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.

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.

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.

Rank Metafields is a new Enterprise feature that writes live product ranking data to your Shopify store as native metafields. No custom API work, no third-party integrations, no developer gymnastics. The data just shows up where your products live and works with everything Shopify already supports.

What Gets Written

For every product in your catalog, Dynasort writes three values:

dynasort.rank_position is your product’s numeric rank within the catalog. Position 1 means this product scores highest according to your sorting recipe. Higher numbers mean lower relative performance.

dynasort.rank_score is the raw weighted composite score that your sorting recipe generates — a combination of revenue, margin, inventory velocity, conversion rate, review scores, and any custom attributes you have configured. This is the actual number Dynasort uses to rank products internally, now surfaced on the product itself.

dynasort.rank_percentile is a 0 to 100 score where 100 means top of catalog and 0 means bottom. This is the number most useful for display logic, conditional rules, and customer-facing copy — it is intuitive to read and easy to build against.

All three are standard Shopify number_integer metafields in the dynasort namespace. They behave identically to any other Shopify metafield, which means they work with themes, Shopify Flow, third-party apps, headless storefronts, and any other system that reads product data.

What You Can Build With It

Theme-level badges and labels. Use metafield-aware theme blocks to display “Top 10% This Week,” “Ranked #3 in Running Shoes,” or any rank-based label on product cards and product detail pages. No developer required after initial setup — the data updates itself.

Email and SMS personalization. Feed rank percentile into Klaviyo, Omnisend, or any other email platform that reads Shopify product data. Automatically promote products that have climbed into the top tier of your catalog. Stop manually curating “best of” product blocks — let rank data do it.

Smarter ad spend. Pipe rank data into your Google Shopping feed or Meta product catalog to bias spend toward products your algorithm already knows convert. If a product is ranking in the top 20% of your catalog, it is probably worth more ad budget than one sitting at the bottom.

Shopify Flow automations. Trigger workflows based on rank changes. Tag products that fall below the 20th percentile for review. Alert your team when a new product breaks into the top 10. Auto-apply or remove “Trending” tags as rank percentile changes.

Headless and custom storefronts. Sort product grids client-side using pre-computed rank data without making real-time API calls. Build custom “Staff Picks” or “Best Sellers” sections that automatically refresh as rank data updates. Feed percentile into personalization engines or recommendation widgets.

Multi-Recipe Support for Enterprise

Enterprise stores running multiple sorting recipes can opt in per-recipe. Each recipe you opt in adds three metafield keys per product, using the recipe ID as a suffix: rank_position_91, rank_score_91, rank_percentile_91 for recipe 91, and separate keys for each additional recipe.

A store running a “Best Sellers” recipe and a “New Arrivals” recipe gets a complete, multi-dimensional ranking profile on every product — making it possible to build themed sections (“New and Trending,” “Value Picks”) where each uses a different recipe’s data.

Technical Details

Metafields are written via Shopify’s metafieldsSet GraphQL mutation in batches of 24, respecting API rate limits. Data refreshes every hour at :30 past the hour, automatically, with no manual action needed after opt-in. All active, draft, and archived products are covered. Products that have been deleted from Shopify but remain in Dynasort’s catalog are automatically excluded.

Availability

Rank Metafields is an Enterprise feature, available now. Merchants on Demo, Basic, and Pro plans will see the option in their recipe settings clearly marked as Enterprise — so you know exactly what you are unlocking when you upgrade.

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

Feature 1: On Sale Option Sorting

If you run a Shopify store and you discount products, you have had this problem: Dynasort sorts your option values by sales, revenue, inventory, or some combination, but it had no way to account for whether a given option was on sale. A red colorway that’s 30% off would sort the same as a full-price red colorway.

That’s fixed.

How it works

Dynasort now automatically detects on-sale option values. An option value (say, “Red” in a Color option) is flagged as on sale if any of its variants have a compare_at_price above the current price. No additional API calls, no extra setup. It uses data already in Dynasort’s pipeline.

When an option is flagged, you get three placement settings per option:

Leave in place keeps things exactly as they were. On-sale options sort normally alongside regular in-stock options. This is the default, so if you do nothing, nothing changes.

Move before in-stock pushes on-sale options to the top of the list, ahead of regular in-stock items. Use this when you want discounts visible immediately: clearance events, sitewide sales, or flash promotions where you want every discounted variant front and center.

Move after in-stock groups on-sale options below regular stock but above sold-out items. Use this when your strategy is full-price first, such as protecting margin on flagship products while still surfacing sale items for shoppers who scroll.

The sort tier logic stacks cleanly with everything else: pinned positions always win, sold-out always goes last, and within each tier, your chosen metric (revenue, sales, inventory, etc.) still determines the order. An orange dot indicator appears in the option value table when on-sale grouping is active, giving you instant visual confirmation of which options are flagged.

