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For a 360-Degree View of the Customer, Combine Active and Passive Observability

Mehdi Daoudi
Catchpoint

Digital businesses don't invest in monitoring for monitoring's sake. They do it to make the business run better. Every dollar spent on observability — every hour your team spends using monitoring tools or responding to what they reveal — should tie back directly to business outcomes: conversions, revenues, brand equity. If they don't? You might be missing the forest for the trees.

As technologists, it's our ability to master the technical complexity involved in delivering successful applications that got us where we are. Yet, focusing too narrowly on technology can sometimes lead us astray. It's not uncommon, for example, for businesses to devote hundreds of hours to inching their digital storefront up in search engine rankings, even as customers struggle with basic functions on the site.

How can you avoid these kinds of pitfalls?

By always keeping a laser focus on the most important aspect of a digital business: the experience of users.

To capture a comprehensive customer view, businesses use a variety of tools, including Real User Monitoring, or RUM (which measures real user interactions), and active observability (simulating synthetic interactions to test the site's response). Too often though, these approaches aren't tied together in a strategic or intentional way. Instead, they exist in silos — sometimes owned by totally different teams — each providing only a partial, fragmented view.

Let's take a closer look at how you can ensure that real and synthetic observability strategies work together to measure what matters most.

Navigating Complexity

The basic goal of prioritizing user experience seems straightforward. Why then do so many businesses struggle to effectively measure it? Because modern digital applications have grown enormously complex.

A typical website now encompasses content and services from literally hundreds of sources: third-party data centers and servers, Domain Name System (DNS) and content delivery network (CDN) providers, load-balancers and site accelerators, social sharing widgets, tracking tags, and more. Problems with any of these elements can disrupt the user experience. That's to say nothing of all the variables on the user's end, such as issues with devices, browsers, and Internet Service Providers (ISPs).

To understand the health of a digital business, you need to observe all these elements and many others. So, modern digital businesses use both real and synthetic monitoring to measure different aspects of how users experience a site. To synthesize them into a holistic observability strategy, however, you need to understand exactly what each perspective shows you — and what it doesn't.

Inside Real User Observability

Real User Monitoring uses code placed on a website or mobile app (typically, the navigation timing API in browsers) to transmit performance metrics around engagement. This data can help you better understand your users — how they get to your site, from which markets and devices, which pages they access most, and more.

This type of observability can play a key role in linking digital interactions with core business metrics. For example, RUM can measure things like:

■ How many customers abandon the site when performance drops by 25%? How about 50%?

■ How do fluctuations in performance levels correlate with conversion rates?

■ When I make changes to my application (adding a new data center, changing CDN provider) what effects do they have on traffic, conversions, and other metrics?

Real user data can be particularly valuable in tracking longer-term trends. By correlating performance data with shopping cart abandonment, bounce rates, time spent on specific pages, and more, you can identify which metrics correlate most strongly with business outcomes. You can then use these insights to identify areas for improvement and prioritize investments towards activities with the most direct impact on revenues.

While RUM insights can be extremely valuable, however, you can't assume they're showing a complete picture of user experience. For example, if DNS issues prevent users from accessing your site, real user metrics won't show you that's happening.

Additionally, passive monitoring tools like RUM, are, well, passive. Anything you do in response those insights is, by definition, reacting to problems after they've already affected customers.

Getting Active

Active observability complements real user monitoring by taking a proactive approach to measuring system health. With active observability, you can continually poke and prod your application by generating synthetic user behavior — on any part of your site, 24x7, from any geography you choose.

Active observability fills in the gaps in passive monitoring, allowing you to spot potential issues before they affect your customers and revenues. It also offers:

Flexibility: Test whatever you want, however you want, from wherever you choose, as often as you choose — without having to wait for real users.

Visibility: Synthetic monitoring measures from the outside-in, capturing performance of both your own systems and third-party elements (DNS, CDNs, ISPs) at every step in the user journey. This also means that, when you detect a problem, you can quickly pinpoint the source.

Validation: With the ability to generate any kind of user behavior, from anywhere, you can measure the performance impact of prospective changes before they go to production.

Business intelligence: Active observability can help you benchmark your performance against the competition, as well as track performance of your digital partners (like DNS or CDN providers) and make sure they're living up to their service-level agreements.

Building Holistic Visibility

Both real and active tools play important roles in a digital observability strategy. To achieve true 360-degree visibility into the customer experience, however, you need to synthesize them within a single strategy. If you're approaching observability strategically, you'll use RUM to understand how real users interact with your site, so you know what to test. And you'll use synthetics to proactively, continually test those components and interactions that have the biggest impact on business outcomes.

Together, these approaches will provide ongoing insights to guide how you invest development and engineering resources — and then validate the effects of those investments. Effectively, you create a continuous feedback loop of measure, respond, and measure again. You end up with much deeper visibility into the customer experience. More important, you have a strategy driven not by technology, but by real-world business concerns.

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 ...

