Marketing Benchmarks Are Lying to You (Sort Of)

The Number That Feels Like a Win (But Might Not Be)

Picture this. Your latest email campaign hits a 22% open rate. You pull up an industry benchmark report, and the average is 15%. You feel good. You share the results with your boss. Everyone's happy.

But what if that 22% and that 15% aren't measuring the same thing? What if the benchmark is counting opens one way, your platform counts them another, and neither number reflects what's actually happening with real human readers?

That's the quiet problem with marketing benchmarks. They look authoritative. They come with clean charts and confident percentages. But underneath, they're built on wildly different assumptions—and most marketers never question them.

Why Two Benchmark Reports Can Look Nothing Alike

Pull benchmark data from three different sources on the same topic, and you'll often get three different answers. This isn't a coincidence. It's a feature of how benchmarks are built.

Almost all published marketing benchmarks come from platform providers—email service providers, ad networks, marketing automation tools. Each platform serves a different mix of customers, uses different data definitions, and has different incentives around what numbers it reports publicly.

The Bot Problem Nobody Talks About Enough

Click bots are one of the messiest variables in the whole equation. Some bots are harmful—they inflate ad clicks on social platforms, costing advertisers real money. Others are actually well-intentioned, like security bots that automatically click email links to check for malware before the message ever reaches a human inbox.

The problem? Both types distort your performance data. A platform that aggressively filters out bot activity will show lower click rates than one that doesn't. Neither number is wrong, exactly—they're just measuring different things.

Here's where it gets uncomfortable. Cleaning up bot data doesn't always benefit a platform financially. Inflated click rates make a platform look stronger against competitors. Some ad networks even profit directly from bot traffic. So when you read a benchmark from a platform provider, it's worth asking: what's their incentive to clean up the data?

Apple Changed the Rules, and Platforms Disagreed on What to Do

In 2021, Apple rolled out Mail Privacy Protection, which pre-loads email content—including tracking pixels—before users actually open messages. This made open rate data unreliable almost overnight.

The response from email platforms was not uniform. Some providers now strip out Apple auto-opens and report only "true" open rates from non-Apple clients. Others still include those auto-opens in their reported figures, which can make open rates look dramatically higher.

If you're comparing your open rate against a benchmark that includes Apple auto-opens, and your platform excludes them, you're comparing apples to oranges. You might think you're underperforming when you're actually doing fine—or vice versa.

Your Industry and Geography Shape the Numbers More Than You'd Expect

Even if two platforms agreed on every technical definition, benchmark numbers would still vary enormously based on who's in the dataset.

Industry Vertical Makes a Massive Difference

Consider email open rates. Across all industries, true open rates (with Apple auto-opens removed) sit around 10%. But for financial services and insurance companies, that figure jumps to nearly 19%—almost double the all-industry number.

Now imagine you work at a bank with a 14% true open rate. Measured against the all-industry average, you look like a star. Measured against your actual peers in financial services, you're below average. Same number, completely different story.

This isn't a minor wrinkle. It's a fundamental flaw in how most teams use benchmarks. An all-industry figure can mask enormous variation at the vertical level.

Where Your Customers Live Affects Engagement Too

Geography plays a bigger role than most marketers realize. Countries like Canada and those in the European Union have strict permission-based marketing laws. Getting someone to opt in is harder under those rules, which means email lists tend to be smaller and more selective.

The tradeoff? Those lists are also more engaged. Recipients who actively chose to receive your emails are more likely to open and click them. So a benchmark from a platform serving mostly European brands will show higher engagement rates than one built on US-heavy data—not because European marketers are better, but because their legal environment filters out disengaged subscribers automatically.

B2B vs. B2C: A Completely Different Sending Reality

B2C brands send far more email than B2B brands. A retailer might send multiple campaigns per week; a software company might send one or two per month. Higher send frequency generally means lower average engagement rates, because recipients get fatigued.

B2C brands also have a much higher proportion of Apple Mail users, which means they're more exposed to the open rate distortion from Mail Privacy Protection. If a benchmark dataset skews heavily B2C, those dynamics are baked into every number.

The Factor Benchmarks Can Never Measure: You

Here's the thing that no benchmark report will ever tell you. Your results aren't just a function of your industry or your platform. They're a function of your specific practices.

