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The Importance of Baselining for End-User Experience Management

Sri Chaganty

If your business depends on mission-critical web or legacy applications, then monitoring how your end users interact with your applications is critical. The end users' experience after pressing the ENTER key or clicking SUBMIT might decide the bottom line of your enterprise.

Most monitoring solutions try to infer the end-user experience based on resource utilization. However, resource utilization cannot provide meaningful results on how the end-user is experiencing an interaction with an application. The true measurement of end-user experience is availability and response time of the application, end-to-end and hop-by-hop.

The responsiveness of the application determines the end user's experience. In order to understand the end user's experience, contextual intelligence on how the application is responding based on the time of the day, the day of the week, the week of the month and the month of the year must be measured. Baselining requires capturing these metrics across a time dimension. The base line of response time of an application at regular intervals provides the ability to ensure that the application is working as designed. It is more than a single report detailing the health of the application at a certain point in time.

"Dynamic baselining" is a technique to compare real response times against historical averages. Dynamic baselining is an effective technique to provide meaningful insight into service anomalies without requiring the impossible task of setting absolute thresholds for every transaction.
A robust user experience solution will also include application and system errors that have a significant impact on the ability of the user to complete a task. Since the user experience is often impacted by the performance of the user's device, metrics about desktop/laptop performance are required for adequate root-cause analysis.

For example, when you collect response time within the Exchange environment over a period of time, with data reflecting periods of low, average, and peak usage, you can make a subjective determination of what is acceptable performance for your system. That determination is your baseline, which you can then use to detect bottlenecks and to watch for long-term changes in usage patterns that require Ops to balance infrastructure capacity against demand to achieve the intended performance.

When you need to troubleshoot system problems, the response time baseline gives you information about the behavior of system resources at the time the problem occurred, which is useful in discovering its cause. When determining your baseline, it is important to know the types of work that are being done and the days and times when that work is done. This provides the association of the work performed with the resource usage to determine whether performance during those intervals is acceptable.

Response time baselining helps you to understand not only resource utilization issues but also availability and responsiveness of services on which the application flow is dependent upon. For example, if your Active Directory is not responding in an optimal way, the end-user experiences unintended latencies with the application's performance.

By following the baseline process, you can obtain the following information:

■ What is the real experience of the user when using any application?

■ What is "normal" behavior?

■ Is "normal" meeting service levels that drive productivity?

■ Is "normal" optimal?

■ Are deterministic answers available? Time to close a ticket, Root cause for outage, Predictive warnings, etc.

■ Who is using what, when and how much?

■ What is the experience of each individual user and a group of users?

■ Dependencies on infrastructure

■ Real-time interaction with infrastructure

■ Gain valuable information on the health of the hardware and software that is part of the application service delivery chain

■ Determine resource utilization

■ Make accurate decisions about alarm thresholds

Response time baselining empowers you to provide guaranteed service levels to your end users for every business critical application which in turns helps the bottom-line of the business.

Sri Chaganty is COO and CTO/Founder at AppEnsure.

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The Importance of Baselining for End-User Experience Management

Sri Chaganty

If your business depends on mission-critical web or legacy applications, then monitoring how your end users interact with your applications is critical. The end users' experience after pressing the ENTER key or clicking SUBMIT might decide the bottom line of your enterprise.

Most monitoring solutions try to infer the end-user experience based on resource utilization. However, resource utilization cannot provide meaningful results on how the end-user is experiencing an interaction with an application. The true measurement of end-user experience is availability and response time of the application, end-to-end and hop-by-hop.

The responsiveness of the application determines the end user's experience. In order to understand the end user's experience, contextual intelligence on how the application is responding based on the time of the day, the day of the week, the week of the month and the month of the year must be measured. Baselining requires capturing these metrics across a time dimension. The base line of response time of an application at regular intervals provides the ability to ensure that the application is working as designed. It is more than a single report detailing the health of the application at a certain point in time.

"Dynamic baselining" is a technique to compare real response times against historical averages. Dynamic baselining is an effective technique to provide meaningful insight into service anomalies without requiring the impossible task of setting absolute thresholds for every transaction.
A robust user experience solution will also include application and system errors that have a significant impact on the ability of the user to complete a task. Since the user experience is often impacted by the performance of the user's device, metrics about desktop/laptop performance are required for adequate root-cause analysis.

For example, when you collect response time within the Exchange environment over a period of time, with data reflecting periods of low, average, and peak usage, you can make a subjective determination of what is acceptable performance for your system. That determination is your baseline, which you can then use to detect bottlenecks and to watch for long-term changes in usage patterns that require Ops to balance infrastructure capacity against demand to achieve the intended performance.

When you need to troubleshoot system problems, the response time baseline gives you information about the behavior of system resources at the time the problem occurred, which is useful in discovering its cause. When determining your baseline, it is important to know the types of work that are being done and the days and times when that work is done. This provides the association of the work performed with the resource usage to determine whether performance during those intervals is acceptable.

Response time baselining helps you to understand not only resource utilization issues but also availability and responsiveness of services on which the application flow is dependent upon. For example, if your Active Directory is not responding in an optimal way, the end-user experiences unintended latencies with the application's performance.

By following the baseline process, you can obtain the following information:

■ What is the real experience of the user when using any application?

■ What is "normal" behavior?

■ Is "normal" meeting service levels that drive productivity?

■ Is "normal" optimal?

■ Are deterministic answers available? Time to close a ticket, Root cause for outage, Predictive warnings, etc.

■ Who is using what, when and how much?

■ What is the experience of each individual user and a group of users?

■ Dependencies on infrastructure

■ Real-time interaction with infrastructure

■ Gain valuable information on the health of the hardware and software that is part of the application service delivery chain

■ Determine resource utilization

■ Make accurate decisions about alarm thresholds

Response time baselining empowers you to provide guaranteed service levels to your end users for every business critical application which in turns helps the bottom-line of the business.

Sri Chaganty is COO and CTO/Founder at AppEnsure.

The Latest

A new study by the IBM Institute for Business Value reveals that enterprises are expected to significantly scale AI-enabled workflows, many driven by agentic AI, relying on them for improved decision making and automation. The AI Projects to Profits study revealed that respondents expect AI-enabled workflows to grow from 3% today to 25% by the end of 2025. With 70% of surveyed executives indicating that agentic AI is important to their organization's future, the research suggests that many organizations are actively encouraging experimentation ...

Respondents predict that agentic AI will play an increasingly prominent role in their interactions with technology vendors over the coming years and are positive about the benefits it will bring, according to The Race to an Agentic Future: How Agentic AI Will Transform Customer Experience, a report from Cisco ...

A new wave of tariffs, some exceeding 100%, is sending shockwaves across the technology industry. Enterprises are grappling with sudden, dramatic cost increases that threaten to disrupt carefully planned budgets, sourcing strategies, and deployment plans. For CIOs and CTOs, this isn't just an economic setback; it's a wake-up call. The era of predictable cloud pricing and stable global supply chains is over ...

As artificial intelligence (AI) adoption gains momentum, network readiness is emerging as a critical success factor. AI workloads generate unpredictable bursts of traffic, demanding high-speed connectivity that is low latency and lossless. AI adoption will require upgrades and optimizations in data center networks and wide-area networks (WANs). This is prompting enterprise IT teams to rethink, re-architect, and upgrade their data center and WANs to support AI-driven operations ...

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...

Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...