What Is Sell-Through Rate?

In plain terms: of the units you had available to sell during a window, how many did you sell? A product that sold 50 of the 60 units it had is moving fast. A product that sold 50 of 1,000 units is not, even though both show the same raw sales number. STR normalizes demand by stock depth, which is exactly what raw sales counts hide. We go deeper in our post on the sell-through rate attribute.

Why Does STR Beat Raw Sales for Some Goals?

Best-selling style sorts reward whatever you stocked deepest, because deep stock generates big absolute numbers. STR cuts through that. It finds the fast movers you bought conservatively, the small-batch products outperforming their inventory, and the items that deserve a reorder and a better position. It is also an inventory health lens: consistently low STR in premium positions means visibility is being spent on products that do not move. If your goal is selling through what you own, rather than just maximizing clicks on already-deep stock, STR is the better compass.

How Do You Sort by STR in Practice?

Use a sorting app that computes STR per product over a window you choose and applies it as a weighted signal through the API. Pure STR sorting can get twitchy on low-stock items (selling 3 of 4 units looks heroic), so blend it with sales velocity and an inventory signal to keep the order stable and buyable.

This is one recipe signal in Dynasort. Install it from the Shopify App Store or see how it works.

A typical collection might convert a handful of times a week. That’s not enough signal to confidently rank forty products against each other — you’d be sorting on noise. So merchants either wait for sales data to slowly accumulate, or they sort on something else and hope.

Meanwhile, shoppers are voting constantly, with a much faster signal: what they click. The new Most Clicked Products attribute turns that signal into sort order. And it does something no Dynasort attribute has done before.

### Every collection, its own ranking

This is the first Dynasort attribute that scores differently in every collection, even when those collections share the same recipe.

That’s worth sitting with for a second, because most “sort by popularity” features treat popularity as one store-wide number. Every collection inherits the same global list. But the same product can be a star in one collection and wallpaper in another, and a single popularity score can’t capture that.

Here’s a concrete example. Collection X and Collection Y both run your “Engagement” recipe. In Collection X, Product A is the most-clicked item. In Collection Y, Product B leads. With Most Clicked Products in the recipe, Product A rises to the top of X while Product B rises to the top of Y — automatically, from that one shared recipe. You don’t build two recipes. You build one, and each collection adapts to its own audience.

### How the scoring works

The merchant picks two things: a timeframe (24 hours to 90 days) for the click-counting window, and how many top products earn points (default 10). The collection’s click leader gets the attribute’s full point weight, second place gets a bit less, and so on down to the last qualifying spot. With the default top 10, first place earns 100% of the points, second earns 90%, sliding to 10th at 10%. Everything below the top 10 earns nothing from this attribute.

Crucially, points scale by rank, not by raw click counts. This is a deliberate design choice, and it solves a real problem.

Raw clicks vary wildly between collections. A niche collection’s leader might get 40 clicks in a month while a flagship collection’s leader gets 4,000. If you scored on raw counts or fixed thresholds, you’d have to tune the attribute differently for every collection, and a single runaway product could flatten all the meaningful differences among everything below it. Rank-based scoring sidesteps all of that. It behaves identically in the niche collection and the flagship, with nothing to configure per collection, and the relative ordering stays meaningful no matter the absolute volume.

### A trending collection that maintains itself

As a Managed Collection condition, the attribute (there called Product Clicks) uses a product’s total clicks across your whole store. That makes a “Trending Now” collection a one-condition setup: for example, products with more than 50 clicks in the last 7 days. Products join the collection as they gain attention and drop out automatically as that attention fades. It’s a storefront merchandising surface that tracks this week’s demand without anyone touching it.

### Where it shines

The most obvious win is for stores with lower sales volume. Clicks accumulate far faster than orders, so your sorting gets smart long before sales data could tell you anything reliable. If you’ve been frustrated that sales-based sorting feels random on your smaller collections, this gives those collections a dense signal to work with.

