Skip to main content

5 Lessons Learned from the 2024 Observability for Retail Report

Nic Benders
New Relic

In the heat of the holiday online shopping rush, retailers face persistent challenges such as increased web traffic or cyber threats that can lead to high-impact outages. With profit margins under high pressure, retailers are prioritizing strategic investments to help drive business value while improving the customer experience.

Published in October, New Relic's 2024 Observability Forecast Report reveals insights into key growth areas, challenges, and trends shaping the observability industry. The report surveyed IT professionals across numerous geographic locations and demographics to inform the understanding of the current state of observability. Of the 1,700 technology professionals surveyed, 148 were associated with the retail industry and consumer-centric sectors.

Image
New Relic Retail

 

This November, New Relic published the State of Observability for Retail Report to share insights about observability's adoption and business impact across the retail industry and consumer-centric sectors.

Here are five key lessons learned from the report:

1. Observability helps retailers respond to outages faster and maintain positive business outcomes

Retailers that utilize observability to deliver business value gain an edge over their competitors. As retail organizations prepare for heightened demand during shopping periods like Black Friday and Cyber Monday, observability solutions help them tackle daily challenges, from mitigating application downtime to optimizing the online customer journey.

The forecast revealed that retailers experienced IT outages with a median annual downtime of 164 hours — or about one week. Though this is 41% lower than other industries, outages can cost organizations up to $1.9 million for every hour of downtime.

However, adopting alerts (62%) and network monitoring (59%) has helped retailers respond better to outages, with a median MTTD (mean-time-to-detection) of only 32 minutes. Further, improvements in network monitoring resulted in only 27% of respondents experiencing high-impact outages weekly. Adopting observability tooling has helped retail organizations maintain positive business outcomes and enhanced customer satisfaction.

2. AI, IoT, and security are driving retailers to adopt observability

In a year of growth, just under half (46%) of retail organizations indicated that an increased focus on security, governance, risk, and compliance was the top technology strategy influencing observability adoption.

Additionally, as AI continues on its meteoric rise, it has remained a significant driver of observability adoption, with 39% of retail respondents identifying AI as a key reason to adopt observability. This trend reflects the sector's commitment to leveraging AI for multiple facets of their work, such as enhancing decision-making and deriving actionable customer insights.

Finally, retail organizations cited IoT as the third most popular driver of observability adoption (32%), underscoring the sector's desire to harness observability for operational excellence.

3. Digital Experience Monitoring (DEM) is on the rise

A large uptick in online shopping has led to an evolved customer journey, meaning digital experience monitoring (DEM) is now a key growth area among retail organizations. DEM combines real user monitoring (RUM) — which covers browser and mobile monitoring — with synthetic monitoring for proactive testing and improvement.

More than half of the respondents (52%) noted that they are preparing to deploy synthetic monitoring within three years, 49% anticipate deploying mobile monitoring, and 42% plan to implement browser monitoring to continue to optimize the online customer journey.

4. The journey to full-stack observability requires tool consolidation

Retail organizations have their sights set on achieving full-stack observability. Though just 18% of organizations have reached this key milestone, retailers are taking strategic steps to overcome issues like an influx of monitoring tools and siloed data, which 35% of retailers identified as major roadblocks on their journey to achieving full-stack observability.

However, some retailers are transitioning through tool consolidation, with retail organizations now using 4.4 tools on average, down from 5.4 in 2023, compared to the broader industry average of 4.5 tools. This proactive tool consolidation effort will support retailers on their way to full-stack observability, with nearly half (43%) of the organizations surveyed planning to further consolidate their observability investments within the next year to access more operational efficiency and maximize their ROI.

5. Investment in observability pays off

Retail organizations reported stronger investments in observability on average, with 74% of respondents indicating annual spending of $1 million or more, while just 2% allocated less than $100,000. This investment delivers significant returns, with retail organizations achieving a median annual return on investment (ROI) of 302%, which is 4x their spending. This further underscores the strategic value of observability in this sector.

In terms of benefits, just under half of respondents (48%) reported improvements in overall system uptime and reliability, while 43% reported a reduction in security risks. Other key benefits called out included operational efficiency (38%), developer productivity (37%), and enhanced customer experience (37%).

Nic Benders is Chief Technical Strategist at New Relic

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 ...

