Skip to main content

Instabug Introduces New Mobile Features

Instabug announces a powerful new suite of features that revolutionize how mobile teams measure, analyze, and improve user app experience. 

The new features – Frustration-Free Sessions, Business Impact Dashboard, and Prioritized Issues List – go beyond crash-free rates to quantify and eliminate user app frustration, empowering mobile teams to take targeted action to boost retention and engagement.

Instabug’s Frustration-Free Sessions consolidates multiple frustration signals – including crashes, slow launches, and network failures – into a single, actionable metric, providing a holistic measure of user frustration and giving teams a clear way to measure and improve user experience.

The Business Impact Dashboard facilitates data-driven decision-making and connects app performance to business outcomes. This ensures teams understand the direct impact of frustration-free sessions on retention and growth.

The Prioritized Issues List ranks issues based on their impact on user frustration and business metrics, eliminating the guesswork and enabling teams to resolve the most critical problems first.

When combined, these features directly link performance improvements to business outcomes, making mobile app success measurable and actionable.

“For years, mobile teams have relied on incomplete metrics that fail to capture the full user experience,” said Kenny Johnston, Chief Product Officer, Instabug. “With the launch of Frustration-Free Sessions, Business Impact Dashboard, and Prioritized Issues List, we are giving teams the capabilities they need to bridge the gap between visibility into the business impact of app quality issues and actually achieving that impact. This is a game-changer for mobile app teams.”

Instabug is committed to revolutionizing mobile app performance by shifting the industry focus from crash-free rates to a more user-centric approach. These new features align with Instabug’s vision of empowering mobile teams with actionable insights that drive technical excellence and business growth. Both technical managers and business executives benefit from this newest suite of features.

Technical managers:

  • Gain a holistic view of app performance by consolidating frustration signals into a single metric.
  • Link technical issues to real business impact, justifying the need for performance investments.
  • Use automatically prioritized insights to efficiently allocate development resources to the most critical issues.
  • Improve collaboration with product and business teams by demonstrating how technical improvements drive user retention and satisfaction.  

Business executives:

  • Gain clarity on how app performance directly affects key business metrics such as retention and churn.
  • Justify and allocate resources more effectively with data-driven insights from the Business Impact Dashboard.
  • Reduce guesswork by using a structured approach to app quality improvement, ensuring investments lead to measurable business impact.
  • Strengthen alignment between technical and business teams by making app performance a shared strategic priority.

Instabug’s new offerings are available as part of its performance monitoring and stability suite, accessible through the Instabug platform. Customers can integrate these features seamlessly into their existing workflows, leveraging real-time data to optimize app performance. 

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.

Instabug Introduces New Mobile Features

Instabug announces a powerful new suite of features that revolutionize how mobile teams measure, analyze, and improve user app experience. 

The new features – Frustration-Free Sessions, Business Impact Dashboard, and Prioritized Issues List – go beyond crash-free rates to quantify and eliminate user app frustration, empowering mobile teams to take targeted action to boost retention and engagement.

Instabug’s Frustration-Free Sessions consolidates multiple frustration signals – including crashes, slow launches, and network failures – into a single, actionable metric, providing a holistic measure of user frustration and giving teams a clear way to measure and improve user experience.

The Business Impact Dashboard facilitates data-driven decision-making and connects app performance to business outcomes. This ensures teams understand the direct impact of frustration-free sessions on retention and growth.

The Prioritized Issues List ranks issues based on their impact on user frustration and business metrics, eliminating the guesswork and enabling teams to resolve the most critical problems first.

When combined, these features directly link performance improvements to business outcomes, making mobile app success measurable and actionable.

“For years, mobile teams have relied on incomplete metrics that fail to capture the full user experience,” said Kenny Johnston, Chief Product Officer, Instabug. “With the launch of Frustration-Free Sessions, Business Impact Dashboard, and Prioritized Issues List, we are giving teams the capabilities they need to bridge the gap between visibility into the business impact of app quality issues and actually achieving that impact. This is a game-changer for mobile app teams.”

Instabug is committed to revolutionizing mobile app performance by shifting the industry focus from crash-free rates to a more user-centric approach. These new features align with Instabug’s vision of empowering mobile teams with actionable insights that drive technical excellence and business growth. Both technical managers and business executives benefit from this newest suite of features.

Technical managers:

  • Gain a holistic view of app performance by consolidating frustration signals into a single metric.
  • Link technical issues to real business impact, justifying the need for performance investments.
  • Use automatically prioritized insights to efficiently allocate development resources to the most critical issues.
  • Improve collaboration with product and business teams by demonstrating how technical improvements drive user retention and satisfaction.  

Business executives:

  • Gain clarity on how app performance directly affects key business metrics such as retention and churn.
  • Justify and allocate resources more effectively with data-driven insights from the Business Impact Dashboard.
  • Reduce guesswork by using a structured approach to app quality improvement, ensuring investments lead to measurable business impact.
  • Strengthen alignment between technical and business teams by making app performance a shared strategic priority.

Instabug’s new offerings are available as part of its performance monitoring and stability suite, accessible through the Instabug platform. Customers can integrate these features seamlessly into their existing workflows, leveraging real-time data to optimize app performance. 

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.