It’s also a merchandising blind-spot finder. The product you assume is your hero, versus the product shoppers actually click, are not always the same thing, and this attribute surfaces the gap. And with a short window like 7 days, your collections follow seasonal demand automatically, leaning into a holiday as interest builds and easing back out as it passes.

### An honest word on feedback loops

There’s a dynamic worth being upfront about: clicks at the top of a collection tend to beget more clicks. A product that’s surfaced gets seen, gets clicked, and that click reinforces its position. Left unchecked, that can become a feedback loop where early attention compounds into permanent placement regardless of merit.

The right way to use this attribute is as one input among several, not the whole sort. Balance it with merchandising attributes like margin, inventory, and newness. Engagement is valuable information about what your shoppers find interesting, but it works best when it informs the sort rather than dictating it. Sophisticated merchants will recognize this immediately, and the attribute is built to be weighted alongside everything else precisely so you can strike that balance.

### A few more notes

Click data updates periodically through the day, not in real time, so expect influence within hours rather than instantly. Brand-new or very low-traffic collections have no click history yet, so the attribute contributes nothing there until shoppers arrive — your other recipe attributes carry the sort in the meantime.

### Pairs with Back In Stock

Most Clicked Products shipped the same day as the Back In Stock attribute, and they’re natural partners. One captures attention, the other captures availability. Used together, your collections stay both interesting and buyable: the products people are clicking rise, and the products that just came back in stock rise, so shoppers see things they want and can actually buy.

### Available now

Most Clicked Products is live today on all plans, as a standard attribute. Add it to a recipe to give each collection its own audience-driven ranking, or use it as a Managed Collection condition to build a self-maintaining trending collection.

Questions or feedback: hello@dynasort.io or in-app chat.

For most stores, the answer is no — and not because anyone decided that. It’s just what happens by default.

### The problem nobody has time to solve

While a product is out of stock, it tends to sink in your collection’s sort order. It’s not selling, it’s not converting, so whatever logic orders your collection pushes it down. That’s reasonable while it’s unbuyable.

The trouble is what happens when it returns. The product comes back in stock and lands wherever it sank — often several pages deep. So at the exact moment back-in-stock demand is highest, your returning bestseller is facing the shoppers least likely to ever see it.

The fix has always been manual: remember that the product restocked, go into the collection, and pin it to the top. Then do that for every collection it belongs to. Then do it again for the next product that restocks, and the next. Across a real catalog, nobody keeps up with this. The restock window quietly passes with the product buried.

### What the Back In Stock attribute does

Back In Stock is a new Dynasort attribute that detects when a product returns to stock and automatically boosts it in your sorted collections for a window you choose, anywhere from 24 hours to 90 days.

Dynasort continuously monitors inventory levels and records a restock event whenever an item crosses from out of stock back to in stock. Any product with a recent qualifying restock gets the attribute’s full boost in any recipe that includes it. No tagging, no manual pinning, no spreadsheets, no remembering.

### The returns filter: the part that matters

Here’s the detail that separates this from a naive implementation.

A customer return puts a unit or two back on the shelf. Technically, that takes a product from zero units to “in stock” — which means a simple back-in-stock boost would fire on it. You’d end up promoting a product that has two returned units and is, for all practical purposes, still sold out. Worse, it would happen constantly, on every minor return across your catalog.

Dynasort handles this with a minimum restock quantity setting, defaulting to 3 units. A restock smaller than that threshold is ignored. Only genuine replenishments — the kind that mean a product is actually buyable again — trigger the boost.

This is the kind of logic you only think to build if you’ve actually lived with inventory data. “Back in stock sorting” is easy to claim. Not firing on a single returned unit is the part that proves the feature was built by people who understand how stock really moves.

### You decide what “back” means

There are two detection modes.

Entire product back in stock fires when a product that was completely sold out has stock again. This is the default, and it matches what most shoppers mean when they say something is “back.”

Any variant back in stock fires when an individual variant returns — a popular size, a sold-out color. This is useful for stores where a single variant coming back is itself worth promoting.

