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4 Points to Consider When Selecting a Mobile App Performance Solution

In mobile, APM has taken on new and critical importance. With mobile apps becoming increasingly vital to a business’ overall performance, it is important to manage 
and improve — not just measure — application performance. Thus the focus and purpose of Mobile Application Performance Management centers on helping companies detect, prioritize, isolate, diagnose, repair, and prevent problems before users or a business are impacted. The goal is to improve customer experience, boost loyalty and increase enterprise efficiency.

When all is said and done, end-user experience with an application is what really matters. Effective mobile application performance management optimizes application availability and response time, ensuring the best user experience.

The following guidelines will arm IT leaders with the necessary steps to finding the right mobile app performance management solution for 100% success.

1. Drill down in the data

Once you pinpoint the cause of a crash, be sure your solution can tie diagnostic data back to your app’s network data, allowing you to isolate issues or track down misbehaving API endpoints. Even better if your solution goes beyond analyzing metrics such as latency, request and data volume, and can filter all of your endpoints grouped by cloud service. That will help you diagnose the errors in detail.

2. Think BIG

Even if you are not a big organization now you should harness a solution that can scale with your business. Solid candidates are ones that have "Mobile First" baked into their corporate DNA and purposely built the capabilities to scale (through a cloud-based infrastructure). These providers are going to be the pros at mobile app management — a position that gives them key insights (drawn from millions of devices and billions of apps) — and makes them a good partner in your wider strategy to make your app succeed.

3. Visibility matters

Monitoring glaring coding mistakes is just as necessary as sorting out smaller edge cases 
to win the battle for users and five-star reviews. Choose a solution that allows you to visualize aggregated data on a dashboard. That’s really the only way to see how people are using your app, account for all variables and explore exactly where errors occur. Even better if the provider has developed an integrated strategy to show you what is actually happening in the field. Access through a single, easy-to-use dashboard is essential to delivering consistent high app performance as part of your mobile app lifecycle.

4. Put business goals first

Obviously a mobile app performance management solution must analyze performance of your mobile sessions. But an effective approach goes an important step further, tying session analysis 
back to your business goals (user retention, completing a level in a game, shopping cart purchase, etc). Most importantly, your solution should provide potential fixes or solutions to performance issues.

Finally, choosing a provider that can go that one step further and sort the crash groups by the number of users affected is a key element. This will allow organizations to focus efforts and resources on fixing the bugs that have impacted the largest number of users.

Jeannie Liou is a Marketing Manager at Crittercism

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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 ...

4 Points to Consider When Selecting a Mobile App Performance Solution

In mobile, APM has taken on new and critical importance. With mobile apps becoming increasingly vital to a business’ overall performance, it is important to manage 
and improve — not just measure — application performance. Thus the focus and purpose of Mobile Application Performance Management centers on helping companies detect, prioritize, isolate, diagnose, repair, and prevent problems before users or a business are impacted. The goal is to improve customer experience, boost loyalty and increase enterprise efficiency.

When all is said and done, end-user experience with an application is what really matters. Effective mobile application performance management optimizes application availability and response time, ensuring the best user experience.

The following guidelines will arm IT leaders with the necessary steps to finding the right mobile app performance management solution for 100% success.

1. Drill down in the data

Once you pinpoint the cause of a crash, be sure your solution can tie diagnostic data back to your app’s network data, allowing you to isolate issues or track down misbehaving API endpoints. Even better if your solution goes beyond analyzing metrics such as latency, request and data volume, and can filter all of your endpoints grouped by cloud service. That will help you diagnose the errors in detail.

2. Think BIG

Even if you are not a big organization now you should harness a solution that can scale with your business. Solid candidates are ones that have "Mobile First" baked into their corporate DNA and purposely built the capabilities to scale (through a cloud-based infrastructure). These providers are going to be the pros at mobile app management — a position that gives them key insights (drawn from millions of devices and billions of apps) — and makes them a good partner in your wider strategy to make your app succeed.

3. Visibility matters

Monitoring glaring coding mistakes is just as necessary as sorting out smaller edge cases 
to win the battle for users and five-star reviews. Choose a solution that allows you to visualize aggregated data on a dashboard. That’s really the only way to see how people are using your app, account for all variables and explore exactly where errors occur. Even better if the provider has developed an integrated strategy to show you what is actually happening in the field. Access through a single, easy-to-use dashboard is essential to delivering consistent high app performance as part of your mobile app lifecycle.

4. Put business goals first

Obviously a mobile app performance management solution must analyze performance of your mobile sessions. But an effective approach goes an important step further, tying session analysis 
back to your business goals (user retention, completing a level in a game, shopping cart purchase, etc). Most importantly, your solution should provide potential fixes or solutions to performance issues.

Finally, choosing a provider that can go that one step further and sort the crash groups by the number of users affected is a key element. This will allow organizations to focus efforts and resources on fixing the bugs that have impacted the largest number of users.

Jeannie Liou is a Marketing Manager at Crittercism

Hot Topics

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 ...