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10 Points to Consider When Choosing a Real User Monitoring Tool

Larry Haig

Tools enabling Real User Monitoring, or RUM (also known as End User Experience, or EUE, and some similar things) have proliferated hugely over the last 10 years, to the point there are now countless hopeful vendors jostling for contention.

RUM certainly adds a powerful additional perspective to external monitoring, and whilst it does not replace "traditional" active (synthetic) testing – they are complimentary – it can dramatically improve some aspects of end-user performance visibility, particularly for companies with a broad international reach.

As always, a crowded marketplace can contain sheep as well as goats. I thought that it may be useful to share some musings on RUM product distinctions. The relative importance of each will depend on particular circumstances, and there is a degree of functional convergence occurring, at least among the players who are carving out significant market share (and who clearly wish to be around for the long-term). Hopefully these will be helpful to the uninitiated who are considering a purchase decision.

Ten points to consider about your potential RUM tool (in no particular order):

1. Sophistication/Coverage

Many RUM products are based on the standard W3C Navigation metrics. Although provision of standard page load metrics is one of the drivers of growth in tooling options, be aware that these are not supported in all browsers, primarily older versions and Safari. In certain cases, basic performance data is collected from these to supplement the core W3C metrics and offer more complete coverage.

Key aspects of sophistication include:

- Ability to record user journeys (logical transactions). Less evolved products act at individual page level only.

- Ability to capture and report individual session-level data – reporting on business relevant metrics such as transaction abandonment and shopping cart conversion by different categories of user.

- Detailed reporting – bounce rate, stickiness (time on site), etc. A tabular comparison between candidate products may be useful here.

- Ability to record "above the line" performance (browser fill aka perceived render time). This metric is rarely estimated by active monitoring tools (the only one of which I am aware is WebPageTest). As such, RUM tooling supporting this give a useful additional perspective into end-user satisfaction. Beware of such metrics as "time to paint" which, whilst conceptually similar are of much less value – the key understanding is the point at which a given user regards the page as having loaded (filled the browser viewport).

2. Standalone or Integrated

RUM tooling operates by instrumenting site pages with a JavaScript beacon that writes back collected data following the unload step. The JavaScript may be deployed in one of two ways:

- By manual instrumentation of the site. This requires insertion of the JavaScript as high in the page headers as possible (unlike behavioral tags, a performance tag must be triggered early in the page download in order to time subsequent steps accurately). Depending upon the number of pages to be instrumented, it may be more practical to insert via a simple cut and paste operation or via an include statement. Manual instrumentation has the disadvantage of introducing an ongoing maintenance overhead for upgrading versions etc.

- By dynamic injection. Some tools offer the option of dynamically injecting the tag from the application server.

Integrated products offer RUM functionality as part of "end to end" visibility of traffic as a component of an Application Performance Management (APM) tool set. While typically involving greater investment, such products often offer better overall value through their enhanced "root cause" isolation ability.

3. Real-Time Reporting

Tools vary in two principal ways with regard to data handling:

- The duration of storage of captured data. As with other monitoring, the problem for vendors storing customer data is that they rapidly become data storage rather than monitoring companies. However, the ability to view trend data over extended periods is extremely useful, so individual vendor strategies to manage that requirement are relevant. This problem is exacerbated (for the vendor) if object level metrics are captured. Understand the options in this area.

- The frequency of update of customer data. This can vary between 24 hours and less than 5 minutes. Near real-time updates are relevant to active operations management, while daily information has limited historic value only.

4. All Traffic or Traffic Sampling

As RUM data is inferential in nature, it is important to capture all visitor traffic rather than a sample. Some tooling offers the option of user defined sampling, often to reduce license costs. This is unlikely to be good practice except possibly in the case of extremely high traffic sites. Within Europe, this situation is exacerbated by EU legislation enabling individual users to opt for "do not send" headers which restrict the transmission of tag based data, further limiting the overall coverage.

5. API

RUM tooling will always provide some output charting, etc. Additional value can be derived from integration of RUM data with outputs from other tooling. This is particularly so for those RUM products that do not report on session level data such as conversion and abandonment rates. In such cases, it is beneficial to combine such data from web analytics with RUM-based performance metrics.

6. Page or Object Level

Although, theoretically, all products could be extended to capture object level rather than page delivery metrics, in practice this tends to be restricted to the capture of specific individual objects (often for reasons associated with data handling as mentioned above).

7. User Event Capture

The ability to record the time between 2 events (e.g. mouse clicks). Such sub-page instrumentation is of value in supporting design and development decisions.

8. Extensibility

The ability to capture and integrate non-performance user data. Examples include: associating user login details with session performance, and collecting details of originating application or database server.

9. Reporting

Extent, type and customizability of in-built reporting. Various aspects include:

- Standard, inbuilt reports – extent and ease of use / comprehensibility.

- Custom reporting functions.

- Ease of data export - for manipulation / integration / display by external (e.g. dashboard) tools.

10. Investment

License models vary. Most are based on some combination of extent of visitor traffic (monthly or annual) and number of domains to be monitored. Some APM vendors include the cost of the RUM component within the overall agent license investment.

Although the importance of any particular aspect will vary depending upon precise use case and the nature of the application, hopefully the above will provide a useful checklist for initial engagement. Happy hunting!

Larry Haig is Senior Consultant at Intechnica.