Configuration is three fields in total: the timeframe (how recent the restock must be), the detection mode (entire product or any variant), and the minimum restock quantity (the returns filter). That’s the whole setup.

### One signal, two surfaces

Because Back In Stock is a standard Dynasort attribute, it works everywhere attributes work.

Inside a recipe, it’s weighted alongside everything else you sort by — sales, margin, newness, clicks, behavioral signals. A restocked product that shoppers were already viewing and adding to cart gets compound priority, surfacing for exactly the reasons you’d want it to.

As a Managed Collection condition, it lets you build a self-maintaining “Back In Stock” collection on your storefront. You set it up once with a single condition: products join the collection when they restock and drop out automatically as the event ages past your window. It’s a merchandising surface that maintains itself.

You can also layer recency. Stack a high-weight 7-day instance with a low-weight 30-day instance, and the boost steps down gracefully as the restock ages — strong while the product just came back, tapering as the moment cools.

### A few honest notes

Detection follows your store’s regular data sync schedule, so a restock is reflected within minutes to about an hour, not literally the instant it happens. Restock history starts accumulating from June 2026, which means the longest windows (60 and 90 days) reach full usefulness once that much history has built up. And products that don’t track inventory in Shopify never trigger the attribute, which is the correct behavior but worth knowing.

### Available now

The Back In Stock attribute is live today on all plans, as a standard attribute. Add it to a recipe, or use it as a Managed Collection condition — or both.

Questions or feedback: hello@dynasort.io or in-app chat.

—

*Install Dynasort free on the Shopify App Store at [dynasort.io](https://dynasort.io)*

“Conversion rate is 2.3% over the last 30 days” is a fact. But it’s a fact with no context. Is 2.3% good for this collection? Is it up or down? Did it move because of the recipe you swapped two weeks ago, or the pins you added last Tuesday, or nothing you did at all? The old Insights left you to answer those questions yourself, usually by exporting data and guessing.

We rebuilt Insights so it answers them for you. This is the largest performance-visibility update we’ve shipped, and it spans six connected features.

Metrics Timeline, on everything

The centerpiece is the Metrics Timeline, now at the bottom of every collection, product, and recipe page. It charts your key rates — conversion, add-to-cart, exit, and bounce — over time.

What makes it more than just a chart is what’s plotted alongside the curve: change markers. Every time you swapped a recipe, added a pin, adjusted attribute weights, started or stopped an A/B test, or made any of a dozen other meaningful configuration changes, a marker appears on the timeline at that exact moment.

This is the difference between seeing a number and understanding it. If your conversion rate climbed over the last two weeks, you can now see whether that climb started the day you swapped recipes — or whether it was already happening and your change had nothing to do with it. Cause and effect, on the same chart.

A dashboard, not a settings panel

Open Dynasort now and the first thing you see is a store-wide performance timeline for the last seven days. Below it sit three Top Movers panels: your top collections, top products, and top recipes.

We chose “movers” deliberately. It’s not just a list of your best performers frozen in place — it surfaces what’s changing, in both directions. Each tile shows a thumbnail trend chart, the current conversion rate, and a colored arrow indicating the percentage change versus the prior week. Green arrows mark wins worth repeating. Red arrows flag problems worth fixing. Click any tile to drill into that entity’s full timeline.

The point is that you no longer have to hunt for what’s working or what’s slipping. The app puts it in front of you the moment you open it.

Stop guessing whether you’re improving

The Insights page now opens with a six-KPI strip: sessions, conversions, conversion rate, cart rate, exit rate, and bounce rate. Each shows the value for your selected window plus a colored arrow showing how it compares to the equivalent prior window.

Pick “30 days” and you immediately see how the last 30 days stack up against the 30 before them. The arrows handle the directional intuition for you — conversion rate up is green, exit rate up is red. “Are we trending up?” used to mean exporting data to a spreadsheet and building a comparison by hand. Now it’s the first thing you see.

