Most inventory guides explain what ABC analysis is. This one shows you how to actually run it on your Shopify data — the formula, a full worked example with 20 products, exactly what to do differently for A, B, and C items, and how it changes your reorder point calculations.
ABC analysis is an inventory categorisation method based on one simple idea: not all products deserve equal attention. In most Shopify stores, roughly 20% of SKUs generate around 70% of total revenue. The remaining 80% of SKUs split the other 30%.
ABC analysis makes this split visible and actionable. It divides your catalog into three tiers:
The value is not in knowing the categories. It is in applying different management rules to each one — different safety stock levels, different reorder frequencies, different monitoring intensity. That is what this guide covers.
ABC analysis uses cumulative revenue percentage. Here is the exact formula:
This is a real ABC analysis calculation for a fictional clothing store with 20 products. Follow the cumulative % column to see exactly where each tier boundary falls.
| # | Product | Revenue | Cumulative | Cumulative % | Tier |
|---|---|---|---|---|---|
| 1 | Black Oversized Tee | $18,400 | $18,400 | 23.0% | A |
| 2 | White Linen Dress | $14,200 | $32,600 | 40.8% | A |
| 3 | Grey Hoodie | $11,800 | $44,400 | 55.5% | A |
| 4 | Navy Chinos | $8,900 | $53,300 | 66.6% | A |
| 5 | Stripe Shirt | $6,200 | $59,500 | 74.4% | A |
| 6 | Floral Blouse | $4,100 | $63,600 | 79.5% | B |
| 7 | Denim Jacket | $3,800 | $67,400 | 84.3% | B |
| 8 | Cargo Shorts | $2,900 | $70,300 | 87.9% | B |
| 9 | Printed Scarf | $2,200 | $72,500 | 90.6% | B |
| 10 | Beanie Hat | $1,800 | $74,300 | 92.9% | C |
| 11 | Belt (Canvas) | $1,400 | $75,700 | 94.6% | C |
| 12 | Novelty Socks 3pk | $1,100 | $76,800 | 96.0% | C |
| 13 | Logo Tote Bag | $900 | $77,700 | 97.1% | C |
| 14 | Retro Cap | $700 | $78,400 | 98.0% | C |
| 15 | Enamel Pin Set | $400 | $78,800 | 98.5% | C |
| 16 | Pocket Square | $320 | $79,120 | 99.0% | C |
| 17 | Hair Clip Set | $280 | $79,400 | 99.2% | C |
| 18 | Luggage Tag | $240 | $79,640 | 99.6% | C |
| 19 | Keyring | $180 | $79,820 | 99.8% | C |
| 20 | Sticker Sheet | $180 | $80,000 | 100.0% | C |
| Total | $80,000 | ||||
This is what most ABC analysis guides skip. Knowing your tiers is useless without changing how you manage each one. Here is the specific action set per tier.
The most important practical application of ABC analysis is adjusting your reorder point safety stock multiplier per tier. The standard reorder point formula is:
The safety stock amount is where ABC tier changes things. Using the same example (6 units/day, 14-day lead time, lead time demand = 84 units):
Two methods — manual via spreadsheet, or automated via an inventory app. Manual is worth doing once so you understand the process. At scale, automation is the only sustainable approach.
In Shopify Admin, go to Analytics → Reports → Sales by product. Set the date range to the last 90 days (or 12 months for seasonal stores). Export as CSV.
In your spreadsheet, create a column for total revenue per product (units sold × average selling price, or use the revenue column directly from the export). One row per product.
Sort your product list descending by revenue. Your A-items will rise to the top.
Add a running total column: each row adds the current product revenue to all previous rows. Then divide each cumulative total by the grand total to get cumulative %. This is the key column.
A = products where cumulative % is 0-70%. B = products where cumulative % is 70-90%. C = products where cumulative % is 90-100%. The first ~20% of your SKU count will land in A. The rest fill B and C.
Update your reorder points, safety stock levels, and monitoring frequency based on the tier each product landed in. See the action section above for exactly what to change per tier.
ABC categories are not permanent. Products move between tiers as sales velocity changes, trends shift, and seasons cycle. Running the analysis once and treating the results as fixed is one of the most common ABC analysis mistakes.
Run a fresh ABC analysis every 90 days regardless of what has changed. This catches slow drifts — a product losing momentum month by month that you would not notice otherwise.
Before a seasonal buy or a large purchase order, always run ABC first. The analysis tells you which products deserve bigger bets and which ones to order conservatively.
A product that goes viral can move from C to A in a week. Run ABC immediately after any significant demand spike so your reorder points update to match the new velocity.
When you remove C-items from your catalog, the remaining products redistribute across the tiers. Products that were low-B-items may now become A-items. Recalculate after major catalog changes.
ABC analysis does not work in isolation — it is the foundation that makes every other inventory practice more effective.
ABC analysis is an inventory categorisation method that divides your products into three tiers based on their revenue contribution. A-items are your top ~20% of SKUs driving ~70% of revenue — your most important products. B-items are the middle tier. C-items are the long tail — many SKUs, little revenue. The method is based on the Pareto principle: a small number of products drive most of the value.
Shopify Admin does not run ABC analysis automatically in its standard reports. To do it manually: export your Sales by Product report from Analytics, calculate cumulative revenue percentage per product in a spreadsheet (sorted highest to lowest), then assign A (0-70%), B (70-90%), and C (90-100%) based on where each product's cumulative % falls. For automated ABC analysis that recalculates from live data, you need a third-party inventory app like Debnix.
The standard ABC breakdown is: A-items = products accounting for the top 70% of cumulative revenue (typically ~20% of your SKUs). B-items = products from 70-90% of cumulative revenue (typically ~30% of SKUs). C-items = products from 90-100% of cumulative revenue (typically ~50% of SKUs). These percentages are guidelines, not rules — your actual store data may produce different splits.
Quarterly is the standard. Some stores with fast-moving seasonal catalogs run it monthly. The key is to recategorise before major buying decisions — before a seasonal buy, before a large reorder, or any time you notice a product's sales velocity has changed significantly. A product can move from C to A after a viral mention and from A to C after a competitor launches a better version.
Yes — and this is exactly why regular recalculation matters. A new product with high early sales is often an A-item in its launch period but may settle into B or C as the novelty fades. A seasonal product may be A in summer and C in winter. Trend-driven products can spike from C to A overnight. Static ABC categories become misleading fast.
Not automatically. C-items often serve a purpose: they complete a range, they are bought alongside A-items as add-ons, or they have loyal niche customers. The question is whether each C-item justifies its inventory investment and shelf space. Review your bottom 10-20% of C-items quarterly and discontinue the ones with very low revenue and no strategic purpose. Run them down to zero stock before discontinuing rather than writing off remaining inventory.
Debnix runs ABC analysis automatically from your live Shopify data. See your A, B, and C items instantly — combined with reorder points, dead stock detection, and profit tracking. $21.99/mo, 30-day free trial.
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