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10 Points to Consider When Choosing a Real User Monitoring Tool

Larry Haig

Tools enabling Real User Monitoring, or RUM (also known as End User Experience, or EUE, and some similar things) have proliferated hugely over the last 10 years, to the point there are now countless hopeful vendors jostling for contention.

RUM certainly adds a powerful additional perspective to external monitoring, and whilst it does not replace "traditional" active (synthetic) testing – they are complimentary – it can dramatically improve some aspects of end-user performance visibility, particularly for companies with a broad international reach.

As always, a crowded marketplace can contain sheep as well as goats. I thought that it may be useful to share some musings on RUM product distinctions. The relative importance of each will depend on particular circumstances, and there is a degree of functional convergence occurring, at least among the players who are carving out significant market share (and who clearly wish to be around for the long-term). Hopefully these will be helpful to the uninitiated who are considering a purchase decision.

Ten points to consider about your potential RUM tool (in no particular order):

1. Sophistication/Coverage

Many RUM products are based on the standard W3C Navigation metrics. Although provision of standard page load metrics is one of the drivers of growth in tooling options, be aware that these are not supported in all browsers, primarily older versions and Safari. In certain cases, basic performance data is collected from these to supplement the core W3C metrics and offer more complete coverage.

Key aspects of sophistication include:

- Ability to record user journeys (logical transactions). Less evolved products act at individual page level only.

- Ability to capture and report individual session-level data – reporting on business relevant metrics such as transaction abandonment and shopping cart conversion by different categories of user.

- Detailed reporting – bounce rate, stickiness (time on site), etc. A tabular comparison between candidate products may be useful here.

- Ability to record "above the line" performance (browser fill aka perceived render time). This metric is rarely estimated by active monitoring tools (the only one of which I am aware is WebPageTest). As such, RUM tooling supporting this give a useful additional perspective into end-user satisfaction. Beware of such metrics as "time to paint" which, whilst conceptually similar are of much less value – the key understanding is the point at which a given user regards the page as having loaded (filled the browser viewport).

2. Standalone or Integrated

RUM tooling operates by instrumenting site pages with a JavaScript beacon that writes back collected data following the unload step. The JavaScript may be deployed in one of two ways:

- By manual instrumentation of the site. This requires insertion of the JavaScript as high in the page headers as possible (unlike behavioral tags, a performance tag must be triggered early in the page download in order to time subsequent steps accurately). Depending upon the number of pages to be instrumented, it may be more practical to insert via a simple cut and paste operation or via an include statement. Manual instrumentation has the disadvantage of introducing an ongoing maintenance overhead for upgrading versions etc.

- By dynamic injection. Some tools offer the option of dynamically injecting the tag from the application server.

Integrated products offer RUM functionality as part of "end to end" visibility of traffic as a component of an Application Performance Management (APM) tool set. While typically involving greater investment, such products often offer better overall value through their enhanced "root cause" isolation ability.

3. Real-Time Reporting

Tools vary in two principal ways with regard to data handling:

- The duration of storage of captured data. As with other monitoring, the problem for vendors storing customer data is that they rapidly become data storage rather than monitoring companies. However, the ability to view trend data over extended periods is extremely useful, so individual vendor strategies to manage that requirement are relevant. This problem is exacerbated (for the vendor) if object level metrics are captured. Understand the options in this area.

- The frequency of update of customer data. This can vary between 24 hours and less than 5 minutes. Near real-time updates are relevant to active operations management, while daily information has limited historic value only.

4. All Traffic or Traffic Sampling

As RUM data is inferential in nature, it is important to capture all visitor traffic rather than a sample. Some tooling offers the option of user defined sampling, often to reduce license costs. This is unlikely to be good practice except possibly in the case of extremely high traffic sites. Within Europe, this situation is exacerbated by EU legislation enabling individual users to opt for "do not send" headers which restrict the transmission of tag based data, further limiting the overall coverage.

5. API

RUM tooling will always provide some output charting, etc. Additional value can be derived from integration of RUM data with outputs from other tooling. This is particularly so for those RUM products that do not report on session level data such as conversion and abandonment rates. In such cases, it is beneficial to combine such data from web analytics with RUM-based performance metrics.

6. Page or Object Level

Although, theoretically, all products could be extended to capture object level rather than page delivery metrics, in practice this tends to be restricted to the capture of specific individual objects (often for reasons associated with data handling as mentioned above).

7. User Event Capture

The ability to record the time between 2 events (e.g. mouse clicks). Such sub-page instrumentation is of value in supporting design and development decisions.

8. Extensibility

The ability to capture and integrate non-performance user data. Examples include: associating user login details with session performance, and collecting details of originating application or database server.

9. Reporting

Extent, type and customizability of in-built reporting. Various aspects include:

- Standard, inbuilt reports – extent and ease of use / comprehensibility.

- Custom reporting functions.

- Ease of data export - for manipulation / integration / display by external (e.g. dashboard) tools.

10. Investment

License models vary. Most are based on some combination of extent of visitor traffic (monthly or annual) and number of domains to be monitored. Some APM vendors include the cost of the RUM component within the overall agent license investment.

Although the importance of any particular aspect will vary depending upon precise use case and the nature of the application, hopefully the above will provide a useful checklist for initial engagement. Happy hunting!

Larry Haig is Senior Consultant at Intechnica.

The Latest

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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