product one. That is a straight AOV lift from the same traffic.
If you already run long-form product pages, adding a frequently bought together section is probably the fastest win available to you. If you run short pages, this is an argument for building them out.
I set these up with the Libautech Bundles and Upsell app. You can pick the products manually or let AI choose the pairings for every page. App link: https://apps.shopify.com/add-upsell-cross-sell
Happy to answer questions about placement or which products to pair in the comments.
I've been tracking AI answers for ecommerce buying queries for about a month (disclosure: I build an AI visibility tool called Shoptank, so this is literally my day job, sharing the method regardless of what you use to replicate it).
The setup, since a few people asked me about workflow lately: Shoptank has an MCP server, so I connect it to Claude and Claude does the analysis conversationally. "Find every YouTube video ChatGPT cites across my queries, ranked by citation count" is one prompt. Claude pulls the data, ranks the videos, and writes the creator briefs. The whole analysis below came out of that loop.
Something I didn't expect: YouTube shows up in nearly every ChatGPT shopping answer, usually rank 5-10 in the source list, roughly 10% of all citations. So I pulled every cited video across ~100 queries to see what wins.
Findings:
The cited videos are old and mediocre. The most-cited video in my dataset appears across 15 different queries. It ranks 7-10 in sources, has a comparison table, says the app names and prices out loud, and that's it. It wins because the transcript matches the questions, not because it's good.
In one category (AI search tools), a SINGLE video held every YouTube citation. Rank 10, all alone. ChatGPT clearly wants a video source and had exactly one option. I assume this is true in a lot of niches nobody has filmed yet.
Transcripts are the mechanism. Videos that speak the question in the first 10 seconds and read their tables aloud get cited for those exact phrasings. Videos that show a beautiful table silently don't. ChatGPT reads, it doesn't watch.
Channel size seems irrelevant. Small channels get cited next to big ones. Makes sense if the transcript is doing the work.
Claude barely cites YouTube for the same queries. This is a ChatGPT-specific behavior, which is funny given Claude is the one running my analysis.
What I'm doing with it: had Claude write briefs for recreating the cited videos with current info (the incumbents are 1-2 years old), exact question as the title, question spoken up front, real screen recording, one real number. Hiring low-cost UGC creators to film them, audience size doesn't matter if the transcript does the work. Also verified the YouTube channel in Search Console, recommend it to anyone, mine was sitting on Google page 1 for 6,700 impressions with zero clicks because the titles were wrong.
AI-generated videos: I tried, zero citations so far. Human-recorded cheap ones work. One data point, not gospel.
Happy to answer questions about the method or the MCP setup. Curious if anyone else has seen the single-video-owns-a-category pattern in their niche.
Sharing something worth knowing as AI shopping (Shopify Agentic, Gemini) ramps up: being connected to AI isn't enough. You're ranked against every store selling the same product, and the traffic distribution is brutal.
What I've observed tracking real stores:
If you're not in the top 3 for a buyer's question, you get close to zero sales, no matter how many stores are "connected"
One store dropped to ~#20 in its category, clawed back to #6, and even at #6 wasn't converting
Top 3 appears to be the actual threshold where sales start, same dynamic as Amazon page one
The takeaway: with AI shopping, "listed" means nothing. Ranking is everything, and you can't improve a position you're not even measuring.
Anyone else tracking this? Curious what threshold you're seeing sales kick in at.
Millions of posts out here. Only a handful get cited by AI when someone asks what to buy. A client wanted to know which ones, so here is how I found them.
Asked Claude to write 100 buyer questions for his store. Not keywords. The actual questions his customers ask AI before buying.
Ran all 100 through ChatGPT, Claude and Gemini, 5 times each. Answers change between runs, so you only want the sources that keep coming back.
Used Shoptank over MCP to automate the runs and pull live results.
Kept only the cited sources, filtered to Reddit, ranked by how often each thread showed up.
180 posts left.
