If you run an online store selling everyday products like food, drinks, household goods or personal care, you’ve probably asked what a “normal” conversion rate looks like. It’s a fair question. It’s also a trap, because published benchmarks for this category are thin, inconsistent and often poorly explained.
This post goes through what has been published, why the numbers disagree, what each metric really measures and how to build a benchmark from your own data that is more useful than any industry average.
What the published numbers say
There’s no single authoritative figure for FMCG. The closest proxies are industry tables for food and beverage, and for beauty and personal care. A few examples from recent roundups:
- Food and beverage: commonly quoted at about 4.9% to 6.2% on average, with some roundups claiming 8% or more for top performers. The attribution is vague. One roundup credits a mix of sources (including Dynamic Yield, IRP Commerce and Smart Insights) without matching each number to a source or sample.
- Beauty and personal care: quoted anywhere from about 2.5% to 3.5% in one roundup, and about 3.5% to 6.8% in another. Those two ranges barely overlap.
- Health: roughly 3% to 5% in one roundup.
- All ecommerce, as a whole: between about 1.4% and 2.9%, depending on the source.
Notice what’s missing: a dedicated figure for grocery or for FMCG as a group, and the method behind most of the category numbers. The table rows are usually not tied to a named study, year or sample size. They should be read as rough indications, not as targets.
Why the numbers disagree
The disagreement isn’t random. A few technical differences explain most of it.
Different denominators
Conversion rate is orders divided by something. That something changes from source to source:
- Sessions: each visit counts. A shopper who visits three times and buys once adds three sessions.
- Users or visitors: each person counts once over the period. The same shopper counts as one.
Session-based rates are lower than user-based rates, because repeat visits increase the denominator. In one roundup, the overall average ranged from 1.4% (a median from Shopify stores, session-based) to 1.6% and 1.89% (also session-based) to 2.5% (session-based, broad mix) to 2.9% (user-based, from a large set of brands). The spread comes largely from what’s being counted and who is being counted.
FMCG makes this difference bigger, because customers return often. A store with many repeat shoppers will look worse on a session basis and better on a user basis.
Different populations
Some data comes from small Shopify stores, some from large brands, some from a single country. A figure from small stores in the UK and Ireland isn’t comparable to one from 400 large brands worldwide.
Different definitions of “conversion”
Some count completed orders, some count checkouts started and some count any purchase event including repeat orders on subscriptions. Check which one you’re comparing to.
Different traffic mixes
Visitors from email and direct (existing customers) convert at much higher rates than visitors from paid social or display. A store with a large email list will look better than one that relies on prospecting ads, even if the site is identical.
Different price points and basket types
A $4 snack and a $60 case of coffee are not the same decision. Lower prices tend to mean quicker decisions and higher conversion. Subscriptions and multi-packs shift the picture again.
Source quality
Benchmarks from vendors and agencies are sometimes recycled from other roundups, with no original data behind them. If a number appears in five places with no study attached to it, it may be one number being copied.
The metrics that matter in FMCG
Conversion rate is only one metric, and for low-priced, high-frequency products, it isn’t the most informative one. Here are the main ones, with definitions.
Conversion rate (CVR) = orders ÷ sessions (or visitors). State which.
Add to cart rate = sessions with an add to cart ÷ sessions. A leading indicator that needs less traffic to measure.
Cart abandonment rate = 1 − (completed orders ÷ carts created). One widely cited aggregate puts the average at 70.22% across 50 studies, though that figure includes many shoppers who were only browsing or comparing. Baymard’s data on US shoppers shows that the most common stated reason for abandoning a cart is extra costs such as shipping, tax and fees (40%), followed by slow delivery (20%) and having to create an account (18%).
Average order value (AOV) = revenue ÷ orders. For low-priced goods, this is often the most important lever, because shipping and handling costs are fixed per order.
Revenue per visitor (RPV) = revenue ÷ visitors. This combines conversion and AOV in one number. A change that lowers conversion slightly but raises AOV a lot can still win on RPV.
Items per order = units ÷ orders. Useful for multipack and bundle tests.
Repeat purchase rate = customers with 2 or more orders in a period ÷ customers with at least one order. This is the metric that makes or breaks FMCG economics. Be careful: the right period (30, 90, 180 days) depends on how fast your product is used up, and a rate for a long period will look higher than one for a short period.
Time between orders = the typical gap for repeat customers. It tells you when reminders and reorders make sense.
Subscription retention = share of subscribers still active after N deliveries.
Margin per order = revenue minus product, shipping, payment and discount costs. A conversion gain bought with a deep discount may reduce profit.
