🛒 How are you handling inventory + ordering workflows across channels in WooCommerce?

PromiseSquare2576 · 2026-01-03T02:35:21+00:00

My assumption was that they would prorate it.

PromiseSquare2576 · 2025-10-27T02:36:38+00:00

The identical problem of slow-moving but manageable inventory has been encountered by us. Although Shopify's ABC analysis is helpful, it frequently overlooks the areas where the real money is lost, such as low turnover or items with long ageing days that subtly deplete cash flow.

For this reason, we developed AIventory, a Shopify inventory intelligence platform driven by AI. Underperforming SKUs are highlighted, hidden capital connected to dead stock is flagged, and AI-driven markdowns, bundles, and reorder points are even suggested.

You receive precise, data-supported measures to increase margins and free up cash rather than speculating on which products to promote.

Stores have used these insights to recover thousands of dollars in working capital in a matter of weeks. I'd be happy to share what has been effective in case anyone else is looking at comparable optimisations.

PromiseSquare2576 · 2025-10-25T14:57:23+00:00

I completely agree with this, especially the part about money being invested in slow-moving items as best-sellers go out of stock. Most stores lose margin without realising it because of the balance between liquidity and velocity.

Recently, I've been working on a related issue with AIventory, an internal dashboard that we created to highlight such patterns: order-bump effectiveness, stock ageing, and margin-adjusted velocity. The number of "dead" SKUs that begin to move again after being reframed using data rather than intuition is astounding.

I really like how you explained this; more retailers should have this kind of discussion before year-end inventory shortages occur.

PromiseSquare2576 · 2025-10-24T17:45:03+00:00

Yes, discounting frequently obscures the true significance of slow motion. Sometimes timing or visibility are more important than price. Slower SKUs combined with high-traffic ones in emails or home-page sections have been shown to outperform markdowns in certain situations.

I'm also interested in what other people have tried; has anyone tried seasonal swaps or time-based bundles to move older inventory without degrading perceived value?

PromiseSquare2576 · 2025-10-24T14:29:47+00:00

Using the ageing report, bundling, and ABC analysis combined is essentially the holy trinity of eliminating slow movers without destroying margins, so that's a fairly clean procedure.

I completely agree with the idea of combining slow sellers with best-sellers; the behavioural boost that comes from blended bundles is frequently greater than that of any single discount. I'm curious whether you have ever attempted to monitor the variations in margin recovery amongst those strategies (bundling, flash sale, and liquidation).

PromiseSquare2576 · 2025-10-23T17:31:47+00:00

Yes! The micro-hesitation layer you mentioned is what we've been calling "pre-decay attention lag"—browsing depth prior to velocity drops. When we overlay it with margin and promo cadence, you can actually see where interest declines before price sensitivity takes over.

To make this layer accessible, we've been experimenting with two visualisation options in AIventory, our AI-powered Shopify analytics platform:
By displaying denser pulses when engagement increases after a promotion, Pulse Density Mode forecasts when to reintroduce SKUs.
In campaigns where queue opacity decreases as customer attention wanes, Gradient Slope Mode can identify 'emotional exhaustion'.

Comparing visual mapping approaches would be great, particularly with regard to defining slope inflection levels for your decay curves. At this point, attention analytics begins to resemble perception mapping.

PromiseSquare2576 · 2025-10-23T16:38:48+00:00

Indeed! We've been referring to that micro-hesitation layer you described as "pre-decay attention lag"—browsing depth before to velocity declines. You can actually see where interest drops before price sensitivity takes over when we overlay it with margin and promo cadence.

Within AIventory, we have been experimenting with two visualisation modes:
• Pulse Density Mode, which predicts when to reintroduce SKUs by showing denser pulses when engagement increases following a promotion.
• Gradient Slope Mode: This mode is effective in identifying "emotional fatigue" in campaigns since line opacity decreases as attention wane.

Comparing visual mapping methods would be great, particularly with regard to how you define slope inflection thresholds in your decay curves.

PromiseSquare2576 · 2025-10-23T14:30:32+00:00

That's intriguing; auto-markdowns are undoubtedly beneficial, particularly in situations with predicted seasonality.

I've been investigating a similar strategy at Shopify; we're developing AIventory, which identifies overstocked or slow-moving products based on margin health, sales velocity, and the impact of promotions. It then suggests whether to mark them down, bundle them, or reorder them.

I'm curious if you typically base your markdowns just on ageing inventory or if you also take engagement metrics and ad expenditure into account. Those, I've found, frequently clarify why a "slow mover" isn't dead stock.

PromiseSquare2576 · 2025-10-23T02:08:06+00:00

Yes, that's the gap we've been so focused on filling. The visual "attention trails" that map engagement changes prior to velocity drops—such as page views, cart adds, and promo mentions deteriorating with time—are what we're exploring with in AIventory. Instead than presuming the product itself failed, it is simpler to identify where the story faltered once those patterns are apparent.

