Disclosure: Dynasort is our app. Every entry here, ours included, gets the same treatment. For the wider category including search and recommendation tools, see our guide to the best Shopify merchandising apps.

What Actually Separates Collection Sorting Apps?

Cadence: how often collections re-sort. During a flash sale or a fast sellout, a collection sorted this morning is already wrong by lunch. Signals: single-metric rules (“sales last 30 days, sold-out to bottom”) versus weighted blends of sales, inventory, margin, reviews and newness. Verification: whether the app measures what its own sorting did to conversion, or leaves you guessing. Pricing model: flat monthly versus scaling with orders or products; the model matters more than the sticker.

The Eight Best Collection Sorting Apps, Reviewed

1. Dynasort

Weighted recipes over 40+ signals, re-sorting as often as every 10 minutes on Enterprise (20 on Pro, 30 on Basic), collection analytics on every plan, A/B testing of sort orders, and pinned products that survive automation. Flat pricing from $99 with a 30-day trial; Built for Shopify certified. To be fair: newer to the App Store than most on this list, with a smaller review count, and it also does option sorting, search and recommendations, which is more than a pure-sorting shopper needs.

2. KX AI Collection Merchandising (Kimonix)

The deepest pure-collection specialist: many-signal ranking, smart collection building, strong case study library. To be fair: update cadence tops out at a few times per day, pricing scales with order volume [VERIFY tiers], and there is no option sorting or Built for Shopify certification. Full side-by-side: Dynasort vs Kimonix.

3. Bestsellers reSort (EGNITION)

The budget veteran: push bestsellers up, sold-out down, on a schedule, with a free plan and paid tiers under $40. [VERIFY] To be fair: single-metric rules, no analytics, no way to know whether the sort helped. Details in our reSort alternative guide.

4. ST: Product & Collection Sort

Multi-signal sorting at a budget price, including sold-out push-down and new-product promotion. To be fair: collections only, and the interface shows its age in places. [VERIFY current feature set]

5. Sort’d Prime Collection Sort

Manual-first: drag-and-drop curation with rules below the fold you hand-place. To be fair: the drag-and-drop that makes it pleasant at 10 collections makes it the bottleneck at 100. See Dynasort vs Sort’d.

6. Sortimize

Rule-based sorting with daily scheduled runs and metric dashboards. [VERIFY features and pricing] To be fair: daily cadence is the slowest on this list, and the signal set is narrower than the blend-based tools.

7. Jedi Collection Sort Pro

Rules-based sorting plus collection creation, including metafield-driven ordering. [VERIFY features and pricing] To be fair: a smaller footprint and track record than the established sorters.

8. SortWise

A newer entrant combining inventory data and analytics-driven strategies. [VERIFY features and pricing] To be fair: young app, short track record; evaluate on trial rather than reviews.

How the Sorting Apps Compare

Which Sorting App Should You Pick?

Under 20 collections and a tight budget: reSort or ST cover the basics. A merchandiser who wants hands on the product order: Sort’d. Collections as your core growth lever, with proof it works: that is the weighted-blend tier, Dynasort or Kimonix, and the differences that decide it are cadence, option sorting, certification and pricing model, all covered in our head-to-head. Whatever you pick, insist on seeing why a product ranked where it did, and measure conversion before and after with full weekly cycles.

Frequently Asked Questions

Do collection sorting apps slow down my storefront?

No. Sorting apps work through Shopify’s Admin API to set the collection order server-side; the storefront serves exactly as it did before, with no added scripts.

Can I still pin specific products to the top?

The serious sorters all support pinning heroes while automation handles the rest. In Dynasort, pinned products survive every re-sort.

Is manual drag-and-drop ever the right answer?

For a handful of small, slow-moving collections, yes. The moment inventory turns fast or collections multiply, manual order decays into chaos ordering, and chaos costs money on every visit.

Dynasort sorts collections by weighted recipes, verifies the lift, and never breaks your pins. Install it from the Shopify App Store or see how it works.

How Do You Create a Best Sellers Collection in Shopify?

If you would rather curate by hand, a manual collection works too, but then nothing updates itself, which defeats the point of a best sellers page.

How Does Shopify Decide What Counts as a Best Seller?

Shopify does not publish the formula. The ranking reflects recent sales activity, but you cannot choose the time window, exclude wholesale or subscription orders, or decide how much recency matters. Two very different businesses get the same black-box ranking. We covered the mechanics and the workarounds in how to sort Shopify collections by best selling, including the storefront URL parameter trick for viewing any collection ranked by sales.

Why Does a Best Sellers Collection Go Stale?

Three failure modes, all built in. The cold start: new products have no sales history, land at the bottom, get no impressions, and never accumulate the sales they would need to climb. The lag: the ranking reflects an unknown window of past sales, so a product that spiked last month can outrank what is selling this week. Sold-out squatters: best selling does not consult inventory, so a sold-out hero product keeps occupying row one while buyable products sit below it.

How Do You Keep a Best Sellers Collection Fresh Automatically?

