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Internet Disruptions Cost E-Commerce Retailers Millions Annually

Howard Beader
Catchpoint

Recent data from eMarketer projects that U.S. retail ecommerce sales will accelerate each year through 2027, reaching more than $1.7 trillion and comprising one-fifth of total retail sales. The great societal shift online is no longer an emerging trend, it's how we live, work and play.

Now that so much of our lives take place online, consumers have increasingly higher expectations about online experience. The consequences of poor experience are significant for e-commerce retailers, affecting sales, revenue, and stock price. New research conducted by Forrester Research on behalf of Catchpoint shows that one cause of poor experiences are disruptions across the "Internet stack," including routers, firewalls, ISPs, DNS, CDNs, cloud services, website payment providers, and video hosting services — which are particularly costly for e-commerce retailers.

The survey found that nearly 40% of e-commerce retailers suffer customer-impacting disruptions, as many as 76 per month on average, and these can cost up to $1 million per month. Despite the frequency and costs of disruption, however, many e-commerce retailers have been slow to adopt new solutions to proactively reduce or eliminate instances and increase their Internet resilience. Less than one-third of respondents in the survey monitor their full Internet stack today.

But the winds are shifting, with more e-commerce retailers adopting new technologies to gain visibility outside their traditional network infrastructure. 61% of survey respondents say they require tools to anticipate, detect, and fix Internet performance problems quickly, indicating a need for better management of Internet performance.

While monitoring the entire Internet stack isn't easy, with thousands of blind spots dispersed geographically that could become disruptions or affect experience, doing nothing isn't an option. This is precisely why adoption of Internet Performance Monitoring (IPM), which provides those capabilities e-commerce retailers say is missing, is growing.

The survey findings make a strong case for IPM, quantifying the consequences of not closely monitoring all aspects of a customer's experience and addressing issues before they happen. With so much at stake, from slow site loading to abandoned shopping carts, there must be zero tolerance for disruption. And this starts with proactive monitoring that anticipates problems instead of reporting on them retrospectively.

As I've written before, the Internet is your new network, and if you're an e-commerce retailer — or any company for that matter, Internet resiliency is critical to the quality and consistency of your digital experience. Our survey shows that e-commerce retailers acknowledge the importance of proactively monitoring the Internet stack, and this industry pivot is certainly a hopeful sign.

Howard Beader is VP of Product Marketing at Catchpoint

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Internet Disruptions Cost E-Commerce Retailers Millions Annually

Howard Beader
Catchpoint

Recent data from eMarketer projects that U.S. retail ecommerce sales will accelerate each year through 2027, reaching more than $1.7 trillion and comprising one-fifth of total retail sales. The great societal shift online is no longer an emerging trend, it's how we live, work and play.

Now that so much of our lives take place online, consumers have increasingly higher expectations about online experience. The consequences of poor experience are significant for e-commerce retailers, affecting sales, revenue, and stock price. New research conducted by Forrester Research on behalf of Catchpoint shows that one cause of poor experiences are disruptions across the "Internet stack," including routers, firewalls, ISPs, DNS, CDNs, cloud services, website payment providers, and video hosting services — which are particularly costly for e-commerce retailers.

The survey found that nearly 40% of e-commerce retailers suffer customer-impacting disruptions, as many as 76 per month on average, and these can cost up to $1 million per month. Despite the frequency and costs of disruption, however, many e-commerce retailers have been slow to adopt new solutions to proactively reduce or eliminate instances and increase their Internet resilience. Less than one-third of respondents in the survey monitor their full Internet stack today.

But the winds are shifting, with more e-commerce retailers adopting new technologies to gain visibility outside their traditional network infrastructure. 61% of survey respondents say they require tools to anticipate, detect, and fix Internet performance problems quickly, indicating a need for better management of Internet performance.

While monitoring the entire Internet stack isn't easy, with thousands of blind spots dispersed geographically that could become disruptions or affect experience, doing nothing isn't an option. This is precisely why adoption of Internet Performance Monitoring (IPM), which provides those capabilities e-commerce retailers say is missing, is growing.

The survey findings make a strong case for IPM, quantifying the consequences of not closely monitoring all aspects of a customer's experience and addressing issues before they happen. With so much at stake, from slow site loading to abandoned shopping carts, there must be zero tolerance for disruption. And this starts with proactive monitoring that anticipates problems instead of reporting on them retrospectively.

As I've written before, the Internet is your new network, and if you're an e-commerce retailer — or any company for that matter, Internet resiliency is critical to the quality and consistency of your digital experience. Our survey shows that e-commerce retailers acknowledge the importance of proactively monitoring the Internet stack, and this industry pivot is certainly a hopeful sign.

Howard Beader is VP of Product Marketing at Catchpoint

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...