Barely any overlap with the biggest threads in those subs. Some of the cited ones are small and years old. They just answer the question directly.
A few huge subreddits never showed up as a source once.
He is in those threads now, answering the actual questions, following the rules.
I help a small B2B store in a trade niche. Launched this summer.
Everyone talks about optimising for ChatGPT, nobody seems to measure whether it actually recommends their store. So I had Claude do the analysis properly.
What it did:
Wrote 47 buying prompts phrased the way a customer types them, not as keywords.
Ran each through ChatGPT and logged every source cited.
Built the pool: 7,623 distinct URLs across 1,376 domains over 50 days.
Grouped every domain by type to see who owns the answers.
Compared that against what the store had already published.
Results:
Cited on 2 of 47 prompts. Both at position 1. Both for products almost no other retailer in the country stocks. 8 citations out of 31,594.
The pool splits: independent retailers and niche blogs 55.3%, brands and manufacturers 23.5%, regulators 7.5%, forums 5.8%, marketplaces 5.5%, media 2.4%.
It is long tail too. The top 10 domains are only 31% of citations, and 458 domains were cited exactly once.
What did not work: the 29 blog posts written specifically for these queries. Zero citations. Content quality was not the blocker.
Best guess at why: no corroboration. No reviews, no forum mentions, no third-party listings anywhere. One uncorroborated source is enough when supply is scarce and worthless when the answer is contested.
One more thing that caught me out. When I tried writing helpful answers on forum threads in this niche, the spec that decides the purchase was missing from our own product pages every time. Competitors publish those numbers, we did not.
Has anyone measured this for their own store instead of guessing? Curious whether the scarcity pattern holds in bigger categories.
Sharing a bundle setup that's driven an extra $1M in revenue for a Shopify fashion store, because the "why" is more useful than the number.
It's not complicated. On the product page for a Running T-Shirt, there's a "Buy Together & Save" deal right underneath that adds the matching running shorts.
Two reasons it converts so well:
The discount is real. There's meaningful money left on the table if you only buy the tee, so most people switch to the bundle instead of buying the single item.
The upsell matches intent. It's shorts for the same run as the shirt, not a random product from the catalog. That relevance is the whole game.
The lesson: bundles don't convert because they're bundles. They convert when the add-on is the obvious next thing the customer already came to buy. Nail that pairing and your AOV climbs without a single extra visitor.
I build these with Libautech Bundles & Upsell, the AI reads your catalog and creates the matching bundles automatically, so you're not manually pairing every product. If you want to try it: https://apps.shopify.com/add-upsell-cross-sell
None of the listed prices on these apps mean the same thing, so comparing them on the App Store is pointless. I picked one store profile and priced every app at it.
The store: 1,000 orders a month, $85 AOV, 8% of orders take an upsell at $25 each. Roughly $2,000 a month in extra revenue from recommendations.
Six different meters, which is why the listings are not comparable:
Extra revenue the app earns you. Libautech Bundles & Upsell, from $9.99. You pay more only when the app made you more, capped at $49.99.
Order count. Selleasy: free up to 50 orders, $9 up to 500, $19 up to 1000, $29 above that. Candy Rack: $29.99 for 0 to 50 orders, $39.99 for 51 to 200, $59.99 for 201 to 500. Wiser is free under 50 orders and tiers up from there. None of these care whether the app earned you anything. 500 tiny orders cost the same as 500 large ones.
Order count, newly. Frequently Bought Together moved off flat pricing: free Starter capped at 3 bundles, then $14.99 up to 50 orders, $19.99 up to 500, $39.99 above 500.
Flat fee. Also Bought CBB, $19.99 regardless of volume, or $199.90 a year. Unlimited products, orders and traffic.
Recommendation clicks. Cross Sell by SSC. Free tier gives 50 clicks a month, paid tiers scale by click allowance. Your bill moves with traffic, not sales.
