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Getting Rid of the Spinner Wheel

Amena Siddiqi

Prior to our current reality in the "new normal," consumers were already reliant on devices to remain connected and carry out daily tasks. With the COVID-19 pandemic, that reliance has grown to dependency, particularly when the app-dependent task is time-sensitive. Indeed, a report by Ericsson found that "delays in video streaming caused stress levels equivalent to the anxiety of taking a math test or watching a horror movie alone, and greater than the stress experienced by standing at the edge of a virtual cliff."

Add a global health pandemic to this predisposition for stress and you have a user group that is less forgiving of the dreaded spinner wheel than ever before. 

To examine how well mobile apps are meeting these high expectations, HeadSpin recently released a new benchmark for measuring app latency. The inaugural report examines the performance of 25 shopping, restaurant and food delivery apps, in five major cities, across popular iOS and Android devices, and multiple service providers. Applications selected for analysis in the report include Target, Amazon, Walmart, Burger King, Grubhub, Uber Eats, and more. The report details which apps are performing best amid the pandemic, and the most crucial contributing factors to user’s overall digital experience.

How Does Your App Stand Up?

The benchmark for contactless e-commerce apps was based on four key performance indicators (KPIs) for the app’s critical user journey: load product time, add to cart time, launch time, and search time. The last two are particularly interesting, so let’s break them down:

Launch- Have you ever gone to open an app, had it take too long, and moved onto another? If an app can’t load at the onset, it's likely a user will move on. In the HeadSpin study, although a few top performing apps took under two seconds to load, a significant proportion of the apps took much longer to load, bringing the average up to 4.1 seconds. According to Google/SOASTA research, as load times progress from one to five seconds, bounce rates increase by 90%.

Search- This is especially important for top retail apps. The report found that top retail apps, such as Walmart and Amazon averaged a search time of 2.4 seconds. Surprisingly, some of the largest retailers featured slow search times (negatively impacting the average), while the relatively new Shop app from Shopify excelled across all metrics, performing 5.5x faster than Amazon in returning search results.

By identifying and optimizing the key performance indicators for their mobile apps, businesses can improve conversions, reduce churn, achieve faster time to market, and publish apps with confidence on day one. 

Top Contributors to App Latency

The study additionally examined the major contributing factors to an app’s slow performance. Notably, the main culprits implicated included:

Slow TLS: Amazon’s iOS app took twice as long to launch compared to Home Depot, Kohl’s, and Best Buy because of slow TLS connections to multiple Amazon hosts.

Duplicate requests: Postmates took four times longer to launch on iOS compared to Uber Eats largely because of numerous connections opened to a Facebook host, and multiple duplicate requests made for the same resource.

SDK bloat: Grubhub was found to be the slowest delivery app to load on Android. This was mainly attributed to multiple calls to 3rd party hosts for initializing SDKs. The app’s performance could be improved by loading SDKs when needed rather than all at once during launch.

Large image files: Pizza Hut’s app launched sluggishly on Android because of large image files and slow server response on the backend. Using a JPEG or WebP file type instead of PNG would allow the screen to load faster with minimal loss of image quality.

Assuring Digital Excellence

Ensuring top quality mobile user experiences is an ongoing process. Businesses can assure optimal digital user experience throughout the app lifecycle by: 

Alerting on high priority issues and detecting build-over-build regressions early.

Testing native/hybrid/web app performance on real devices and real networks before, during, and after launch.

Automating functional, performance, and load testing end-to-end across applications, devices, and networks.

Analyzing performance and UX data with state-of-the-art AI and computer vision technology.

Monitoring and baselining live app KPIs by location, device, OS & carrier networks.

With all the changes that have and will continue to take place around digital connectivity and the app development ecosystem, we are losing patience with the spinner wheel, and have very little tolerance for latency. As brick and mortar businesses begin to open in the wake of the COVID-19 crisis, contactless options will continue to be in high demand as consumers err on the side of caution over in-person shopping. For now, as businesses and consumers remain dependent on digital commerce, more organizations will shift to web and mobile operations, and slow apps that do not deliver usable content promptly will lose out in the long run.

