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The Deeper Problem Under HealthCare.gov's Rocky Start

Even as the federal government appears to have untangled the worst of HealthCare.gov's problems, the finger pointing and agonizing about what went wrong with the Affordable Care Act's centerpiece website are unlikely to die down any time soon.

The political dimensions aside, there's a persistent curiosity about how such a high-profile project could have failed so spectacularly. It was possibly the world's most important IT project of the moment, yet it performed as if it were rolled out the door without so much as a cursory kick of the tires.

That's because it probably was – and that's far from unusual.

A recent LinkedIn/Empirix survey found that at most companies and public agencies, pre-deployment testing is half-hearted at best and non-existent at worst. Public agencies and private companies alike have abysmal records for testing customer-facing IT projects, such as customer service and e-commerce portals.

This is despite the importance that most organizations place on creating a consistently positive customer experience; almost 60 percent of the contact center executives interviewed for Dimension Data's 2012 Contact Center Benchmarking Report named customer satisfaction as their most important metric.

It's not that IT doesn't test anything before they roll out a project. It's that they don't test the system the way customers will interact with it. They test the individual components — web interfaces, fulfillment systems, Interactive Voice Recognition systems (IVRs), call routing systems — but not the system as a whole under real-world loads. This almost guarantees that customers will encounter problems that will reflect on the company or agency.

Empirix and LinkedIn surveyed more than 1,000 executives and managers in a variety of industries. The survey asked how companies:

- tested new customer contact technology before it was implemented

- evaluated the voice quality of customer/service agent calls

- monitored overall contact center performance to maintain post-implementation quality

The results are a series of contradictions. While it appears from overall numbers that pre-deployment testing rates are high — 80 percent or better — the numbers are actually much less impressive than they appear.

In truth, the overall picture isn't good. More than 80 percent of respondents said their companies do not test contact center technology under real-world conditions before go-live. They do some form of testing, but it's not comprehensive enough to reveal all of the issues that can affect customer service.

They're a little bit better about testing upgrades to existing systems: 82 percent reported testing upgrades. There's grade inflation in this number, however. Sixty-two percent use comparatively inaccurate manual testing methods.

While better than not testing at all, manual testing does not accurately reflect real-world conditions. Manual tests usually occur during off-peak times, which do not accurately predict how systems will work at full capacity. Because manual testing is difficult to repeat, it is usually done only once or twice. That makes it harder to pinpoint problems — and ensure they are resolved — even if they are detected pre-deployment.

Another 20 percent don't test new technology at all; they just "pray that it works” (14 percent) or react to customer complaints (3 percent). The remaining 3 percent are included with the non-testers because they only test major upgrades. They're included with the non-testers because of the obvious flaw in their reasoning that only major upgrades are test-worthy. A small change can erode performance or cause a system crash just as easily as a major upgrade. In fact, small upgrades can create performance drags that are harder to pinpoint because unlike large upgrades, they do not have the IT organization's full attention.

Only about 18 percent of respondents said that their companies use automated testing for all contact center upgrades. That's the second-largest block of users after the manual testing group, but a low overall percentage of the total. These companies use testing software to evaluate the performance of new functionality, equipment, applications and system upgrades under realistic traffic conditions. This approach yields the most accurate results and rapid understanding of where and why problems are occurring.

The Spoken Afterthought

HealthCare.gov's problems highlighted shortcomings with web portal testing, but voice applications face similar neglect. Indeed, when the President advised people to use their phone to call and apply for healthcare, many of the call centers set up to field applicants also had trouble handling the spike in caller traffic.

Voice quality can be a significant drag on short- and long-term call center ROI. Contact center agents who must ask customers to repeat themselves because of poor voice connections — or worse, ask customers to hang up and call in again — are less productive than those who can hear customers clearly. In the long term, repetition and multiple calls erode customer satisfaction levels.

The vast majority of professionals who responded to the LinkedIn/Empirix survey — 68 percent reported that their companies never monitor contact center voice quality. Only 14 percent continuously monitor voice quality, while the remaining 17 percent periodically monitor on a daily, weekly or monthly basis.

This failure carries heavy risks. Globally, 79 percent of consumers replying to a Customer Experience Foundation survey said they experienced poor voice quality on contact center calls. Almost as many — 68 percent — said they will hang up if they experience poor voice quality. If they are calling about a new product or service, they will likely call a competing company instead.

Between misdirected efforts and testing rates like these, it's no wonder people aren't surprised when a major initiative like online healthcare enrollment goes off the rails, or customers calling a contact center get funneled down a blind alley in the IVR system. Customers who run into obstacles like those are on a fast track to becoming former customers.

Testing and performance monitoring can effectively stem those losses. Businesses that test and monitor customer service systems are better able to achieve maximum ROI on their customer service systems (CSS) by identifying and remediating problems quickly. An end-to-end monitoring solution provides organizations with deep visibility into complex customer service technology environments, enabling businesses to reduce the time it takes to understand the source of a problem — and fix it — before customers ever notice the glitch.

ABOUT Matthew Ainsworth

Matthew Ainsworth is Senior Vice President, Americas and Japan at Empirix. He has 15 years of experience in contact centers and unified communications solutions.

