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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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The Latest

CEOs are committed to advancing AI solutions across their organization even as they face challenges from accelerating technology adoption, according to the IBM CEO Study. The survey revealed that executive respondents expect the growth rate of AI investments to more than double in the next two years, and 61% confirm they are actively adopting AI agents today and preparing to implement them at scale ...

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A major architectural shift is underway across enterprise networks, according to a new global study from Cisco. As AI assistants, agents, and data-driven workloads reshape how work gets done, they're creating faster, more dynamic, more latency-sensitive, and more complex network traffic. Combined with the ubiquity of connected devices, 24/7 uptime demands, and intensifying security threats, these shifts are driving infrastructure to adapt and evolve ...

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The development of banking apps was supposed to provide users with convenience, control and piece of mind. However, for thousands of Halifax customers recently, a major mobile outage caused the exact opposite, leaving customers unable to check balances, or pay bills, sparking widespread frustration. This wasn't an isolated incident ... So why are these failures still happening? ...

Cyber threats are growing more sophisticated every day, and at their forefront are zero-day vulnerabilities. These elusive security gaps are exploited before a fix becomes available, making them among the most dangerous threats in today's digital landscape ... This guide will explore what these vulnerabilities are, how they work, why they pose such a significant threat, and how modern organizations can stay protected ...

The prevention of data center outages continues to be a strategic priority for data center owners and operators. Infrastructure equipment has improved, but the complexity of modern architectures and evolving external threats presents new risks that operators must actively manage, according to the Data Center Outage Analysis 2025 from Uptime Institute ...

As observability engineers, we navigate a sea of telemetry daily. We instrument our applications, configure collectors, and build dashboards, all in pursuit of understanding our complex distributed systems. Yet, amidst this flood of data, a critical question often remains unspoken, or at best, answered by gut feeling: "Is our telemetry actually good?" ... We're inviting you to participate in shaping a foundational element for better observability: the Instrumentation Score ...

We're inching ever closer toward a long-held goal: technology infrastructure that is so automated that it can protect itself. But as IT leaders aggressively employ automation across our enterprises, we need to continuously reassess what AI is ready to manage autonomously and what can not yet be trusted to algorithms ...

Much like a traditional factory turns raw materials into finished products, the AI factory turns vast datasets into actionable business outcomes through advanced models, inferences, and automation. From the earliest data inputs to the final token output, this process must be reliable, repeatable, and scalable. That requires industrializing the way AI is developed, deployed, and managed ...

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