Every Shopify store owner knows the feeling: you’ve spent money driving traffic, your products are solid, your images look great — but your collection pages just aren’t converting the way they should. The problem often isn’t what you’re selling. It’s the order customers see it in.

At Dynasort, we track exactly how our AI-powered collection sorting performs against Shopify’s default sorting — across thousands of real stores, millions of products, and billions of sort executions. August 2026 was our strongest month of data yet, and the results make a clear case: the order of your products matters enormously, and getting it right can double your conversions. Let’s dig into the numbers.

Key Metrics: How Dynasort Collections Performed vs. Shopify Default

We measure four core metrics that together paint a complete picture of collection page performance. Here’s how Dynasort-sorted collections compared to Shopify’s default sorting in August.

Conversion Rate (CVR): 1.44% vs. 0.71% — a 102.82% improvement

This is the metric that matters most to your bottom line. Conversion rate measures the percentage of collection page visitors who ultimately complete a purchase. Dynasort collections converted at more than double the rate of default-sorted collections.

To put that in practical terms: if your store gets 10,000 collection page visits per month and your average order value is $75, the difference between a 0.71% and 1.44% CVR is roughly $548 in additional monthly revenue — from the exact same traffic. No extra ad spend. No new products. Just a smarter product order.

Cart Rate: 6.7% vs. 3.02% — a 121.85% improvement

Cart rate measures how often visitors add a product to their cart from a collection page. This is the earliest and most reliable signal that a collection page is doing its job — surfacing the right products to the right shoppers at the right time.

Dynasort collections achieved a 121.85% higher cart rate than default sorting. That’s more than double the add-to-cart activity, which means Dynasort isn’t just nudging behavior slightly — it’s fundamentally changing how effectively your collection pages engage shoppers.

Exit Rate: 60.71% vs. 65.1% — a 6.74% improvement

Exit rate tracks the percentage of visitors who leave your site entirely from a collection page. A high exit rate means your collection page is a dead end rather than a gateway deeper into your store.

Dynasort reduced exit rates by 6.74%. While this may sound modest compared to the conversion and cart rate improvements, consider the scale: across over 5.5 million collection sessions in August alone, that improvement represents hundreds of thousands of additional visitors who stayed on-site, browsed further, and gave your store another chance to earn their business.

Bounce Rate: 47.35% vs. 55.06% — a 14% improvement

Bounce rate measures single-page sessions — visitors who land on a collection page and leave without interacting at all. It’s a harsh indicator of first-impression failure.

Dynasort brought bounce rates down by 14%, from 55.06% to 47.35%. That means more shoppers are finding something worth clicking on within the first few seconds. When your highest-potential products lead the page instead of being buried on row five, visitors stick around.

Platform Scale: Dynasort by the Numbers

August’s data isn’t drawn from a handful of test stores. These results reflect the performance of a mature, large-scale platform:

• 37,526 collections tracked across the Dynasort platform
• 3,441 collections actively sorted by Dynasort’s AI
• 1,023,530 products managed — over one million individual SKUs optimized
• 2.65 billion sort executions performed throughout the month
• $238.15 million in catalog revenue managed across Dynasort-enabled stores
• 5.52 million total collection sessions measured

These aren’t projections or lab results. This is production data from real Shopify stores selling real products to real customers. The scale of data behind these benchmarks is what gives us — and our merchants — confidence that the improvements are meaningful and repeatable.

What This Means for Merchants

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

Your default collection sort is likely costing you sales. Shopify’s built-in sorting options — bestselling, newest, price-based, alphabetical — are static and one-dimensional. They don’t account for real-time customer behavior, inventory changes, margin targets, or seasonal trends. August’s data shows that stores relying on these defaults are converting at roughly half the rate of stores using AI-driven sorting.

Small changes at the collection level compound fast. Most conversion optimization advice focuses on checkout flows, product pages, or ad targeting. Collection pages are often overlooked — but they’re frequently the highest-traffic pages on your store and the primary way customers browse your catalog. Improving performance here has an outsized impact.

You don’t need more traffic — you need smarter merchandising. The stores in our August data didn’t get more visitors to see these improvements. They got better results from the visitors they already had. In a landscape where acquisition costs keep climbing, that kind of efficiency gain is the highest-leverage move you can make.

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

August’s results reinforce what we’ve seen month after month: AI-sorted collections outperform default sorting across every metric that matters — more add-to-carts, higher conversion rates, lower bounce rates, and fewer exits.

The best part? You don’t have to take our word for it. Dynasort offers a free trial so you can see the impact on your own store, with your own products and your own customers.

