Benchmarking engagement metrics against sector norms

Two retailers both report a 3% email click-through rateclick-through rateClick-Through Rate (CTR) is the percentage of people who click a link, ad, or call to action out of those who viewed it.View full definition →. One divides clicks by emails delivered to the whole list; the other divides clicks by opens, and only mails people who bought in the last 90 days. Those are not the same measurement, and neither is comparable to the median in a vendor benchmark report. Most arguments about "are we good?" are denominator arguments wearing a costume.

This lesson is about comparison discipline: which engagement benchmarks are honest for your category and basket frequency, and where sector medians quietly mislead.

Why sector matters more than "industry average"

"Retail" is not one industry for benchmarking purposes. Purchase frequency, basket size and consideration time vary enormously:

• Grocery: high frequency, low consideration, habitual. Shoppers buy milk weekly without comparing five brands.
• Apparel: medium frequency, higher consideration, driven by browsing and discovery.
• Electronics: low frequency, high consideration, long research cycles (a laptop purchase can involve weeks of comparison).

A "good" click-through rate (CTR, the share of recipients who click a link after receiving it) in grocery looks mediocre in electronics, and the reverse.

Click-through rate: email and app benchmarks

CTR = (clicks ÷ delivered emails) × 100. Say "delivered", not "sent", and not "opened", every single time you quote the number.

As-of-2025 estimates, drawn from sources like Klaviyo's ecommerce benchmarks and Mailchimp's sector reports. Both companies sell email marketing software, so their samples are their own customer bases, which lean direct-to-consumer and under-represent supermarket chains and big-box electronics:

Worked example: an apparel brand sends a promotional email to 100,000 subscribers and gets 2,800 clicks. CTR = (2,800 ÷ 100,000) × 100 = 2.8%. That sits comfortably inside the apparel band. The same 2.8% in electronics would be below norm; in grocery it would be excellent.

Two traps hide in a table like that one. Published benchmarks are usually medians across accounts, so a 2,000-subscriber shop counts as much as one with two million: the median describes a typical account, not a typical email. And open rate has been unreliable as a comparison since Apple's Mail Privacy Protection arrived in September 2021 and began pre-fetching images for Apple Mail users, registering opens no human performed. Compare clicks. Treat any open rate spanning that period as inflated by an amount nobody can size.

Cart abandonment: the metric everyone misreads

Cart abandonment rate = (1 − [completed purchases ÷ carts created]) × 100.

The Baymard Institute, an independent UX research group, puts the average ecommerce cart abandonment rate around 70% (estimate, based on aggregated studies, see Baymard's cart abandonment research). Sector spread is wide:

• Grocery: often lower, roughly 50% to 65% (estimate). Shoppers are completing a planned task; friction is the main enemy (delivery slots, minimum basket).
• Apparel: high, often 70% to 80% (estimate). Comparison across tabs and wishlist-style carts inflate it.
• Electronics: also 70%+ (estimate), driven by research cycles: add to cart on one site, compare specs elsewhere, maybe return.

The denominator problem: "carts created" is not a standard event. Some platforms count add-to-cart per session, some per identified user, some only carts that reached checkout initiation. A shopper who adds on mobile at lunch and buys on desktop that evening is one abandoned cart plus one order if you count sessions, and a single clean conversion if you stitch identity. Grocery's flattering numbers are partly this: a saved basket that persists between visits gets amended rather than abandoned, so it never enters the numerator at all. Write down which event your platform counts before you compare with anyone.

High is not automatically bad: a shopper adding three sizes of the same jacket to compare, then removing two, is ordinary browsing. Treating 75% in apparel as a crisis misreads the category.

What is diagnostic: the drop-off between the stages the funnelfunnelThe customer journey from awareness to purchase, typically Awareness, Interest, Consideration, Decision, Action, with prospects narrowing at each stage.View full definition →-mapping lesson lays out, not the headline number. A spike at the payment step points to unexpected shipping costs, forced account creation or too few payment methods. That is fixable, and worth tracking against your own past self more than against an external average.

One more honesty check for fashion. Engagement benchmarks say nothing about what comes back. Zalando built its proposition on free returns, and European fashion return rates commonly sit near half of items shipped. In that world a campaign that lifts CTR and completed checkouts can lift net revenue by very little, and a brand comparing its abandonment rate to Zalando's is comparing against a business that has deliberately priced returns into the funnel. Keep a returns-adjusted number beside the engagement one.

