Skip to main content

Holiday Retail Booming - Dependable Application Performance is a Must

Matthieu Silbermann

Retail forecasts were on target for the extended Thanksgiving weekend this year, with sales figures for the period indicating it was the strongest start of the holiday shopping season ever recorded . But while Cyber Monday was touted as the best time for online bargains, billions of dollars still went to brick-and-mortar retailers offering steep discounts.

One trend that caught retailers by surprise this year was the speed with which all of the mayhem came to a halt. Traffic at many locations dissipated by mid-afternoon on Black Friday as savvy shoppers took advantage of mobile applications and interactive, in-store sales tools to target bargains within minutes of crossing the threshold.

In-store purchasing systems like tablet-based applications gave retailers the upper hand in helping consumers save time shopping – which is critical as the holiday season comes to a close. Rather than leaving customers to their own devices, a sales associate can use an app to input the consumers' needs and quickly determine what product – and at what price – is the best fit.

This past year, a British consumer technology retailer implemented such a program in their stores. To help streamline operations among 1,300 locations, their tablet-based application relies upon lightning-fast broadband to run tablets used by sales associates on the floor. This helps the customer speed up their buying experience, allowing them to more easily explore other purchases while freeing up sales associates to pursue a greater number of potential leads.

In-store purchasing systems like this are not only great for spotting bargains, but also for speeding up and easing an otherwise frenzied shopping season. This is especially true on such storied shopping holidays as Black Friday when discounted inventories are limited. Once prized sales merchandise is no longer available, shoppers will need all the help they can get in the weeks leading up to the New Year to find suitable alternatives while prices are still low.

Now that the holiday shopping season is in full swing, retailers and buyers alike must ensure that their resources are dependable. Not only will stores need to utilize advanced shopping technology, but also systems to support their apps and devices that are running on all cylinders. An application performance tool can guarantee delivery of actionable visibility and in-depth network and application performance information to IT managers to make sure systems aren't overwhelmed during high periods of traffic. By allowing full control of application flow, IT complexity is simplified and the customer experience is reshaped – hopefully resulting in higher customer spending.

Holiday purchases are estimated to account for 19 percent of all retail sales in 2015, which in total will come to $3.2 trillion dollars, according to the National Retail Federation. The weekend following Thanksgiving only accounts for a small portion of that total, which means retailers need to be on their top game throughout the rest of the month if they want to capitalize on the season's financial potential.

Matthieu Silbermann is Director Product Management at InfoVista.

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 ...

Holiday Retail Booming - Dependable Application Performance is a Must

Matthieu Silbermann

Retail forecasts were on target for the extended Thanksgiving weekend this year, with sales figures for the period indicating it was the strongest start of the holiday shopping season ever recorded . But while Cyber Monday was touted as the best time for online bargains, billions of dollars still went to brick-and-mortar retailers offering steep discounts.

One trend that caught retailers by surprise this year was the speed with which all of the mayhem came to a halt. Traffic at many locations dissipated by mid-afternoon on Black Friday as savvy shoppers took advantage of mobile applications and interactive, in-store sales tools to target bargains within minutes of crossing the threshold.

In-store purchasing systems like tablet-based applications gave retailers the upper hand in helping consumers save time shopping – which is critical as the holiday season comes to a close. Rather than leaving customers to their own devices, a sales associate can use an app to input the consumers' needs and quickly determine what product – and at what price – is the best fit.

This past year, a British consumer technology retailer implemented such a program in their stores. To help streamline operations among 1,300 locations, their tablet-based application relies upon lightning-fast broadband to run tablets used by sales associates on the floor. This helps the customer speed up their buying experience, allowing them to more easily explore other purchases while freeing up sales associates to pursue a greater number of potential leads.

In-store purchasing systems like this are not only great for spotting bargains, but also for speeding up and easing an otherwise frenzied shopping season. This is especially true on such storied shopping holidays as Black Friday when discounted inventories are limited. Once prized sales merchandise is no longer available, shoppers will need all the help they can get in the weeks leading up to the New Year to find suitable alternatives while prices are still low.

Now that the holiday shopping season is in full swing, retailers and buyers alike must ensure that their resources are dependable. Not only will stores need to utilize advanced shopping technology, but also systems to support their apps and devices that are running on all cylinders. An application performance tool can guarantee delivery of actionable visibility and in-depth network and application performance information to IT managers to make sure systems aren't overwhelmed during high periods of traffic. By allowing full control of application flow, IT complexity is simplified and the customer experience is reshaped – hopefully resulting in higher customer spending.

Holiday purchases are estimated to account for 19 percent of all retail sales in 2015, which in total will come to $3.2 trillion dollars, according to the National Retail Federation. The weekend following Thanksgiving only accounts for a small portion of that total, which means retailers need to be on their top game throughout the rest of the month if they want to capitalize on the season's financial potential.

Matthieu Silbermann is Director Product Management at InfoVista.

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 ...