Fix the window, the cold start and the inventory blindness, and the collection maintains itself. In practice that means computing the order yourself from signals you control: windowed sales velocity (so this week beats last quarter), a deliberate newness weight (so fresh products start visible and then earn their position from real performance), and an inventory signal (so sold-out products sink and restocks come back automatically). Shopify’s built-in option cannot blend signals, but an app working through the API can recompute a weighted order on a schedule. Dynasort builds exactly these blended recipes and re-sorts collections as often as every 10 minutes on Enterprise (20 on Pro, 30 on Basic).

Should “Best Selling” Be Your Default Sort Everywhere?

No. A dedicated best sellers collection is the one place where pure sales history is the point: shoppers clicked it to see what everyone buys. Your category collections have a different job, balancing proven sellers with new arrivals, margins and stock position, which is why they deserve a blended sort instead. And new arrivals need their own treatment everywhere, or the feedback loop above quietly strangles your next bestseller at birth.

Frequently Asked Questions

How often does Shopify update the best selling order?

Shopify recalculates it on its own schedule and does not publish the cadence. In practice the order shifts over hours and days, not minutes, which is one reason fast-moving stores compute their own ranking.

Can I exclude certain products from a best sellers collection?

Yes, through the automated collection’s conditions: tag products you want out (or in) and add a matching condition. What you cannot do natively is exclude specific order types (wholesale, subscriptions) from the sales counting itself.

Does a best sellers collection help SEO?

Modestly and indirectly: it earns internal links from your navigation, matches “brand + best sellers” searches, and concentrates engagement on one strong URL. It will not rank for competitive terms by itself.

Dynasort keeps best sellers collections honest: windowed sales, newness and inventory in one recipe. Install it from the Shopify App Store or see how collection sorting works.

What Does a High-Converting Collection Page Look Like?

What Sort Order Converts Best?

Sort order is the silent salesperson on every collection page, and the default options sell badly: “Manual” decays into chaos as the catalog changes, and “Best selling” lags demand, buries new products and ignores inventory (we broke down why in sorting by best selling). The orders that convert blend signals: recent sales velocity, stock position, margin, review ratings and newness, weighted for the collection’s job. Across stores running Dynasort, collections sorted this way converted 92.65% better than default Shopify sorting in June 2026.

How Many Products Should a Collection Page Have?

Fewer than you think. When we analyzed engagement across collection sizes in our choice overload study, oversized collections showed measurably weaker engagement: past a certain size, every added product dilutes attention rather than adding opportunity. The practical guidance from how many products should a collection have: split big generic collections into specific ones with a clear promise, and let a small catalog run fewer, richer collections instead of a taxonomy built for a catalog you do not have.

Which Filters Belong on a Collection Page?

Filters should match how customers think, not how your admin is organized. “Occasion,” “fit” and “material” out-convert internal product types every time, because shoppers arrive with a use case, not your taxonomy. Build filter values from the words in your search logs and reviews. And keep the filter count honest: every filter that almost nobody uses is visual noise on the row that matters.

Where Should the Collection Description Go?

Collection descriptions do real work for SEO and for AI assistants reading your catalog, but their placement is a conversion decision: long copy above the grid pushes products below the fold on mobile, which taxes every visit to help the occasional crawler. The pattern that serves both: one or two keyword-relevant sentences up top, products immediately visible, and the longer descriptive copy below the grid where engines and agents still read it.

Do Images and Grid Consistency Matter?

Mixed backgrounds and crops tank scannability; uniform grids read as trustworthy. Keep first images consistent in style and framing, and use badges (New, Bestseller, Low stock, Back in stock) sparingly and honestly to guide scanning: a grid where everything is badged has no badges at all.

How Do You Measure a Collection Page?

Per collection, not sitewide: conversion rate of sessions that viewed the collection, add-to-cart rate from the page, and click distribution by grid position (top rows dominating is normal; the question is whether the right products occupy them). Watch weekly trends, not daily swings. If your analytics cannot answer “did re-sorting this collection help,” you are flying blind on the highest-leverage setting the page has.

Frequently Asked Questions

Does changing the collection sort order hurt SEO?

No. Product order on a collection page is not a ranking factor for the page, and reordering products does not change the page’s URL, title or indexed content. We covered the detail in does collection sorting affect SEO.

Do collection descriptions actually help SEO?

Yes: they are often the only crawlable prose on the page, they define what the collection is about for search engines and AI assistants, and they support the title and heading. Placement matters more than length; see above.

What is a good conversion rate for a collection page?

Baselines vary too much by vertical and traffic mix for a universal number. Benchmark each collection against your own store average, then judge changes against that baseline over full weeks.

Data referenced: June 2026 performance report, the choice overload study.

Dynasort keeps every collection’s first rows earning their spot. Install it from the Shopify App Store or see collection sorting in action.

Full disclosure before we start: Dynasort is our app, and it is on this list. So you can trust the rest of the page, every entry including ours gets the same treatment.

What Does a Shopify Merchandising App Actually Do?

A Shopify merchandising app decides which products appear first. Shopify decides what appears on each surface: which products are in the collection, which match the search, which get recommended. The app controls the order. Since the top rows of any grid absorb most of the clicks, that ordering decision is quietly one of the highest-leverage settings in a store: in our study of 188,000 storefront search clicks, 28.5% went to position 1 and roughly half never went past the top three results.

Which Apps Cover Which Surfaces?