Try Dynasort free on the Shopify App Store →

Your products deserve to be seen in the right order. Let the data do the selling.

Two datasets. First, the fleet: every storefront search that flowed through stores on the Dynasort platform over the last 30 days, bots excluded. Second, a deep dive into one high-traffic apparel and jewelry store’s full year of search tracking. Everything is aggregated or anonymized, and as always we will tell you exactly how far each number can honestly be stretched.

The fleet, in two numbers

Just over 1.34 million searches in a month is a lot of shoppers typing exactly what they want into a box. Search users are widely known to be the highest-intent visitors on a store, which is what makes both of those tiles matter: the surface is busy, and one in every 22 of those high-intent moments ends in a blank page.

Where clicks actually land: one store, one year

Fleet totals tell you how big the surface is. To see how shoppers behave on it, we went deep on a single high-traffic apparel and jewelry store: 365 days of tracking, 188,323 clicks on search results. To be clear up front, this is one store we studied closely, not a fleet average.

Only 28.5 percent of clicks landed on the first result. Nearly half of all clicks, 49.4 percent, landed past the top three, and the average click sat at position 4.51. The detail that surprised us most: positions 7 through 12, the bottom half of a typical first screen, collected exactly as many clicks as position 1.

That says two things at once. Shoppers on this store scan well past the top, so a good product at position 9 is not lost. But position still carries enormous weight per slot: one slot got 28.5 percent of clicks, and it took six slots further down to match it. Whatever sits at the top gets a structural advantage, and default text relevance hands that advantage out with no idea what is in stock, what is selling, or what just launched. That is the whole argument for putting merchandising signals in charge of the order, and it is why we built Dynasort Search.

Zero-result searches are shoppers telling you what they wanted

The same store gave us a year of dead ends to study: 602,891 searches, of which 37,183 returned nothing at all. That is a 6.2 percent zero-result rate, a bit above the 4.5 percent fleet figure for the last 30 days.

A zero-result search is the most honest feedback a store ever gets. Nobody types into a search box by accident. This store’s zero-result queries clustered into three buckets: catalog gaps, synonym gaps, and typos. The single largest source of unmet demand was a product category the store simply did not carry, followed by obvious typo variants of things it did.

Dynasort tracks every storefront search on a connected store, including the dead ends, so this list is not a research exercise. It is sitting in your search report: the exact words shoppers used, ranked by how often they hit nothing. Some entries are a merchandising fix, some are a synonym you can add in five minutes, and some are a product line your shoppers keep asking for by name.

How to read these numbers honestly

This is a data post in a series whose whole point is honest math, so here are the limits.

• The click position data comes from one store. It is a large, clean, year-long sample, but a store with a different catalog, theme, or grid layout will scan differently. Treat the shape as a well-documented example, not a law.
• Zero-result rates vary widely by catalog breadth. A store with 40 products will see far more dead ends than a store with 40,000, and neither number is a report card.
• Re-ranking does not fix zero-result searches. If nothing matches, there is nothing to reorder. The fix for dead ends is usually catalog or synonym work. Re-ranking helps the other 95 percent of searches, the ones that do match and then come back in an order chosen by text relevance alone.
• None of this is a causal claim about conversion. Search users convert at multiples of browsing shoppers across the industry because high-intent people search, not because a search box works magic. What the data supports is narrower and still valuable: search is a busy, high-intent surface, position on it carries real weight, and the default order is decided by a signal that knows nothing about your business.

What to do with it

If half the clicks on a busy store land past the top three, the order of the whole first page is merchandising real estate, not just slot one. Dynasort Search puts your recipe in charge of that order: recent sales, inventory depth, newness, margin, or any custom signal you build, on both the results page and the predictive dropdown. Shopify still decides what matches. Your business decides what comes first. Monday’s launch announcement has the full picture, and tomorrow we close the week with a practical guide to search recipes and result cards.

Install Dynasort free for 30 days or read the search docs. Already a Dynasort merchant? Your search report is already collecting this data for your store, and a free setup session gets search live with you on a screen share.

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

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

First, the honest math

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

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

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

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

What the fleet actually runs on

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

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

What works

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

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

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

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

What underperforms

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

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

The humbling one

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

A coin flip.

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

The merchants who build their own signals

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

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

The shelf nobody has found

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

How we measured, so you can judge it

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

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

Before = collections on Shopify’s default sort order. After = collections actively sorted by Dynasort. July 1 to July 31, 2026, all stores, aggregated and anonymized.

