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The "Crash and Burn" Report Findings

Andrew Levy

The correlation between mobile app crashes and increasing churn rates (or declining user retention) has long been suspected. In the report, titled Crash and Churn, Apteligent set out to understand the impact of per user crash rate on churn, using both approaches to the definition of churn. Whereas an app's crash rate is the total number of crashes divided by number of app loads, the analysis employed a per user crash rate to allow us to consider the segments of the population experiencing that issue.

The report contains many key takeaways for digital marketers, product managers, and mobile development teams:

Crashes can increase churn by as much as 534 percent

This represents a six-times increase from your "average" churn rate. The report found a more accurate depiction of crash and churn relation when viewed through Android devices over IOS. IOS displays a lower churn rate compared to Android, but, due to the platform design, users cannot send a crash report until the next time a user opens the app. And, in some cases, the user could use the app, the app crashes and that user may never go back to that app again so, that crash won't be counted as a churn cause.

Crashes have a significant impact on next day app opens by as much as 8x the normal rate

The report calculates how likely a user would be to return the day following a crash taking into consideration that 1.8 percent of users don't return to an app the next day even after a crash-free experience. As the per user crash rate approaches 100 percent, the churn rate increases to almost 15 percent. Again, IOS limitations led us to believe that Android had more accurate data.

Less engaged users, or users with lower app opens per day, tend to churn at higher rates based on crashes

The fewer apps a user engages with, the more sensitive they are to crashes, increasing their churn rate. The report notes that as crashes per day increase, we see a steady stream of churning users, especially those that load an app ten times or fewer per day. Perhaps one of the most interesting behaviors discovered was that the inverse of this is true as well. The more a user uses an app, the more resilient they become with crashes.

The impact of crashes on churn also varies by app store category

Shopping and finance apps, the most revenue-critical, were particularly vulnerable to crashes causing increased churn rates, while games and travel were much more resilient. While the data proves the aforementioned to be true, we cannot prove why users of games and travel are more forgiving of app crashes. We can only speculate that games and travel apps crashing may not be be enough to sway users away from addictive games or travel necessities, regardless of frustration with the experience.

For app owners, the report underscores the immediate return on investment that comes with applying the right resources to app performance. There are immediate and medium term revenue losses associated with churning app users and customers. In addition, the cost of acquiring new users is much higher than those associated with retaining existing users.

In 2017, the organizations who win on mobile will be those that select vendors applying proven data science techniques to big data collection. Data, without insights, is noise.

Andrew Levy is Co-Founder and Chief Strategy Officer of Apteligent.

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The "Crash and Burn" Report Findings

Andrew Levy

The correlation between mobile app crashes and increasing churn rates (or declining user retention) has long been suspected. In the report, titled Crash and Churn, Apteligent set out to understand the impact of per user crash rate on churn, using both approaches to the definition of churn. Whereas an app's crash rate is the total number of crashes divided by number of app loads, the analysis employed a per user crash rate to allow us to consider the segments of the population experiencing that issue.

The report contains many key takeaways for digital marketers, product managers, and mobile development teams:

Crashes can increase churn by as much as 534 percent

This represents a six-times increase from your "average" churn rate. The report found a more accurate depiction of crash and churn relation when viewed through Android devices over IOS. IOS displays a lower churn rate compared to Android, but, due to the platform design, users cannot send a crash report until the next time a user opens the app. And, in some cases, the user could use the app, the app crashes and that user may never go back to that app again so, that crash won't be counted as a churn cause.

Crashes have a significant impact on next day app opens by as much as 8x the normal rate

The report calculates how likely a user would be to return the day following a crash taking into consideration that 1.8 percent of users don't return to an app the next day even after a crash-free experience. As the per user crash rate approaches 100 percent, the churn rate increases to almost 15 percent. Again, IOS limitations led us to believe that Android had more accurate data.

Less engaged users, or users with lower app opens per day, tend to churn at higher rates based on crashes

The fewer apps a user engages with, the more sensitive they are to crashes, increasing their churn rate. The report notes that as crashes per day increase, we see a steady stream of churning users, especially those that load an app ten times or fewer per day. Perhaps one of the most interesting behaviors discovered was that the inverse of this is true as well. The more a user uses an app, the more resilient they become with crashes.

The impact of crashes on churn also varies by app store category

Shopping and finance apps, the most revenue-critical, were particularly vulnerable to crashes causing increased churn rates, while games and travel were much more resilient. While the data proves the aforementioned to be true, we cannot prove why users of games and travel are more forgiving of app crashes. We can only speculate that games and travel apps crashing may not be be enough to sway users away from addictive games or travel necessities, regardless of frustration with the experience.

For app owners, the report underscores the immediate return on investment that comes with applying the right resources to app performance. There are immediate and medium term revenue losses associated with churning app users and customers. In addition, the cost of acquiring new users is much higher than those associated with retaining existing users.

In 2017, the organizations who win on mobile will be those that select vendors applying proven data science techniques to big data collection. Data, without insights, is noise.

Andrew Levy is Co-Founder and Chief Strategy Officer of Apteligent.

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 ...

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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.

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