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When Every Experience Matters, Bad Software Can Have Devastating Consequences

Marcus Merrell
Sauce Labs

One idea those in the DevOps world have been preaching for years is the importance of customer experience. Build a product that will delight your customers and they will keep coming back, or so the conventional wisdom goes. On the flip side, deliver poor CX and people will vote with their feet (out the digital door).

But just how damaging can it be for a brand when a consumer discovers a bug on a company's website or mobile app?


This information can be hard to quantify, but it's exactly what the new Sauce Labs report, Every Experience Matters is here to explain.

Most developers don't realize how closely their work is tied to a brand's success and revenue stream, but in a world where consumers have so many choices, user expectations can make or break a transaction. Today we'll look at a few of the insights that came from the report.

Every brand is a digital brand. Every company, regardless of industry, has a web presence that represents the primary way users engage with that company. Any time we browse a web page or mobile app, there is the possibility of a coding error crashing the site or lowering its performance in a way that a user would notice.

How common are these bugs?

Nearly a quarter of consumers (23%) say they encounter an error or experience issue that keeps them from accomplishing a task online at least once a day. In other words, millions of consumers are logging on to shop, pay bills, connect with people, and other important daily tasks, only to encounter a problem that stops them in their tracks. These bugs lead to abandoned carts, negative reviews and a loss of brand equity. Eventually, these errors start to negatively impact a company's reputation and destroy trust.

Patience may be a virtue, but it's also one that most consumers lack: when we drill into the data, the results are alarming. Almost one in five consumers (18%) will immediately abandon a transaction the moment they encounter an error. The majority of them will just go directly to your competition. 64% are a bit more forgiving and they will give you the benefit of the doubt by refreshing your page 2-3 times before eventually giving up, but the bottom line here is that your customer's time is money. Without swift resolution, they have no problems taking their business elsewhere.


To make matters worse, consumers aren't shy about sharing their negative experiences. About half of those surveyed (49%) reported discussing negative experiences with family and friends meaning that errors and challenges can spread to potential customers via word of mouth. A quarter (25%) took the extra step of writing a negative review for the world to see. (Just sorting by new on Reddit will show plenty of these.) Most concerning were the 20% that said after experiencing one error on a brand's website or app, they would leave as a customer forever.

These statistics may sound daunting, but all they really do is confirm our instincts that consumers are impatient, and our users aren't too forgiving. The easy fix for a developer is to catch errors before they are released to the masses, in the form of an aggressive testing strategy.

Whether a developer operates in a low-code environment or relies heavily on automation testing, there are tools that can put your customer first and set you up for success. Test features like API testing and Error Monitoring can catch bugs earlier in the SDLC and help devs and testers catch problems quickly and save the company money. In a DevOps world increasingly obsessed with speed, we must remember the importance of quality. To your users, every experience matters, so we should always be mindful to put our best foot forward and protect brand reputation at all costs.

Marcus Merrell is VP of Technology Strategy at Sauce Labs

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 ...

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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 ...

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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 ...

When Every Experience Matters, Bad Software Can Have Devastating Consequences

Marcus Merrell
Sauce Labs

One idea those in the DevOps world have been preaching for years is the importance of customer experience. Build a product that will delight your customers and they will keep coming back, or so the conventional wisdom goes. On the flip side, deliver poor CX and people will vote with their feet (out the digital door).

But just how damaging can it be for a brand when a consumer discovers a bug on a company's website or mobile app?


This information can be hard to quantify, but it's exactly what the new Sauce Labs report, Every Experience Matters is here to explain.

Most developers don't realize how closely their work is tied to a brand's success and revenue stream, but in a world where consumers have so many choices, user expectations can make or break a transaction. Today we'll look at a few of the insights that came from the report.

Every brand is a digital brand. Every company, regardless of industry, has a web presence that represents the primary way users engage with that company. Any time we browse a web page or mobile app, there is the possibility of a coding error crashing the site or lowering its performance in a way that a user would notice.

How common are these bugs?

Nearly a quarter of consumers (23%) say they encounter an error or experience issue that keeps them from accomplishing a task online at least once a day. In other words, millions of consumers are logging on to shop, pay bills, connect with people, and other important daily tasks, only to encounter a problem that stops them in their tracks. These bugs lead to abandoned carts, negative reviews and a loss of brand equity. Eventually, these errors start to negatively impact a company's reputation and destroy trust.

Patience may be a virtue, but it's also one that most consumers lack: when we drill into the data, the results are alarming. Almost one in five consumers (18%) will immediately abandon a transaction the moment they encounter an error. The majority of them will just go directly to your competition. 64% are a bit more forgiving and they will give you the benefit of the doubt by refreshing your page 2-3 times before eventually giving up, but the bottom line here is that your customer's time is money. Without swift resolution, they have no problems taking their business elsewhere.


To make matters worse, consumers aren't shy about sharing their negative experiences. About half of those surveyed (49%) reported discussing negative experiences with family and friends meaning that errors and challenges can spread to potential customers via word of mouth. A quarter (25%) took the extra step of writing a negative review for the world to see. (Just sorting by new on Reddit will show plenty of these.) Most concerning were the 20% that said after experiencing one error on a brand's website or app, they would leave as a customer forever.

These statistics may sound daunting, but all they really do is confirm our instincts that consumers are impatient, and our users aren't too forgiving. The easy fix for a developer is to catch errors before they are released to the masses, in the form of an aggressive testing strategy.

Whether a developer operates in a low-code environment or relies heavily on automation testing, there are tools that can put your customer first and set you up for success. Test features like API testing and Error Monitoring can catch bugs earlier in the SDLC and help devs and testers catch problems quickly and save the company money. In a DevOps world increasingly obsessed with speed, we must remember the importance of quality. To your users, every experience matters, so we should always be mindful to put our best foot forward and protect brand reputation at all costs.

Marcus Merrell is VP of Technology Strategy at Sauce Labs

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 ...