What Is ABC Inventory Analysis?
ABC analysis comes from a much older idea in economics called the Pareto principle, or the 80/20 rule: in many systems, roughly 80% of outcomes come from roughly 20% of causes. Applied to a product catalog, this usually means a small share of SKUs generates a large share of revenue, while a long tail of products each contributes very little on its own.
The point of classifying products isn't to declare some SKUs unimportant — it's to allocate limited attention efficiently. A merchant with 40 products and a merchant with 4,000 products both have the same number of hours in a day. Reviewing every SKU with equal frequency doesn't scale, and it isn't necessary.
ABC analysis solves this by sorting products into three groups:
| Category | Typical Contribution | Business Importance | Management Approach |
|---|---|---|---|
| A Products | High | Critical | Frequent monitoring |
| B Products | Medium | Important | Regular review |
| C Products | Low | Lower individual impact | Efficient management |
The exact percentage split (commonly cited as roughly 70–80% of revenue from A products, 15–20% from B, and the remainder from C) varies significantly by business, category, and catalog size. These figures are a starting reference point, not a rule to force onto every store.
How Does ABC Analysis Work for a Shopify Store?
The process is the same regardless of store size — only the tooling changes as catalogs grow. In order:
Collect product sales data — typically revenue or units sold over a defined period (30, 90, or 365 days).
Calculate each product's contribution — its revenue as a percentage of total store revenue for that period.
Rank products from highest to lowest contribution.
Calculate cumulative contribution — running total of contribution percentage as you move down the ranked list.
Divide products into A, B, and C based on where cumulative contribution crosses defined thresholds.
Review inventory and replenishment strategy for each tier based on its classification.
Worked Example
Consider a small Shopify store selling five products over a 30-day period:
| Product | Revenue | % of Total Revenue | Cumulative % | Classification |
|---|---|---|---|---|
| Product A | $8,000 | 53.3% | 53.3% | Tier A |
| Product B | $4,000 | 26.7% | 80.0% | Tier A |
| Product C | $1,500 | 10.0% | 90.0% | Tier B |
| Product D | $1,000 | 6.7% | 96.7% | Tier B |
| Product E | $500 | 3.3% | 100.0% | Tier C |
Total revenue: $15,000. Using a common threshold — A up to ~80% cumulative, B up to ~95%, C beyond that — Products A and B fall into tier A, Products C and D into tier B, and Product E into tier C. Two SKUs out of five generate 80% of revenue, which is a fairly typical A-tier concentration, though the exact ratio always depends on the store.
How to Perform ABC Analysis in Shopify
Method 1: Manual Spreadsheet Analysis
This is the standard starting point for smaller catalogs:
- Export product or order data from Shopify admin (Analytics → Reports, or a sales-by-product export)
- Load the export into Excel or Google Sheets
- Sort products by revenue (or units sold) in descending order
- Add a column calculating each product's percentage of total revenue:
=Product Revenue / SUM(All Revenue) - Add a running total column for cumulative percentage
- Assign A, B, or C based on where each product's cumulative percentage falls relative to your chosen thresholds
This works well up to a few hundred SKUs. Beyond that, manual re-classification on any regular cadence becomes a meaningful time cost, and it's easy for the analysis to go stale between updates.
Method 2: Using Shopify Analytics
Shopify's built-in reporting can show sales by product, which is the raw input ABC analysis needs. It does not natively output ABC classifications, cumulative contribution percentages, or tier labels — that layer of calculation has to be added by the merchant, typically by exporting the underlying sales data into a spreadsheet or another tool.
Method 3: Using an Inventory Analytics Platform
As catalogs grow past a few hundred SKUs, span multiple locations, or change frequently, manual spreadsheet analysis becomes harder to maintain. Dedicated inventory analytics tools automate the ranking and classification step and keep it current as new sales data comes in, which matters most for stores with:
- Hundreds or thousands of SKUs
- Multiple stock locations
- Frequent inventory changes
- Replenishment decisions that need to happen faster than a manual re-export cycle allows
Why Revenue Alone Is Not Enough
Basic ABC analysis run on revenue alone can be misleading if it's the only signal used to make inventory decisions. Two products can generate identical revenue and require completely different inventory treatment.
