Industry Benchmarks
Cross-vertical conversion benchmarks for online retail — see where your category sits on CR, AOV, abandonment, and repeat rate, and which neighboring verticals are worth learning from.
Industry Benchmarks
Cross-vertical reference values for conversion rate, AOV, cart abandonment, and repeat purchase rate that show where your category typically sits.
Industry benchmarks are aggregated metric ranges grouped by vertical — apparel, beauty, home, electronics, food and beverage, supplements — so you can compare your store's conversion rate, average order value, cart abandonment, and repeat purchase rate against a relevant peer set rather than a generic e-commerce average.
The point of slicing by industry is that buyer behaviour changes more by what you sell than by how big you are. A 1.4% conversion rate is mediocre for a beauty subscription brand and excellent for a €600 sofa. Benchmarks anchor that judgement, surface the gap to median, and hint at which neighbouring verticals already solved the same friction.
Most teams compare themselves against a single "e-commerce average" number and conclude the wrong thing. A blended 2.5% conversion rate hides the fact that supplements convert near 4% and furniture closer to 0.8% — averaging them tells you nothing about your store.
Useful benchmarking starts with picking the right peer set: same vertical, similar order value tier, comparable traffic source mix. Only then does the gap between your numbers and the median become an actionable diagnostic rather than a vanity stat.
Typical DTC conversion metrics by vertical (median ranges across Shopify, WooCommerce, and Magento stores in the €1M–€15M revenue band)
| Vertical | Conversion rate | AOV (€) | Cart abandonment | Repeat rate (90d) |
|---|---|---|---|---|
| Beauty & cosmetics | 2.8% – 3.6% | 45 – 75 | 68% – 74% | 28% – 38% |
| Apparel & accessories | 1.8% – 2.6% | 70 – 120 | 70% – 78% | 22% – 30% |
| Supplements & wellness | 3.2% – 4.4% | 55 – 90 | 65% – 72% | 35% – 48% |
| Food & beverage | 2.4% – 3.4% | 40 – 70 | 67% – 73% | 40% – 55% |
| Home & furniture | 0.6% – 1.2% | 180 – 450 | 75% – 84% | 8% – 14% |
| Consumer electronics | 1.0% – 1.8% | 120 – 280 | 72% – 80% | 12% – 20% |
Read the table as four-way trade-offs, not isolated targets. High AOV verticals (furniture, electronics) buy convertibility with longer consideration cycles and higher abandonment. High-frequency verticals (supplements, food) trade ticket size for repeat rate. Your strategy should look like your column, not someone else's.
Median conversion rate by DTC vertical
How to use benchmarks without being misled
Benchmarks are most dangerous when they're used as goals. A vertical median is the middle of the distribution — half the stores in your category are below it, and copying their patterns won't get you to the top quartile. Treat the median as a floor and the 75th percentile as the realistic stretch target.
The second trap is mixing traffic sources. A store running 80% paid social against one running 80% branded search will show wildly different conversion rates in the same vertical. Before you panic about a benchmark gap, segment your own data by channel and compare like with like.
One benchmark you should ignore
The "average e-commerce conversion rate is 2-3%" stat that appears in every quarterly report is a blended number across hundreds of verticals and order-value tiers. Quoting it in a board meeting tells your CFO almost nothing about whether your store is healthy. Always pair any benchmark with the segment cut it came from.
Learning from neighbouring categories
The most useful benchmark move is sideways, not upwards. If you sell apparel, you'll learn more about repeat-rate mechanics from food and beverage brands (who've cracked subscription cadence) than from luxury watches. If you sell furniture, electronics teams have already solved long-consideration checkout for you.
Pick one neighbouring vertical per quarter and audit how they handle the metric you're weakest on. A beauty brand worried about abandonment can borrow checkout patterns from supplements; a furniture store with low repeat rate can study how electronics handle accessory cross-sell. Adjacency is where the cheap wins hide.
Frequently asked questions
There's no universal answer — it depends entirely on what you sell. Beauty and supplements stores typically run 3-4%, apparel 2%, and furniture under 1%. Find your vertical row in the table above, then aim for the upper end of the range as a realistic 12-month target.
The widely quoted "2-3% e-commerce average" is a blended number across all verticals and price points. If you sell high-AOV items like furniture or electronics, you should expect 0.8-1.5% and that's healthy. Compare against your vertical, not the blend.
Conversion benchmarks shift slowly — vertical medians move 10-20% over 18-24 months as cohorts change devices, traffic mix, and checkout UX. Refresh your reference numbers annually, and re-check after any major platform shift (iOS privacy update, Shopify checkout extensibility migration, etc.).
Both, but for different reasons. The vertical median tells you what's structurally possible given buyer behaviour in your category. Direct competitor data (when you can get it via SimilarWeb, public earnings, or audits) tells you what's achievable given your specific positioning.
Industry benchmarks aggregate across a vertical regardless of size or geography. Peer benchmarks narrow further — same vertical, same revenue band, same primary market. Peer is more actionable; industry is easier to source. Use industry to set context, peer to set targets.
Cart abandonment is heavily skewed by how you define a "cart" — some stores count any add-to-cart, others only count checkout-started. Always check the definition before comparing. A 75% rate measured at add-to-cart is often equivalent to 55% measured at checkout-started.
Yes, significantly. Mobile typically converts 30-50% lower than desktop in every vertical, even though it carries 60-75% of traffic. Always segment benchmarks by device — a blended number hides the fact that your mobile experience might be the entire problem.
They're inversely correlated across verticals: high-AOV categories (furniture €350, electronics €200) convert at 1% or below, while low-AOV categories (supplements €70, beauty €55) convert at 3-4%. Within a single store, though, raising AOV usually doesn't tank CR — they move independently.
Yes — compare each funnel stage to its vertical median separately. If product-page-to-cart matches benchmark but cart-to-checkout lags by 15 points, you've localised the leak to the cart drawer. Stage-level benchmarking is far more diagnostic than headline conversion rate.
Import 12-18 months of GA4 data so you have a real seasonal baseline before you start testing. Without historical data, your first three months of "benchmarking" are just noise — you can't tell improvement from January-to-February seasonality.
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