No black box. You choose the signal, QSortby scores your catalog in real time and keeps in-stock first — or personalizes per shopper — then proves the lift with a built-in A/B test and revenue attribution. Here’s the inside.
Set the rule per collection, over a window you pick — a rolling last N hours or days, anywhere from an hour to a year, or a fixed range like last week, a specific month, or any two dates. Change it anytime; preview before it goes live.
A ranking is only useful once it reaches the storefront, and that is where most merchandising apps ask for trust they have not earned — they publish on their own schedule, in their own markup. QSortby does neither.
Preview, manual order, manual membership and product exclusions are on every plan, including Free.
Beyond one order for everyone, QSortby can render a per-visitor feed. Visitors are classified into types you define, and each type gets a ranking that blends weighted signals — a live, personal front row.
Recommendation engines rank on purchase history, so a first-time visitor gets nothing and a returning one gets last year's taste. QSortby reads this visit — pace, dwell, revisits, comparisons, hesitation — and reads the intent behind this visit from their first pause, before they've bought anything.
Personalization is never used to manufacture pressure — no false urgency, no exploiting a low mood. The point is relevance, not leverage.
You don’t have to trust the sort. QSortby runs it as an experiment and measures the money it actually drove.
Illustrative. QSortby splits visitors between your current order and the new sort, then reports the real funnel each drove.
Attribution is influenced / last-touch — it shows which sort a converting shopper engaged with, an honest directional read of lift, not a lab-grade incrementality study.
Once a shopper has chosen, the question changes from "what should they find" to "what else belongs in this order". QSortby answers it from the same live signals — so every suggestion is something that's selling and in stock, never a static list you maintain by hand.
Sold-out and already-in-cart products are filtered out of every surface, and a failed request hides the section rather than blocking the cart. On Growth, the session read chooses between the candidates in the cart, popup, checkout and thank-you offers — same cart, different suggestions for a shopper who is comparing carefully and one who is buying a gift in a hurry.
Install free and run it on one collection — or book a 30-minute demo and we’ll screen-share the experiment + attribution on your real collections.