Consider two hypothetical products, each generating $10,000 in monthly revenue:
Product X
1,000 units/month at $10 each · 15% margin · 4-day supplier lead time · low demand volatility. Needs tight, fast replenishment — stockouts affect more transactions.
Product Y
20 units/month at $500 each · 40% margin · 90-day supplier lead time · high demand volatility. Carries more stockout risk and generates more profit per unit.
Both land in the same revenue-based A tier. Revenue alone doesn't surface this difference. A more complete view of product importance typically weighs several signals together:
- Sales velocity — how fast the product sells
- Units sold — total volume, separate from revenue
- Revenue contribution — the classic ABC input
- Profit margin — revenue doesn't equal profitability
- Inventory value — capital tied up in stock on hand
- Days of stock remaining — how close to a stockout
- Lead time — how long replenishment takes (see supplier management)
- Supplier reliability — consistency of fulfillment
- Seasonality — whether current demand reflects a typical pattern
- Demand volatility — how predictable future demand is
ABC analysis is a useful starting filter, not a complete decision framework on its own.
ABC Analysis vs. Other Product Analysis Methods
| Method | What It Measures | Best Use Case | Limitation |
|---|---|---|---|
| ABC Analysis | Revenue/sales contribution | Prioritizing monitoring effort across a catalog | Ignores margin, volatility, and lead time |
| XYZ Analysis | Demand variability/predictability | Deciding safety stock levels | Doesn't account for revenue impact on its own |
| FSN Analysis | Movement frequency (Fast/Slow/Non-moving) | Identifying dead or slow stock | Doesn't distinguish why a product is slow-moving |
| Sales Velocity Analysis | Units sold per time period | Setting reorder timing | Can overweight cheap, high-volume items |
| Profitability Analysis | Margin and contribution to profit | Deciding where to invest marketing/stock capital | Can undervalue loss-leader or bundle products |
| Inventory Turnover Analysis | How often stock is sold and replaced | Identifying overstock or capital inefficiency | Doesn't explain root cause of low turnover |
None of these methods is complete in isolation. A common approach is combining ABC with XYZ (sometimes called ABC-XYZ analysis) — classifying products by both revenue contribution and demand predictability — to get a two-dimensional view. An "A" product with volatile demand needs different safety stock than an "A" product with stable demand, even though both are top revenue drivers.
How to Use ABC Analysis to Make Better Inventory Decisions
A Products
- Monitor frequently — weekly or even daily for fast-moving A products
- Prioritize stockout prevention; the revenue and customer impact of running out is highest here (see reorder points)
- Build tighter reorder planning around actual lead times
- Give these products the most visibility in dashboards and reports
- Evaluate supplier risk carefully — a disruption here has outsized impact
B Products
- Monitor for movement — B products can shift into A (growing demand) or C (declining demand) over time
- Watch for emerging trends that suggest a product is gaining momentum
- Apply a moderate, not maximal, level of review frequency
C Products
- Avoid unnecessary overstock — tying up capital in slow contributors is a common inefficiency
- Simplify replenishment (larger batch orders, less frequent review)
- Consider bundling C products with A or B products to move inventory
- Periodically evaluate for rationalization — but don't assume low revenue means low strategic value
Low revenue does not automatically mean a product is unimportant. A C-tier product might be a loss-leader that drives traffic, a complementary item that supports A-product sales, or a newly launched SKU that hasn't ramped up yet. Classification should inform decisions, not replace judgment about a product's role in the catalog.
