Retail & CX metrics glossary
The ABC curve ranks what sells. Here is how to build it — and what it structurally cannot see.
ABC analysis classifies whatever you sell — SKUs, categories, suppliers, even customers — by how much each one contributes to a total, usually revenue. It borrows the Pareto principle: in most retail assortments, roughly 20% of the items concentrate about 80% of the value. Ranking everything by contribution and cutting the ranked list into classes A, B and C turns a flat product list into a map of where the money actually is.
The ABC curve is the visual form of that ranking: items ordered from largest to smallest contribution, with the accumulated share of revenue climbing steeply at first and then flattening into a long tail. It is one of the oldest tools in retail management precisely because it directs scarce resources — buying budget, shelf space, replenishment discipline, seller attention — toward the few items that move the result. This page covers how to build the curve step by step, the classic cut-offs per class, a working calculator, and the structural blind spot the curve inherits from its own data.
ABC analysis (ABC curve)
Class A ≈ 80% of value · B ≈ 15% · C ≈ 5%
Class A = the top-ranked items — typically 10–20% of items concentrating 70–80% of value · Class B = the intermediate block — typically 20–30% of items adding 15–20% of value · Class C = the long tail — typically 50–70% of items accounting for only 5–10% of value
Revenue share calculator
A fashion retailer ranks its 400 SKUs by last quarter's revenue. The top 72 SKUs — 18% of the assortment — accumulate 79% of revenue: class A. The next 96 SKUs add another 16%: class B. The remaining 232 SKUs, 58% of the catalog, account for just 5%: class C. One SKU that sold $38,000 out of $950,000 total holds a share of (38,000 ÷ 950,000) × 100 = 4.0% — a heavyweight class A item on its own. From that ranking, the buyer tightens service levels on the 72 A items and reviews the C tail for discontinuation candidates.
Classic ABC cut-offs by class
The exact thresholds are a management convention, not a law of nature. The cuts most commonly used in retail and inventory management:
| Class A | 70–80% of value · 10–20% of items |
|---|---|
| Class B | 15–20% of value · 20–30% of items |
| Class C | 5–10% of value · 50–70% of items |
Treat these cut-offs as a starting convention — the useful thresholds are the ones that create classes your team will actually treat differently. Keep them stable over time so classifications stay comparable from one period to the next.
How to build an ABC curve, step by step
Step 1 — choose the unit and the value. Decide what you are classifying (SKUs, categories, suppliers, customers) and by which value (revenue is the default; contribution margin or units sold answer different questions). Pick a representative period — a quarter smooths out promotions and stockouts better than a single month.
Step 2 — rank and accumulate. Sort every item from largest to smallest value, compute each item's share of the total, then compute the running accumulated share down the list. The accumulated column is the curve itself: it typically races to 80% within the first fifth of the items and then crawls through the tail.
Step 3 — cut the classes. Draw the class boundaries where the accumulated share crosses your thresholds — classically ~80% for the A/B cut and ~95% for the B/C cut. Then revisit the classification every quarter or season: assortments rotate, and yesterday's A item can quietly slide into B while a rising C item earns promotion.
What retailers use the ABC curve for
Inventory and replenishment: class A items get the tightest service levels, the most safety stock attention and the most frequent counts — a stockout on an A item costs more revenue than the entire C tail sells in a week. Class C items get simpler rules: larger review intervals, minimum-order batches, or active pruning of the assortment.
Buying and negotiation: the curve tells the buying team where negotiation effort pays. A 2-point cost improvement on class A moves the company's margin; the same effort on a C supplier barely registers. Many chains run a supplier-level ABC exactly for this reason.
Attention on the sales floor — and beyond products: the same logic applies to customers (an ABC of clients by revenue or lifetime value shows which relationships deserve dedicated care) and to sales effort, where A items deserve prime display space, guaranteed availability and seller fluency. That last use is where the tool is strongest — and, as the next section shows, where it can quietly mislead.
The blind spot: the curve shows what sells, not what would sell
An ABC classification is built from sales history, and sales history records what the store actually offered, displayed and recommended — not what shoppers would have bought if the conversation had gone differently. A high-margin accessory that sellers never mention will post weak revenue, land in class C, lose shelf space and attention because it is class C, and sell even less. The classification becomes self-fulfilling.
This matters most for attachment and margin items: warranties, services, complementary products, premium variants. Their natural sales channel is the in-person conversation, so their position on the curve reflects seller behavior as much as customer demand. Before pruning the C tail, it is worth asking a different question: which of these items were ever actually offered?
The practical fix is to pair the curve with interaction-level data. Sales history says what left the store; only the conversation says what was proposed, what was asked for and unavailable, and what never came up at all. A C item that is offered and refused is a fair discontinuation candidate. A C item that never enters the conversation is an untested hypothesis.
The curve shows what sells. We show what never enters the conversation.
Cognifyze captures the in-person sales interaction itself — with consent, without identifying any individual shopper — and measures what the ABC curve structurally cannot: which products are actually offered, which are asked about and missing, and which high-margin C items never enter the conversation at all. The curve ranks the past; the conversation on the floor decides the next ranking.
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.
Find out which products your team never offers — book an executive diagnostic.
Related metrics and guides
ABC analysis — frequently asked questions
What is ABC analysis?
A classification method that ranks items — SKUs, categories, suppliers or customers — by their contribution to a total value, usually revenue, and cuts the ranking into classes: A for the few items that concentrate most of the value, B for the intermediate block, C for the long tail. It applies the Pareto principle to direct management attention where it pays most.
What are the classic ABC cut-offs?
The most common convention: class A holds 70–80% of the value with 10–20% of the items; class B adds 15–20% with 20–30% of the items; class C covers the remaining 5–10% of value spread across 50–70% of the items. The thresholds are a convention — adjust them to create classes your operation can treat differently.
Should I build the ABC curve on revenue or on margin?
Build both if you can. The revenue curve shows where volume and cash flow live; the margin curve shows where profit lives — and the two rankings often disagree. An item can be class A in revenue and mediocre in margin, or the reverse. Buying and pricing decisions usually deserve the margin view; replenishment and availability decisions, the revenue view.
How often should the ABC classification be updated?
Quarterly is a solid default for most retail assortments; seasonal businesses should re-cut at each season change. Updating too often makes classes unstable and erodes the operational routines attached to them; updating too rarely leaves the store managing last year's assortment. Whatever the cadence, keep the thresholds fixed so movements between classes are real signals.
Can a C item become an A item?
Yes — and that is the curve's most underused insight. Some C items are genuinely weak demand, but others are simply never offered: they lack display space, seller fluency or availability precisely because they are class C. Before discontinuing the tail, test whether the item enters the sales conversation at all; an item that is never proposed has never actually been tested by the market.