The Eight Best Shopify Merchandising Apps, Reviewed

1. Dynasort

The job: all four surfaces from one set of rules. Collections, product options and swatches, search result re-ranking, and recommendations are ordered by “recipes” you build from 40+ weighted signals: sales, inventory, margin, review ratings, freshness, metafields and more.

Standout: breadth plus transparency. One recipe logic runs everywhere, you can read exactly why a product ranks where it does, and collection analytics ship on every plan. Search and recommendations rank live on every request; collections re-sort as often as every 10 minutes on Enterprise (20 on Pro, 30 on Basic). Built for Shopify certified.

Pricing: flat plans at $99, $249 and $499 per month sized by catalog, with a 30-day free trial. No order-volume component, so the bill does not climb with your sales.

To be fair: we are the newer entrant. Our review count is small next to incumbents that have been in the App Store for years, and if all you want is a one-collection drag-and-drop tidy-up, we are more tool than you need.

2. KX AI Collection Merchandising (Kimonix)

The job: collection sorting and smart collection building, plus email-linked recommendations.

Standout: depth on the collection problem, ranking on a large number of signals, and an unusually strong library of named case studies. If you want to read stories from stores like yours before installing anything, Kimonix publishes more of them than anyone in this category.

Pricing: free entry tier, then paid tiers that scale with your store’s order volume. [VERIFY current tiers]

To be fair: collections update on a schedule measured in hours, up to a few times per day, not minutes, which matters during fast sellouts and flash sales. There is no product option or swatch sorting, and it is not a Built for Shopify certified app. Our Dynasort vs Kimonix page holds the full side-by-side.

3. Boost AI Search & Filter

The job: search and filtering first, with merchandising controls layered on top of search results and filtered collection views.

Standout: mature, widely installed search product with strong filter building. If broken storefront search is your main complaint, Boost is a serious candidate, and its merchandising rules (pin, boost, demote) live inside that search experience.

To be fair: it is a search app that merchandises, not a merchandising app. Collection sorting outside the search-and-filter context and variant ordering are not the focus, and replacing your theme’s native search UI is a bigger integration than an API-based sorter. Pricing scales with product count. [VERIFY current tiers] Full comparison: Dynasort vs Boost.

4. Bestsellers reSort (EGNITION)

The job: budget rule-based collection sorting: push bestsellers up, push sold-out products down, on a schedule.

Standout: simplicity and price. The rules are easy to reason about, the app has done this one job for years, and there is a free plan. [VERIFY tiers]

To be fair: single-metric rules rather than weighted blends, no search, recommendations or option sorting, and no analytics to tell you whether the sort actually helped. Fuller treatment in our Bestsellers reSort alternative guide.

5. ST: Product & Collection Sort

The job: data-driven collection sorting on a budget, including push-down of sold-out items and promotion of new products. Good value for stores that want more than one signal without enterprise pricing. To be fair: primarily a collection tool; search and recommendations need other apps. [VERIFY current feature set]

6. Sort’d Prime Collection Sort

The job: hands-on collection management: drag-and-drop curation with rules to automate the rest. Merchandisers who want to hand-place hero products and automate below them like this model. To be fair: manual-first is also the ceiling: the more collections you run, the more the drag-and-drop becomes the bottleneck the automation was supposed to remove. See Dynasort vs Sort’d.

7. Entaice AI Collection Sort

The job: automated collection sorting designed to keep working when shoppers apply filters. The filter-aware angle is the standout: sorted order holds inside filtered views. To be fair: a narrower feature set than the other sorters here, and a smaller track record.

8. Searchspring (Enterprise)

The job: search, merchandising and recommendations as a managed enterprise platform: one vendor for the whole discovery stack with onboarding services, which large teams value. To be fair: enterprise pricing, contracts and implementation timelines. If you are not doing enterprise volume, this category (Searchspring, Nosto, Algolia and friends) is more platform than a Shopify store needs. Trade-offs on our comparisons hub.

Flat Pricing or Usage Pricing: Which Costs More?

The sticker price matters less than the pricing model. Flat monthly plans (Dynasort, reSort, Sort’d) cost the same in your best month as your worst. Order-volume plans (Kimonix) and product-count plans (Boost) grow as you grow, which is painless at install time and noticeable a year later. Neither is wrong, but know which curve you are signing:

How Do You Choose the Right Merchandising App?

Three questions settle it. Which surface is bleeding? If search is broken, start with a search-first tool. If collections are stale, start with a sorter. If you want all four surfaces governed by one logic, that is the case Dynasort was built for. Does pricing scale with your success? See above. Can you see why it ranked what it ranked? Black-box ordering is fine until a bestseller lands on page 3 and nobody can say why. Prefer tools that show their reasoning, whichever tool that is.

Frequently Asked Questions

Do merchandising apps slow down my store?

API-based sorters (Dynasort, Kimonix, reSort) reorder through Shopify’s own system, so the storefront serves exactly as fast as before. Search-replacement apps add their own frontend, which is usually fast but is real added surface area.

Does automated sorting actually lift conversion?

Across stores running Dynasort, sorted collections converted 92.65% better than default Shopify sorting in our June 2026 performance report, methodology included. Your store is not an average, which is why we would rather you run the 30-day trial and read your own numbers.

