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3 Approaches to End-User Experience Monitoring

Sridhar Iyengar

The volume of transactions running through websites and mobile apps make customer-facing applications crucial to online businesses. If these applications perform well for their users, they generate revenue for the business. If they don't, they affect the credibility of the business, which in turn affects the overall revenue. It is therefore imperative that businesses understand how well their revenue-critical applications are behaving for their end users.

From an IT team's point of view, understanding the user experience of their applications is becoming challenging as technology evolves. Newer and more complex applications are being written using an assortment of languages. These applications are being deployed on a wide variety of infrastructure components. To add to that, today's users access these modern applications on a variety of devices such as the Web, smartphones, tablets and smart watches.

Fortunately, there are a few means available through which businesses can determine the user experience of their Web applications. Let's take a look at three common approaches:

Real User Monitoring (RUM)

Real user monitoring is a passive monitoring approach that involves collecting metrics at the browser level to accurately determine the application performance as perceived by the end users. Monitoring at the browser level is achieved by injecting JavaScript snippets into the header and footer of the HTML code of the Web application. This code will ascertain the full-page load experience — including downloading the assets from the content delivery network (CDN), rendering the page and executing the JavaScript from the browser's perspective. Additional instrumentation can be used to collect more metrics by injecting additional JavaScript code.

The data gathered through RUM provides answers to questions about user experience such as:

■ How long did it take to load the full page?

■ What is the response time from a network perspective (redirection time, DNS resolution time, connection time)?

■ What is the time interval between sending the request and receiving the first byte of response?

■ What is the time taken by the browser to receive the response and render the page?

■ Are there any problems on the page? If yes, what caused the problem?

■ How is the performance when the application is accessed from different countries?

■ What is the response time across different browsers? Do new application updates affect the performance in a specific version of the browser?

■ How does the application perform in different platforms such as desktop, Web and mobile?

The biggest advantage of monitoring real user data is that it relies on actual traffic to take measurements. There is no need to script the important use cases, which can save a lot of time and resources.

Real user monitoring captures everything as a user goes through the application, so performance data will be available irrespective of what pages the user sees. This is particularly useful for complex apps in which the functionality or content is dynamic.

Server-Side Monitoring

Although user experience is best tracked at the browser level, application performance monitoring at the server side also provides insight into end-user performance. Server-side monitoring is mostly used in conjunction with real user monitoring. This is because problems originating on the server side can only be efficiently detected using server-side monitoring.

Monitoring performance on the server side involves agent-based instrumentation technology for acquiring and transmitting data. This monitoring approach is used to watch user transactions in real time and troubleshoot in case of issues such as slowness or application bugs.

Developers have to install agents on the application server to help capture and visualize transactions end-to-end, with performance statistics across all components, from the URL down to the SQL level. This visual breakdown reveals the flow of all the user transactions being executed in each layer of the application infrastructure.

Server-side monitoring helps track response time and throughput taken by each application component, with the option to trace transactions end-to-end via code analysis. This helps the IT Operations/DevOps teams identify slow Web transactions and then isolate performance issues down to the level of the specific application code that caused them. The underlying database is also monitored most of the time to determine slow database calls, database usage and overall database performance. With server-side monitoring, users will be able to identify the SQL queries executed during a transaction and thus identify the worst performing queries.

Synthetic Transaction Monitoring

Synthetic transaction monitoring is an active monitoring technique based on the concept of simulating the actions of an end user on a Web application. This method involves the use of external monitoring agents executing pre-recorded scripts that mimic end-user behavior at regular time intervals. The monitoring agents are usually very light and do not create any additional load on network traffic.

Most application performance monitoring solutions provide recorder tools to capture the actions or paths a typical end user might take in an application, such as log in, view product, search and check out. These recordings are saved as scripts, which are then executed by the monitoring agents from different geographical locations.

Technically, there are two different approaches to generating requests. Some solutions replay recorded HTTP traffic patterns, while others drive real browser instances. The second approach is more useful for modern applications that make a lot of JavaScript, CSS and Ajax calls.

Since synthetic transaction monitoring involves sending requests across the network, it can measure the response time of application servers and network infrastructure. This type of monitoring does not require actual Web traffic, so you can use this approach to test your Web applications prior to launch — or anytime you like. Many companies use synthetic monitoring before entering production in the form of automated integration tests with Selenium.

Synthetic monitoring does have its limitations, though. Since the monitoring is based on pre-defined transactions, it does not monitor the perception of real end users. Transactions have to be “read-only” because they would otherwise set off real purchase processes. This limits the usage to a certain subset of your business-critical transactions.

The best approach is to use synthetic transaction monitoring as a reference measurement that will help identify performance degradation, detect network problems and notify in case of errors.

Every business is different and has its own requirements that can help to choose which type of monitoring to implement. An ideal strategy would be to use active and passive monitoring techniques side by side so that no stone is left unturned in the pursuit to monitor end-user experience.