Related Links:

www.empirix.com

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The Deeper Problem Under HealthCare.gov's Rocky Start

Even as the federal government appears to have untangled the worst of HealthCare.gov's problems, the finger pointing and agonizing about what went wrong with the Affordable Care Act's centerpiece website are unlikely to die down any time soon.

The political dimensions aside, there's a persistent curiosity about how such a high-profile project could have failed so spectacularly. It was possibly the world's most important IT project of the moment, yet it performed as if it were rolled out the door without so much as a cursory kick of the tires.

That's because it probably was – and that's far from unusual.

A recent LinkedIn/Empirix survey found that at most companies and public agencies, pre-deployment testing is half-hearted at best and non-existent at worst. Public agencies and private companies alike have abysmal records for testing customer-facing IT projects, such as customer service and e-commerce portals.

This is despite the importance that most organizations place on creating a consistently positive customer experience; almost 60 percent of the contact center executives interviewed for Dimension Data's 2012 Contact Center Benchmarking Report named customer satisfaction as their most important metric.

It's not that IT doesn't test anything before they roll out a project. It's that they don't test the system the way customers will interact with it. They test the individual components — web interfaces, fulfillment systems, Interactive Voice Recognition systems (IVRs), call routing systems — but not the system as a whole under real-world loads. This almost guarantees that customers will encounter problems that will reflect on the company or agency.

Empirix and LinkedIn surveyed more than 1,000 executives and managers in a variety of industries. The survey asked how companies:

- tested new customer contact technology before it was implemented

- evaluated the voice quality of customer/service agent calls

- monitored overall contact center performance to maintain post-implementation quality

The results are a series of contradictions. While it appears from overall numbers that pre-deployment testing rates are high — 80 percent or better — the numbers are actually much less impressive than they appear.

In truth, the overall picture isn't good. More than 80 percent of respondents said their companies do not test contact center technology under real-world conditions before go-live. They do some form of testing, but it's not comprehensive enough to reveal all of the issues that can affect customer service.

They're a little bit better about testing upgrades to existing systems: 82 percent reported testing upgrades. There's grade inflation in this number, however. Sixty-two percent use comparatively inaccurate manual testing methods.

While better than not testing at all, manual testing does not accurately reflect real-world conditions. Manual tests usually occur during off-peak times, which do not accurately predict how systems will work at full capacity. Because manual testing is difficult to repeat, it is usually done only once or twice. That makes it harder to pinpoint problems — and ensure they are resolved — even if they are detected pre-deployment.

Another 20 percent don't test new technology at all; they just "pray that it works” (14 percent) or react to customer complaints (3 percent). The remaining 3 percent are included with the non-testers because they only test major upgrades. They're included with the non-testers because of the obvious flaw in their reasoning that only major upgrades are test-worthy. A small change can erode performance or cause a system crash just as easily as a major upgrade. In fact, small upgrades can create performance drags that are harder to pinpoint because unlike large upgrades, they do not have the IT organization's full attention.

Only about 18 percent of respondents said that their companies use automated testing for all contact center upgrades. That's the second-largest block of users after the manual testing group, but a low overall percentage of the total. These companies use testing software to evaluate the performance of new functionality, equipment, applications and system upgrades under realistic traffic conditions. This approach yields the most accurate results and rapid understanding of where and why problems are occurring.

The Spoken Afterthought

HealthCare.gov's problems highlighted shortcomings with web portal testing, but voice applications face similar neglect. Indeed, when the President advised people to use their phone to call and apply for healthcare, many of the call centers set up to field applicants also had trouble handling the spike in caller traffic.

Voice quality can be a significant drag on short- and long-term call center ROI. Contact center agents who must ask customers to repeat themselves because of poor voice connections — or worse, ask customers to hang up and call in again — are less productive than those who can hear customers clearly. In the long term, repetition and multiple calls erode customer satisfaction levels.

The vast majority of professionals who responded to the LinkedIn/Empirix survey — 68 percent reported that their companies never monitor contact center voice quality. Only 14 percent continuously monitor voice quality, while the remaining 17 percent periodically monitor on a daily, weekly or monthly basis.

This failure carries heavy risks. Globally, 79 percent of consumers replying to a Customer Experience Foundation survey said they experienced poor voice quality on contact center calls. Almost as many — 68 percent — said they will hang up if they experience poor voice quality. If they are calling about a new product or service, they will likely call a competing company instead.

Between misdirected efforts and testing rates like these, it's no wonder people aren't surprised when a major initiative like online healthcare enrollment goes off the rails, or customers calling a contact center get funneled down a blind alley in the IVR system. Customers who run into obstacles like those are on a fast track to becoming former customers.

Testing and performance monitoring can effectively stem those losses. Businesses that test and monitor customer service systems are better able to achieve maximum ROI on their customer service systems (CSS) by identifying and remediating problems quickly. An end-to-end monitoring solution provides organizations with deep visibility into complex customer service technology environments, enabling businesses to reduce the time it takes to understand the source of a problem — and fix it — before customers ever notice the glitch.

ABOUT Matthew Ainsworth

Matthew Ainsworth is Senior Vice President, Americas and Japan at Empirix. He has 15 years of experience in contact centers and unified communications solutions.

Related Links:

www.empirix.com

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.