Can I just use Shopify’s built-in sort options?

You can, and for small catalogs “Best selling” is a reasonable default. Its limits (black-box logic, no inventory awareness, cold-start burial of new products) are covered in our guide to sorting collections by best selling.

Data referenced: Dynasort June 2026 performance report, 30 days of storefront search behavior (188k clicks). Competitor facts from public App Store listings and vendor sites, August 2026; re-verify pricing before relying on it.

Dynasort merchandises collections, options, search and recommendations from one set of rules. Install it from the Shopify App Store or see how it works.

How Do Native Shopify Product Recommendations Work?

Shopify’s recommendation engine powers the “Related products” and “Complementary products” sections most themes ship with. Related products are inferred automatically from product descriptions, collections and purchase history. Complementary products are pairs you set by hand in the Search & Discovery app (“this tripod goes with this camera”).

What you control natively: which complementary pairs exist, and whether the sections render. What you do not control: how automatic suggestions are ranked, whether sold-out or low-margin products appear, and what shows when Shopify has no confident suggestion for a product.

Which Recommendation Placements Convert Best?

Not all slots are equal, and the right products differ per slot:

What Should Rank Your Recommendations?

Whatever engine picks the candidate products, something has to decide their order, and that ordering carries the same leverage as collection sort order: the first suggestion gets a disproportionate share of clicks. The signals worth blending, in rough order of usefulness:

• Sales performance, the base signal, ideally windowed so last quarter’s hit does not outrank this week’s.
• Inventory, so recommendations never dead-end in a sold-out product, and overstock gets a nudge when you need it to move.
• Margin, because two products with equal appeal rarely have equal economics.
• Review ratings, a trust signal shoppers already weigh, pulled from whichever review platform you run.
• Freshness, so new products get seen before their sales history exists.

This is exactly what Dynasort’s recommendation recipes do: the same weighted rules that order your collections rank your recommendation slots, computed live on every request, across product, cart, collection, home, thank you and order status placements, with no theme changes.

Do Personalized Recommendations Beat Merchandised Ones?

Per-shopper personalization (“shoppers like you bought”) is the premium promise of enterprise vendors, and on large catalogs with heavy traffic it earns real lifts. But it needs volume to work: on a typical Shopify store’s traffic, per-shopper models spend most sessions with too little data and quietly fall back to popularity anyway.

Merchandised recommendations, ranked by your data and your goals and the same for every shopper, are the honest baseline that works at any traffic level. They are also auditable: you can always answer “why is this product recommended first.” Start there; graduate to personalization when your traffic can feed it.

How Do You Measure Whether Recommendations Work?

Check all three per placement rather than in aggregate. Flying blind on recommendations is how “you might also like” rows quietly show sold-out products for months.

Frequently Asked Questions

Are Shopify’s native recommendations good enough?

For a small catalog with healthy stock levels, often yes. The cracks appear when inventory is volatile (dead-end suggestions), when margins vary widely (the engine cannot see them), or when new products need visibility (no history means no ranking).

Do recommendation apps slow the storefront down?

It depends on the integration. Widgets injected by theme scripts add frontend weight. Dynasort renders through your theme’s own sections with ranking computed server-side per request, so there is no added storefront script latency.

Can recommendations be A/B tested?

Ordering can, the same way collection sort order can: serve two ranking rules to split traffic and compare conversion. Test one variable with a success metric chosen before you start.

Dynasort ranks your recommendations with the same recipes that sort your collections, live on every request. Install it from the Shopify App Store or see recommendations in action.

We decided to stop answering from instinct. We read every sorting recipe running on Dynasort, more than 200 distinct ranking signals, 161 of them built by merchants themselves, and then watched more than 20 million shopper sessions flow through those collections over 60 days. Everything below is aggregated and anonymized. No store is named, no store is identifiable.

Some of what we found flatters us. Some of it does not. You are getting all of it.

First, the honest math

Collections that merchants sort with Dynasort convert at nearly twice the rate of collections left on autopilot. 1.20 percent versus 0.63 percent, measured across millions of sessions.

Great headline. Now watch what happens when the comparison gets fairer.

Compare store medians instead of raw traffic and the gap drops to about 25 percent. Compare sorted against unsorted collections inside the same store, the fairest cut we can make, and sorted collections win in 56 percent of stores with a median gain under 4 percent.

Two times, then 25 percent, then 4 percent. The number shrinks every time the comparison gets more honest, because merchants sort the collections that already matter and the stores that bother sorting are the stores that bother with everything else too. Most merchandising case studies you will ever read stop at the first number. We think the whole ladder tells you more: the honest gain is smaller than the marketing gain, and it is still real, still repeatable, and still sitting there unclaimed on most stores.

What the fleet actually runs on

Ten built-in signals plus merchant-built custom attributes account for four out of five live recipe attachments: inventory quantity, 30-day sales, on-sale status, age of newest variant, 7-day sales, all-time sales, sold-out status, full size run, variants in stock, and days since published.

Everything else is a long tail. That tail is where the interesting stuff lives, in both directions.

