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The Importance of CX Observability for DevOps Teams: A $1 Billion Missed Opportunity

James Isaacs
Cyara

A company will thrive or go out of business based on its reputation. A brand's reputation is dictated by several different factors, but customer experience (CX) remains the most critical when it comes to customer loyalty, trust and ultimately, sales. Studies have shown that 96% of consumers say CX is a key factor in their choice of loyalty to a brand and 85% of buyers are willing to pay more for great CX.

Yet, every day, companies are missing CX "red flags" because they don't have the tools to observe CX processes or metrics. Even basic errors or defects in automated customer interactions are left undetected for days, weeks or months, leading to widespread customer dissatisfaction. In fact, poor CX and digital technology investments are costing enterprises billions of dollars in lost potential revenue.

You can't fix what you can't see. Without observability into critical data about CX quality, there will always be bugs and errors that live out in the CX system for far too long. These technical issues tamper with the reliability of the CX software, which is so integral to the customer journey now that most businesses are operating on a digital-first approach. Reliability has never been more important for enterprises as they navigate digital transformation, especially cloud migration and automation.

Cultivating Observability in CX Software

Even in today's digital economy, many companies are still missing the mark when it comes to creating a memorable and positive CX through digital and voice channels. Most of the gap can be solved, however, if businesses invest in increasing their observability of CX processes and insightful metrics such as call center data, churn rate and net promoter scores (NPS).

Investing in greater observability allows enterprises to better collect and analyze data on every component of a system, application and infrastructure — from performance to security to accessibility. This information allows DevOps teams to glean insights into the reliability of actions performed within a unique business environment, such as an interactive voice response (IVR) system or a contact center. While an observability approach has been quickly adopted by software development and engineering teams, the practice of observability is still lagging when it comes to CX-related systems and solutions.

Gaining Real-time CX Insights

The fact of the matter is that for even the best DevOps teams, flaws creep into production. While DevOps teams do their best to foresee and predict potential gaps, they need a little help. Automated testing early in the development cycle, and throughout the entire process, is a proven way to identify potential flaws and mitigate errors before it becomes a costly threat to customers' experience. This requires automation because the faster a data set can be analyzed, the more value it will deliver.

Automated CX testing solutions work by generating synthetic interactions — either a single or thousands — to test the customer journey from the outside-in, from the network through IVRs to digital apps and routing systems, all the way to agents at their desktops — engaging in systems just as a customer would. Testing elements such as connectivity, responsiveness, quality and functionality can help DevOps teams ensure quality is achieved throughout the entire CX development life cycle. You can then dictate what data should be collected, who will own and examine the data and why it matters in the business' unique context to ensure the information collected within the customer journey is useful and impactful for the organization.

Be a Champion for CX Success

While the virtues of improving CX through automated testing are well known, it is worth mentioning the importance of executives championing CX observability and making the investment to prioritize it. Considering the potential revenue lost annually due to poor CX, it is important for executives to champion CX improvement efforts. For CX digital transformation initiatives to succeed, they must be led by an executive leader who can assert the need to drive cultural change and strategic investments. Appointing an executive to this role will ensure that CX initiatives are not siloed from DevOps teams, but also from the rest of the enterprise, including sales, marketing, operations and more. The organization's digital transformation champion must have broad authority that covers budget, people and processes in order to be effective in their role.

Online and in-person CX will always play a key role in brand loyalty. In an increasingly digital age, the reliability of our brand's digital frameworks will play an undeniable role in achieving brand loyalty and reputation through positive customer experiences. With greater observability and automated testing capabilities, enterprises can be empowered to innovate faster and deliver higher-quality solutions that will improve customer interactions and solidify brand loyalty for years to come.

James Isaacs is President of Cyara

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

The Importance of CX Observability for DevOps Teams: A $1 Billion Missed Opportunity

James Isaacs
Cyara

A company will thrive or go out of business based on its reputation. A brand's reputation is dictated by several different factors, but customer experience (CX) remains the most critical when it comes to customer loyalty, trust and ultimately, sales. Studies have shown that 96% of consumers say CX is a key factor in their choice of loyalty to a brand and 85% of buyers are willing to pay more for great CX.

Yet, every day, companies are missing CX "red flags" because they don't have the tools to observe CX processes or metrics. Even basic errors or defects in automated customer interactions are left undetected for days, weeks or months, leading to widespread customer dissatisfaction. In fact, poor CX and digital technology investments are costing enterprises billions of dollars in lost potential revenue.

You can't fix what you can't see. Without observability into critical data about CX quality, there will always be bugs and errors that live out in the CX system for far too long. These technical issues tamper with the reliability of the CX software, which is so integral to the customer journey now that most businesses are operating on a digital-first approach. Reliability has never been more important for enterprises as they navigate digital transformation, especially cloud migration and automation.

Cultivating Observability in CX Software

Even in today's digital economy, many companies are still missing the mark when it comes to creating a memorable and positive CX through digital and voice channels. Most of the gap can be solved, however, if businesses invest in increasing their observability of CX processes and insightful metrics such as call center data, churn rate and net promoter scores (NPS).

Investing in greater observability allows enterprises to better collect and analyze data on every component of a system, application and infrastructure — from performance to security to accessibility. This information allows DevOps teams to glean insights into the reliability of actions performed within a unique business environment, such as an interactive voice response (IVR) system or a contact center. While an observability approach has been quickly adopted by software development and engineering teams, the practice of observability is still lagging when it comes to CX-related systems and solutions.

Gaining Real-time CX Insights

The fact of the matter is that for even the best DevOps teams, flaws creep into production. While DevOps teams do their best to foresee and predict potential gaps, they need a little help. Automated testing early in the development cycle, and throughout the entire process, is a proven way to identify potential flaws and mitigate errors before it becomes a costly threat to customers' experience. This requires automation because the faster a data set can be analyzed, the more value it will deliver.

Automated CX testing solutions work by generating synthetic interactions — either a single or thousands — to test the customer journey from the outside-in, from the network through IVRs to digital apps and routing systems, all the way to agents at their desktops — engaging in systems just as a customer would. Testing elements such as connectivity, responsiveness, quality and functionality can help DevOps teams ensure quality is achieved throughout the entire CX development life cycle. You can then dictate what data should be collected, who will own and examine the data and why it matters in the business' unique context to ensure the information collected within the customer journey is useful and impactful for the organization.

Be a Champion for CX Success

While the virtues of improving CX through automated testing are well known, it is worth mentioning the importance of executives championing CX observability and making the investment to prioritize it. Considering the potential revenue lost annually due to poor CX, it is important for executives to champion CX improvement efforts. For CX digital transformation initiatives to succeed, they must be led by an executive leader who can assert the need to drive cultural change and strategic investments. Appointing an executive to this role will ensure that CX initiatives are not siloed from DevOps teams, but also from the rest of the enterprise, including sales, marketing, operations and more. The organization's digital transformation champion must have broad authority that covers budget, people and processes in order to be effective in their role.

Online and in-person CX will always play a key role in brand loyalty. In an increasingly digital age, the reliability of our brand's digital frameworks will play an undeniable role in achieving brand loyalty and reputation through positive customer experiences. With greater observability and automated testing capabilities, enterprises can be empowered to innovate faster and deliver higher-quality solutions that will improve customer interactions and solidify brand loyalty for years to come.

James Isaacs is President of Cyara

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...