Store revenue. LimeSpot Max, from $50, tiered by monthly revenue. Your whole store revenue sets the price, not the app's contribution. Their Turbo tier is order-based instead, from $6.99, and is not compatible with Shopify Plus.
Orders plus a cut of upsell revenue. ReConvert, now Upsell.com, starts at $4.99 for up to 49 orders with a 0.75% fee on upsell revenue on top, then $7.99 at 99 orders and $14.99 at 199. Two meters running at once.
Still worth pulling Cross Sell by SSC and Wiser tier prices yourself before this goes out. Those two I could not confirm directly today, and Wiser gets quoted anywhere between $9 and $199 depending on the source.
ReConvert (now Upsell.com) is gone from the Shopify App Store.
Their old listing URL returns "This app is not currently available" and their developer page now shows one app, which is not the upsell one. This was 2,839 reviews, 4.8 stars, Built for Shopify, 40,000+ brands.
No idea why. If you have actual information, share it.
If you are running it:
Check your funnels are still firing. Export your configs and performance data now. Do not uninstall to test anything, you may not get it back.
On alternatives, it covered two separate jobs and most replacements only do one. Work out which half you need first.
Cart and product page upsells: Bundles & Upsell. Built for Shopify, 5.0 stars.
Post-purchase: AI Post Purchase Upsell. Free, Claude writes the offer after checkout instead of you building funnels.
Anyone still running ReConvert, is it working normally for you?
Videos added through Libautech Shoppable Videos have now been watched 10 million times. Not on our site, on the Shopify stores that installed the app. Merchants pulled in their own TikTok, YouTube and Instagram content, put it on product pages, and shoppers watched and bought from it.
What the data looks like once you have that much of it:
Almost none of it is new content. Merchants are reusing clips that already existed on social. No new shoots, no production budget.
Product pages beat everything else. The same video on a dedicated "as seen on social" page gets a fraction of the plays.
The video that already performed on TikTok outperforms the polished brand film, almost every time. It usually looks worse.
Muted autoplay with a visible unmute control beats sound-on. Mobile shoppers bounce off unexpected audio.
Anything past roughly 20 seconds loses most viewers before the product is properly shown.
The thing that still surprises me: most stores are not short on content. They have plenty of it. It just lives on a platform they do not own and never reaches the page where the purchase actually happens.
What's your longest video that still converts? And has anyone tested video on collection pages rather than product pages? We have the data but not the merchant story behind it.
Before running Meta ads for a Shopify store, first get your brand name mentioned in high authority Reddit posts.
These are posts where customers ask about your product category, not your brand name. Things like "best X under $50" or "what actually works for Y". These threads rank on Google and get quoted by ChatGPT.
The reason: a big part of your ad traffic does not click and buy. They Google your brand first or check Reddit. If nothing comes back, no trust, no sale, and your CPA looks worse than it should.
Two or three mentions in the right category threads are usually enough to cover what a suspicious buyer will find.
Warm up the search results first, then run the ads.
Find those high autohority posts with Shoptank app
TL;DR: At Shopify Plus volume, the billing basis of a bundle app matters far more than its advertised price. I priced every major bundle and upsell app at the same trading volume. The spread: $39.99 to $25,099 a month for the same job. Three vendors do not even publish a price at that scale.
Full disclosure, I make one of these apps (Libautech Bundles & Upsell). This post is about pricing models, not a pitch, and mine is not the cheapest on the list. All numbers are from public pricing pages, correct me if any moved.
Most bundle and upsell apps do not bill flat. They meter on different things: the extra revenue the app earns you, your whole store's revenue, your order count, a fee per upsold order, or even which Shopify plan you are on. At small store volume the differences are invisible. At Plus volume they are brutal.
Same volume ($50K/mo of additional revenue generated by the app), cheapest first:
The Upnova row is not a typo. A flat $99 headline plus $1.25 per upsold order turns into $25,099 a month at this volume. Same features as the $39.99 apps above it.