A change log you don’t have to maintain

Below every timeline chart is a chronological list of every meaningful configuration change: what changed, when, and by how much. Attribute weight bumps, A/B test starts and stops, manual sorts, pin updates, recipe swaps — all named with the friendly attribute and recipe names you actually use, not internal IDs.

For teams sharing Dynasort access, this is a shared audit trail. You can see what a colleague changed and tie it directly to the movement in the metrics above it. For solo merchants, it’s a changelog you get for free instead of trying to remember what you did and when.

Recipe performance that’s actually trustworthy

Recipe pages get the same timeline treatment, with one subtle but important detail handled correctly: time-accurate attribution.

If you moved a collection from Recipe A to Recipe B last Tuesday, Recipe A’s history correctly shows the traffic from before Tuesday, and Recipe B shows the traffic after. The data reflects what was actually true at each point in time, rather than taking today’s recipe assignments and projecting them backward across the whole history. That means when you compare two recipes, you’re comparing real performance, not an artifact of who happens to be assigned where right now.

Click a row, land on the chart

Finally, a small quality-of-life addition: clicking any collection, product, or recipe row in the Insights table takes you straight to that entity’s page, already scrolled to its timeline. No scrolling past settings to find the data you came for.

Built to be reliable from day one

A few things happening beneath the surface, for merchants who care about the plumbing:

Enterprise stores were backfilled with 90 days of historical timeline data, so your charts are populated immediately rather than starting empty. The underlying analytics pipeline was rebuilt to keep high-volume stores current — previously, very large stores could silently fall days behind; now the pipeline catches up adaptively. And the entire system is timezone-correct, so international merchants see complete data through their own “yesterday,” not the server’s.

Available now

The reworked Insights is live today on all plans. Open the app and you’ll land on the new dashboard. From there, every collection, product, and recipe has its timeline waiting at the bottom of the page.

Questions or feedback: hello@dynasort.io or in-app chat.

Every product in your Shopify collection has a position. And that position is either working for you or against you. When a customer lands on a collection page, the order of your products determines whether they scroll, click, add to cart, or leave. Most stores still rely on Shopify’s default sorting — manual arrangements, alphabetical order, or basic “best selling” logic that updates slowly and doesn’t adapt to real-time shopping behavior.

Dynasort exists to fix that. Our AI engine continuously re-sorts your product collections based on live performance data, putting the right products in front of the right shoppers at the right time. Every month, we publish a transparent performance report comparing Dynasort-sorted collections against Shopify’s default sorting across the same stores. May 2026 was our strongest month yet — and the numbers tell a clear story about what intelligent merchandising can do for your bottom line.

Key Metrics: Dynasort vs. Shopify Default Sorting

We measure four core shopping behavior metrics across every collection session. Here’s how Dynasort-enabled collections performed against Shopify’s default sorting in May 2026.

Conversion Rate (CVR): 1.58% vs. 0.69% — 129% Improvement

Conversion rate is the metric that matters most. It measures the percentage of collection visitors who complete a purchase. Dynasort collections converted at 1.58%, compared to 0.69% for default-sorted collections — a 128.99% improvement. To put that in plain terms: for every 1,000 visitors landing on a collection page, Dynasort-sorted collections generated roughly 9 additional purchases compared to the default. At scale, that’s transformative. This isn’t the result of driving more traffic or running steeper discounts. It’s the same products, the same collection pages, just sorted more intelligently.

Cart Rate: 6.69% vs. 2.71% — 147% Improvement

Cart rate tracks the percentage of collection visitors who add at least one product to their cart. This is the clearest signal of purchase intent, and it’s where Dynasort showed its largest advantage in May. Dynasort collections achieved a 6.69% cart rate, more than double the 2.71% rate on default-sorted collections — a 146.86% improvement. When products that match a visitor’s likely preferences appear higher in the collection, the friction between browsing and buying drops dramatically. More adds-to-cart means a fuller pipeline for your checkout flow, your abandoned cart emails, and your retargeting campaigns.