What works

Demoting sold-out products is the closest thing to free money in the data. Recipes that push sold-out products down, or hide them outright, show the lowest bounce rate of any major signal family: about a third of sessions bounce, against nearly half fleet-wide. Conversion runs roughly 55 percent above the sorted-fleet average. Nothing kills buying momentum like a wall of products nobody can buy, and the data says shoppers punish it every single day.

Freshness sells, measured the right way. Among widely used signals, age of newest variant posts the best median collection conversion in the fleet. Notice it is not “days since the product was created.” Ranking on how recently you added or refreshed variants rewards restocks and new colorways, not just new SKUs. Merchants using it are quietly outperforming the ones sorting by product creation date.

Sales windows of 7 and 30 days are the sweet spot. Recent enough to track demand, wide enough to be statistically calm.

The margin surprise. We expected to write that sorting by profit hurts conversion, that shoppers can somehow smell it. The data refused to cooperate. Stores that blend margin into their recipes convert above the sorted-fleet median. Sorting profit-aware does not automatically cost you sales, so if you have been treating margin as a guilty secret in your merchandising, stop.

What underperforms

Twitchy time windows. One-day and three-day windows underperform their 7-day and 30-day siblings in every signal family where both exist: sales, revenue, and cart adds. A day of data is small enough that the sort order reshuffles on noise, and a collection that reshuffles daily is a collection nobody recognizes on their second visit.

Raw view counts. Recipes ranking on product views sit below the fleet median at every window we offer. Views measure attention, not intent, and ranking by views is a feedback loop: whatever was on top yesterday collects views and stays on top tomorrow. Rank by carts and purchases instead; they measure what shoppers do, not what they scroll past. Fewer stores run these recipes, so treat this one as a strong pattern rather than a law.

The humbling one

We would love to tell you that hand-tuned recipes crush our seeded defaults. In stores running both, tuned recipes beat the defaults about half the time.

A coin flip.

That is not an argument against tuning. It is an argument against trusting anyone’s gut, including ours, and it is exactly why A/B testing is built into Dynasort. Our own biggest completed on-versus-off test so far: sorting on ran about 35 percent higher conversion across roughly 8,000 sessions, and it is still not statistically significant. Plenty of vendors would have shipped that as a case study headline. We are telling you it needs more traffic, because when we finally publish lift numbers, we want them to survive the ladder from the top of this post.

The merchants who build their own signals

161 of the ranking signals in the fleet are custom attributes merchants built themselves: restock dates, sell-through targets, seasonal flags, supplier priorities pushed in through the API.

Here is the stat that made us sit up. Three out of four merchant-built attributes are live on a storefront right now. For built-in signals, dragged down by starter recipes that never get attached, it is under one in ten. When a merchant invests the ten minutes to define a signal only their business has, they use it, they keep it, and they sort real traffic with it. If you have a spreadsheet column somewhere that secretly runs your merchandising, that column wants to be a custom attribute.

The shelf nobody has found

And then there are the signals almost nobody has discovered: back-in-stock demand, most-clicked, location-level stock, search-behavior signals. Adoption is close to zero. That one is on us, not on you, and it is getting fixed. If you want to be the merchant who gets there before your competitors do, the shelf is open.

How we measured, so you can judge it

Sixty days of data. Conversion means the session viewed the collection and completed checkout in the same session. Collections whose recipe or status changed mid-window were excluded, A/B variant traffic was excluded, and where a single high-traffic store distorted a segment we checked medians and reran the numbers without it. Everything here is correlation across a fleet, not a controlled experiment, which is precisely why the product ships with a testing tool. Run your own experiment; your store outranks our averages.

Install Dynasort free for 30 days or read the docs to see every ranking signal, including the shelf nobody has found yet.

That’s the point, but it has a cost. Once things work, nobody looks at them again. And stores change: catalogs grow, traffic shifts, a collection that converted well in March quietly stops converting in June. The data to catch all of this has been sitting in your Insights the whole time. Someone just has to read it every week.

Now something does.

What it is

Weekly Recommendations is a new card on your Dynasort dashboard, included on every paid plan. Every Monday morning it reviews your store’s own data and posts 3 to 6 prioritized recommendations: specific, quantified, and each linked to the exact place in Dynasort where you act on it.

Not “improve your product pages.” More like: “This collection drew 8,500 sessions in the last 28 days but converts at a third of your store average, and it still runs your generic default recipe. Here is the recipe that’s outperforming everything else. Consider assigning it.”

What it looks at

Each week’s review covers your last 28 days and how they compare to the 28 before, including:

• Enabled collections converting well below your store average, ranked by traffic so the highest-impact fixes come first
• Collections whose conversion or revenue dropped sharply this week versus last
• Enabled collections with no recipe assigned, and high-traffic collections still on the default recipe
• Recipes that haven’t been touched in months while their collections decline
• Your best-performing recipe, and the laggard collections that aren’t using it
• Collections where shoppers add to cart normally but complete purchase far less than average, which usually points to price, shipping, or stock rather than sorting. When that’s the story, it says so.
• Plan features you already pay for but aren’t using, like A/B testing, suggested only when your data indicates they’d help
• Tracking anomalies. In our first week, it flagged two stores whose orders mostly bypass the online storefront, which makes conversion look artificially low. Knowing that is worth more than any sorting tweak.