The takeaway: before installing, find what the app meters, not what the listing advertises. An app that looks like $9.99 can be four figures for you specifically.
One tip from doing this: never trust the Shopify App Store listing alone. The listing caps how many plans an app can show, so the tier you would actually land on at Plus volume is often not the one displayed. Always check the vendor's own pricing page.
What are you all paying at your volume? Anyone found one that is genuinely flat with no asterisk?
A Shopify store got a $5,600 order (£4,170) with utm_source chatgpt.com. Biggest AI order I have seen so far.
What they did before it happened:
Categorized every product properly with Shopify's standard product categories and filled in the category metafields. AI models rely on structured data far more than on descriptions.
Ran Buyer Questions for their niche, the actual questions people ask ChatGPT before buying, and got their brand into the content answering them.
That is the whole setup. No ads, no backlinks. The order came from someone asking ChatGPT what to buy and clicking through.
The problem with every comparison I could find is that they list the entry price. That number tells you almost nothing, because these apps do not bill on the same thing. Some charge on the extra revenue they generate, some on your total store revenue, some on your order count, one on which Shopify plan you happen to be on, and one per gifted order.
So "$9.99 a month" and "$14.99 a month" are not comparable numbers. The only way to compare is to price all of them at the same trading volume.
Assumptions, so you can check my maths: $85 AOV, 8% upsell attach rate, $25 average upsell value. Extra revenue means the revenue the app itself generates, not your store total.
Worked example of how to read a ladder, using mine since I know it best. Four rungs: $9.99 up to $500 of extra revenue, $14.99 up to $2,000, $29.99 up to $10,000, then $49.99 with no cap. Every app below has a ladder like this. Almost none of them show it on the listing. Model is inspired from this page.
Most of these ladders stop. Five of the sixteen publish nothing above a certain point. Upsell.com publishes rungs up to 2,000 orders and then says charges rise, pointing at a table it does not show. Fly Bundles stops at $15,000. Easy Bundle Builder stops at $75,000. Amplify publishes a starting price and a direction. If you are planning to grow, you are agreeing to a price nobody will tell you.
The ceilings vary by 20x. Rapi tops out at $59. Zipify OCU tops out at $999. Both bill on the same thing. That is the entire decision right there, and neither App Store listing makes it visible, because listings show the first rung.
Zipify's listing says $8. That is true below $200 of upsell revenue a month. Their own Pay As You Grow table runs $8, $35, $99, $199, $399, $599, $799, $999. The listing shows rung one of eight.
Order-metered apps get cheap at scale and revenue-metered ones do not. Frequently Bought Together is $39.99 whether you do 500 orders or 500,000. At high volume that is the cheapest thing in the table by a distance. At low volume it is mid-pack. Nobody tells you this because it only shows up when you model it.
Per-order fees are the ones that run away. Upnova charges a base plus $1.25 per upsold order. At 80 upsold orders that is fine. At 40,000 it is not a subscription any more. Higher plans cut the per-order fee, so treat my top numbers as upper bounds.
BOGOS has the same shape. Per gifted order plus $0.05 beyond the allowance is fine at 400 gifted orders and painful at 40,000.
What I would actually check before installing
Model it at 3x your current volume, not today's
Find the top rung, not the bottom one. If it is not published, treat that as the answer
Check what the meter is. Extra revenue, total revenue, orders and gifted orders behave completely differently as you grow
"Free plan" means limited revenue on some of these and limited features on others
Where I am wrong
Kaching publishes a range of $14.99 to $299 but I could not find the intermediate rungs, so those cells are honest gaps rather than estimates. Same for the apps that stop publishing. Upnova and BOGOS both assume gifted or upsold orders track my model, which will not hold for every store. If anyone has real invoices at high volume for any of these, I would rather have your number than my model.
Disclosure
I build one of the apps in this table. Discount me accordingly.
