For many ecommerce brands, stress-testing their digital systems in preparation for the holidays is an essential ritual. Retailers need to ensure their systems can scale to upcoming seasonal traffic spikes like Cyber Week so they can capture the attention and wallet share of the numerous consumers hunting online for deals.
However, while the holiday shopping rush comes with significant opportunity — Cyber Monday 2023 alone is estimated to generate $13.7 billion in sales revenue — it also comes with risks. During an especially popular shopping day like Black Friday or Cyber Monday, a consumer has even less patience for a slow, disjointed user experience or outage, and such a disruption can lead to lasting damage to a retailer’s brand reputation.
So how can retail leaders create a more resilient online shopping journey this holiday season — one that keeps customers engaged, reduces cart abandonment rates and creates greater brand loyalty so shoppers keep returning? The answer can be found in establishing an effective observability practice.
Used by software developers, site reliability engineers and IT operations teams, observability is a technology practice that provides greater end-to-end visibility across one’s applications and infrastructure in order to find and fix problems faster and to reduce the impact of unplanned downtime. It can help improve the reliability of a retailer’s site by providing proactive maintenance, early detection and real-time guidance to identify areas for improvement. This enables ITOps and engineering teams at retail brands to find and address issues faster and with more precision, so those issues never impact the consumer and their shopping journey.
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It’s no surprise that observability innovations are relevant to retailers that are preparing for the holidays by looking to minimize outages and slow, unresponsive page loads. The observability toolkit has recently been bolstered by emerging innovations and practices that can help retailers ultimately foster a more seamless and cohesive user experience — from home page to cart checkout.
Leverage AI to Predict and Prevent Disruptions Through Accurate Alerting
AIOps is an innovative practice within the observability space that can help increase the speed and efficiency of the online marketplace. Similar to how AI often helps to accelerate different processes and practices through automated insights, AIOps enables ITOps and engineering professionals to gain insights from large operational datasets and speed up incident detection and resolution times of any IT stressors. The practice of AIOps can help improve the performance and reliability of retailer’s IT systems and applications through reductions in mean time to repair (MTTR.)
AIOps also can equip retailers with better understanding and context of any IT incidents. Research has shown that in comparison to legacy monitoring tools, AIOps tools often outperform in a number of ways — including more effectively determining the technical root cause of an issue and helping predict potential problems before they turn into customer-impacting incidents.
This sort of insight can reduce alert fatigue, prevent false positives and help ITOps and engineering teams direct their energy to the most critical issues, so consumers can browse, click and “add to cart” with minimal disruptions.
Embrace a Customer-Centric Digital Strategy Through DEM
Another relevant observability practice for creating a smooth shopping journey is Digital Experience Monitoring (DEM). Foundational to fostering a delightful and cohesive online customer experience — especially during high traffic periods like the holidays — DEM provides online retailers with the tools and insights needed to proactively optimize their systems for scalability and benchmark performance against predefined standards.
Within the DEM practice, Synthetic Monitoring serves as your scout, simulating user interactions and testing your ecommerce website to ensure it’s running smoothly. Diving even deeper into the DEM toolkit, Real User Monitoring (RUM) acts like a virtual shopping companion, showing you how fast pages load and transactions are completed for actual customers and helping to pinpoint exactly the source of any delays or glitches.
Within the race to provide a reliable online shopping experience during the holiday season, observability emerges as the unsung hero. As retailers gear up to capture the attention and wallet share of holiday shoppers, observability practices like AIOps and DEM prove invaluable in streamlining alerts, enhancing user experiences and safeguarding against disruptions.
By leveraging AI for predictive insights and embracing a customer-centric digital strategy, retailers can not only meet the challenges of peak traffic but also grow brand loyalty that keeps customers returning for more. This holiday season, the path to success lies in building a resilient shopping journey, ensuring that every click leads to a delighted customer.
Patrick Lin is currently the SVP and GM, Observability at Splunk. He joined Splunk through the acquisition of SignalFx in 2019, where he was the Chief Product Officer. Prior to SignalFx, Lin held a variety of PM leadership roles, including VP Product Management at VMware, the pioneer in x86 virtualization software. He started his career with Bain & Company, a strategic management consulting firm, and holds an MBA from INSEAD, and MS, BS and BA degrees in Electrical Engineering and East Asian Studies from Stanford University.
