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End User Monitoring - Reports of EUM's Death Have Been Greatly Exaggerated

Larry Haig

Once upon a time (as they say) client side performance was a relatively straightforward matter. The principles were known (or at least available – thank you, Steve Souders et al), and the parameters surrounding delivery, whilst generally limited in modern terms (IE5 /Netscape, dialup connectivity anyone?) were at least reasonably predictable.

This didn't mean that enough people addressed client side performance (then or now for that matter), despite the alleged 80% of delivery time spent on the user machine, and the undoubted association between application performance and business outcomes.

From a monitoring and analysis point of view, synthetic external testing (or end user monitoring) did the job. Much has been written (not least by myself) on the need to apply best practice, and to select your tooling appropriately. The advent of “real user monitoring” (RUM) came some 10 years ago – a move at first decried, then rapidly embraced, by most of the “standalone” external test Vendors. The undoubted advantages of real user monitoring in terms of breadth of coverage and granular visibility to multiple user end points – geography, O/S, device, browser – tended for a time to mask the different, though complementary strengths of consistent, repeated performance monitoring at page or individual (eg 3rd party) object level.

Fast forward to today, though, and the situation demands a variety of approaches to cope with the extreme diverseness of delivery conditions. The rise and rise of mobile (just as one example, major UK retailer JohnLewis.com quoted over 60% of digital orders derived from mobile devices during 2015/16 peak trading) brings many challenges to Front-End Optimization (FEO) practice. These include: diversity of device types and version; browsers; and limiting connectivity conditions.

This situation is compounded by development of the applications themselves. As far as the web is concerned, monitoring challenges are introduced by, amongst other things: Single Page Applications (either full or partial); “server push content”; and mobile “WebApps” driven by service worker interactions. Mobile Applications, whether native or hybrid, present their own analysis challenges, which I will address subsequently also.

This already rich mix is further complicated by business demands for more on-site content – multimedia and other rich content, exotic fonts, and more. Increasingly large amounts of client side logic, whether as part of SPAs or otherwise, demand focused attention to avoid unacceptable performance in edge case conditions.

As if this wasn't enough, the (final!) emergence of HTTP/2 introduces both advantages and anti-patterns relative to former best practice.

The primitive simplicity of page onload navigation timing endpoints has moved from beyond irrelevance to becoming positively misleading, regardless of the type of tool used.

So, these changes require an increased subtlety of approach, combined with a range of tools to ensure that FEO recommendations are both relevant and effective.

I will provide some thoughts in subsequent blogs as to effective FEO approaches to derive maximum business benefit in each of these cases.

The bottom line is, however, that FEO is more important than ever in ensuring optimal business outcomes from digital channels.

Larry Haig is Senior Consultant at Intechnica.

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End User Monitoring - Reports of EUM's Death Have Been Greatly Exaggerated

Larry Haig

Once upon a time (as they say) client side performance was a relatively straightforward matter. The principles were known (or at least available – thank you, Steve Souders et al), and the parameters surrounding delivery, whilst generally limited in modern terms (IE5 /Netscape, dialup connectivity anyone?) were at least reasonably predictable.

This didn't mean that enough people addressed client side performance (then or now for that matter), despite the alleged 80% of delivery time spent on the user machine, and the undoubted association between application performance and business outcomes.

From a monitoring and analysis point of view, synthetic external testing (or end user monitoring) did the job. Much has been written (not least by myself) on the need to apply best practice, and to select your tooling appropriately. The advent of “real user monitoring” (RUM) came some 10 years ago – a move at first decried, then rapidly embraced, by most of the “standalone” external test Vendors. The undoubted advantages of real user monitoring in terms of breadth of coverage and granular visibility to multiple user end points – geography, O/S, device, browser – tended for a time to mask the different, though complementary strengths of consistent, repeated performance monitoring at page or individual (eg 3rd party) object level.

Fast forward to today, though, and the situation demands a variety of approaches to cope with the extreme diverseness of delivery conditions. The rise and rise of mobile (just as one example, major UK retailer JohnLewis.com quoted over 60% of digital orders derived from mobile devices during 2015/16 peak trading) brings many challenges to Front-End Optimization (FEO) practice. These include: diversity of device types and version; browsers; and limiting connectivity conditions.

This situation is compounded by development of the applications themselves. As far as the web is concerned, monitoring challenges are introduced by, amongst other things: Single Page Applications (either full or partial); “server push content”; and mobile “WebApps” driven by service worker interactions. Mobile Applications, whether native or hybrid, present their own analysis challenges, which I will address subsequently also.

This already rich mix is further complicated by business demands for more on-site content – multimedia and other rich content, exotic fonts, and more. Increasingly large amounts of client side logic, whether as part of SPAs or otherwise, demand focused attention to avoid unacceptable performance in edge case conditions.

As if this wasn't enough, the (final!) emergence of HTTP/2 introduces both advantages and anti-patterns relative to former best practice.

The primitive simplicity of page onload navigation timing endpoints has moved from beyond irrelevance to becoming positively misleading, regardless of the type of tool used.

So, these changes require an increased subtlety of approach, combined with a range of tools to ensure that FEO recommendations are both relevant and effective.

I will provide some thoughts in subsequent blogs as to effective FEO approaches to derive maximum business benefit in each of these cases.

The bottom line is, however, that FEO is more important than ever in ensuring optimal business outcomes from digital channels.

Larry Haig is Senior Consultant at Intechnica.

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...