Exit Rate: 59.18% vs. 66.78% — 11.38% Improvement

Exit rate measures how often a collection page is the last page a visitor sees before leaving your store entirely. A high exit rate means your collection page is a dead end rather than a gateway. Dynasort reduced exit rate from 66.78% to 59.18% — an 11.38% improvement. That means more visitors continued browsing your store after viewing a Dynasort-sorted collection. They clicked into product pages, explored other collections, or moved toward checkout. Fewer exits mean more opportunities to convert on every session.

Bounce Rate: 44.22% vs. 55.76% — 20.7% Improvement

Bounce rate captures the percentage of visitors who land on a collection page and leave without interacting at all — no clicks, no scrolls, no engagement. Dynasort brought bounce rate down from 55.76% to 44.22%, a 20.7% improvement. This metric speaks directly to first impressions. When the first rows of a collection feature products that are relevant, visually compelling, and aligned with current demand, visitors stay. When they see stale or irrelevant products, they leave. Dynasort’s AI ensures that first impression is optimized continuously.

Platform Scale: May 2026 by the Numbers

Dynasort’s performance data isn’t drawn from a small test group. In May 2026, our platform operated at significant scale:

• 40,512 collections tracked across all connected stores
• 3,548 collections actively sorted by Dynasort’s AI engine
• 982,742 products managed across the platform
• 2.49 billion sort operations executed — every sort reflecting real-time data analysis
• $301.6 million in catalog revenue managed across Dynasort-enabled stores
• 6.02 million total collection sessions analyzed

These numbers reflect a platform that’s processing massive volumes of merchandising decisions every day. Each of those 2.49 billion sorts represents Dynasort’s AI evaluating product performance signals, identifying patterns, and repositioning products to maximize engagement and revenue. The performance improvements reported above aren’t theoretical — they’re drawn from over six million real shopping sessions across nearly a billion dollars in managed catalog value.

What This Means for Merchants

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

Your collection sort order is a conversion lever. Most merchants spend heavily on traffic acquisition — ads, SEO, influencer partnerships — but neglect what happens when that traffic actually arrives. Collection page sorting is one of the highest-leverage, lowest-cost optimizations available to you. The data shows it can more than double your conversion rate.

Default sorting is leaving money on the table. Shopify’s built-in sorting options are functional, but they’re static. They don’t learn from visitor behavior, they don’t adapt to inventory changes in real time, and they don’t differentiate between a first-time visitor and a returning customer. The 129% CVR gap and 147% cart rate gap in May’s data represent the cost of that rigidity.

The impact compounds. Better sorting doesn’t just improve one metric in isolation. Lower bounce rates feed into higher cart rates, which feed into higher conversions, which feed into better revenue per session. When your collection pages keep visitors engaged from the first scroll, every downstream metric benefits.

Start Sorting Smarter

May 2026 reinforced what our data has shown month after month: AI-driven collection sorting meaningfully outperforms the default across every metric that matters to your store’s profitability. The stores using Dynasort aren’t working harder — they’re letting their product data work harder for them.

Ready to see what smarter sorting can do for your store? Try Dynasort free on the Shopify App Store and let your collections start optimizing themselves.

What min and max actually do

Every numeric attribute you use for sorting has a minimum and a maximum value. These two numbers do more work than they appear to.

When Dynasort scores a product, each attribute’s raw value gets normalized into a 0 to 1 contribution. The min is the floor and the max is the ceiling: a product at or below the min contributes 0, a product at or above the max contributes 1, and anything in between is interpolated linearly. For attributes where lower is better, like bounce rate or exits, the score is inverted.

This is a clean, standard way to turn wildly different metrics — revenue in dollars, conversion rate as a percentage, inventory as a count — into comparable scores that can be weighted and combined. But it only works well if the min and max reflect your actual catalog. Set the revenue ceiling too low and half your products max out at a score of 1, flattening the signal. Set it too high and nothing ever approaches the top, compressing everything toward zero.