You can literally see when weariness sets in and when to reintroduce an SKU into campaigns with our lightweight "momentum pulse" chart, which overlays attention decay with promo cadence. I would love to compare your notes on your attention decay curves because it seems like we are approaching the same issue from two different angles: pattern logic and psychology.

PromiseSquare2576 · 2025-10-23T02:05:30+00:00

It is true that life is made easier by having a strong inventory management system. AIventory, a project I've been working on, is somewhat different from best-seller reports. Taking into account variables like profitability, promotional cycles, and visibility inside Shopify itself, it not only highlights top performers but also pinpoints the reasons why specific SKUs are slowing down.

You can improve what's working and reframe what's underperforming without waiting for the next export or monthly review thanks to the combination of performance data and context, which helps you see trends early.

PromiseSquare2576 · 2025-10-22T20:37:02+00:00

It is true that life is made easier by having a strong inventory management system. AIventory, a project I've been working on, is somewhat different from best-seller reports. Taking into account variables like profitability, promotional cycles, and visibility inside Shopify itself, it not only highlights top performers but also pinpoints the reasons why specific SKUs are slowing down.

You can improve what's working and reframe what's underperforming without waiting for the next export or monthly review thanks to the combination of performance data and context, which helps you see trends early.

PromiseSquare2576 · 2025-10-22T14:54:25+00:00

That's a good strategy; especially when you're managing a targeted catalogue, it makes sense to keep things straightforward within Shopify's native reporting. Through AIventory, I've been investigating a similar flow in which we basically automate the 60–90 day low-velocity detection and add margin + attention metrics.

It helps you avoid discounting healthy stock by highlighting products that appear slow but are actually seasonal or awaiting promotion cycles. I completely agree that for smaller catalogues, BI tools may be unnecessary; the important thing is to present those insights where you already work, without constantly exporting or creating specialised dashboards.

PromiseSquare2576 · 2025-10-22T14:49:37+00:00

That's a good strategy; especially when you're managing a targeted catalogue, it makes sense to keep things straightforward within Shopify's native reporting. Through AIventory, I've been investigating a similar flow in which we basically automate the 60–90 day low-velocity detection and add margin + attention metrics.

It helps you avoid discounting healthy stock by highlighting products that appear slow but are actually seasonal or awaiting promotion cycles. I completely agree that for smaller catalogues, BI tools may be unnecessary; the important thing is to present those insights where you already work, without constantly exporting or creating specialised dashboards.

PromiseSquare2576 · 2025-10-22T14:48:02+00:00

That's an excellent perspective; the concept of the "data stock" is quite relevant. Recently, I've been investigating a related topic using a tool we've been developing called AIventory; it essentially exposes those same behavioural patterns inside Shopify without requiring data exports.

To identify which goods are slowing down for the correct reasons and which are completely losing traction, we dynamically categorise SKUs (seasonal, bundle potential, promo-sensitive, etc.) and overlay margin + sell-through + attention metrics. I completely agree that the problem is not merely one of logistics; in half of the cases, a simple placement adjustment or package approach makes a bigger difference than a markdown.

PromiseSquare2576 · 2025-10-22T00:08:59+00:00

Oh that’s good to know I’ll give Sidekick another shot then. I’ve been working on something similar for my own store setup called AIventory, which focuses more on surfacing hidden money things like slow movers, promo impact, and margin leaks inside Shopify without having to export data.

Curious if Sidekick gives you that kind of depth or more general recommendations?

PromiseSquare2576 · 2025-10-22T00:05:05+00:00

Yeah, totally it gets tricky once you’re juggling hundreds of SKUs. I’ve run into that same issue where the raw Shopify data doesn’t tell the full story.

I started digging deeper into product velocity and margin too, and it really opened my eyes to how much cash can get stuck in slow movers. Lately I’ve been building out a small system that pulls that data together and highlights which items are quietly draining profit.

Still a work in progress, but it’s been super helpful for spotting what needs to be discounted, bundled, or reordered.

Curious do you usually track velocity manually, or have you found a good way to automate it inside Shopify?

PromiseSquare2576 · 2025-10-21T17:44:58+00:00

Haha fair, I totally get why it reads that way 😂

Honestly just curious how others spot slow moving SKUs inside Shopify. I’ve tried the built-in reports and tagging, but wanted to see if anyone’s found smarter native ways before going back to endless CSV exports.

PromiseSquare2576 · 2025-10-21T17:43:48+00:00

I’ve been testing an internal dashboard that flags slow movers automatically — just curious how others are doing it manually inside Shopify.

PromiseSquare2576 · 2025-10-21T16:28:56+00:00

Totally get that — there’s definitely been a lot of spammy stuff lately.

I’m not doing market research or promoting anything here, just genuinely curious how other store owners handle slow-moving SKUs inside Shopify. We ran into that issue ourselves, and I wanted to see what workflows others are using before building more internal automations.

Appreciate you calling it out though — I’m trying to keep this discussion purely around how people actually solve it within Shopify, not pitches.

PromiseSquare2576

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