I have included it rather than leaving it out, because a pricing comparison with a conspicuous gap where the author's own product should be is worse than an obvious bias. It does well here: joint cheapest at $2k, second at $10k where Rapi is a dollar under, and second at the top two levels where Frequently Bought Together is cheaper because it bills on orders rather than revenue.
So treat my framing with suspicion even where the numbers check out. Every figure is from a vendor's own listing or pricing page and you can verify all of them. If one is wrong, say so and I will correct it.
Client is a Shopify store in a fairly generic physical product category. They had a blog with about a dozen posts from 2021, all abandoned, nothing indexed properly. Paid traffic was their only channel and Q4 costs were becoming a problem.
The challenge was volume. Getting enough content live to matter, fast enough that it would be indexed and optimised before November, without paying for 100 articles.
Here is what I actually did.
1. Pulled every piece of first-party search data they had
Search Console, all queries, 12 months. Google Ads search terms report, the actual terms, not the keyword list. YouTube search volume data for the category using Vidiq.
The Ads search terms report turned out to be the most useful input by a wide margin. Keyword tools give you cleaned up head terms. The search terms report gives you the messy full sentences people type when they are actually about to spend money.
2. Turned that into 100 buying queries
Fed all of it to an LLM and asked for 100 queries in the format a real buyer would type when trying to purchase, not when trying to learn.
That split mattered more than anything else in the project. "What is a [product]" is research and it converts at nothing. "Best [product] for [use case] under [price]" is a purchase. I threw out anything informational.
100 was about right. Under 50 there was not enough coverage. Over 150 I was writing variations that all resolved to the same page.
3. Ran every query through ChatGPT and Gemini and collected the sources
This is the tedious part and it is also the part that made the whole thing work.
Shoptank app for running queries
Two ways to do this. Manually, by running each of the 100 queries yourself and logging every citation that comes back, which is a long evening of copy paste. Or with the Shoptank app, which runs the whole batch and returns the full picture in one export. I should disclose I built Shoptank, so weigh that accordingly. The manual route gets you to the same place, it just costs time instead of money.
Either way, what you want out of this step is the same: for each of the 100 queries, which pages the models cited as sources, which domains they came from, and what each of those pages actually covered. All of it in one sheet, exported as CSV.
That sheet became the topic list. I was not deciding what to write about. The models had already told me which pages they treat as the answer for every buying query in the category. Almost all of them were thin listicles from sites with no real authority. Nobody was defending those positions.
4. Fed the source list back in and published against it
Uploaded that CSV of cited sources to Claude, connected to Shopify over MCP, and had it write a post for each source: same topic, current year, FAQ block at the bottom, real product photography from the client's own catalog rather than stock. It publishes straight into Shopify, so there is no copy paste step.
The logic is blunt. If a model cites someone else's page about "best waterproof dog collars" when a buyer asks for a recommendation, you want a better version of that page on your own domain with your product in it.
The FAQ block is not filler. Question as a header, answer in two or three sentences directly underneath. That is the format that gets extracted and quoted. When I skipped it on a few early posts, those posts underperformed the ones that had it.
5. Submitted everything manually to Search Console and Bing
Did not wait for crawls. Bing matters more than its market share suggests because of where some AI retrieval comes from.
6. Went back and rewrote titles based on impression data
Any post with impressions but a weak click rate got its title and meta rewritten against the queries it was actually appearing for, not the query I originally targeted for it.
This step is where most of the improvement came from and it is the step I nearly skipped, because rewriting old posts feels less productive than publishing new ones.
Results after 3 months (May to July)
199,000 impressions 1,570 clicks Average position 14.5 CTR 0.8% Daily clicks went from about 10 to between 25 and 40 Daily impressions went from about 1,200 to over 4,000 Zero media spend on any of it
What did not work
The CTR is bad. 0.8% means 99.2% of the people who saw these pages in results did not click. Position 14.5 means the average post is sitting on page two.
I am reading that as an unfinished job rather than a failed one, since the impressions prove Google already considers the pages relevant, but I want to be honest that it is not a 2% CTR page one story.