Email and app engagement: frequency changes the yardstick

• Grocery apps are opened weekly or more; engagement lives in session frequency, not one-off campaign opens.
• Apparel engagement spikes around drops and sales, then goes quiet, so campaign-level metrics matter more.
• Electronics engagement concentrates in a decision window, then falls to near zero until the next upgrade (a phone every two to three years).

Run the arithmetic per subscriber instead of per send and the ranking inverts. A grocery retailer mailing four times a week at 1.8% CTR produces about 0.29 clicks per subscriber per month. An electronics retailer mailing twice a month at 4% produces 0.08. Per send, electronics wins by roughly two to one. Per subscriber per month, grocery wins by about three and a half to one. Neither should copy the other's cadence, and neither should be judged on the other's yardstick.

A useful cross-sector comparison is repeat visit rate within 30 days, which normalises for these rhythms better than raw session counts.

Putting it together: a simple benchmarking checklist

When a new engagement number lands, run it through four questions:

1. Which sector cohort am I actually in? A regional grocery app has little to learn from Zalando's fashion app numbers.
2. What is the denominator, exactly? Delivered or sent. Opens or delivered. Sessions or identified users. All carts or checkout-initiated carts.
3. Which stage does this measure? Top-of-funnel clicks and cart-to-order sit at different points and carry different thresholds.
4. Is the benchmark recent and sourced? Norms move. Online grocery adoption after 2020 permanently reset grocery app baselines.

A repeatable calculation, fine in a spreadsheet or a five-line script:

def ctr(clicks, delivered):
    return round((clicks / delivered) * 100, 2)

def cart_abandonment(carts_created, orders_completed):
    return round((1 - (orders_completed / carts_created)) * 100, 2)

def clicks_per_subscriber_month(ctr_pct, sends_per_month):
    return round((ctr_pct / 100) * sends_per_month, 3)

print(ctr(4200, 100000))                  # 4.2% -> within electronics norm
print(cart_abandonment(10000, 2600))      # 74.0% -> normal for the category
print(clicks_per_subscriber_month(1.8, 16))  # 0.288, grocery cadence
print(clicks_per_subscriber_month(4.0, 2))   # 0.08, electronics cadence

The discipline matters more than the code: compute the same way every time, tag each figure with sector, denominator and date, and store the benchmark alongside it so nobody re-litigates "is 3% good?" from memory next quarter.

A note on European vs. US benchmarks

European engagement figures track US patterns directionally (apparel and electronics abandon more than grocery), but absolute levels differ because the payment mix differs. Markets with heavy buy-now-pay-later use or dominant local rails such as iDEAL in the Netherlands often show lower checkout-stage abandonment, since the friction that produces the payment-step spike is smaller. Treat any single "Europe average" with suspicion: it blends national payment and logistics environments that have almost nothing in common. Country-level benchmarks are more decision-useful than a blended EU figure.

🎬 [VIDEO: "Ecommerce Benchmarks Explained" - youtube.com/results?search_query=ecommerce+benchmarks+cart+abandonment+explained - search results for current explainer videos on reading cart abandonment and CTR benchmarks by sector; use to see live dashboard walkthroughs]

Key Takeaways

• Never benchmark across sectors as if "retail" were one category. Grocery, apparel and electronics differ structurally in frequency and consideration, which drives different "normal" CTR and abandonment levels.
• Estimate ranges (2025 to 2026): email CTR roughly 1.5-2.5% grocery, 2-3.5% apparel, 3-5% electronics; cart abandonment around 70% overall, lower in grocery (50-65%), higher in apparel and electronics (70-80%+). Check the source date, and remember most published figures are medians across accounts, not across sends.
• The denominator decides the answer. Delivered versus opened, session carts versus stitched-identity carts, persistent grocery baskets that are never technically abandoned: all of these move the number by more than the sector gap does.
• Open rate stopped being a fair comparison after Apple's Mail Privacy Protection landed in September 2021. Benchmark clicks.
• Per-send CTR and clicks per subscriber per month can rank two brands in opposite orders. Pick the one that matches your cadence, and keep a returns-adjusted view in categories where half the units come back.