Which Products Actually Deserve a Discount?

A product deserves a discount when cutting its price would bring in meaningfully more buyers, not because it's overstocked, a competitor just marked it down, or the team has a feeling about it. That's a measurable question, not a guess. The answer lives in a product's own price-and-order history, specifically how much its order volume moved the last time its price actually changed, and it's usually a smaller list than the "let's put half the catalog on sale" instinct suggests.

Why Some Products Respond to Discounts and Others Don't

The technical term for this is price elasticity: how much demand for a specific product changes when its price changes. A product is elastic when a price cut brings in a lot more buyers, usually because there are close alternatives out there and shoppers are actively comparing. A product is inelastic when a discount barely moves the needle, usually because the buyer already wanted that specific item regardless of price.

Discounting an inelastic product is close to pure margin loss, since the customers who buy it would mostly have bought it anyway at full price. Discounting an elastic product is where a sale actually earns its keep, since the lower price is what convinces new buyers to convert. Picking discount candidates without knowing which category a product falls into means some of the "sale" is really just a discount handed to customers who never needed one.

A Worked Example: Two Products, Same Discount, Different Outcomes

Say a store runs a 15% discount across two products that look similar on paper, both mid-catalog sellers with comparable order volume the month before.

Product A is a basic, replenishment-style item, the kind of thing a customer rebuys out of habit. Its elasticity is close to -0.4. A 15% price cut moves order volume up by roughly 6%, not enough extra revenue to cover the margin given up on every unit that would have sold anyway. The sale technically "worked" in that units moved, but it mostly paid existing customers to buy something they were already going to buy.

Product B sits in a crowded, comparison-heavy category with several close substitutes. Its elasticity is closer to -2.1. The same 15% cut moves volume up by roughly 30%, and the added unit volume more than covers the margin given up per unit. This is the sale that was worth running.

Same discount, same starting order volume, opposite outcomes, and the only variable that predicted the difference was each product's own elasticity. That's the number a "which products should we discount" decision actually needs, not a shared instinct applied evenly across the catalog.

What the Data Shows About the Spread Across Categories

The gap between an elastic and inelastic product is bigger than most catalogs assume, and it shows up even within a single category. Our sourced reference on price elasticity by category compiles real, peer-reviewed coefficients across ecommerce verticals, and the range inside apparel alone makes the point: basic t-shirts sit around -1.17, while casual and athletic wear runs as high as -2.86, one of the most elastic figures measured in that entire dataset. Household appliances, by contrast, average closer to -0.35 across refrigerators, washers, and dishwashers, goods people research carefully and don't casually price-compare on a whim.

Applied to a quarterly discount decision, that spread means a flat 20% markdown across an entire "sale" collection is very likely overcorrecting on some products and undercutting margin on others, since a basic tee and a piece of athletic wear sitting in the same sale bucket don't actually behave the same way once the price drops, even though they're in the "same" apparel category on a spreadsheet.

A Simple Way to Pick Candidates From Your Own Catalog

Start with the products already generating meaningful volume, since a discount decision on a low-traffic SKU won't move enough units to learn much either way. From there:

1. Find the last real price change for each candidate. Even a small change, a shipping-cost pass-through, a routine adjustment, still tells you something, as long as it wasn't bundled with a sale that also changed other variables at the same time.
2. Check whether order volume moved noticeably in response. A product that barely reacted to a past price change is a weak discount candidate. A product that reacted sharply is a strong one.
3. Weight recent data more than old data. A price reaction from two years ago is a weaker signal than one from the last two quarters, since customer behavior and competitive context shift.
4. Don't trust a single data point. One price change with one noisy result isn't a model, it's an anecdote. The more price variation in a product's history, the more confidently you can call its elasticity.

Tools built specifically for this, including Zorin for Shopify and WooCommerce stores, run this calculation automatically by fitting an elasticity model to a store's own order history and returning a per-product score along with a confidence level, so the decision doesn't rely on memory of what happened during a promotion six months ago. A Weak confidence label on a candidate is itself useful information: it means the history doesn't yet support a strong call, and that product is a worse bet for this quarter's sale than one with a Strong confidence read behind it.