ABC Analysis for Multi-Location Shopify Stores
Store-wide ABC classification can hide meaningful differences at the location level. A product can be:
- Category A globally — a top revenue driver across the whole business
- Category B in one location — solid but not dominant demand in a specific region
- Category C in another location — barely selling in a location where regional preferences differ
This matters because inventory allocation decisions happen at the location level, not just the store level. A product that's globally "A" but weak in a specific warehouse doesn't need the same stock depth there as it does at a location where it's the top seller. Running ABC analysis only at the store-wide level can lead to over-stocking a slow location and under-stocking a fast one for the same SKU.
Merchants operating multiple locations generally need to layer location-level demand and transfer patterns on top of store-wide classification, rather than relying on a single global tier to drive stocking decisions everywhere.
Common Mistakes Shopify Merchants Make
Using revenue only
Ignores margin and inventory cost; a high-revenue, low-margin product may deserve less priority than a lower-revenue, high-margin one. Combine revenue with profitability signals.
Ignoring profit margins
A product can be revenue-A and margin-C. Decisions based on revenue alone can misallocate capital toward low-profit SKUs.
Never updating classifications
Product performance shifts with seasonality, trends, and lifecycle stage. Re-run the analysis on a regular cadence — monthly or quarterly is common.
Treating classifications as permanent
A product's tier is a snapshot, not a label. Products move between tiers as demand changes.
Ignoring seasonality
A product that looks like a C in the off-season may be an A during its peak period. Consider seasonal windows separately when relevant.
Using the same reorder strategy for every product
A-tier products generally need tighter, more frequent reorder cycles than C-tier products; applying one uniform policy under- or over-serves most of the catalog.
Ignoring multi-location demand
Store-wide classification can mask meaningful location-level differences. A product that is globally A may be C at a specific location with weak regional demand.
Focusing only on top sellers
Overweighting A products while ignoring B and C tiers can miss both overstock risk in C and rising stars still classified as B.
Overstocking slow-moving inventory
Treating C products with the same order quantities as A products ties up capital and warehouse space unnecessarily.
Making decisions from raw spreadsheets without inventory context
Revenue rank alone, without stock levels, lead times, or reorder points layered in, produces an incomplete picture for actual purchasing decisions.
How Supremo Helps Shopify Merchants Turn Product Data Into Inventory Decisions
As a Shopify store grows, manually sorting products and comparing inventory signals becomes increasingly difficult. The goal is not simply to generate another report — it is to help merchants understand which products require attention and why.
Supremo is a Shopify inventory management app built to help merchants move from raw product and sales data toward actionable inventory decisions. An inventory-focused platform can help with:
- Ongoing product and inventory visibility as the catalog and sales data change
- Surfacing which products need attention, rather than requiring a merchant to re-run a manual analysis
- Supporting reordering decisions based on stock levels and sales patterns
- Managing inventory across multiple locations, where store-wide averages can obscure what's actually happening at each site
Example: A Shopify Store Using ABC Analysis
Illustrative Example — Northfield Goods
Starting point: 120 active SKUs across five categories (kitchenware, textiles, lighting, storage, decor), with no formal prioritization — every product reviewed on the same monthly cycle regardless of performance.
Product performance data: A 90-day sales export shows wide variation — the top 18 SKUs (15% of the catalog) account for roughly 78% of revenue, while the bottom 40 SKUs each contribute less than 0.5% individually.
ABC classification: Applying cumulative contribution thresholds, Northfield classifies 18 SKUs as A, 34 as B, and 68 as C.
Inventory insights: Cross-referencing classification with stock data reveals that three A-tier SKUs are within a week of stocking out, while several C-tier SKUs are sitting on six months of inventory relative to their sales pace.
Replenishment priorities: Northfield reorders the at-risk A-tier products immediately, sets tighter reorder points for the rest of the A tier, and pauses reordering on the overstocked C-tier items until existing stock depletes further.