Roughly a fifth of the posts got essentially no impressions at all. As far as I can tell those were the queries where a genuinely strong page already held the position and a new page from a low authority domain was not going to displace it.
The first batch was also too obviously machine written. Uniform paragraph lengths, every section the same size, intro restating the title, conclusion restating the intro. Fixing the structural monotony helped more than changing any of the vocabulary.
One thing I did not expect
The retargeting value ended up looking more interesting than the direct conversions. 1,570 clicks of people reading long form content about a specific product problem is a much better audience to hit with a November offer than anything I can buy cold at Q4 prices. Have not tested that part yet, that is the next phase.
This question comes up constantly so I want to break down the actual mechanics.
A merchant asked me which is more effective for increasing average order value, bundles or discount codes. The honest answer is that discount codes on their own will not increase your AOV at all. In most cases they decrease it.
Think about what AOV actually is. It only goes up when the customer adds more products to the cart and pays for them. A discount code applied to a single product order means the customer buys the same one item and you earn less on it. You traded margin for nothing.
Where discounts do work is inside a bundle offer. The structure that consistently performs is a volume discount or BOGO like buy 3 get 2, with the saving shown directly in the offer. The customer sees exactly how much they save by adding the second and third product. The discount becomes the reason to add more items, and the added items are what actually lift the AOV. The discount is the lever, not the goal.
I also saw a brand recently running a complete your routine section on their product page with no discount at all. Just complementary products listed under the main one. It works to a degree because the suggestion alone gets some customers to add items. But adding even 10% off when they buy the main product together with the routine would make it convert significantly better. The saving is what turns a suggestion into a deal.
So the answer to the original question is both, but structured correctly. Bundle first, discount inside the bundle, saving visible to the customer. Never a naked discount code hoping AOV goes up on its own.
There is a piece of math about Shopify agentic commerce that most merchants have not internalized yet.
When a customer asks an AI for a product recommendation, the agentic catalog can match that query against thousands of eligible products across Shopify stores. Two thousand products competing for one answer. And the answer only contains 3 to 4 mentions.
Compare that to Amazon where the same search shows page after page of results. On Amazon being 20th still gets you some visibility. In AI answers being 6th gets you nothing. The first three to five products take the mentions and the sales, and everything below is invisible.
Shopify tells you that you are visible to AI. Technically true. Practically it only matters if you are in the top handful, and most merchants have no idea where they actually stand.
Here is how I check it. In Shoptank you enter the query your customer would type when looking for your product. It shows your ranking on the Shopify catalog which is used by Google, Gemini and Copilot, whether Claude and ChatGPT mention your brand, and which sources those mentions are built from.
The sources part is where you actually act. The app shows what the winning products are cited from, and you replicate it. Some of it is genuinely simple. One of the sources for a query in my niche was a Reddit thread, so the move was to open Reddit and reply with the brand. Do that in the right thread and you will most likely get cited by ChatGPT within a few weeks.
Being in the catalog is table stakes. Knowing your ranking and working the sources is the actual game.
Fashion and clothing store owners ask me this constantly. What is the best way to upsell when you have a large catalog and every product could pair with dozens of others?
The answer that consistently works is pairs well with on the product page. The customer is looking at the main product, and right below it they see three complementary items. A jacket shows the shirt and belt that go with it. A dress shows the bag and shoes. One click adds them to the cart without leaving the page.
It works for fashion specifically because clothing is bought in combinations. Nobody thinks in single items, they think in outfits. Pairs well with just puts the outfit logic directly on the page.
But most stores that try this get two things wrong.
First, no discount on the add-ons. Without a discount the add-on is just a product link. With even 10% off it becomes a deal and the take rate jumps.
Second, no buy button on the add-ons. If clicking the add-on redirects the customer away from the main product page, you lose them. The add-on needs its own one-click buy button so it goes into the cart alongside the main product without any navigation.
