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6 Key Factors of a Failsafe Performance Strategy

User Expectations Demand a New, More Comprehensive Approach to Performance Monitoring
Mehdi Daoudi
Catchpoint

The digital business era is placing a premium on strong end-user performance (speed) for all websites, mobile sites and applications. If a company wants to thrive in this environment, it must focus on delivering the highest quality user interactions possible. However, the truth is that businesses across industries are struggling to meet users' rising performance expectations. According to Radware, only 12 percent of e-commerce sites are currently meeting customer expectations for load times of three seconds or less. And that's an industry known for taking steps to improve performance!

Failing to deliver strong experiences can negatively impact a company's profits and brand reputation. Staying ahead of the game from a performance perspective really comes down to preparation and monitoring. If a comprehensive performance strategy is deployed, organizations are less likely to fall behind.

Today, a failsafe performance management strategy consists of these six key factors:

1. Get As Close to Your Users As Possible

Geography plays a major role in the user experience. The further away a user is from an organization's datacenter, the more performance tends to degrade. Therefore, it's crucial to measure performance as geographically close to real users as possible.

For example, the Internet landscape in China is very different from North America, so monitoring from a datacenter in California won't give you an accurate depiction of what users in China are experiencing. Considering the rapid growth occurring in the China e-commerce market, strong web performance will surely play a role in determining which global companies successfully establish a foothold there – and which ones do not. Catchpoint recently analyzed the in-China web performance of 21 leading US and global retailers – all big-name companies – and found that only about half of them are currently delivering satisfactory performance there.

2. Don't Forget Internal Users

Organizations tend to focus more on ensuring strong performance for external users, namely customers. While the customer experience is a top priority, delivering excellent performance for internal applications is also critical to your business. Internal users rely on enterprise applications on a daily basis, and if their performance is poor, productivity could diminish. In many cases, internal applications ultimately serve a customer-facing purpose (for instance, a bank teller's app in a remote branch), so poor performance for these types of applications can in fact tarnish external reputation.

As noted above, performance tends to decline the further away users are from the data center – and this is also true for internal applications. Fortunately, new solutions enable organizations to measure performance in remote locations, in a simple, cost-effective manner that doesn't require extra IT resources and integrates seamlessly with larger, enterprise-wide performance monitoring initiatives.

3. Reactive Monitoring Is Dead

The traditional approach to performance monitoring has typically emphasized detecting and diagnosing problems; however, today, this just isn't enough. Today's customers take to social media networks to vent frustration almost immediately after a bad experience, so by the time a problem has occurred it's too late.

Adding to this challenge, increased IT complexity makes it very difficult to pinpoint and identify the source of problems quickly. Many businesses are using several measurement solutions to enhance their performance strategy, yet all of those layers take time to sift through.

This is why it's important to deploy a solution that interoperates seamlessly with other solutions to support a comprehensive, enterprise-wide monitoring strategy. Performance monitoring data offers deeper insights when correlated and analyzed in aggregate, so interoperability between tools can significantly enrich and optimize overall efforts. Ultimately, the result is improved productivity and the ability to find and fix application performance problems faster, ideally before end users become aware of them. For example, if performance for a particular set of applications declines, the IT team is in a better position to spot a trend and quickly identify the culprit, like one slow server or database supporting the apps.

4. IT Operational Excellence

IT operations teams used to define excellence as delivering “good enough” performance on the least amount of resources. Today, IT operational excellence is being redefined as the optimization of IT to support exceptional customer experiences.

For example, in a virtualized environment there may be a certain level of CPU utilization (less than 100 percent) where application performance begins to suffer. In this example, 100 percent utilization is not the ideal. As described previously, using more than one advanced analytics platform can help you discover these thresholds. IT operational excellence, when delivering the best customer experience possible is the focus, is a significant benefit of an evolved APM strategy.

5. Pay Attention to Third-Party Services

While it's important to use third-party services to deliver a richer and more satisfying customer experience that will help drive conversions, they can also become a performance nightmare if mismanaged. Prioritizing the needed third parties over supplemental third parties can prevent performance problems associated with this complexity.