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3 Approaches to End-User Experience Monitoring

Sridhar Iyengar

The volume of transactions running through websites and mobile apps make customer-facing applications crucial to online businesses. If these applications perform well for their users, they generate revenue for the business. If they don't, they affect the credibility of the business, which in turn affects the overall revenue. It is therefore imperative that businesses understand how well their revenue-critical applications are behaving for their end users.

From an IT team's point of view, understanding the user experience of their applications is becoming challenging as technology evolves. Newer and more complex applications are being written using an assortment of languages. These applications are being deployed on a wide variety of infrastructure components. To add to that, today's users access these modern applications on a variety of devices such as the Web, smartphones, tablets and smart watches.

Fortunately, there are a few means available through which businesses can determine the user experience of their Web applications. Let's take a look at three common approaches:

Real User Monitoring (RUM)

Real user monitoring is a passive monitoring approach that involves collecting metrics at the browser level to accurately determine the application performance as perceived by the end users. Monitoring at the browser level is achieved by injecting JavaScript snippets into the header and footer of the HTML code of the Web application. This code will ascertain the full-page load experience — including downloading the assets from the content delivery network (CDN), rendering the page and executing the JavaScript from the browser's perspective. Additional instrumentation can be used to collect more metrics by injecting additional JavaScript code.

The data gathered through RUM provides answers to questions about user experience such as:

■ How long did it take to load the full page?

■ What is the response time from a network perspective (redirection time, DNS resolution time, connection time)?

■ What is the time interval between sending the request and receiving the first byte of response?

■ What is the time taken by the browser to receive the response and render the page?

■ Are there any problems on the page? If yes, what caused the problem?

■ How is the performance when the application is accessed from different countries?

■ What is the response time across different browsers? Do new application updates affect the performance in a specific version of the browser?

■ How does the application perform in different platforms such as desktop, Web and mobile?

The biggest advantage of monitoring real user data is that it relies on actual traffic to take measurements. There is no need to script the important use cases, which can save a lot of time and resources.

Real user monitoring captures everything as a user goes through the application, so performance data will be available irrespective of what pages the user sees. This is particularly useful for complex apps in which the functionality or content is dynamic.

Server-Side Monitoring

Although user experience is best tracked at the browser level, application performance monitoring at the server side also provides insight into end-user performance. Server-side monitoring is mostly used in conjunction with real user monitoring. This is because problems originating on the server side can only be efficiently detected using server-side monitoring.

Monitoring performance on the server side involves agent-based instrumentation technology for acquiring and transmitting data. This monitoring approach is used to watch user transactions in real time and troubleshoot in case of issues such as slowness or application bugs.

Developers have to install agents on the application server to help capture and visualize transactions end-to-end, with performance statistics across all components, from the URL down to the SQL level. This visual breakdown reveals the flow of all the user transactions being executed in each layer of the application infrastructure.

Server-side monitoring helps track response time and throughput taken by each application component, with the option to trace transactions end-to-end via code analysis. This helps the IT Operations/DevOps teams identify slow Web transactions and then isolate performance issues down to the level of the specific application code that caused them. The underlying database is also monitored most of the time to determine slow database calls, database usage and overall database performance. With server-side monitoring, users will be able to identify the SQL queries executed during a transaction and thus identify the worst performing queries.

Synthetic Transaction Monitoring

Synthetic transaction monitoring is an active monitoring technique based on the concept of simulating the actions of an end user on a Web application. This method involves the use of external monitoring agents executing pre-recorded scripts that mimic end-user behavior at regular time intervals. The monitoring agents are usually very light and do not create any additional load on network traffic.

Most application performance monitoring solutions provide recorder tools to capture the actions or paths a typical end user might take in an application, such as log in, view product, search and check out. These recordings are saved as scripts, which are then executed by the monitoring agents from different geographical locations.

Technically, there are two different approaches to generating requests. Some solutions replay recorded HTTP traffic patterns, while others drive real browser instances. The second approach is more useful for modern applications that make a lot of JavaScript, CSS and Ajax calls.

Since synthetic transaction monitoring involves sending requests across the network, it can measure the response time of application servers and network infrastructure. This type of monitoring does not require actual Web traffic, so you can use this approach to test your Web applications prior to launch — or anytime you like. Many companies use synthetic monitoring before entering production in the form of automated integration tests with Selenium.

Synthetic monitoring does have its limitations, though. Since the monitoring is based on pre-defined transactions, it does not monitor the perception of real end users. Transactions have to be “read-only” because they would otherwise set off real purchase processes. This limits the usage to a certain subset of your business-critical transactions.

The best approach is to use synthetic transaction monitoring as a reference measurement that will help identify performance degradation, detect network problems and notify in case of errors.

Every business is different and has its own requirements that can help to choose which type of monitoring to implement. An ideal strategy would be to use active and passive monitoring techniques side by side so that no stone is left unturned in the pursuit to monitor end-user experience.

The Latest

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...