The Leaderboard Illusion: Why Published Email Benchmarks Reward Conformity and Punish Ambition

Every year, a familiar ritual plays out across the email marketing industry. ESPs, research firms, and trade publications release their annual benchmark reports. Marketing teams download them, circulate them in Slack channels, and use them to evaluate whether their programs are performing adequately. Quarterly reviews reference them. Budget justifications cite them. Hiring decisions are shaped by them.

And yet, the numbers in those reports are, at best, an incomplete picture of what elite email programs actually achieve—and at worst, a strategically curated representation designed to serve the interests of the organizations that produce and populate them.

This is not a fringe critique. It is a structural observation about how benchmark data is generated, who contributes to it, and what incentives govern that contribution.

How Industry Benchmarks Are Actually Built

Most published email benchmark reports draw from one of two data sources: self-reported survey data from marketing professionals, or aggregated sending data from ESP customer bases. Both sources introduce systematic distortions that are rarely acknowledged in the reports themselves.

Self-reported survey data is vulnerable to the most basic of measurement problems: people report numbers that reflect favorably on their programs. This is not necessarily deliberate deception. It is the natural consequence of asking professionals to recall and characterize their own performance without providing audited verification. The result is a dataset that skews toward the upper range of actual performance for most respondents.

ESP-aggregated data presents a different problem. When an ESP publishes benchmark data derived from its customer base, it is simultaneously publishing a performance claim about the quality of its platform. An ESP whose aggregate customer open rates are below the industry average has a direct commercial incentive to either exclude low-performing accounts from the benchmark calculation, apply methodological adjustments that improve the aggregate figure, or simply not publish benchmark data at all. The ESPs most likely to publish benchmark reports are those whose numbers look favorable—introducing selection bias into the data that shapes industry-wide expectations.

The Strategic Underreporting Dynamic

Beyond the structural issues with data collection, there is a more deliberate dynamic at work among high-visibility email programs—the brands and agencies whose metrics, if accurately reported, would significantly shift industry averages upward.

Consider the incentive structure facing a major retail brand whose email program consistently achieves open rates and click-through rates well above published industry averages. If that brand's actual performance data were incorporated into industry benchmarks, it would raise the bar against which competitors measure themselves. Competitors would adjust their strategies, increase their investment, and potentially close the performance gap. The brand's competitive advantage, at least as measured by public benchmarks, would diminish.

The rational response—and the one that sophisticated email programs often take, whether consciously or not—is to ensure that publicly visible performance data remains conservative. This can be accomplished through several mechanisms: contributing data to benchmark surveys that reflects average or below-average segments rather than top-performing ones, structuring public case studies around modest improvement narratives rather than absolute performance claims, and declining to participate in benchmark surveys entirely, which removes high-performing data points from the aggregate.

The result is a published benchmark landscape that is systematically skewed toward the middle of the actual performance distribution—or below it—while the top of the distribution remains largely invisible.

The Follower's Disadvantage

For marketers who use published benchmarks as genuine performance guides, this dynamic creates what might be called the follower's disadvantage. By targeting the published average, they are targeting a number that has been depressed by structural reporting biases and strategic understatement. They are, in effect, optimizing toward a performance level that the best programs in their industry cleared years ago and no longer consider competitive.

This is not a theoretical concern. Consider the trajectory of email open rate benchmarks over the past decade. Published averages in many industries have hovered in ranges that, given the changes in privacy-driven measurement introduced by Apple's Mail Privacy Protection and similar tools, are now largely unmeasurable in the same way they were when the benchmarks were established. Programs that built their performance targets around these figures are now measuring against a baseline that reflects neither current competitive reality nor current measurement methodology.

The marketers most harmed by this dynamic are those in mid-market organizations without the resources to build proprietary benchmarking capabilities. They rely on published data because it is the only external reference available to them. And that data, for all the reasons outlined above, is a poor map of the territory they are actually trying to navigate.

What High-Performing Programs Actually Use Instead

Elite email programs do not, by and large, use published industry benchmarks as primary performance guides. They use them for one purpose: external communication. When presenting to stakeholders who are unfamiliar with email performance nuance, published benchmarks provide a recognizable reference point. For internal optimization, they rely on different frameworks.

The first is proprietary historical benchmarking—evaluating current performance against the program's own historical trajectory rather than against external averages. This approach captures program-specific variables that industry averages cannot account for, including list composition, audience demographics, product category, and sending infrastructure.

The second is competitive intelligence derived from direct observation rather than published reports. Tools that track email sending behavior, creative strategy, and frequency patterns across competitor programs provide a more accurate picture of what competitors are actually doing than anything those competitors would voluntarily report to a benchmark survey.

The third is segment-level performance disaggregation. Rather than measuring aggregate open rates or click rates against an industry average, high-performing programs track performance by acquisition source, engagement tier, product category, and customer lifecycle stage. The resulting data is too granular and too specific to be compared directly against published benchmarks—which is precisely the point. It reflects the actual performance drivers of the specific program rather than the averaged-out performance of a heterogeneous industry sample.

Rethinking the Role of Published Benchmarks

None of this is to suggest that published benchmark data has no value. For organizations entering a new vertical, benchmarks provide a rough orientation to the performance landscape. For identifying broad trends—declining engagement across an industry, shifting open rate patterns driven by platform changes—aggregate data retains diagnostic utility.

The error is in treating published benchmarks as performance targets rather than as rough orientation tools. A benchmark that reflects the averaged, self-reported, selection-biased performance of a heterogeneous industry sample is not a standard to aspire to. It is, at best, a floor—and a poorly measured one at that.

The programs that consistently outperform their categories are not doing so by targeting the published average and exceeding it marginally. They are operating from a fundamentally different performance framework—one that uses external benchmarks sparingly, builds internal reference standards rigorously, and treats competitive advantage as something to be protected rather than disclosed.

For marketers who have built their strategies around published industry averages, the most important question is not whether they are meeting the benchmark. It is whether the benchmark they are meeting bears any meaningful relationship to what genuine performance leadership in their category actually requires.