In addition, organizations must have a way to isolate third parties and understand how their individual performance impacts the performance of their sites or applications overall, and quickly remove any services causing problems.

This is an especially important exercise when it comes to mobile sites and applications, as constrained mobile networks often exaggerate performance problems. The potential for third parties to wreak havoc was demonstrated during the 2015 holiday season. Overall, the leading mobile retail sites performed well, in spite of being “heavier” and delivering more content. When problems did occur, they were almost always found to be the result of malfunctioning third parties. This suggests that while mobile sites seem to have gotten a handle on delivering their own content well, they are still struggling to gain control over elements outside their own four walls.

Mobile traffic has increased immensely in recent years, making it a vital component to businesses. Reducing the number of third parties on mobile sites can support faster load times and better overall mobile user experiences.

6. Comprehensive Synthetic Monitoring with Real End-User Measurement

Synthetic monitoring is a proactive approach which monitors website availability and performance by generating synthetic-user traffic from cloud resources in various geographies. Regardless of the volume of traffic a site has at any given time, organizations can rely on synthetic monitoring to provide a realistic view of performance for users across a wide range of geographies. The limitation of synthetic monitoring, however, is that it does not show what actual users are doing once they enter a site or application.

Real-user monitoring (RUM) can bridge this gap by giving an understanding of what users do once they enter a site or application, like the most common landing pages and conversion paths. This can help an organization understand what parts of a site or application must be prioritized for optimization. However, this is a reactive approach, so performance problems will only be caught as they happen. Combining synthetic monitoring with real-user measurement is the best method to get a holistic understanding of performance across geographies; identify and fix problems before end users become aware; and see what website and application areas should be performance priorities.

In the digital business era, these six factors play a key role in defining a successful performance monitoring strategy. Today, a “satisfactory” user experience is not enough to propel a business, and with competition just a click away, delivering exceptional user experiences takes on the utmost of importance. Businesses across industries need to rethink their performance monitoring strategies, making them more comprehensive and proactive than ever before.

Mehdi Daoudi is CEO and Co-Founder of Catchpoint

The Latest

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 ...

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

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 ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

6 Key Factors of a Failsafe Performance Strategy

User Expectations Demand a New, More Comprehensive Approach to Performance Monitoring
Mehdi Daoudi
Catchpoint

The digital business era is placing a premium on strong end-user performance (speed) for all websites, mobile sites and applications. If a company wants to thrive in this environment, it must focus on delivering the highest quality user interactions possible. However, the truth is that businesses across industries are struggling to meet users' rising performance expectations. According to Radware, only 12 percent of e-commerce sites are currently meeting customer expectations for load times of three seconds or less. And that's an industry known for taking steps to improve performance!

Failing to deliver strong experiences can negatively impact a company's profits and brand reputation. Staying ahead of the game from a performance perspective really comes down to preparation and monitoring. If a comprehensive performance strategy is deployed, organizations are less likely to fall behind.

Today, a failsafe performance management strategy consists of these six key factors:

1. Get As Close to Your Users As Possible

Geography plays a major role in the user experience. The further away a user is from an organization's datacenter, the more performance tends to degrade. Therefore, it's crucial to measure performance as geographically close to real users as possible.

For example, the Internet landscape in China is very different from North America, so monitoring from a datacenter in California won't give you an accurate depiction of what users in China are experiencing. Considering the rapid growth occurring in the China e-commerce market, strong web performance will surely play a role in determining which global companies successfully establish a foothold there – and which ones do not. Catchpoint recently analyzed the in-China web performance of 21 leading US and global retailers – all big-name companies – and found that only about half of them are currently delivering satisfactory performance there.

2. Don't Forget Internal Users

Organizations tend to focus more on ensuring strong performance for external users, namely customers. While the customer experience is a top priority, delivering excellent performance for internal applications is also critical to your business. Internal users rely on enterprise applications on a daily basis, and if their performance is poor, productivity could diminish. In many cases, internal applications ultimately serve a customer-facing purpose (for instance, a bank teller's app in a remote branch), so poor performance for these types of applications can in fact tarnish external reputation.