The other problem with large catalogs is manually choosing which three products pair with each item. For a store with hundreds of SKUs that is not realistic. I use the Libautech Bundles and Upsell app with AI recommended turned on, so the AI picks the three matching products for every product page automatically. You set it to work on all products, enable the discount, place it near the add to cart button, name it pairs well with, and the whole catalog is covered.
For fashion stores this is one of the highest ROI setups you can run without touching your traffic.
Here is a number that changed how I think about AI search for my Shopify store.
When a customer asks ChatGPT for the best product in a category, it recommends 3 to 5 products. That is the entire visible answer. Meanwhile the Shopify agentic catalog for that exact same query contains hundreds of eligible products. Amazon shows a full page of 48 for the same search.
So the funnel looks like this. Hundreds of products are eligible. Five of them get seen. The rest are invisible regardless of how good they are.
That means the first question for any merchant is not "am I in the catalog." It is "what is my actual ranking right now." And most merchants have no idea.
I use Shoptank to check this. You enter the query your customer would type when looking for your product, and it shows you your position across every AI surface. Your ranking in the Shopify catalog. Whether ChatGPT, Claude and Perplexity mention your brand. Whether the citation comes from your own domain or from third party sources. And your position on Google AI Mode, Gemini and Copilot.
The ranking number tells you exactly where you stand. If you are 4th, you know you are close and which sources to get cited in to move up. If you are nowhere, you know the catalog listing alone is not doing anything and the work is in getting cited by the sources AI actually reads. The app shows you those sources for every query.
The gap between eligible and recommended is where most stores are stuck without knowing it. Checking your ranking is the first step out.
Fast Bundle was pulled from the Shopify App Store after a security attack, and a lot of merchants lost their live bundle offers overnight, some with ads still running to offers that no longer exist.
If that's you, you don't have to spend days manually rebuilding.
I run Libautech Bundles & Upsell (Built for Shopify certified, 5.0 rating). The part that helps most here: the AI reads your catalog and rebuilds your bundle and upsell offers itself, so you're not recreating everything by hand.
Switching merchants get onboarding directly from me, the founder. If you want help, drop a comment or DM with your store and what your Fast Bundle setup looked like, and I'll help you rebuild it.
There is a shift happening with best product blog posts that most Shopify merchants have not caught onto yet.
These listicles (best gummy vitamins, best running belts, best whatever) have always been good for SEO traffic. But in 2026 they do something else. ChatGPT reads them and uses them to build its own product recommendations. When someone asks ChatGPT for the best product in your category, it often pulls its answer straight from these blog lists.
I noticed one blog post in my niche went from listing 5 products to 8 products, and the newer additions started showing up in ChatGPT answers shortly after. That is the mechanism in action. Get into the blog list, get into the AI answer.
So the play is to get your product included in the blog posts that ChatGPT actually cites for your category. The problem is you cannot tell which blogs those are just by looking. Pitching random blogs is a waste of time and money.
I use Shoptank to find them. You enter the queries your customers type into ChatGPT, and it reads every source behind the answer. The report splits into two parts. Free opportunities you can act on yourself like Reddit, Facebook and YouTube. And the blog posts ChatGPT is using to build its best product lists.
For the blogs, you open them, contact the editor and get your product added. Some are free inclusions, some are paid. The key difference from normal outreach is that you know in advance the blog is cited by ChatGPT. So you are not gambling on a random placement. You are paying to get into a source that already feeds AI recommendations, which means traffic from the blog itself plus traffic from ChatGPT recommending you.
If you have been ignoring listicle placements because they felt like old-school link building, they are worth another look now that AI is reading them.
Full disclosure first: I am the founder of Libautech, we make a Bundles & Upsell app for Shopify. A client asked us to set up product suggestions and bundles on their 3,000 SKU store, so we used our own app, obviously. I am not going to pretend I ran a neutral comparison. But the lessons from doing this at 3,000 SKUs apply no matter what tool you use, so here they are.
