Templates & tools
The ABC curve: your revenue is more concentrated than you think.
In almost every store, a small fraction of SKUs pays the rent: typically around 20% of items generate close to 80% of revenue. The ABC curve makes that concentration visible so you can decide with it — which items can never stock out, which deserve a monthly review, and which are quietly eating shelf space and working capital. You don't need software for this: a spreadsheet, one export from your POS and four formulas are enough.
This page has the whole method: a worked example already classified, the exact Excel and Google Sheets formulas, the cutoffs, and what to do with each class. If you want the print-ready version to share with whoever runs your inventory, we'll email you the PDF. Either way, run it on your own numbers this week — the first curve is always a surprise.
Worked example — 10 rows, already classified
90-day sales by SKU, sorted by revenue (total: 200,000)
Cutoffs: A = items up to 80% of cumulative revenue; B = 80–95%; C = above 95%. In this example, 6 SKUs deliver 80% of revenue and the 40-SKU long tail delivers the last 5% — that concentration is exactly what the curve exists to show.
| # | SKU / Item | Revenue | % of total | Cumulative % | Class |
|---|---|---|---|---|---|
| 1 | Runner X sneaker | 42,000 | 21% | 21% | A |
| 2 | Trail jacket | 36,000 | 18% | 39% | A |
| 3 | Urban backpack | 28,000 | 14% | 53% | A |
| 4 | Training legging | 24,000 | 12% | 65% | A |
| 5 | Performance tee | 18,000 | 9% | 74% | A |
| 6 | Running socks (3-pack) | 12,000 | 6% | 80% | A |
| 7 | Sports cap | 11,000 | 5.5% | 85.5% | B |
| 8 | Thermal bottle | 10,000 | 5% | 90.5% | B |
| 9 | Wristband kit | 9,000 | 4.5% | 95% | B |
| 10 | Remaining 40 SKUs (long tail) | 10,000 | 5% | 100% | C |
Get the guide as a print-ready PDF
The worked example, the formulas and the cutoffs — formatted to print and share with whoever runs your inventory.
How to build it, step by step
- Export sales by SKU for the last 90 days from your POS or ERP — revenue per item, not units — and paste into Excel or Google Sheets: SKU in column B, revenue in column C.
- Sort by revenue, descending (Data → Sort by column C, largest to smallest). The curve only works on an ordered list — this step is what turns a report into a curve.
- Calculate % of total in column D: in D2, type =C2/SUM($C$2:$C$100) (adjust $C$100 to your last row), format as percentage and fill down. The same formula works in Google Sheets.
- Calculate cumulative % in column E: in E2, type =SUM($D$2:D2) and fill down — the anchored start makes each row add everything above it. The last row must read 100%; if it doesn't, the range in step 3 is wrong.
- Classify with the cutoffs in column F: =IF(E2<=80%,"A",IF(E2<=95%,"B","C")) — then act by class. A: never stock out (safety stock, daily availability check). B: review monthly — these are the candidates to rise or fall. C: shrink the assortment, renegotiate terms or discontinue.
The mistakes that ruin an ABC analysis
Running it once and never updating. The curve is a photograph, and assortments move: seasonality, launches and price changes reshuffle classes. Rebuild it quarterly — an ABC from last year is a map of a store that no longer exists.
Classifying by units instead of revenue — or ignoring margin entirely. Units inflate cheap items into false As. Revenue is the standard baseline, and the stronger variant is ABC by margin contribution: an item can be class A in revenue and class C in profit, and that difference changes what you promote.
Treating class C as junk. Some C items are strategic: the complementary product that pulls an A sale (the sock next to the sneaker), the item that brings a customer profile in, the minimum assortment your category needs. Cut C with the curve in one hand and the role of each item in the other.
The curve shows what sells. We show what never enters the conversation.
The ABC curve is a photograph of what already sold — it says nothing about why. A high-margin B item that salespeople never mention will never become an A, and no spreadsheet will ever show you that: the realized curve is decided in the sales conversation, product by product. Cognifyze measures those in-person conversations — which items get offered, demonstrated and attached — with consent and without identifying any individual shopper, so you can see the assortment your customers actually hear about, not just the one on the shelf.
In measured deployments, making the interaction visible moved same-store conversion from 51.5% to 79.5% (+28pp, p<0.001), with 383% ROI and payback in 1.4 months.
See which products your floor never offers — book an executive diagnostic.
Related tools and guides
ABC analysis — frequently asked questions
What are the ABC cutoff points?
The classic convention: class A covers items up to 80% of cumulative revenue, B covers 80–95%, C is everything above 95%. They come from the Pareto principle, not from law — some retailers use 70/90 for tighter A control. Pick one convention and keep it stable so periods stay comparable.
Should I rank by revenue, units or margin?
Revenue is the standard baseline and the right first curve. Units mislead — cheap fast-movers look like As while carrying the store nowhere. The advanced version ranks by margin contribution (units × unit margin): run it as a second curve and study the items whose class changes between the two.
How often should I redo the analysis?
Quarterly is the practical rhythm for most physical retail — enough to catch seasonality and launches without chasing noise. Redo it immediately after events that reshape the assortment: a category reset, a big price reposition or a new collection landing.
How many SKUs should end up in each class?
There is no fixed quota — the cutoffs are on cumulative revenue, not on item counts. In practice class A usually lands around 10–20% of SKUs, B around 20–30%, and C is the long majority. If half your SKUs come out as A, your assortment is unusually flat — or the ranking wasn't sorted.
Should I just discontinue everything in class C?
No. C is where you look hardest, not where you cut blindly. Separate the dead weight (no sales, no role) from strategic C: complements that pull A sales, items that serve a niche you want in the store, and minimum-assortment pieces. Kill the first group, keep the second on purpose.