How you acquire subscribers matters enormously. A list built through aggressive lead generation tactics—contests, co-registration, gated content with minimal friction—will behave very differently from a list built through genuine interest and explicit permission. The first list might be large. The second will almost certainly be more engaged.

Your segmentation approach matters too. Sending the same message to your entire list is a fundamentally different strategy than sending targeted content to specific segments based on behavior or preferences. The latter almost always outperforms the former, but both approaches will show up in the same benchmark category.

Triggered campaigns—automated emails sent based on user actions like purchases, sign-ups, or abandoned carts—consistently outperform batch-and-blast campaigns. If your program is heavily weighted toward triggered messaging, your metrics will naturally look better than a competitor who relies mostly on scheduled broadcasts. A benchmark can't see any of that nuance.

This is actually the most useful thing benchmarks can do for you. When your numbers fall short of a relevant, well-matched benchmark, it's a signal worth investigating. But the benchmark can only wave a flag. It can't tell you whether the problem is your subject lines, your list quality, your send frequency, or something else entirely. That diagnosis has to come from you.

Benchmarks Don't Measure What Actually Matters to Your Business

Most published benchmarks focus on what's called channel health metrics—open rates, click rates, deliverability, unsubscribe rates. These are useful signals, but they sit at the top of the funnel. They tell you whether people are engaging with your messages. They don't tell you whether that engagement is turning into revenue.

A campaign with a modest open rate but a strong conversion rate is more valuable than one with a high open rate and no conversions. But the first campaign would look worse in a benchmark comparison. The second would look better.

This gap matters especially when you're reporting to leadership. If your boss asks whether the email program is working, an open rate comparison to an industry average is a thin answer. The more meaningful question is whether email is driving pipeline, purchases, or customer retention—and those outcomes rarely appear in standard benchmark reports.

Building your own internal benchmarks around revenue impact—cost per acquisition from email, revenue per email sent, customer lifetime value by acquisition channel—gives you a much clearer picture of actual performance than any third-party report can.

How to Actually Use Benchmarks Without Getting Burned

None of this means benchmarks are useless. They're just frequently misused. Here's a more grounded approach.

Get Benchmarks From Your Own Platform First

Your platform provider's benchmark data is the most directly comparable to your own numbers, because it's built on the same technical definitions and the same data infrastructure. Ask your provider for benchmark data filtered by your industry and your primary geography. That removes most of the noise from platform-to-platform variation.

Ask Hard Questions Before Trusting Any Number

Before you use any benchmark, find out whether it includes or excludes Apple auto-opens. Ask how the provider handles bot filtering. Find out whether the dataset skews B2B or B2C, and whether it's segmented by industry. If a benchmark report doesn't answer these questions, treat the numbers with appropriate skepticism.

Track Direction, Not Just Position

The most valuable way to use a benchmark isn't to ask "are we above or below average?" It's to ask "are we moving in the same direction as the benchmark, or a different one?"

If industry open rates fell by two percentage points over the past year and yours fell by one, you actually improved your relative position—even though your absolute rate went down. Conversely, if your open rate held steady while the benchmark rose, you're losing ground even though your number looks the same.

Tracking the year-over-year change in your metrics against the change in the benchmark gives you a much more honest read on performance than any single snapshot comparison.

Build Internal Benchmarks From Your Own History

Your best benchmark is your own past performance. Track your metrics consistently over time, segment them by campaign type, audience, and channel, and build a picture of what good looks like for your specific program. External benchmarks can provide useful context, but your internal trend line is the most honest signal you have.

Stop Letting Averages Make Your Decisions

Benchmarks are a starting point, not a verdict. They're built on assumptions that may not match your situation, measured with definitions that vary by provider, and filtered through incentives that don't always align with giving you the most accurate picture.

That doesn't mean you should ignore them. It means you should read them carefully, ask questions about how they were built, and treat them as one input among many—not as a scoreboard that determines whether your program is succeeding or failing.

The teams that get the most value from benchmarks are the ones that use them to spark better questions, not to produce easy answers. When your numbers diverge from a benchmark, that's an invitation to investigate. When they align, that's not necessarily a reason to relax. The real work is always in understanding why your metrics are what they are—and benchmarks, on their own, can't do that for you.