https://preview.redd.it/pnhmu4wr3rhh1.png?width=2688&format=png&auto=webp&s=2ef001beafaa3b51bea38c4b93613c4a4f770c2e

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.

App	Bills on	$2k extra rev	$10k	$100k	$1M
Libautech	extra revenue	$14.99	$29.99	$49.99	$49.99
Moon Bundles	extra revenue	$14.99	$29.99	$59.99	$59.99
Fly Bundles	extra revenue	$19	$49	$99	$99
Koala Bundles	extra revenue	$19.99	$59.99	$59.99	$59.99
AOV.ai	your Shopify plan	$25.99	$45.99	$75.99	$75.99
Rapi Bundles	extra revenue	$29	$29	$59	$59
Kaching Bundles	extra revenue	$29.99	$99	$299	$299
Pumper Bundles	extra revenue	$29.99	$49.99	$49.99	$49.99
BOGOS	per gifted order	$29.99	$49.99	~$210	~$2,010
Frequently Bought Together	your order count	$39.99	$39.99	$39.99	$39.99
Fast Bundle	extra revenue	$49	$139	$299	$299
Easy Bundle Builder	extra revenue	$49	$99	$399	$599
Zipify OCU	extra revenue	$99	$399	$999	$999
Upsell.com / ReConvert	orders or upsell rev, whichever is higher	$79.99	$149.99	not published	not published
Upnova	flat fee plus per upsold order	$199	~$599	~$5,100	~$50,100
Amplify (PickyStory)	your whole store revenue	$299.50	rises with store revenue	not published	not published

What actually surprised me

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.

Shopify store Google Search results

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.

https://preview.redd.it/5cxc33jpibhh1.png?width=1295&format=png&auto=webp&s=26e2fbefe04cc0e54e711d285f9922b5fd2941bf

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.

I set these up with the Libautech Bundles and Upsell app. App link: https://apps.shopify.com/add-upsell-cross-sell

Happy to answer questions about discount levels or bundle structures for different product types in the comments.

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.

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

Happy to answer questions about checking rankings for your niche in the comments.

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.

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

Happy to answer questions about checking rankings for your niche in the comments.

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.

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

Happy to answer questions about placement or discount levels for clothing stores in the comments.

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.

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

Happy to answer questions about checking rankings for specific niches in the comments.

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.

https://preview.redd.it/lwmrrrbgpjeh1.jpg?width=1600&format=pjpg&auto=webp&s=3672b992e35c3c8c58f1b2d1bf8eb34cdea71689

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.

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

Happy to answer questions about how to pitch editors or find the right blogs for your niche in the comments.

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:

1. 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.
2. 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.
3. 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.
4. 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:

1. Take the exact question your customer would type into ChatGPT. Not a keyword, the full question.
2. 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.
3. 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.
4. Then go after the paid or pitched sources. The editorial listicles and blog roundups that keep getting cited.
5. 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.

Shopify App link if useful: https://apps.shopify.com/shoptank

If you drop your niche in the comments I will tell you what kind of sources usually dominate it.

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.

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.

Shoptank citation report: buyer queries checked across ChatGPT, Claude, Perplexity, and Shopify Catalog.

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 cited sources Shoptank found for this niche, ranked by citation count.

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.