For FMCG benchmarks, repeat purchase rate and retention usually matter more than first-order conversion, but published figures for them are even harder to find and compare than conversion rates. Treat any single number with caution.
Read more: A/B Testing vs. Multivariate Testing: Which One Should You Use?
How to build a benchmark from your own data
An internal benchmark is more trustworthy than an industry one because you control the definitions. Here’s a simple method.
1. Define each metric precisely
Write the formula down. Say whether you count sessions or users. Say whether subscription renewals count as orders. Use the same definitions each time.
2. Segment before you average
A blended number hides the real picture. Calculate each metric separately for:
- Device (mobile, desktop, tablet)
- New vs. returning visitors
- Traffic source (email, organic, paid, direct)
- Landing page type (home, category, product)
- Basket type (single item, multipack, subscription)
You’ll often find that your blended rate is made up of very different pieces. A new mobile visitor from paid social may convert at a fraction of the rate of a returning desktop visitor from email.
3. Use enough history, and mind the seasons
FMCG demand moves with paydays, weekends, holidays, weather and promotions. Use at least 8 to 12 weeks, and ideally the same period last year, and flag promotion periods. Compare like with like: a promotional week with another promotional week.
4. Add the spread, not just the average
Record the week-to-week variation. If your weekly conversion rate bounces between 3.8% and 4.6% without any changes, a test result of “+0.2%” is well inside normal noise. Understanding natural variation helps you judge which differences are meaningful.
A simple measure is the standard deviation of weekly rates. A rough guide: a difference smaller than the usual week-to-week swing is unlikely to be a reliable signal without a proper test.
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5. Use the benchmark to size tests
Your baseline decides how much traffic a test needs. At a 5% false positive rate and 80% power, approximate visitors needed per variant are:
| Baseline conversion | Detect 10% relative lift | Detect 20% relative lift |
| 2% | about 80,700 | about 21,100 |
| 3% | about 53,200 | about 13,900 |
| 5% | about 31,200 | about 8,200 |
A store converting at 5% needs fewer visitors than one converting at 2% to detect the same relative change. That’s one practical reason higher-converting FMCG categories are easier to test, though it also means very small percentage-point changes need large samples.
6. Re-baseline regularly
Your numbers change with traffic mix, pricing, product range and seasons. Refresh the benchmark every quarter.
Using external benchmarks sensibly
External figures still have a role, as long as you treat them carefully.
- Use them as a sanity check, not a goal. If your conversion rate is 0.4% and published ranges are in the 2% to 6% region, there may be a tracking error or a traffic quality problem worth investigating.
- Check the definition. Sessions or users? Orders or checkouts? What population?
- Prefer sources with a method. A figure with a stated sample, period and denominator is worth more than one without.
- Compare ranges, not points. A single “average” hides a wide spread.
- Look at the shape. The relationship between your metrics (add to cart rate to checkout to order) often tells you more than any single number.
What about benchmarks for test results?
People often ask how large a lift a typical test produces. There’s no reliable published standard for FMCG, and the numbers that circulate tend to come from selected winners, which overstates what’s typical. In practice, most tests produce small effects or none. Large wins are rare, and when they appear they should be re-tested. Plan on gains from a steady series of tests and don’t expect any single result to change the business.
Common benchmark mistakes
- Comparing a session-based rate to a user-based one
- Comparing your blended rate to a category figure with no stated method
- Using a number with no source, year or sample
- Ignoring traffic mix when comparing
- Judging first-order conversion for a category built on repeat purchases
- Treating a benchmark as a target, and cutting margin to reach it
Summary
Published FMCG conversion benchmarks are rough, and they disagree for good reasons: different denominators, populations, definitions and traffic mixes. Treat them as a sanity check. The benchmark that helps you make decisions is the one you build from your own data, with exact definitions, split by segment and with the normal week-to-week variation recorded.
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FAQs
What is a good conversion rate for an FMCG or grocery store?
Published ranges for food and beverage often fall around 4.9% to 6.2%, but the sources, methods and samples are usually unclear. Your own rate depends on traffic mix, price point and how conversion is defined, so compare against your own history first.
Why do conversion rate benchmarks differ so much?
Mainly because they count different things. Some divide orders by sessions and others by unique users. Populations, traffic sources, price points and definitions also differ.
Which metrics matter more than conversion rate in FMCG?
Average order value, revenue per visitor, repeat purchase rate and margin per order. Because customers buy often, the value of a customer over time usually matters more than the first order.
Is a 70% cart abandonment rate normal?
It’s close to the average reported across studies, but that figure includes many people who were browsing or comparing. Look at where in checkout your shoppers leave, and the reasons, such as extra costs, delivery speed and forced account creation.
