When free shipping thresholds work
Shipping cost is the single most-cited reason for cart abandonment, and "free shipping over $X" attacks it twice at once: it removes the cost objection for your best orders and it gives everyone else a reason to add one more item. Customers routinely spend an extra $15 to "save" $7 of shipping, and that behaviour is remarkably reliable.
But the offer is not free for you. Every qualifying order gives up the shipping fee you used to collect and still costs you the carrier fee, which is now higher because the uplifted orders are bigger and heavier. A threshold pays for itself only when the margin on the extra basket value covers both. Set the threshold too low and you're subsidising orders that needed no persuasion; too high and nobody stretches, so you keep the downside and lose the behaviour change.
How the calculator works
For each candidate threshold, orders split into two groups:
- Orders that reach it: their AOV rises by your uplift estimate, they pay no shipping fee, and you absorb a carrier cost scaled to the bigger basket. COGS scales with order value too.
- Orders that don't: the same as today. Baseline AOV, and the customer pays your shipping charge.
qualifying orders × (profit per qualifying order − profit per baseline order)
That one line is the whole economics of a threshold: the offer wins exactly when a qualifying order out-earns a baseline order. In the example data, every extra dollar a customer adds to their basket keeps 50 cents after COGS (45%) and heavier shipping (5 cents per dollar). A $48 uplift on the $100 threshold therefore turns into $24 of extra per-order margin against a $7 baseline. Multiply by how many orders qualify, and you have each bar on the chart.
By default the model holds total order count fixed: the classic, conservative framing where the offer only re-shapes baskets. In reality, advertising free shipping usually lifts conversion as well; the optional "conversion lift" input adds that effect and unlocks the reach break-even analysis.
Estimating reach and uplift from your own data
The two per-threshold inputs are estimates of customer behaviour, and both are readable from data you already have:
- Reach %: pull an order-value histogram (Shopify Analytics or GA4, last 60–90 days). Count orders already at or above the candidate threshold, then add a third to a half of the orders within about 25% below it. Those are the customers a threshold visibly moves. That total, as a share of all orders, is your reach.
- AOV uplift: qualifying orders tend to land at or just above the threshold: people add the cheapest item that gets them over the line. If you have run a threshold before, measure the real gap between free-shipping orders and your overall AOV; if not, assume qualifying orders average the threshold amount itself (the calculator suggests exactly this when you add a row).
Sense-check that the two agree. If you claim 35% of orders will reach a $125 threshold from a $55 AOV, you're claiming a third of your customers will more than double their basket. The calculator flags one version of this inconsistency (an uplift too small to actually reach the threshold); optimistic reach numbers are on you.
A worked example
The example store has a $55 AOV, 45% COGS, a $6.99 shipping charge and about $7.50 of profit per order, roughly $225 a month at 30 orders. Three thresholds are on the table: $75, $100 and $125, with reach falling (35% → 25% → 15%) as the ask gets bigger and uplift rising ($22 → $48 → $72).
All three beat the baseline. The $75 threshold adds about $42 a month: qualifying orders earn $11.50 against the baseline's $7.49, but the uplift is modest. The $100 threshold adds roughly $128. The $125 threshold wins at about $131 extra profit: qualifying orders earn $36.50 each, nearly five times baseline. Note how close $100 and $125 are, though, and how much more demanding the $125 assumption is (15% of customers stretching $70 past today's AOV). When two thresholds are that close, pick the one with the more believable assumptions, not the bigger bar.
The break-even panel explains why this setup is forgiving: with 50 cents kept per extra dollar, covering the $6.99 fee needs only a $13.98 uplift, and all three thresholds sit further above the current AOV than that. Any order that genuinely reaches the threshold is already past break-even, so the offer can only lose on orders it fails to move.
Break-even, done honestly
A quick word on what this calculator deliberately doesn't show. Threshold tools love a "break-even reach %", the share of qualifying orders needed for the offer to pay off. In the standard fixed-traffic model that number doesn't exist: if qualifying orders out-earn baseline orders the offer wins at any reach, and if they don't it loses at any reach. The reach just scales the result. (The original version of this tool printed a break-even reach that was mathematically always 0%.)
What does have a break-even is the uplift: the basket growth needed to cover the fee you give up, which is your shipping charge divided by the margin you keep on each extra dollar. Once you model the offer lifting overall conversion, reach develops a real break-even, sometimes as a ceiling: extra conversions are pure upside, so a threshold with weak per-order economics stays net positive only while few orders qualify. The analysis panel covers each case and tells you which one applies.
Assumptions and limitations
- Two clean groups. Orders either behave exactly like today or jump to the uplifted AOV. Real order distributions are messier around the threshold.
- Reach and uplift are your estimates. The model makes them comparable and shows break-evens. It cannot validate them. Ground both in your order histogram.
- Costs scale linearly. COGS as a flat % of order value and carrier cost as base + per-dollar. Carrier rate cards are steppier than that in reality.
- No margin mix effects. Threshold-chasers often add low-margin filler items; blended COGS % can drift up during the offer.
- Snapshot, not LTV. Free shipping can lift repeat rates and long-term value, which is upside this model leaves out.
Set the threshold from this model, then watch two numbers for the first month: the share of orders qualifying (against your reach estimate) and the AOV of qualifying orders (against your uplift estimate). If either misses badly, re-run the numbers before the offer trains your customers. For a second pair of eyes on the whole shipping strategy, talk to us.
