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Why Does Your App Only Get a 3-Star Rating?

Michael Gopshtein

As part of our work on the AppPulse Mobile project, we have been digging into hundreds of user comments for many mobile apps, trying to understand what makes an application get a 3-star average user rating.

Interestingly, we could identify two main categories of applications: “Consistent 3” and “Accidental 3”.

“Consistent 3” ratings are all about functionality – users complain about missing features or inconvenient flows in the app. For such applications the majority of user ratings range around three stars, so the application average rating is consistent with most of individual comments.

The “Accidental 3” category of applications is more interesting in a sense. Ratings from most of the users are separated to two distinct groups. Half of the users really like the application; they are excited with the value it brings them, and are happy to give it a 5-star rating. Other users complain about stability and performance issues – application crashes and slow loading times – and rate the app as 1-star or 2-star in the store.

These “Accidental 3” ratings can be easily understood – it is practically impossible to test your application on a whole matrix of device types, OS versions and network connectivity flaws. As a result, some users experience severe quality issues, are unable to benefit from the full value of the application, and express their frustration in low ratings in the app store.

If you want to be on top of how users rate your app, many tools monitor your application ratings on the app store. However, this is not enough. If you really want to understand why users rate your app this way, you need a tool that can track your app’s real user experience and show you where you should improve. This may be exactly the tool you need to gain the missing 2 stars!

Michael Gopshtein is Team Manager in AppPulse Mobile R&D, HP Software.

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Why Does Your App Only Get a 3-Star Rating?

Michael Gopshtein

As part of our work on the AppPulse Mobile project, we have been digging into hundreds of user comments for many mobile apps, trying to understand what makes an application get a 3-star average user rating.

Interestingly, we could identify two main categories of applications: “Consistent 3” and “Accidental 3”.

“Consistent 3” ratings are all about functionality – users complain about missing features or inconvenient flows in the app. For such applications the majority of user ratings range around three stars, so the application average rating is consistent with most of individual comments.

The “Accidental 3” category of applications is more interesting in a sense. Ratings from most of the users are separated to two distinct groups. Half of the users really like the application; they are excited with the value it brings them, and are happy to give it a 5-star rating. Other users complain about stability and performance issues – application crashes and slow loading times – and rate the app as 1-star or 2-star in the store.

These “Accidental 3” ratings can be easily understood – it is practically impossible to test your application on a whole matrix of device types, OS versions and network connectivity flaws. As a result, some users experience severe quality issues, are unable to benefit from the full value of the application, and express their frustration in low ratings in the app store.

If you want to be on top of how users rate your app, many tools monitor your application ratings on the app store. However, this is not enough. If you really want to understand why users rate your app this way, you need a tool that can track your app’s real user experience and show you where you should improve. This may be exactly the tool you need to gain the missing 2 stars!

Michael Gopshtein is Team Manager in AppPulse Mobile R&D, HP Software.

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Developers building AI applications are not just looking for fault patterns after deployment; they must detect issues quickly during development and have the ability to prevent issues after going live. Unfortunately, traditional observability tools can no longer meet the needs of AI-driven enterprise application development. AI-powered detection and auto-remediation tools designed to keep pace with rapid development are now emerging to proactively manage performance and prevent downtime ...

Every few years, the cybersecurity industry adopts a new buzzword. "Zero Trust" has endured longer than most — and for good reason. Its promise is simple: trust nothing by default, verify everything continuously. Yet many organizations still hesitate to implement Zero Trust Network Access (ZTNA). The problem isn't that ZTNA doesn't work. It's that it's often misunderstood ...

For many retail brands, peak season is the annual stress test of their digital infrastructure. It's also when often technical dashboards glow green, yet customer feedback, digital experience frustration, and conversion trends tell a different story entirely. Over the past several years, we've seen the same pattern across retail, financial services, travel, and media: internal application performance metrics fail to capture the true experience of users connecting over local broadband, mobile carriers, and congested networks using multiple devices across geographies ...

PostgreSQL promises greater flexibility, performance, and cost savings compared to proprietary alternatives. But successfully deploying it isn't always straightforward, and there are some hidden traps along the way that even seasoned IT leaders can stumble into. In this blog, I'll highlight five of the most common pitfalls with PostgreSQL deployment and offer guidance on how to avoid them, along with the best path forward ...

The rise of hybrid cloud environments, the explosion of IoT devices, the proliferation of remote work, and advanced cyber threats have created a monitoring challenge that traditional approaches simply cannot meet. IT teams find themselves drowning in a sea of data, struggling to identify critical threats amidst a deluge of alerts, and often reacting to incidents long after they've begun. This is where AI and ML are leveraged ...

Three practices, chaos testing, incident retrospectives, and AIOps-driven monitoring, are transforming platform teams from reactive responders into proactive builders of resilient, self-healing systems. The evolution is not just technical; it's cultural. The modern platform engineer isn't just maintaining infrastructure. They're product owners designing for reliability, observability, and continuous improvement ...

Getting applications into the hands of those who need them quickly and securely has long been the goal of a branch of IT often referred to as End User Computing (EUC). Over recent years, the way applications (and data) have been delivered to these "users" has changed noticeably. Organizations have many more choices available to them now, and there will be more to come ... But how did we get here? Where are we going? Is this all too complicated? ...

On November 18, a single database permission change inside Cloudflare set off a chain of failures that rippled across the Internet. Traffic stalled. Authentication broke. Workers KV returned waves of 5xx errors as systems fell in and out of sync. For nearly three hours, one of the most resilient networks on the planet struggled under the weight of a change no one expected to matter ... Cloudflare recovered quickly, but the deeper lesson reaches far beyond this incident ...

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Every modern industry is confronting the same challenge: human reaction time is no longer fast enough for real-time decision environments. Across sectors, from financial services to manufacturing to cybersecurity and beyond, the stakes mirror those of autonomous vehicles — systems operating in complex, high-risk environments where milliseconds matter ...