Every month we publish the same four numbers: how collection pages actively sorted by Dynasort performed against collection pages left on Shopify’s default order, across every store on the platform. July’s numbers are above, drawn from 8.1 million collection page sessions. Sorted collections won on every metric we track, and the conversion gap was exactly two to one.

How to read these numbers honestly

These are observational fleet numbers, not a controlled experiment, and we want you to read them that way. Merchants tend to switch on automated sorting for the collections that already matter most, and the stores that bother sorting are usually the stores that bother optimizing everything else too. Some of that two-to-one gap is the sorting. Some of it is selection. Anyone who tells you a fleet-wide comparison is pure product effect is selling you something.

The clean way to know what sorting does for your store is the A/B test built into Dynasort: run automated sorting against your default order on the same collection and let your own traffic decide. Our monthly numbers tell you the direction and the size of the opportunity. Your test tells you the truth.

What each metric is saying

Conversion rate, 1.48% versus 0.74%. The percentage of collection page visitors who completed a purchase. On 10,000 collection visits a month, that spread is the difference between 74 orders and 148 orders from traffic you already paid for.

Cart rate, 7.03% versus 3.01%. How often visitors add to cart from a collection page. This was July’s biggest relative gap, and it is the upstream action that feeds everything else. When the right products surface earlier in the scroll, more shoppers take the first commitment step.

Exit rate, 58.81% versus 64.51%. How often a collection page is the last page a visitor sees. Lower is better. Fewer exits means more browsing, more chances to convert, and stronger engagement signals.

Bounce rate, 44.42% versus 53.15%. Single-page sessions that land and leave. If you drive paid traffic straight to collection pages, this is the number to stare at: on sorted collections, meaningfully more of those clicks stuck around.

The scale behind the numbers

Scale is why the pattern is worth publishing: it holds month after month across store sizes, industries, and catalog shapes. Scale is also why we keep the caveat above. At this volume the difference is consistent, and consistency is still not the same thing as causation. That distinction is the whole reason testing is built into the product.

What to do with this

Your default sort order is static. Whether it is manual, alphabetical, best-selling, or newest-first, it does not adapt to what is selling right now. Automated sorting re-ranks continuously on real performance signals.

Collection pages are your highest-leverage surface. Merchants obsess over product pages and checkout while most product discovery happens in the grid. July’s cart rate gap shows how much demand sits in a poorly ordered one.

Test before you trust anyone’s percentages, including ours. Pick one high-traffic collection, run the built-in A/B test for a few weeks, and make the call on your own data.

Install Dynasort free for 30 days and let your collections earn their order. Setup takes minutes, and the test results are yours either way.

Reference version of this report, with the month-by-month data series and methodology: Sorted vs Default Collections

Every Shopify store owner knows the feeling. You’ve spent money driving traffic, your products are solid, your photos look great — but visitors land on a collection page and leave without buying. The problem often isn’t what you’re selling. It’s the order you’re showing it in.

June 2026 marked another strong month for Dynasort merchants, and the data tells a compelling story. Across over 5.6 million collection page sessions, stores using Dynasort’s AI-powered sorting consistently outperformed default Shopify sorting on every key metric — from conversion rate to bounce rate. Below, we’ll break down exactly what these numbers mean and why they matter for your bottom line.

Key Metrics: Dynasort vs. Default Shopify Sorting

We measure Dynasort’s performance by comparing shopping behavior on collections where our AI sorting is active against collections using Shopify’s default sort order. Here’s how June 2026 shook out.

Conversion Rate (CVR): 92.65% Improvement

Dynasort: 1.31% → Shopify Default: 0.68%

Conversion rate is the metric that pays the bills. It measures the percentage of collection page visitors who complete a purchase. In June, Dynasort collections converted at nearly double the rate of default-sorted collections — a 92.65% improvement.

To put that in practical terms: for every 1,000 visitors to a default-sorted collection, roughly 7 people buy. With Dynasort, that number jumps to 13. At scale, that difference represents significant revenue without spending a single additional dollar on advertising.

Cart Rate: 134.05% Improvement

Dynasort: 6.53% → Shopify Default: 2.79%

Cart rate tracks the percentage of visitors who add at least one product to their cart from a collection page. This was Dynasort’s strongest metric in June, with AI-sorted collections seeing a 134.05% higher add-to-cart rate.

This metric matters because it captures buying intent upstream. Even visitors who don’t complete checkout in that session have entered your funnel. They’re remarketing candidates. They’ve shown interest. A higher cart rate means more opportunities to close sales — now or later.

Exit Rate: 8.4% Improvement

Dynasort: 61.83% → Shopify Default: 67.5%

Exit rate measures the percentage of visitors for whom a collection page was the last page they viewed before leaving your store. A lower exit rate means more visitors continue browsing deeper into your site.