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Getting Rid of the Spinner Wheel

Amena Siddiqi

Prior to our current reality in the "new normal," consumers were already reliant on devices to remain connected and carry out daily tasks. With the COVID-19 pandemic, that reliance has grown to dependency, particularly when the app-dependent task is time-sensitive. Indeed, a report by Ericsson found that "delays in video streaming caused stress levels equivalent to the anxiety of taking a math test or watching a horror movie alone, and greater than the stress experienced by standing at the edge of a virtual cliff."

Add a global health pandemic to this predisposition for stress and you have a user group that is less forgiving of the dreaded spinner wheel than ever before. 

To examine how well mobile apps are meeting these high expectations, HeadSpin recently released a new benchmark for measuring app latency. The inaugural report examines the performance of 25 shopping, restaurant and food delivery apps, in five major cities, across popular iOS and Android devices, and multiple service providers. Applications selected for analysis in the report include Target, Amazon, Walmart, Burger King, Grubhub, Uber Eats, and more. The report details which apps are performing best amid the pandemic, and the most crucial contributing factors to user’s overall digital experience.

How Does Your App Stand Up?

The benchmark for contactless e-commerce apps was based on four key performance indicators (KPIs) for the app’s critical user journey: load product time, add to cart time, launch time, and search time. The last two are particularly interesting, so let’s break them down:

Launch- Have you ever gone to open an app, had it take too long, and moved onto another? If an app can’t load at the onset, it's likely a user will move on. In the HeadSpin study, although a few top performing apps took under two seconds to load, a significant proportion of the apps took much longer to load, bringing the average up to 4.1 seconds. According to Google/SOASTA research, as load times progress from one to five seconds, bounce rates increase by 90%.

Search- This is especially important for top retail apps. The report found that top retail apps, such as Walmart and Amazon averaged a search time of 2.4 seconds. Surprisingly, some of the largest retailers featured slow search times (negatively impacting the average), while the relatively new Shop app from Shopify excelled across all metrics, performing 5.5x faster than Amazon in returning search results.

By identifying and optimizing the key performance indicators for their mobile apps, businesses can improve conversions, reduce churn, achieve faster time to market, and publish apps with confidence on day one. 

Top Contributors to App Latency

The study additionally examined the major contributing factors to an app’s slow performance. Notably, the main culprits implicated included:

Slow TLS: Amazon’s iOS app took twice as long to launch compared to Home Depot, Kohl’s, and Best Buy because of slow TLS connections to multiple Amazon hosts.

Duplicate requests: Postmates took four times longer to launch on iOS compared to Uber Eats largely because of numerous connections opened to a Facebook host, and multiple duplicate requests made for the same resource.

SDK bloat: Grubhub was found to be the slowest delivery app to load on Android. This was mainly attributed to multiple calls to 3rd party hosts for initializing SDKs. The app’s performance could be improved by loading SDKs when needed rather than all at once during launch.

Large image files: Pizza Hut’s app launched sluggishly on Android because of large image files and slow server response on the backend. Using a JPEG or WebP file type instead of PNG would allow the screen to load faster with minimal loss of image quality.

Assuring Digital Excellence

Ensuring top quality mobile user experiences is an ongoing process. Businesses can assure optimal digital user experience throughout the app lifecycle by: 

Alerting on high priority issues and detecting build-over-build regressions early.

Testing native/hybrid/web app performance on real devices and real networks before, during, and after launch.

Automating functional, performance, and load testing end-to-end across applications, devices, and networks.

Analyzing performance and UX data with state-of-the-art AI and computer vision technology.

Monitoring and baselining live app KPIs by location, device, OS & carrier networks.

With all the changes that have and will continue to take place around digital connectivity and the app development ecosystem, we are losing patience with the spinner wheel, and have very little tolerance for latency. As brick and mortar businesses begin to open in the wake of the COVID-19 crisis, contactless options will continue to be in high demand as consumers err on the side of caution over in-person shopping. For now, as businesses and consumers remain dependent on digital commerce, more organizations will shift to web and mobile operations, and slow apps that do not deliver usable content promptly will lose out in the long run.

Hot Topics

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...