Every number it quotes is your own, pulled from the same Insights data you already see in the app. It cannot invent statistics and it cannot link to things that don’t exist in your store. If your store is new and doesn’t have enough traffic yet, it says so and waits.

It learns what you want

Every recommendation has three controls: mark it done, dismiss it, or rate it with a thumbs up or down. That feedback feeds the next week’s review. Dismissed recommendations don’t come back unless the underlying number gets meaningfully worse. Thumbs tell it which categories of advice you want more of and which to drop.

And it writes in your language. If you run your Shopify admin in German, Czech, Hebrew, or any of the other languages Dynasort supports, the recommendations arrive in that language, quoting revenue in your currency.

What it costs

Nothing extra. Weekly Recommendations is included on all paid plans, from Basic up. There’s nothing to install or configure: if you’re on a paid plan, it’s already on your dashboard.

If you’re evaluating Dynasort, every paid plan starts with a 30-day free trial.

Start your free trial or book a demo.

Now you can find out.

Dynasort A/B testing for collection sort order is live in beta on every Dynasort plan. Here’s what it does, how it works, and why we built it the way we did.

Why Shopify merchandising A/B testing has always been a pain

Testing sort order on Shopify has historically required one of two bad options: either you eyeball before-and-after metrics across arbitrary date ranges (no control group, seasonality baked in, gut-feel conclusions), or you try to implement a proper split test — which means cookies, theme code changes, fighting Shopify’s edge caching, and often a third-party CRO platform that doesn’t understand merchandising at all.

Per-visitor splits are especially messy in a collection context. A visitor sees Variant A on desktop. They come back on mobile and get Variant B. The cache serves stale sort orders to half your traffic. The data gets noisy fast, and you end up with a “significant” result that isn’t actually telling you anything useful.

There’s a better approach.

How Dynasort’s strict alternation works

Instead of splitting visitors, Dynasort alternates the entire collection between two variants on a configurable time window — anywhere from 1 hour to 24 hours. During any given window, every shopper sees the same sort order. When the interval elapses, the collection flips to the other variant. Back and forth, for as long as the test runs.

This is called strict alternation, and it sidesteps the entire per-visitor mess. There’s no cookie logic. No theme changes. No cache-busting gymnastics. Shopify’s edge caching works with you instead of against you, because everyone in a window sees the same version. The experience is consistent across devices by definition.

The tradeoff is that you need to run the test long enough to smooth out time-of-day and day-of-week variation. A one-hour window run for three days gives you different data than a six-hour window run for two weeks. We recommend longer windows and longer test durations for most merchants — and the results interface tells you when you don’t have enough data yet.

What you can test

There are two test types.

Dynasort ON vs OFF compares your currently active Dynasort recipe against the collection’s native Shopify sort order — Best Selling, Manual, Created Date, whatever you had before. This is the “prove it” test. If you’ve ever wanted to know whether your automated sorting is actually outperforming the default, this is how you find out. It’s also the first test we’d suggest running on any collection where you’re not already confident in the data.

Recipe A vs Recipe B compares two Dynasort recipes head-to-head. You might be deciding between weighting revenue vs inventory, or testing whether a margin-optimized recipe converts better than a velocity-based one. Same alternation mechanic, same metrics, same significance reporting.

Reading the results

Dynasort tracks four metrics per variant: Conversion Rate, Cart Rate, Exit Rate, and Bounce Rate. Each one shows you the per-variant value, the lift percentage, and a Yes/No significance flag.

The significance calculation uses a two-proportion z-test with pooled variance and a two-tailed p-value — the same methodology used by Optimizely and VWO. “Significant” means p < 0.05, which is the standard threshold for this type of test. We’re not doing anything exotic with the math; we’re applying established CRO methodology to a context where it’s rarely been available before.

One thing we’re deliberate about: Dynasort enforces a minimum of 100 sessions per variant before surfacing results as reliable. Until you hit that threshold, the UI tells you clearly that your data isn’t ready. This matters more than it might seem. Small-sample significance results are one of the most common ways A/B tests produce confident wrong answers — a test with 40 sessions per variant and p = 0.04 is not a test you should be making decisions from.

When the test ends, you pick the winner. Dynasort doesn’t auto-apply a result, because a 4% conversion lift on a variant that burns through your lowest-margin inventory isn’t necessarily the right call. The merchant context matters. You make the decision.

Who this is for

If you have collections with meaningful traffic — a few thousand sessions a month or more — A/B testing is worth running. The most direct use case is validating your current sort strategy on your highest-traffic collections. If Dynasort is doing its job, the data should show it. If the default Shopify sort is actually outperforming on a specific collection, you’d want to know that too.

Agencies and CRO consultants managing Dynasort for clients will also find this useful as a reporting and validation tool — something concrete to show that merchandising decisions are being made on data, not intuition.

This is a beta release. The core mechanics are solid, but we’re actively collecting feedback on test configuration, reporting clarity, and edge cases. If you hit something unexpected, in-app support gets to us directly.

Get started

Full docs, including setup walkthrough and how to interpret results: https://docs.dynasort.io/ab-testing/

The first test to run: Dynasort ON vs OFF on your highest-traffic collection.