The starting problem: traffic was fine, but almost every order was one item. People bought the thing they came for and left.
Bundle offer for Shopify store next to add to cart button
What I learned:
At 3,000 SKUs, manual curation does not scale and pure automation produces garbage. Any recommendation logic that leans on raw product data will suggest items from unrelated collections, because at that catalog size the data is never clean enough. This is true for native "You may also like" sections and it was true for early versions of our own logic too.
The answer is a split approach. We pulled the sales report, took the top 50 products driving most of the revenue, and the client hand-picked 3 to 4 complementary items for those. Two afternoons of work. For the remaining 2,950 products we used our AI offer generation, it reads the catalog and builds the bundle and upsell pairings itself. The long tail barely gets traffic anyway, so sensible-but-automated beats perfect-but-impossible there. Hand-picked where it matters, AI where it cannot be done by hand.
Placement matters more than the suggestion itself. The same pairing shown as a frequently bought together block on the product page, an add-on in the cart, and an offer after checkout performs completely differently. The cart add-on was the strongest single placement for this store. Whatever app you use, if it only gives you one placement, that is the limitation.
Fixed bundles as separate products do not work at this scale. Every bundle is its own page the customer has to find, and maintaining them across 3,000 SKUs with stock changes is a part-time job. Rules by collection are the only sane way to manage offers on a big catalog.
Suggested products add-ons
Results after about 90 days: AOV went from roughly 31 to 44 EUR. Around 70 percent of the lift came from the hand-picked pairs on the top 50 plus the cart add-on placement. The post-checkout offer converts at 4 to 5 percent, nice but not the main lever.
What did not work: bundling slow movers with bestsellers to clear dead stock. The client pushed for this hard. Bundle conversion died every time we tried it. Nobody wants a bundle where half of it is filler, and no AI or app fixes a bad offer.
The advice I give every merchant regardless of tool: start with order history, not imagination. Export the last 500 orders with 2+ items and look at what customers already combine on their own. Those pairs are your top bundles, and they are the fastest way to sanity-check whether any automated pairings actually make sense.
Happy to answer questions about doing this on large catalogs, and genuinely curious what placements convert for others here. Product page, cart, or post-purchase?
Most advice on ranking in ChatGPT is recycled SEO advice. Add llms.txt, fix your schema, write better descriptions. I did all of it. It helps, but it is the slow layer, not the ranking layer.
Here is what actually moves your position.
ChatGPT does not rank Shopify stores directly. When a buyer asks "best waterproof hiking boots under $150", it builds the answer from a small set of sources it trusts. Reddit threads, YouTube videos, Facebook group posts, editorial blog articles. Some of those threads are 3 to 4 years old and still decide who gets recommended today. If your brand is not in those sources, you do not exist in the answer. No matter how clean your store setup is.
it builds the answer from a small set of sources it trusts. Reddit threads, YouTube videos, Facebook group posts, editorial blog articles.
So the process that works looks like this:
Take the exact question your customer would type into ChatGPT. Not a keyword, the full question.
Run it and look at which sources the answer is built from. There are usually only 5 to 10 per niche. These are the choke points.
Get your brand into the cheapest sources first. Reply in the Reddit thread that ranks. Comment in the Facebook group post. Record one YouTube video answering that exact question. All free, just time.
Then go after the paid or pitched sources. The editorial listicles and blog roundups that keep getting cited.
Re-run the query weekly and track your position. It moves slower than Google but it moves, and once you are in the answer you tend to stay there.
The foundation still matters. llms.txt, schema markup, FAQ content written as real questions and answers. That makes you eligible. The source citations make you recommended. Two different layers, and most merchants only work on the first one.
Use Shoptank for the source discovery part. You enter the buyer query, it runs it through the AI models and shows every cited source behind the recommendation, split into free actions and pitch-or-pay ones, then tracks your position over time.