The problem nobody revisits

Here’s what happens in practice. During setup, you configure your attributes and set reasonable min/max values, often using the recommendation Dynasort already shows next to each field. Then you move on. The store runs. Months pass.

In those months, your catalog grows. Your traffic patterns shift. Your best-selling products change. The revenue distribution that made sense in October looks nothing like the one in March. But those min/max values are still sitting where you left them, quietly calibrated to a version of your store that no longer exists.

Nothing breaks, exactly. The sorting still runs. But it’s running on stale calibration, and the further your data drifts from those original numbers, the less your scores mean.

The fix used to be manual: go back into each attribute, look at the current recommendation, and copy the new numbers into the fields. Almost nobody did this regularly, because it’s the kind of maintenance task that’s easy to forget and hard to prioritize.

Check a box, and it’s handled

Now there’s a simpler option. On any numeric attribute, you’ll find a new checkbox labeled “Auto-update from recommendations,” with help text that reads “Refresh minimum and maximum from data each day.”

Check it and save. Two things happen.

First, Dynasort immediately recomputes that attribute’s min and max from your current store data and applies them. Second, from that point forward, a nightly process keeps them in sync automatically. You don’t approve each update — opting in is the approval. The values simply stay current.

The recommendation logic is the same one Dynasort has always used for its on-screen suggestion: the floor is set at the 5th percentile of your product distribution, and the ceiling at the 90th percentile. This trims outliers on both ends so a single extreme product doesn’t distort the whole range. For traffic-derived attributes like conversions, sales, and cart adds, the distribution comes from your recent data window. For static catalog attributes like inventory, margin, and product age, it comes from your product data directly.

Built to be conservative

This feature changes values automatically, so we designed it to be cautious by default.

It only touches attributes you explicitly check. Every other attribute stays exactly as you set it. It only changes min and max — never your weights, your attribute status, or any other setting. There are guardrails on every update: if a recommendation would produce an inverted range (min greater than max) or an invalid value, the write is skipped. And when there isn’t a usable recommendation for one side of the range, your existing value on that side is preserved rather than blanked out.

Everything is computed from data already inside Dynasort. There are no external API calls, and nothing is pushed to Shopify as part of this feature. It’s purely an internal recalibration of the numbers your sorting already depends on.

What it covers

Auto-update works on 20 numeric attribute types. That includes traffic and event-derived metrics — cart adds, cart rate, bounces, bounce rate, conversions, conversion rate, exits, exit rate, product views, revenue, sales, and sell-through rate — as well as static catalog values like inventory quantity, per-location inventory, product margin, discount amount, discount percent, age of newest variant, days since created, and days since published.

Attributes that aren’t numeric ranges — booleans, metafields, product tags, options, and similar — don’t have min/max values, so the checkbox doesn’t appear for them.

You can see the status of every attribute at a glance on the attributes index, where a new “Auto” column shows a green On badge for enabled attributes, a grey Off badge for eligible ones you haven’t enabled, and nothing for attribute types that don’t support it.

Available now, every plan

This is live today on every Dynasort plan, from Demo to Enterprise. There’s no upgrade required and no add-on. Open any numeric attribute and you’ll find the toggle directly below the min and max fields.

Set it once, and stop thinking about it. Which, for a maintenance task, is exactly the point.

Questions or feedback: hello@dynasort.io or in-app chat.

If you run a Shopify store with 50+ collections, you know the drill. You log into Dynasort, scroll to find the right collection, check what recipe is assigned, maybe enable sorting on a few more, repeat. Multiply that by every seasonal shift, every new product line, every merchandising experiment. The UI is fine — but clicking isn’t always the fastest path.

The Dynasort Connector changes that.

What the Connector Is

The Connector is a secure API that lets you plug Dynasort into any AI assistant — Claude, ChatGPT, or anything that supports tool use through a system prompt. Once connected, your AI assistant becomes a merchandising co-pilot. You describe what you want in plain English, and it handles the work: enabling collections, assigning recipes, triggering sorts, checking configurations, adjusting product boosts.

