Cart Abandonment Rate
Cart abandonment rate is the share of shoppers who add to cart but don't buy. Here's the formula, vertical benchmarks, and what a "good" number actually looks like.
Cart Abandonment Rate
The percentage of shoppers who add an item to cart but leave without completing the purchase.
Cart abandonment rate measures the gap between purchase intent and purchase completion. It counts every session where a shopper added at least one product to their cart, then divides the ones who didn't check out by that total. The metric is a direct read on checkout friction: pricing surprises at the shipping step, slow page loads, forced account creation, and limited payment options all push the rate up.
Typical online stores sit between 60% and 80%. A rate below 50% usually signals a strong checkout experience; above 85% suggests something is broken — or that paid traffic is sending hyper-low-intent visitors who use the cart as a wishlist.
Cart abandonment is one of the highest-leverage numbers on a store's dashboard. Visitors who add to cart have already done the hard work — discovery, product selection, intent — so every abandoned cart is a near-miss on revenue you've already paid to acquire.
It's distinct from checkout abandonment, which only counts sessions that reached the checkout page. Cart abandonment is the broader funnel: add-to-cart through order confirmation. Tracking both side-by-side tells you whether the leak is between cart and checkout, or inside checkout itself.
Cart Abandonment Rate = (1 - Completed Purchases / Carts Created) × 100
Carts Created
Carts Created
Unique sessions where a shopper added at least one item to the cart in the period.
Completed Purchases
Completed Purchases
Orders placed (and not cancelled) by those same sessions in the period.
A Shopify apparel store runs the numbers for October. They had 12,400 sessions that added something to cart, and 2,976 of those sessions placed an order.
Carts Created: 12,400
Completed Purchases: 2,976
→ 76.0% cart abandonment rate
76% sits in the typical band for apparel. Not a fire — but with 9,424 abandoned carts and an AOV of €68, even a 3-point improvement is roughly €25k of recovered monthly revenue.
The denominator matters. Some platforms count line-item additions, others count unique sessions, and a few count distinct shoppers. Pick one definition and hold it constant — comparing a session-based rate this quarter to a shopper-based rate next quarter will mislead every decision downstream.
Typical cart abandonment rate by vertical (online retail)
| Vertical | Typical range | Strong (top quartile) | Concerning (bottom quartile) |
|---|---|---|---|
| Apparel & accessories | 68% – 78% | < 65% | > 82% |
| Beauty & personal care | 65% – 75% | < 62% | > 80% |
| Home & furniture | 78% – 88% | < 74% | > 90% |
| Electronics | 75% – 85% | < 70% | > 88% |
| Food & beverage | 60% – 72% | < 58% | > 76% |
| Health & supplements | 62% – 72% | < 58% | > 78% |
The biggest movers on this number are predictable: showing shipping cost on the product page (not at step three), offering the payment methods your audience actually uses (Klarna, Apple Pay, iDEAL by region), guest checkout, and a sub-2-second checkout load time. Email and SMS recovery flows catch the carts you couldn't save in-session — Klaviyo's published benchmarks put a well-tuned three-step abandonment series at 8-12% recovery.
Cart abandonment rate FAQ
Anything under 65% is strong for most online retail verticals. The global average across stores is roughly 70%, and high-consideration categories like furniture and electronics regularly sit in the 80s without anything being broken — shoppers are price-comparing across tabs.
Cart abandonment measures the full funnel from add-to-cart to purchase. Checkout abandonment only measures sessions that reached the checkout page and didn't complete. Cart abandonment is always higher because it includes shoppers who never even started checkout.
Three usual suspects: a checkout bug or slow load on mobile, a surprise cost (shipping, tax, fees) appearing late in the flow, or paid traffic that's mis-targeted and using the cart to save items rather than buy. Run a session replay on ten abandoned sessions and the pattern usually shows up within the first three.
It depends on your attribution window. Most analytics tools (including GA4) credit a return-and-purchase session to the original cart if it happens within the lookback window — typically 7 or 30 days. Set this once and document it; comparing a 1-day window to a 30-day window will swing the number by 5-10 points.
Use the funnel exploration with `add_to_cart` as step one and `purchase` as the final step. The drop-off between those events is your abandonment rate. Shopify's native analytics shows the same number under Behavior → Online store conversion rate.
Yes — a three-message recovery series typically recovers 8-12% of abandoned carts and reduces the rate by 3-5 percentage points overall. The first email within one hour does most of the work; later messages mainly catch shoppers who genuinely needed time.
Significantly. Shipping cost is the single most-cited reason for abandonment in every published survey. Stores that move from paid shipping to a free-shipping threshold (e.g. free over €50) commonly see abandonment drop 4-8 points, with the threshold also lifting AOV.
They're inverse views of the same funnel section. Conversion rate is purchases ÷ sessions; cart abandonment is non-purchases ÷ carts. A store with a 3% conversion rate and a 35% add-to-cart rate has roughly 91% cart abandonment — useful to sanity-check the math each way.
Mobile abandonment typically runs 8-15 points higher than desktop and deserves its own diagnosis. Common mobile-specific causes: tiny form fields, no Apple Pay or Google Pay, address autocomplete missing, and a checkout that's been responsively shrunk rather than properly redesigned for thumb use.
Weekly at a rolled-up level, with an alert if it moves more than 3 points week-on-week — that's usually a deploy or a tracking break, not real shopper behaviour. Run a deeper diagnosis quarterly, segmented by device, traffic source, and new vs returning.
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