.d53b12e5.png&w=3840&q=75 "Trivas.ai")
by Trivas.aiA brand can grow 40% year over year and still be losing money on half its catalog. It happens more than founders want to admit. Revenue is easy to see. It shows up on the Shopify dashboard every morning, it goes in the board deck, it makes everyone feel good. Margin is harder to see, especially per product, which is exactly why it gets ignored until cash gets tight.
A product profitability analysis for ecommerce is the practice of figuring out what each SKU actually earns after every direct and allocated cost, not just COGS subtracted from retail price. That distinction matters. Plenty of brands know their gross margin looks fine on paper, then wonder why the bank balance doesn't match the P&L.
This becomes urgent at a specific moment: once a brand moves past early scrappiness and starts scaling ad spend, adding SKUs, and selling across more than one channel. At $50k a month, you can eyeball profitability. At $2M a month across Shopify, Amazon, and a few marketplaces, you can't. The spreadsheet that worked at 30 SKUs falls apart at 300.
This guide covers the full cost stack you need before calling anything "profit," the metrics that actually matter, the mistakes that quietly skew the numbers, a step-by-step process for running the analysis, and how to turn the output into pricing, bundling, and assortment decisions.
Most profitability math starts and stops at COGS. That's the first mistake. Here's what actually belongs in the stack.
Direct costs. Unit COGS, obviously. But also inbound freight (which fluctuates more than most brands track) and duties or tariffs, which can quietly add 10-25% to landed cost depending on category and country of origin.
Marketplace and platform fees. Amazon referral fees (typically 8-15% depending on category) and FBA fulfillment fees. Shopify payment processing. 3PL pick, pack, and storage fees, which scale with order volume and SKU velocity in ways that are easy to underestimate.
Variable costs people forget. Return and refund rates per SKU, not blended across the catalog. A hoodie with sizing issues might return at 18% while your bestselling accessory returns at 2%. Chargebacks. Discount codes and promo redemptions, which rarely get mapped back to the specific products they were used on.
Allocated marketing cost. This is the one most brands get wrong. Meta, Google, and Amazon Ads spend usually sits as one blended company-wide number. But if you're running category-specific campaigns or product-targeted ads, that spend needs to be attributed down to the SKU or category level. Otherwise you have no idea if a product is profitable or just riding on the halo of your best-performing ad set.
Cash basis vs accrual. Cash tracking tells you what hit the bank this week. Accrual tells you what a product actually cost and earned in the period it was sold, even if the invoice or payout landed later. Ecommerce brands need both: cash for runway, accrual for real per-SKU margin. Mixing the two is how "we're profitable" turns into a surprise cash crunch two months later.
Getting this cost stack mapped correctly is most of the work. Once it's right, the math downstream is simple.
Contribution margin per unit. Revenue minus all variable costs (COGS, fees, shipping, returns, allocated marketing). This is the core number for any SKU-level decision. If contribution margin is negative, nothing else about that product matters.
Gross margin percentage per SKU vs blended store-wide gross margin. A store-wide blended margin of 55% sounds healthy. It can still be hiding a dozen SKUs running at 8% or losing money outright. Blended numbers are built to look reassuring. That's exactly why they're dangerous for decision-making.
Margin after marketing (MAM), or true ROAS. A SKU can post a 4x ROAS and still lose money once you account for landed cost, fees, and returns. ROAS measures ad efficiency, not product profitability. These are different questions, and conflating them is one of the most common ways brands fool themselves into scaling spend on the wrong product.
CAC and LTV by product category. This matters most for brands running an intentional loss-leader or bundle strategy. A $15 starter kit that loses money on the first order but drives a $300 LTV customer is a fine trade. The same kit losing money with no repeat purchase behind it is just a leak.
Inventory carrying cost. Slow-moving SKUs tie up warehouse space and working capital. A product with a 6% gross margin advantage over another can still be the worse choice if it sits for 90 days before it sells.
Want to see how a few of these metrics interact in dashboard form? The BI reporting product is built around surfacing contribution margin and MAM at the SKU level rather than burying them in a blended summary.
A few patterns show up again and again when brands run their first real product profitability analysis.
Relying on blended AOV and blended margin. Averages flatten everything. A catalog with one breakout hero product and twenty mediocre performers will show a healthy blended average while hiding exactly which SKUs need attention.