It’s not a chatbot bolted onto Dynasort. It’s a real API with 16 actions covering the operations you actually do most — list and update collections, bulk toggle sorting, assign recipes across many collections at once, sort or revert, manage product-level option sorting, and boost individual products in the ranking algorithm.

How It Works

Four steps to get running:

1. Generate an API key on the Connector page in your Dynasort dashboard. The key is shown once and stored as a SHA-256 hash — save it somewhere safe.
2. Copy the pre-built system prompt we provide. It tells your AI assistant how to use the Connector correctly.
3. Paste it into your AI assistant of choice (Claude Projects, a ChatGPT custom GPT, whatever you use).
4. Start a conversation. The assistant will ask for your API key on first use, then you’re off.

What You Can Actually Do

Here’s what a normal session looks like. These are real prompts the Connector handles today:

• “Enable sorting on all my summer collections using the Best Sellers recipe.”
• “What recipe is assigned to my New Arrivals collection, and when did it last sort?”
• “Set a boost of +50 on product SKU MM-HOODIE-NAVY so it surfaces higher in the lowcountry collection.”
• “Disable variant sorting on the t-shirt category — I want sizes to stay in manual order there.”
• “Re-sort the entire Sale collection right now.”

You can chain these. Ask the assistant to audit every collection that’s missing a recipe, then bulk-assign one. Ask it which collections sorted in the last 24 hours. Ask it to revert a collection to its previous order if a sort didn’t land right.

The point isn’t novelty — it’s that you stop context-switching between tabs and start operating Dynasort at the speed you think.

Security

Every key uses Bearer token authentication and is hashed with SHA-256 before storage (which is why we only show it to you once). Requests are rate-limited at 60 per minute. Revoke any key from the Connector page at any time.

Who It’s For

The Connector is included on Pro ($119/mo) and Enterprise ($249/mo) plans. It’s built for merchants who’ve outgrown clicking — agencies managing multiple stores, brands with deep catalogs, anyone whose merchandising workflow is a recurring time sink.

If you’re on Starter and most of your work happens across a handful of collections, the UI is probably still the right tool. If you’re managing dozens or hundreds, the Connector pays for itself the first week.

Getting Started

Full documentation, the system prompt, and example workflows are at docs.dynasort.io/connector.

If you’re already on Pro or Enterprise, the Connector page is live in your dashboard now. If you’re on Starter and want in, upgrade from your billing page and you’ll see it appear.

Stop clicking. Start asking.

Every product collection page on your Shopify store is quietly making a decision for your customers: what to look at first, what to consider next, and what to skip entirely. Most stores leave that decision to Shopify’s default sorting — alphabetical, best-selling, or manual arrangements that don’t adapt to real-time shopper behavior. And every month, the data tells us the same story: that’s leaving money on the table.

In April 2026, Dynasort tracked over 6.1 million collection page sessions across thousands of active Shopify stores. The results reinforce what we’ve seen month after month — AI-driven collection sorting meaningfully outperforms static defaults across every key shopping behavior metric. This report breaks down exactly what happened, what the numbers mean, and why it matters for your bottom line.

Key Metrics: How Dynasort Collections Performed Against Shopify Defaults

We measure Dynasort’s impact by comparing shopping behavior on collections actively sorted by our AI against collections using Shopify’s default sorting logic. Here’s how April 2026 shook out:

Conversion Rate (CVR): 1.52% vs. 0.64% — a 137.5% improvement

This is the metric that matters most. Conversion rate measures the percentage of collection page visitors who ultimately completed a purchase. Dynasort collections converted at 1.52%, compared to 0.64% on default-sorted collections. That’s not a marginal gain — it’s more than double the conversion rate.

What’s driving this? When your collection page surfaces the right products for each visitor based on real behavioral signals — not just static rules — shoppers find what they want faster and buy with more confidence. A 137.5% CVR improvement means that for every 1,000 visitors hitting a Dynasort-sorted collection, roughly 9 more customers are completing a purchase than they would on a default page. Scale that across thousands of daily sessions and the revenue impact compounds quickly.

