Skip to main content

Back to School: 2 Seconds Will Make or Break Mobile Retailers

Ann Ruckstuhl

As we enter August, back to school shopping season is in full swing. While research shows that a lot of back to school shopping still happens in brick-and-mortars, Americans are increasingly turning to their computers and mobile devices to stock up on back to school essentials.


According to the NPD Group, last year the e-commerce channel gained $90 million in dollar share growth versus brick-and-mortar, and predictions are that this year the gains could be even higher. As for mobile, new data shows that smartphone purchases for back to school have doubled.

But with this increase in mobile traffic comes a heightened expectation.

In 2014, the page load time that yielded the best conversion rate was six seconds. Now it's two. In other words, consumers will only give an online or mobile store two seconds to work — if a site or app jams or is slow, then it's hasta la vista.

The Continuing Rise of Mobile

SOASTA conducted a year over year comparison study of digital back to school traffic, discovering that in 2014, just over 60 percent of total traffic came from desktop users, while around 33 percent came from smartphones.

Just one year later, for the same set of sites, 65 percent of traffic came from smartphones, while 25 percent came from desktop. And this traffic does not solely consist of people browsing or window shopping on mobile devices only to return home and make purchases from their desktop computers. In 2014, the peak conversion rate for this set of sites was a mere 0.4 percent. Just twelve months later, the peak conversion rate for the same set of sites was over 2.2 percent — that's a change of 450 percent.

Americans Hate Delays

In a Harris poll of more than 2,000 Americans, 91 percent of back to school shoppers said they find making online purchases stressful, with 27 percent citing slow load times and 25 percent frustrated by pages crashing in the middle of a transaction.


If two seconds is fast, how do we define slow? SOASTA data shows that, while in 2014, conversion rates declined slowly after their peak at six seconds, in 2015, conversions take a sharper downturn. For both mobile and desktop devices, the "poverty line" — the point at which conversion rates dip down and plateau — begins at a page load time of four seconds and establishes itself at six seconds.

Retailers Have No Time to Spare

Huge improvements have been made — and continue to be made — in the mobile web and app space, both in terms of performance and user interface, and these figures illustrate that Americans are embracing the flexibility and portability of shopping from their smartphones. As Americans do more and more of their shopping from mobile devices, having a well-designed, high-functioning mobile website or mobile app is not a luxury for retailers but a mission-critical necessity.

Poor digital performance is now being measured by retailers in terms of lost customers and revenue — and the back to school shopping season, which accounts for 35 percent of the $11.8 billion in yearly sales in the US, is a true testing ground that will determine the winners and the losers in retail.

Ann Ruckstuhl is CMO of SOASTA.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

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.

Back to School: 2 Seconds Will Make or Break Mobile Retailers

Ann Ruckstuhl

As we enter August, back to school shopping season is in full swing. While research shows that a lot of back to school shopping still happens in brick-and-mortars, Americans are increasingly turning to their computers and mobile devices to stock up on back to school essentials.


According to the NPD Group, last year the e-commerce channel gained $90 million in dollar share growth versus brick-and-mortar, and predictions are that this year the gains could be even higher. As for mobile, new data shows that smartphone purchases for back to school have doubled.

But with this increase in mobile traffic comes a heightened expectation.

In 2014, the page load time that yielded the best conversion rate was six seconds. Now it's two. In other words, consumers will only give an online or mobile store two seconds to work — if a site or app jams or is slow, then it's hasta la vista.

The Continuing Rise of Mobile

SOASTA conducted a year over year comparison study of digital back to school traffic, discovering that in 2014, just over 60 percent of total traffic came from desktop users, while around 33 percent came from smartphones.

Just one year later, for the same set of sites, 65 percent of traffic came from smartphones, while 25 percent came from desktop. And this traffic does not solely consist of people browsing or window shopping on mobile devices only to return home and make purchases from their desktop computers. In 2014, the peak conversion rate for this set of sites was a mere 0.4 percent. Just twelve months later, the peak conversion rate for the same set of sites was over 2.2 percent — that's a change of 450 percent.

Americans Hate Delays

In a Harris poll of more than 2,000 Americans, 91 percent of back to school shoppers said they find making online purchases stressful, with 27 percent citing slow load times and 25 percent frustrated by pages crashing in the middle of a transaction.


If two seconds is fast, how do we define slow? SOASTA data shows that, while in 2014, conversion rates declined slowly after their peak at six seconds, in 2015, conversions take a sharper downturn. For both mobile and desktop devices, the "poverty line" — the point at which conversion rates dip down and plateau — begins at a page load time of four seconds and establishes itself at six seconds.

Retailers Have No Time to Spare

Huge improvements have been made — and continue to be made — in the mobile web and app space, both in terms of performance and user interface, and these figures illustrate that Americans are embracing the flexibility and portability of shopping from their smartphones. As Americans do more and more of their shopping from mobile devices, having a well-designed, high-functioning mobile website or mobile app is not a luxury for retailers but a mission-critical necessity.

Poor digital performance is now being measured by retailers in terms of lost customers and revenue — and the back to school shopping season, which accounts for 35 percent of the $11.8 billion in yearly sales in the US, is a true testing ground that will determine the winners and the losers in retail.

Ann Ruckstuhl is CMO of SOASTA.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

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.