As noted above, performance tends to decline the further away users are from the data center – and this is also true for internal applications. Fortunately, new solutions enable organizations to measure performance in remote locations, in a simple, cost-effective manner that doesn't require extra IT resources and integrates seamlessly with larger, enterprise-wide performance monitoring initiatives.

3. Reactive Monitoring Is Dead

The traditional approach to performance monitoring has typically emphasized detecting and diagnosing problems; however, today, this just isn't enough. Today's customers take to social media networks to vent frustration almost immediately after a bad experience, so by the time a problem has occurred it's too late.

Adding to this challenge, increased IT complexity makes it very difficult to pinpoint and identify the source of problems quickly. Many businesses are using several measurement solutions to enhance their performance strategy, yet all of those layers take time to sift through.

This is why it's important to deploy a solution that interoperates seamlessly with other solutions to support a comprehensive, enterprise-wide monitoring strategy. Performance monitoring data offers deeper insights when correlated and analyzed in aggregate, so interoperability between tools can significantly enrich and optimize overall efforts. Ultimately, the result is improved productivity and the ability to find and fix application performance problems faster, ideally before end users become aware of them. For example, if performance for a particular set of applications declines, the IT team is in a better position to spot a trend and quickly identify the culprit, like one slow server or database supporting the apps.

4. IT Operational Excellence

IT operations teams used to define excellence as delivering “good enough” performance on the least amount of resources. Today, IT operational excellence is being redefined as the optimization of IT to support exceptional customer experiences.

For example, in a virtualized environment there may be a certain level of CPU utilization (less than 100 percent) where application performance begins to suffer. In this example, 100 percent utilization is not the ideal. As described previously, using more than one advanced analytics platform can help you discover these thresholds. IT operational excellence, when delivering the best customer experience possible is the focus, is a significant benefit of an evolved APM strategy.

5. Pay Attention to Third-Party Services

While it's important to use third-party services to deliver a richer and more satisfying customer experience that will help drive conversions, they can also become a performance nightmare if mismanaged. Prioritizing the needed third parties over supplemental third parties can prevent performance problems associated with this complexity.

In addition, organizations must have a way to isolate third parties and understand how their individual performance impacts the performance of their sites or applications overall, and quickly remove any services causing problems.

This is an especially important exercise when it comes to mobile sites and applications, as constrained mobile networks often exaggerate performance problems. The potential for third parties to wreak havoc was demonstrated during the 2015 holiday season. Overall, the leading mobile retail sites performed well, in spite of being “heavier” and delivering more content. When problems did occur, they were almost always found to be the result of malfunctioning third parties. This suggests that while mobile sites seem to have gotten a handle on delivering their own content well, they are still struggling to gain control over elements outside their own four walls.

Mobile traffic has increased immensely in recent years, making it a vital component to businesses. Reducing the number of third parties on mobile sites can support faster load times and better overall mobile user experiences.

6. Comprehensive Synthetic Monitoring with Real End-User Measurement

Synthetic monitoring is a proactive approach which monitors website availability and performance by generating synthetic-user traffic from cloud resources in various geographies. Regardless of the volume of traffic a site has at any given time, organizations can rely on synthetic monitoring to provide a realistic view of performance for users across a wide range of geographies. The limitation of synthetic monitoring, however, is that it does not show what actual users are doing once they enter a site or application.

Real-user monitoring (RUM) can bridge this gap by giving an understanding of what users do once they enter a site or application, like the most common landing pages and conversion paths. This can help an organization understand what parts of a site or application must be prioritized for optimization. However, this is a reactive approach, so performance problems will only be caught as they happen. Combining synthetic monitoring with real-user measurement is the best method to get a holistic understanding of performance across geographies; identify and fix problems before end users become aware; and see what website and application areas should be performance priorities.

In the digital business era, these six factors play a key role in defining a successful performance monitoring strategy. Today, a “satisfactory” user experience is not enough to propel a business, and with competition just a click away, delivering exceptional user experiences takes on the utmost of importance. Businesses across industries need to rethink their performance monitoring strategies, making them more comprehensive and proactive than ever before.

Mehdi Daoudi is CEO and Co-Founder of Catchpoint

The Latest

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 ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

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 ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...