Cart Rate: 6.82% vs. 2.75% — a 148% improvement

Cart rate tracks how often visitors add a product to their cart from a collection page. In April, Dynasort collections saw a 6.82% cart rate versus 2.75% for Shopify defaults — a 148% improvement and our strongest-performing metric this month.

This number reflects purchase intent. When more people are adding to cart, it means the products they’re seeing first are relevant, appealing, and well-matched to what they came looking for. A higher cart rate also feeds your abandoned cart recovery flows, giving you more opportunities to close sales even when shoppers don’t buy immediately.

Exit Rate: 58.58% vs. 61.35% — a 4.52% improvement

Exit rate measures the percentage of visitors whose last pageview was the collection page — meaning they left your store entirely without navigating deeper. Dynasort collections held that number to 58.58%, compared to 61.35% on default-sorted pages.

A 4.52% improvement here might sound modest compared to the CVR and cart rate numbers, but consider what it represents at scale: across millions of sessions, that’s tens of thousands of additional shoppers staying on your site, browsing product pages, and moving further into your funnel instead of bouncing to a competitor.

Bounce Rate: 44.55% vs. 49.99% — a 10.88% improvement

Bounce rate captures single-page sessions — visitors who landed on a collection page and left without any further interaction. Dynasort-sorted collections reduced bounce rate by 10.88%, from nearly 50% down to 44.55%.

This tells us something important about first impressions. When a shopper lands on a collection page and immediately sees products that feel relevant, they engage. When they see a poorly ordered wall of products that doesn’t match their intent, they leave. Nearly an 11% improvement in bounce rate means Dynasort is doing its job in that critical first moment.

Platform Scale: Dynasort by the Numbers

April 2026 was another month of significant scale across the platform:

• 41,197 collections tracked across all connected stores, with 3,561 collections actively sorted by Dynasort’s AI
• 1,088,209 products managed — over a million SKUs being intelligently positioned in real time
• 2.65 billion collection sorts executed during the month, meaning our algorithms continuously re-optimized product order based on fresh behavioral data
• $272.9 million in catalog revenue managed, representing the total product value across stores relying on Dynasort
• 6,150,039 total collection sessions analyzed, providing the robust data foundation behind every number in this report

These aren’t theoretical projections. Every metric in this report is drawn from real sessions, real shopping behavior, and real purchase outcomes across a diverse base of Shopify merchants.

What This Means for Merchants

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

Your collection page order is a conversion lever — treat it like one. Most merchants obsess over ad creative, landing pages, and checkout optimization. Collection page sorting rarely gets the same attention, yet it directly influences whether shoppers find products they want to buy. The 137.5% CVR gap suggests this is one of the highest-impact, lowest-effort optimizations available.

More add-to-carts means more recovery opportunities. Even if your conversion rate stays flat, a 148% improvement in cart rate dramatically expands your remarketing and abandoned cart audiences. Every add-to-cart is a signal of intent that your email and SMS flows can act on.

Reducing bounce and exit rates has compounding effects. Shoppers who stay on your site longer see more products, build more brand familiarity, and are more likely to return — even if they don’t purchase today. Lower bounce rates also send positive engagement signals that can benefit your SEO and paid ad quality scores over time.

You don’t need to manage this manually. Dynasort executed over 2.6 billion sort operations in April alone. No merchandising team can match that frequency or responsiveness. The AI adapts continuously to shifting customer behavior, seasonal trends, and inventory changes without requiring you to touch a thing.

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

The gap between AI-optimized and default-sorted collections isn’t narrowing — it’s consistent, measurable, and significant. If your collection pages are still relying on Shopify’s built-in sorting, April’s data makes a strong case for change.

Try Dynasort free and let your collections start working harder — automatically.

Now you can find out.

Dynasort A/B testing for collection sort order is live in beta on Pro and Enterprise plans. 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’re on Pro or Enterprise and 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.

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.