Ignoring return rate differences. Apparel sizing issues and hardware defects don't behave the same way, and treating all returns as a flat percentage across the catalog erases the signal entirely.
Spreading marketing spend evenly across SKUs. If 70% of your ad budget is going toward three hero products, those three products need to carry 70% of the allocated ad cost, not an even split across the catalog. Even allocation makes low-spend SKUs look artificially profitable and hero SKUs look worse than they are.
Blending Amazon and Shopify into one number. Amazon's fee structure, ad auction dynamics, and fulfillment costs are fundamentally different from Shopify's. Merging them into a single blended margin number produces something that's technically a number but not actually useful for any decision.
Running the analysis quarterly instead of continuously. A lot can shift in three months: supplier costs change, a competitor cuts prices, return rates creep up on a specific SKU. By the time a quarterly review catches it, you've already absorbed the damage for 90 days.
Step 1: Centralize the data. Pull unit economics from every sales channel, Shopify, Amazon, Walmart, wherever you sell, into one place. This sounds obvious and is still where most brands get stuck, because each channel reports cost and revenue differently.
Step 2: Map every cost bucket to every SKU. COGS, fees, shipping, returns, and allocated ad spend all need to sit against the specific product they belong to, not a company-wide bucket.
Step 3: Calculate and rank. Work out contribution margin and margin-after-marketing per SKU, then rank the full catalog from most to least profitable. This ranked list is usually the first moment founders actually see which products are carrying the business.
Step 4: Segment the list. Break it down by category, channel, and price tier. Patterns show up fast this way, maybe every low-margin SKU traces back to one supplier, or every underperformer is concentrated on a single channel's ad account.
Step 5: Set a cadence. Weekly or monthly, not quarterly. This isn't a one-off spreadsheet exercise, it's an ongoing operating habit. For teams building this out, the forecasting and simulation product can project how margin shifts play out before you commit to a pricing or sourcing change.
The analysis itself isn't the point. What you do with it is.
Repricing and renegotiating. Once true landed cost is visible, it's often obvious which SKUs need a price increase or a harder supplier conversation. Brands routinely discover they've been underpricing a product for a year simply because nobody had the full cost stack in view.
Bundling for blended lift. Pairing a high-margin product with a low-margin one can raise blended profitability on an order without hurting conversion, especially when the low-margin item is something customers already expect to buy alongside the hero product.
Cutting ad spend on margin-negative winners. This is the uncomfortable one. A SKU with great ROAS and negative margin-after-marketing is a product you're paying to lose money on faster. Cutting or reducing spend there, even if it stings on the top line, usually improves cash position within a month.
Deciding what to discontinue. Not every unprofitable SKU should die. Some earn their keep through assortment breadth or as an entry point for new customers. But chronically unprofitable SKUs with no acquisition value are just quietly draining cash, and the product profitability analysis is what tells you which is which.
Spreadsheets work fine at 20 SKUs and one channel. They stop working somewhere around the point where you're pulling cost data from a 3PL invoice, fee reports from two marketplaces, and ad spend from three platforms, then trying to reconcile all of it by hand every month.
The core problem is that the data lives in separate systems that were never built to talk to each other. Ad platforms report spend by campaign, not by SKU. 3PLs invoice by order volume, not by product margin impact. Marketplace fee reports come as flat exports that need manual mapping back to your catalog. Doing this by hand once is tedious. Doing it every month, accurately, as the catalog grows, is close to impossible without something breaking.
Centralizing that data in a warehouse like Redshift changes the timeline entirely: profitability calculations that used to take a finance lead three days at month-end can update daily instead. Layer an AI model on top of that centralized data and it can flag a margin drop on a specific SKU the week it happens, not the quarter after it's already cost you five figures.
The shift that matters here is simple to describe and hard to execute: moving from blended, backward-looking profit reports to per-SKU, near-real-time margin visibility. Most brands know they should be doing this. Few actually have the infrastructure to do it without a finance team burning a week every month on spreadsheets.
Start small if the full catalog feels overwhelming. Pick one channel or one product category, build the cost stack properly, and get comfortable with the metrics before expanding it company-wide.
If you want to see how this looks with your own catalog and cost data instead of a hypothetical, founders and growth leads can get a closer look here.
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