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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

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

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

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...