5 Lessons Learned from the 2024 Observability for Retail Report

Nic Benders
New Relic

In the heat of the holiday online shopping rush, retailers face persistent challenges such as increased web traffic or cyber threats that can lead to high-impact outages. With profit margins under high pressure, retailers are prioritizing strategic investments to help drive business value while improving the customer experience.

Published in October, New Relic's 2024 Observability Forecast Report reveals insights into key growth areas, challenges, and trends shaping the observability industry. The report surveyed IT professionals across numerous geographic locations and demographics to inform the understanding of the current state of observability. Of the 1,700 technology professionals surveyed, 148 were associated with the retail industry and consumer-centric sectors.

Image
New Relic Retail

 

This November, New Relic published the State of Observability for Retail Report to share insights about observability's adoption and business impact across the retail industry and consumer-centric sectors.

Here are five key lessons learned from the report:

1. Observability helps retailers respond to outages faster and maintain positive business outcomes

Retailers that utilize observability to deliver business value gain an edge over their competitors. As retail organizations prepare for heightened demand during shopping periods like Black Friday and Cyber Monday, observability solutions help them tackle daily challenges, from mitigating application downtime to optimizing the online customer journey.

The forecast revealed that retailers experienced IT outages with a median annual downtime of 164 hours — or about one week. Though this is 41% lower than other industries, outages can cost organizations up to $1.9 million for every hour of downtime.

However, adopting alerts (62%) and network monitoring (59%) has helped retailers respond better to outages, with a median MTTD (mean-time-to-detection) of only 32 minutes. Further, improvements in network monitoring resulted in only 27% of respondents experiencing high-impact outages weekly. Adopting observability tooling has helped retail organizations maintain positive business outcomes and enhanced customer satisfaction.

2. AI, IoT, and security are driving retailers to adopt observability

In a year of growth, just under half (46%) of retail organizations indicated that an increased focus on security, governance, risk, and compliance was the top technology strategy influencing observability adoption.

Additionally, as AI continues on its meteoric rise, it has remained a significant driver of observability adoption, with 39% of retail respondents identifying AI as a key reason to adopt observability. This trend reflects the sector's commitment to leveraging AI for multiple facets of their work, such as enhancing decision-making and deriving actionable customer insights.

Finally, retail organizations cited IoT as the third most popular driver of observability adoption (32%), underscoring the sector's desire to harness observability for operational excellence.

3. Digital Experience Monitoring (DEM) is on the rise

A large uptick in online shopping has led to an evolved customer journey, meaning digital experience monitoring (DEM) is now a key growth area among retail organizations. DEM combines real user monitoring (RUM) — which covers browser and mobile monitoring — with synthetic monitoring for proactive testing and improvement.

More than half of the respondents (52%) noted that they are preparing to deploy synthetic monitoring within three years, 49% anticipate deploying mobile monitoring, and 42% plan to implement browser monitoring to continue to optimize the online customer journey.

4. The journey to full-stack observability requires tool consolidation

Retail organizations have their sights set on achieving full-stack observability. Though just 18% of organizations have reached this key milestone, retailers are taking strategic steps to overcome issues like an influx of monitoring tools and siloed data, which 35% of retailers identified as major roadblocks on their journey to achieving full-stack observability.

However, some retailers are transitioning through tool consolidation, with retail organizations now using 4.4 tools on average, down from 5.4 in 2023, compared to the broader industry average of 4.5 tools. This proactive tool consolidation effort will support retailers on their way to full-stack observability, with nearly half (43%) of the organizations surveyed planning to further consolidate their observability investments within the next year to access more operational efficiency and maximize their ROI.

5. Investment in observability pays off

Retail organizations reported stronger investments in observability on average, with 74% of respondents indicating annual spending of $1 million or more, while just 2% allocated less than $100,000. This investment delivers significant returns, with retail organizations achieving a median annual return on investment (ROI) of 302%, which is 4x their spending. This further underscores the strategic value of observability in this sector.

In terms of benefits, just under half of respondents (48%) reported improvements in overall system uptime and reliability, while 43% reported a reduction in security risks. Other key benefits called out included operational efficiency (38%), developer productivity (37%), and enhanced customer experience (37%).

Nic Benders is Chief Technical Strategist at New Relic

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 ...