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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.

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If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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 ...

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