Better operational decisions: Going forward, Northfield reviews A-tier stock weekly, B-tier monthly, and C-tier quarterly — replacing the uniform monthly review cycle that treated all 120 SKUs the same way.
Advanced: From ABC Analysis to Inventory Intelligence
ABC classification is one layer of a broader inventory picture, not a complete decision system by itself. A more complete framework — sometimes described as moving from product classification to inventory intelligence — combines several dimensions:
- Product importance (ABC classification)
- Sales velocity (how fast a product is currently moving — see inventory forecasting)
- Stock levels (what's actually on hand right now)
- Reorder points (the stock level that should trigger a new order — see what is a reorder point)
- Lead times (how long replenishment actually takes per supplier)
- Forecasted demand (where sales are trending, not just where they've been)
- Location-level inventory (how stock and demand differ across warehouses or stores)
- Overstock risk (capital tied up in excess inventory)
- Stockout risk (probability of running out before replenishment arrives)
Each of these answers a different question. ABC classification answers "how important is this product to the business?" Reorder points and lead times answer "when do I need to act?" Location-level data answers "where does this decision need to happen?" Layering these together gives a more complete picture than any single method alone.
Frequently Asked Questions
What is ABC analysis in Shopify?
ABC analysis is a method of classifying Shopify products into three tiers — A, B, and C — based on their contribution to revenue or sales, so merchants can prioritize inventory attention toward the products that matter most to the business.
How do I perform ABC analysis for Shopify products?
Export sales data by product, calculate each product's percentage of total revenue, rank products from highest to lowest, calculate cumulative percentage contribution, and assign A, B, or C based on defined cumulative thresholds.
What are A, B, and C products?
A products are high-contribution SKUs that warrant the closest monitoring; B products are moderate contributors reviewed regularly; C products are low individual contributors managed more efficiently, often with simplified reorder cycles.
Is ABC analysis based on revenue or units sold?
It's most commonly based on revenue contribution, but it can also be run on units sold, profit contribution, or a combination of signals depending on what the business wants to prioritize.
How often should I update ABC classifications?
There's no universal rule, but monthly or quarterly re-analysis is common. Faster-moving catalogs or highly seasonal businesses may benefit from more frequent updates.
Can a product move from C to A?
Yes. Classification reflects a snapshot of recent performance, not a permanent label. Products commonly move between tiers as demand, seasonality, or marketing focus changes.
Does Shopify have ABC analysis built in?
Shopify's native analytics provide the underlying sales data (revenue and units by product) but do not natively calculate ABC classifications or cumulative contribution percentages — that calculation has to be added separately, whether manually or through another tool.
How is ABC analysis different from inventory turnover analysis?
ABC analysis measures a product's contribution to revenue or sales; inventory turnover measures how often stock is sold and replaced over a period. A product can be high-revenue (A) but still have poor turnover if too much stock is held relative to its sales pace.
Can ABC analysis help prevent stockouts?
Indirectly. By identifying which products deserve the closest monitoring, ABC analysis helps merchants focus stockout-prevention effort on the SKUs where a stockout would have the largest business impact — but it needs to be paired with stock-level and lead-time data to actually prevent one.
How does ABC analysis work for multi-location inventory?
A product's classification can differ by location — a SKU might be A store-wide but only B or C at a specific location with weaker regional demand. Location-level analysis is needed alongside store-wide classification to make accurate stocking decisions per site.
Should I stop selling C-category products?
Not automatically. Low revenue contribution doesn't necessarily mean a product is strategically unimportant — it may support other sales, serve a loyal niche, or be newly launched. C-tier classification should prompt a review, not an automatic decision to discontinue.
What is the best way to analyze thousands of Shopify products?
Spreadsheet-based ABC analysis becomes difficult to maintain past a few hundred SKUs, especially with frequent inventory changes or multiple locations. At that scale, a dedicated inventory analytics tool that automates ranking and keeps classifications current is generally more practical than manual re-exports.