Reference version of this post, with the full testing methodology and statistics: Strict Alternation Testing

Every product collection on your Shopify store is a silent sales pitch. The order your products appear in shapes what customers click, what they add to cart, and whether they buy — or bounce.

Most merchants don’t think much about collection sorting. They leave it on Shopify’s default settings — alphabetical, best-selling, newest first — and hope for the best. But “hope” isn’t a merchandising strategy. In March 2026, Dynasort tracked over 6.3 million collection sessions across thousands of Shopify stores, and the data tells a clear story: AI-optimized product sorting dramatically outperforms the default. Across every key metric — conversion rate, cart rate, exit rate, and bounce rate — Dynasort-sorted collections delivered meaningfully better results. Let’s break down exactly what that looks like.

Key Metrics: Dynasort vs. Shopify Default Sorting

Here’s how collections using Dynasort’s AI sorting compared to collections using Shopify’s standard sorting options in March 2026.

Conversion Rate: 1.74% vs. 0.71% (↑ 145.07%)

This is the headline number, and it’s the one that matters most. Conversion rate measures the percentage of collection visitors who completed a purchase. Dynasort collections converted at 1.74% compared to just 0.71% for default-sorted collections — a 145% improvement.

To put that in perspective: if your store gets 10,000 collection visits per month and your average order value is $75, moving from 0.71% to 1.74% CVR means going from roughly $5,325 in revenue to approximately $13,050. That’s an additional $7,725 per month — from the same traffic you’re already paying for. No new ad spend. No redesign. Just smarter product ordering.

Cart Rate: 7.3% vs. 2.94% (↑ 148.3%)

Cart rate measures the percentage of collection visitors who added at least one product to their cart. Dynasort collections hit 7.3% compared to 2.94% for default sorting — a 148% lift.

This metric reveals something important about intent. When products are sorted in an order that matches what shoppers are actually looking for, they engage. They don’t just browse — they take action. A higher cart rate means your collections are doing their job: surfacing the right products to the right shoppers at the right time.

Exit Rate: 57.24% vs. 64.4% (↓ 11.12% improvement)

Exit rate tracks how often a collection page is the last page a visitor sees before leaving your store entirely. Dynasort collections had an exit rate of 57.24% compared to 64.4% for default-sorted collections — an 11.12% improvement.

A lower exit rate means more shoppers are continuing to browse your store after viewing a collection. They’re clicking into product pages, exploring other categories, or heading to checkout. Poorly sorted collections, on the other hand, act like dead ends. When customers don’t see anything compelling near the top, they leave.

Bounce Rate: 43.18% vs. 53.62% (↓ 19.47% improvement)

Bounce rate measures visitors who land on a collection page and leave without interacting at all — no clicks, no scrolls, nothing. Dynasort collections reduced bounce rate to 43.18% versus 53.62% for Shopify defaults, a 19.47% improvement.

This is your first-impression metric. Nearly 1 in 5 additional visitors stayed and engaged when collections were sorted by Dynasort’s AI instead of Shopify’s static rules. For merchants spending money on paid traffic that lands on collection pages, this improvement alone can meaningfully change your ROAS.

Platform Scale: March 2026 by the Numbers

These results aren’t pulled from a handful of test stores. Here’s the scale Dynasort operated at in March:

• 40,490 collections tracked across the platform
• 3,556 collections actively sorted by Dynasort’s AI
• 1,069,782 products managed across all connected stores
• 2.23 billion collection sorts executed — that’s Dynasort’s algorithm continuously reordering products based on real-time performance signals
• $286.4 million in catalog revenue managed across all Dynasort-connected stores
• 6.32 million total collection sessions measured

This is production-scale machine learning applied to a problem most merchants solve with gut instinct or ignore entirely. Every sort decision is informed by actual shopping behavior — clicks, cart adds, purchases, and more — not static rules that go stale the day you set them.

What This Means for Merchants

If you’re running a Shopify store, here are the practical takeaways from this data:

Your collection sort order is a conversion lever. It’s not a cosmetic detail. The difference between a well-sorted and poorly-sorted collection is, based on March’s data, a 145% gap in conversion rate. Few optimizations on your store can deliver that kind of impact with zero ongoing effort.

Static sorting rules decay. “Best-selling” sorting sounds logical, but it creates feedback loops — top sellers stay on top, and new or seasonal products get buried. Dynasort’s AI continuously re-evaluates and adapts, which is why metrics like cart rate and bounce rate also improve, not just conversions.

You don’t need more traffic; you need more from your traffic. Every percentage point of bounce rate reduced and every additional cart add represents revenue you’re currently leaving on the table. With acquisition costs continuing to climb across Meta, Google, and TikTok, squeezing more value from your existing sessions is the highest-leverage move you can make.

Start Sorting Smarter

March 2026’s data reinforces what we’ve seen month after month: AI-optimized product sorting consistently and significantly outperforms Shopify’s default options. Higher conversion rates. More cart adds. Fewer bounces. More engaged shoppers.

Dynasort works in the background — no manual merchandising, no rules to configure, no ongoing maintenance. Install it, enable it on your collections, and let the algorithm do what static sorting can’t.

Try Dynasort free on the Shopify App Store →

Your products deserve to be seen in the right order. Your revenue depends on it.