For a 360-Degree View of the Customer, Combine Active and Passive Observability

Mehdi Daoudi
Catchpoint

Digital businesses don't invest in monitoring for monitoring's sake. They do it to make the business run better. Every dollar spent on observability — every hour your team spends using monitoring tools or responding to what they reveal — should tie back directly to business outcomes: conversions, revenues, brand equity. If they don't? You might be missing the forest for the trees.

As technologists, it's our ability to master the technical complexity involved in delivering successful applications that got us where we are. Yet, focusing too narrowly on technology can sometimes lead us astray. It's not uncommon, for example, for businesses to devote hundreds of hours to inching their digital storefront up in search engine rankings, even as customers struggle with basic functions on the site.

How can you avoid these kinds of pitfalls?

By always keeping a laser focus on the most important aspect of a digital business: the experience of users.

To capture a comprehensive customer view, businesses use a variety of tools, including Real User Monitoring, or RUM (which measures real user interactions), and active observability (simulating synthetic interactions to test the site's response). Too often though, these approaches aren't tied together in a strategic or intentional way. Instead, they exist in silos — sometimes owned by totally different teams — each providing only a partial, fragmented view.

Let's take a closer look at how you can ensure that real and synthetic observability strategies work together to measure what matters most.

Navigating Complexity

The basic goal of prioritizing user experience seems straightforward. Why then do so many businesses struggle to effectively measure it? Because modern digital applications have grown enormously complex.

A typical website now encompasses content and services from literally hundreds of sources: third-party data centers and servers, Domain Name System (DNS) and content delivery network (CDN) providers, load-balancers and site accelerators, social sharing widgets, tracking tags, and more. Problems with any of these elements can disrupt the user experience. That's to say nothing of all the variables on the user's end, such as issues with devices, browsers, and Internet Service Providers (ISPs).

To understand the health of a digital business, you need to observe all these elements and many others. So, modern digital businesses use both real and synthetic monitoring to measure different aspects of how users experience a site. To synthesize them into a holistic observability strategy, however, you need to understand exactly what each perspective shows you — and what it doesn't.

Inside Real User Observability

Real User Monitoring uses code placed on a website or mobile app (typically, the navigation timing API in browsers) to transmit performance metrics around engagement. This data can help you better understand your users — how they get to your site, from which markets and devices, which pages they access most, and more.

This type of observability can play a key role in linking digital interactions with core business metrics. For example, RUM can measure things like:

■ How many customers abandon the site when performance drops by 25%? How about 50%?

■ How do fluctuations in performance levels correlate with conversion rates?

■ When I make changes to my application (adding a new data center, changing CDN provider) what effects do they have on traffic, conversions, and other metrics?

Real user data can be particularly valuable in tracking longer-term trends. By correlating performance data with shopping cart abandonment, bounce rates, time spent on specific pages, and more, you can identify which metrics correlate most strongly with business outcomes. You can then use these insights to identify areas for improvement and prioritize investments towards activities with the most direct impact on revenues.

While RUM insights can be extremely valuable, however, you can't assume they're showing a complete picture of user experience. For example, if DNS issues prevent users from accessing your site, real user metrics won't show you that's happening.

Additionally, passive monitoring tools like RUM, are, well, passive. Anything you do in response those insights is, by definition, reacting to problems after they've already affected customers.

Getting Active

Active observability complements real user monitoring by taking a proactive approach to measuring system health. With active observability, you can continually poke and prod your application by generating synthetic user behavior — on any part of your site, 24x7, from any geography you choose.

Active observability fills in the gaps in passive monitoring, allowing you to spot potential issues before they affect your customers and revenues. It also offers:

Flexibility: Test whatever you want, however you want, from wherever you choose, as often as you choose — without having to wait for real users.

Visibility: Synthetic monitoring measures from the outside-in, capturing performance of both your own systems and third-party elements (DNS, CDNs, ISPs) at every step in the user journey. This also means that, when you detect a problem, you can quickly pinpoint the source.

Validation: With the ability to generate any kind of user behavior, from anywhere, you can measure the performance impact of prospective changes before they go to production.

Business intelligence: Active observability can help you benchmark your performance against the competition, as well as track performance of your digital partners (like DNS or CDN providers) and make sure they're living up to their service-level agreements.

Building Holistic Visibility

Both real and active tools play important roles in a digital observability strategy. To achieve true 360-degree visibility into the customer experience, however, you need to synthesize them within a single strategy. If you're approaching observability strategically, you'll use RUM to understand how real users interact with your site, so you know what to test. And you'll use synthetics to proactively, continually test those components and interactions that have the biggest impact on business outcomes.

Together, these approaches will provide ongoing insights to guide how you invest development and engineering resources — and then validate the effects of those investments. Effectively, you create a continuous feedback loop of measure, respond, and measure again. You end up with much deeper visibility into the customer experience. More important, you have a strategy driven not by technology, but by real-world business concerns.

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 ...