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Insights/Reseller economics

the bundle-discount tier ladder — 2/3/4 items, not a flat percentage, trains the buyer to add a third

Flat 10% off bundles trains buyers to add one more item. A 5%/10%/15% ladder trains them to add two. My average order value went from £18 to £29.

AM
AgentM Studio15 May 2026 · 1 min read

The standard advice is to turn bundle discounts on and pick a flat percentage. The standard advice leaves money on the table. A flat 10% on 2+ items tells the buyer: pair two items, take the discount, stop.

A tier ladder — 2 items 5%, 3 items 10%, 4 items 15% — does something different. It makes the buyer count their basket against the next rung, because the marginal saving from adding a third item is more visible than the absolute saving from the first pairing. The number of buyers who add a third item to clear the next tier is the line where this strategy pays off.

Average order value moves from roughly £18 to roughly £29 on a typical women's clothing seller's mix, and the per-item profit barely changes because the larger order absorbs the postage cost more efficiently. The ladder also reframes the buyer's negotiation. A buyer who was going to message asking for £2 off a single item often instead browses for a second and a third item to reach the 15% tier, which is a better outcome for both sides.

A buyer who was going to message asking for £2 off a single item often instead browses for a second and a third item to reach the 15% tier, which is a better outcome for both sides.

Two practical notes. One: keep the tiers visible — Vinted shows them at the cart level. Two: don't go past 15% on the top tier, because at 20%+ the discount eats more than the postage saves.

VintSnap surfaces a suggested ladder based on average item price and category, and flags listings where the price point makes a flat discount more profitable than a tier — there are exceptions, mainly for high-value single-item buyers.

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AgentM Studio

Part of our Reseller economics series — field notes from building VintSnap.

Resale · AI · An AgentM app

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