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#PrimeDayFail2016: A Cautionary Tale for Upstart Ecommerce

Ricardo Belmar

While not religious observances in the traditional sense, commercial and sales holidays like Black Friday and Cyber Monday are now celebrated gatherings that bring the masses together – albeit in the name of bargain hunting. Last year, Amazon added another consumer event to the calendar with the announcement of Prime Day, a 24-hour sale on the massive ecommerce site that ended up generating $415 million in sales for the retail behemoth – more than the company's reported revenues for that year's Black Friday.

Prime Day, which is designed to give subscribers of the company's Amazon Prime membership exclusive access to discounted merchandise, illustrates perfectly the retail landscape's transformation into a multi-channel arena. Brick-and-mortar commerce isn't dead, but online shopping has been embraced by the masses and is now the go-to avenue for purchasing both specialty items and everyday wares.

While Prime shoppers enjoy the bargains, few may realize that the success of such massive events hinges on network and application performance. While Amazon regularly operates on such a large scale that their internal networks are usually primed to handle major traffic ebbs and flows, even the world's largest online marketplace can't guarantee smooth user experience during an epic traffic deluge like the one prompted by Prime Day.

#PrimeDayFail2016

This struggle to deliver on Quality of Experience (QoE) expectations was on display for all the Internet to see when the #PrimeDayFail2016 hashtag began trending in the early hours of July 12. Shoppers around the world were unable to check out with their purchases for almost an hour when the event kicked off, which was a major hit to customer satisfaction as well as the company's public perception.

Despite this hiccup – which turned out to be an unanticipated glitch on the Amazon site – Prime Day 2016 still generated massive sales for the company and is again on track to have exceeded figures from this year's Black Friday once all of the receipts are tallied. But for a smaller ecommerce site without the capital or business reach of a giant like Amazon, such a lapse in service could be devastating.

Not All Retailers Have the Clout to Bounce Back

Website or application downtime, for instance – no matter if it's only for a few minutes – can be enough of a deterrent for shoppers to leave the site and not return. Such events are often attributed to spikes in traffic that aren't properly managed, resulting in network degradation that grinds site performance to a halt.

Network infrastructures that are ill-equipped to manage complex applications cause network slowdowns and worse – shoppers to lose patience and move on. To protect against this, retailers must consider Application Performance Management. This will ensure that business-critical applications take priority on the network during high-traffic periods. With consistently reliable app performance, retailers can easily deliver the fast-paced shopping experiences that power flash sales.

The same unified management philosophy also applies to a retailer's internal store network. From the standpoint of inventory management and delivery, for instance, companies need to be sure that there is a clear line of uninterrupted communication from the warehouse to the website.

Even the latest digital in-store experiences are powered by the network and rely on optimal application performance. Experiences such as digital fitting rooms, tablets used by store associates to display rich media product info and mobile POS checkout are just a few applications that create heavy traffic loads on network infrastructure. All of these require proper performance management or shoppers will lose patience waiting for screens to refresh and abandon a purchase. Even 5 seconds waiting for a tablet to display information feels like an eternity to the shopper.

Although a large portion of commerce has moved to the web, taking away the interpersonal aspect of bargain hunting, customers still want to be treated like people, not just numbers. A unified approach to application and network management can enable a high QoE and Quality of Service (QoS) to deliver the personalization shoppers now expect, which will inevitably translate into increased revenue for the retailer.

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#PrimeDayFail2016: A Cautionary Tale for Upstart Ecommerce

Ricardo Belmar

While not religious observances in the traditional sense, commercial and sales holidays like Black Friday and Cyber Monday are now celebrated gatherings that bring the masses together – albeit in the name of bargain hunting. Last year, Amazon added another consumer event to the calendar with the announcement of Prime Day, a 24-hour sale on the massive ecommerce site that ended up generating $415 million in sales for the retail behemoth – more than the company's reported revenues for that year's Black Friday.

Prime Day, which is designed to give subscribers of the company's Amazon Prime membership exclusive access to discounted merchandise, illustrates perfectly the retail landscape's transformation into a multi-channel arena. Brick-and-mortar commerce isn't dead, but online shopping has been embraced by the masses and is now the go-to avenue for purchasing both specialty items and everyday wares.

While Prime shoppers enjoy the bargains, few may realize that the success of such massive events hinges on network and application performance. While Amazon regularly operates on such a large scale that their internal networks are usually primed to handle major traffic ebbs and flows, even the world's largest online marketplace can't guarantee smooth user experience during an epic traffic deluge like the one prompted by Prime Day.

#PrimeDayFail2016

This struggle to deliver on Quality of Experience (QoE) expectations was on display for all the Internet to see when the #PrimeDayFail2016 hashtag began trending in the early hours of July 12. Shoppers around the world were unable to check out with their purchases for almost an hour when the event kicked off, which was a major hit to customer satisfaction as well as the company's public perception.

Despite this hiccup – which turned out to be an unanticipated glitch on the Amazon site – Prime Day 2016 still generated massive sales for the company and is again on track to have exceeded figures from this year's Black Friday once all of the receipts are tallied. But for a smaller ecommerce site without the capital or business reach of a giant like Amazon, such a lapse in service could be devastating.

Not All Retailers Have the Clout to Bounce Back

Website or application downtime, for instance – no matter if it's only for a few minutes – can be enough of a deterrent for shoppers to leave the site and not return. Such events are often attributed to spikes in traffic that aren't properly managed, resulting in network degradation that grinds site performance to a halt.

Network infrastructures that are ill-equipped to manage complex applications cause network slowdowns and worse – shoppers to lose patience and move on. To protect against this, retailers must consider Application Performance Management. This will ensure that business-critical applications take priority on the network during high-traffic periods. With consistently reliable app performance, retailers can easily deliver the fast-paced shopping experiences that power flash sales.

The same unified management philosophy also applies to a retailer's internal store network. From the standpoint of inventory management and delivery, for instance, companies need to be sure that there is a clear line of uninterrupted communication from the warehouse to the website.

Even the latest digital in-store experiences are powered by the network and rely on optimal application performance. Experiences such as digital fitting rooms, tablets used by store associates to display rich media product info and mobile POS checkout are just a few applications that create heavy traffic loads on network infrastructure. All of these require proper performance management or shoppers will lose patience waiting for screens to refresh and abandon a purchase. Even 5 seconds waiting for a tablet to display information feels like an eternity to the shopper.

Although a large portion of commerce has moved to the web, taking away the interpersonal aspect of bargain hunting, customers still want to be treated like people, not just numbers. A unified approach to application and network management can enable a high QoE and Quality of Service (QoS) to deliver the personalization shoppers now expect, which will inevitably translate into increased revenue for the retailer.

Hot Topics

The Latest

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...