Dynasort collections saw an 8.4% reduction in exit rate compared to default sorting. That might sound modest next to the cart rate numbers, but think about what it represents: thousands of additional visitors each month who stayed on your store instead of leaving. Every one of those visitors is another chance to make a sale.

Bounce Rate: 16.72% Improvement

Dynasort: 47.85% → Shopify Default: 57.46%

Bounce rate tracks single-page sessions — visitors who land on a collection page and leave without interacting further. In June, Dynasort collections reduced bounce rate by 16.72%.

High bounce rates are expensive. You paid to get that visitor to your store, and they left immediately. When AI sorting puts the most relevant products in front of shoppers from the first scroll, they’re more likely to engage. A nearly 10-percentage-point drop in bounce rate means substantially more of your paid and organic traffic is actually working for you.

Platform Scale: June 2026 by the Numbers

Dynasort’s performance data isn’t based on a handful of test stores. Here’s the scale behind the numbers:

• 40,768 collections tracked across the Dynasort platform
• 3,150 collections actively sorted by Dynasort’s AI
• 1,024,832 products managed across all connected stores
• 2.48 billion collection sorts executed — the cumulative number of times Dynasort’s algorithm has optimized product order
• $508.6 million in catalog revenue managed across Dynasort-connected stores
• 5,623,928 total collection sessions recorded in June

These aren’t projections or lab results. This is real shopping behavior from real customers across a diverse range of Shopify stores managing over half a billion dollars in catalog value.

What This Means for Merchants

The takeaway from June’s data is straightforward: the order your products appear in directly affects whether people buy them.

Default Shopify sorting options — alphabetical, best-selling, newest, price — are static. They don’t adapt to what’s actually converting. Dynasort’s machine learning analyzes real-time shopping behavior and continuously re-sorts your collections to surface the products most likely to lead to a purchase.

Here’s what that means in practice:

• You get more revenue from existing traffic. No increased ad spend required. The visitors you’re already paying for simply convert at a higher rate.
• Your ad spend becomes more efficient. When more visitors add to cart and fewer bounce, your cost per acquisition drops.
• It compounds over time. Dynasort’s algorithm learns continuously. As it processes more data from your store, its sorting decisions become more precise.

The merchants seeing the best results aren’t doing anything complicated. They install the app, enable it on their collections, and let the AI do what static sort rules can’t — adapt in real time.

Start Sorting Smarter

June 2026 reinforced what months of data have consistently shown: AI-sorted collections outperform default sorting across every metric that matters to your business.

If you’re still relying on manual or default sort orders for your Shopify collections, you’re likely leaving conversions on the table.

Try Dynasort free and see what smarter sorting does for your store.

“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.

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.

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

We recently analyzed conversion data from over 1,100 Shopify collections across our merchant base, and the results challenge a common assumption in e-commerce: that more product options lead to more sales.

The Data

Here’s what we found:

• Collections with fewer than 10 products: 2.30% CVR
• Collections with 11-20 products: 2.17% CVR
• Collections with 51-100 products: 2.00% CVR
• Collections with 501-1000 products: 1.88% CVR

That’s an 18.4% decline in conversion rate from the smallest to largest collections.

Why This Matters

The psychology behind this is well-documented. When customers face too many choices, decision-making becomes harder, not easier. They experience what researchers call “choice overload” — and often, they simply leave without buying anything.

Think about your own shopping behavior. When you’re browsing a collection with 8 curated items, you can mentally process each option. When you’re scrolling through 500 products, you’re overwhelmed before you even start.

What You Can Do

This doesn’t mean you should delete products from your store. Instead, think about how you’re organizing them:

1. Create focused, themed collections — “Summer Wedding Guest Dresses” converts better than “All Dresses”
2. Use merchandising rules to surface your best products — Don’t bury bestsellers on page 3
3. Consider pagination or filtering carefully — Make it easy to narrow down, not just scroll endlessly

The most successful merchants we work with treat their collections like curated experiences, not product dumps. They understand that their job isn’t just to show inventory — it’s to help customers find what they need quickly and confidently.

The Bottom Line

Sometimes the best merchandising decision is showing less. Your conversion rate will thank you.

About this data: This analysis is based on aggregated, anonymized conversion data from more than 1,100 Shopify collections on stores running Dynasort, collected through Shopify web pixel events and analyzed in November 2025. Figures describe patterns across our merchant base as a whole, not any individual store.

The practical follow-up to this data is collection page best practices, which covers product count alongside sort order, filters and descriptions.