Introduction

What if the order of products on your collection pages was quietly costing you sales every single day?

That’s the question Dynasort answers every month, and February 2026’s data makes the case more clearly than ever. Across over 6.2 million collection page sessions, Shopify stores using Dynasort’s AI-powered sorting consistently and significantly outperformed stores relying on Shopify’s default collection order. We’re not talking about marginal gains. We’re talking about conversion rate improvements north of 64%, cart rates that more than doubled, and meaningful reductions in both bounce and exit rates. These aren’t projections or lab results — they’re real numbers from real stores selling real products to real customers. Let’s break down exactly what happened in February and what it means for your store.

Key Metrics: How Dynasort Collections Performed Against Shopify Defaults

Every month, we compare shopping behavior on Dynasort-sorted collection pages against collection pages using Shopify’s default sorting. Here’s what February’s data showed across four critical metrics.

Conversion Rate (CVR): 64.79% Improvement
Dynasort collections converted at 1.17%, compared to 0.71% on default-sorted collections. That’s a 64.79% lift.

Conversion rate is the bottom line — the percentage of sessions that result in a completed purchase. A higher CVR means more of your existing traffic is turning into paying customers without you spending an additional cent on ads. For a store doing $50,000 a month in revenue, a 64.79% improvement in collection page CVR could represent tens of thousands of dollars in additional annual revenue from the same traffic you’re already paying for.

Cart Rate: 128.57% Improvement
Dynasort collections saw a 6.4% cart rate versus just 2.8% on Shopify default collections — a 128.57% increase.

Cart rate measures the percentage of visitors who add at least one product to their cart. This is arguably the most telling engagement signal on a collection page. It means shoppers are finding products they want faster. When your best-performing, most relevant products surface near the top of a collection, visitors don’t have to scroll and hunt. They see something they like, and they act. More than doubling the cart rate suggests that Dynasort’s sorting algorithm is doing exactly what it’s designed to do: putting the right products in front of the right shoppers at the right time.

Exit Rate: 7.7% Improvement
Exit rate dropped from 66.51% on default collections to 61.39% on Dynasort collections.

Exit rate tells you how often a collection page is the last page a visitor sees before leaving your store entirely. A lower exit rate means shoppers are continuing their journey — clicking into product pages, exploring more of your catalog, moving deeper into your funnel. A 7.7% improvement here means Dynasort collections are doing a better job of keeping shoppers engaged and on-site.

Bounce Rate: 13.91% Improvement
Bounce rate fell from 54.13% to 46.6% on Dynasort-sorted collections.

Bounce rate measures single-page sessions — visitors who land on a collection page and leave without interacting further. High bounce rates on collection pages are often a sign that shoppers aren’t seeing anything compelling above the fold. By intelligently sorting products based on performance signals, Dynasort reduces that friction. A nearly 14% improvement in bounce rate means thousands more visitors each month are sticking around long enough to discover products they want to buy.

Platform Scale: Dynasort by the Numbers

February 2026 reinforced Dynasort’s position as a trusted sorting engine operating at serious scale across the Shopify ecosystem.

• 41,007 collections tracked across all connected stores, with 3,207 collections actively using Dynasort’s AI sorting
• 1,016,023 products managed — over one million individual products organized and optimized by our algorithm
• 1,648,278,946 collection sorts executed in a single month — that’s over 1.6 billion sort operations ensuring products are positioned for maximum performance
• $213,398,562 in catalog revenue managed, meaning Dynasort is actively influencing the merchandising of over $213 million worth of product inventory
• 6,223,020 total collection sessions tracked, providing a massive and statistically robust dataset behind every metric reported above

These aren’t small-sample experiments. This is production-grade AI sorting running at scale across a diverse range of stores, categories, and price points — and the results are consistent month after month.

What This Means for Merchants

If you’re running a Shopify store and you’re still relying on manual sorting, best-selling defaults, or alphabetical order for your collections, February’s data highlights a significant opportunity you’re likely leaving on the table.

You don’t need more traffic — you need smarter merchandising. The stores in this dataset aren’t using some secret ad strategy or viral marketing hack. They’re showing the same products to the same audiences. The difference is which products appear first, second, and third on a collection page. That sequencing has an outsized impact on whether a visitor adds to cart or bounces.

AI sorting compounds over time. Dynasort’s algorithm continuously learns from shopping behavior, seasonality, inventory changes, and conversion patterns. The longer it runs, the better it gets at surfacing the products most likely to convert for your specific store and audience. February’s results reflect months of accumulated learning across the platform.

The setup cost is near zero. Unlike a site redesign, a new ad campaign, or a merchandising hire, enabling Dynasort takes minutes. You install the app, select the collections you want optimized, and the algorithm goes to work. There’s no manual rule-building, no spreadsheet analysis, no guesswork.

Ready to See What Smarter Sorting Can Do for Your Store?

February’s numbers tell a clear story: AI-optimized collection sorting drives more engagement, more add-to-carts, and more conversions from the traffic you already have.

If you’re a Shopify merchant looking for a high-leverage, low-effort way to improve store performance, there’s no reason not to test it for yourself.

Try Dynasort free today →

Your collections are already getting traffic. Let Dynasort make sure that traffic sees the right products first.