The setting is per-option, per-product. You can apply “Move before in-stock” to Color and “Leave in place” to Size on the same product. Automated background re-sorts respect the setting the same way manual saves do.

Feature 2: Auto Merchandise from Anywhere

This one is smaller, but if you spend significant time in your Shopify admin, you’ll feel it immediately.

Dynasort now integrates into Shopify’s native “More Actions” menu on product pages, collection pages, and the collections index. The links are:

Auto Merchandise Product on any product page jumps directly into Dynasort’s controls for that product.

Auto Merchandise Collection on any collection page opens Dynasort’s sorting settings for that collection.

New Managed Collection from the collections index launches straight into the collection creation workflow in Dynasort.

Previously, getting from a specific product or collection in Shopify admin to the corresponding Dynasort controls required opening a separate tab, navigating to Dynasort, and searching for the item. It was a constant context switch for anyone actively managing merchandising. Now it’s one click from wherever you already are.

Available Now

Both features are live for all Dynasort users on every plan tier, including Demo, Basic, Pro, and Enterprise. No upgrade, no additional configuration required beyond enabling On Sale Option Sorting on the products where you want it.

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

Homer and the Dynasort Team

Install Dynasort free on the Shopify App Store at dynasort.io

This week we shipped two new sorting attributes that give merchants sharper signals and more control. Both are live now and included in every Dynasort plan.

The Problem with Sales Rank

Sales rank is intuitive. Put your bestsellers first, customers find popular products faster, conversions go up. Simple enough.

Except it ignores a critical variable: inventory.

A product that has sold 1,000 units over the past 90 days but has 8 units left in stock is not a product you should be leading your collection with. You’re funneling traffic toward a near-stockout, manufacturing frustration, and burning potential conversions that could have gone to products you can actually fulfill.

This is where Sell Through Rate changes the picture.

Feature 1: Sell Through Rate (STR) Attribute

Sell Through Rate measures how quickly a product is moving through available inventory. It’s not just “what sold the most” but “what is selling fastest relative to what we have.”

High STR means a product is popular and moving quickly. Used alongside inventory data, it gives you a much more honest picture of what deserves front-page placement in your collections.

With STR now available as a native Dynasort sorting attribute, you can build recipes that factor in velocity alongside sales, inventory levels, and conversion data. The result is collections that reflect what you can actually sell, not just what sold well in the past.

Feature 2: Alphabetical Metafield Sorting

Shopify metafields let merchants store custom data against products: material type, brand, fit, season, country of origin, and dozens of other attributes depending on the catalog.

Until now, sorting by those metafield values alphabetically required custom workarounds or manual ordering. That’s done.

Alphabetical metafield sorting is now a first-class option in Dynasort. Pick any metafield, sort ascending or descending, and let automation handle the rest. Whether you’re organizing by brand name, material, or a custom taxonomy you’ve built, it works without any extra configuration.

Available Now

Both attributes are live in your Dynasort account today. No upgrade required, no new billing, no setup beyond adding them to your sorting recipe.

If you’ve been working around inventory blind spots or fighting with manual metafield ordering, now you don’t have to.

Questions or feedback? Reach us at hello@dynasort.io or through in-app chat.

Feature 1: Auto Sorting for New Collections

Until now, when you created a new Shopify collection, there was a gap between when the collection went live and when Dynasort’s automated sorting kicked in. For busy teams managing large catalogs, this meant new collections sometimes faced real customers with default, unsorted product ordering.

That gap is gone.

With Auto Sorting for New Collections, Dynasort now detects new collections the moment they’re created and immediately applies your configured sorting rules. There’s nothing to configure, no checklist to remember, no delay. Your merchandising logic is active from day one.

This is especially valuable for merchants who frequently launch new seasonal collections, run promotions with dedicated collection pages, or are scaling their catalog quickly.

Feature 2: Bulk Editing for Products & Collections

Managing sorting configurations across a large catalog used to require updating collections and products one at a time. If your strategy changed — new metric priorities, a seasonal shift, a site-wide promotion — implementing it was a manual slog.

Bulk editing solves that. You can now select multiple collections or products and update their sorting rules in a single action. Whether you’re managing 20 collections or 2,000, what used to take hours now takes seconds.

Available Now — No Upgrade Required

Both features are live in your Dynasort account right now. No additional setup, no new billing tier. Open the app and you’ll find Auto Sorting in your Collections settings and Bulk Editing available wherever you manage collections and products.

As always, we’re building based on what merchants actually need. If you have feedback on either feature — or ideas for what to tackle next — reach out at hello@dynasort.io or use the in-app chat.

— Wade & the Dynasort Team