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Retailers undergoing digital transformation often encounter significant challenges in ensuring a great customer experience. While new technologies and processes can enhance scalability and agility, they also make website infrastructure more complex and prone to unforeseen issues.
Retailers that use Datadog are able to simultaneously understand the health of their IT infrastructure and how customers are interacting with their digital storefronts. These joint insights allow for quick remediations, targeted application improvements, and issue preemption.
“Datadog allows us to know when there is an issue with a particular segment of the website, how our customers might be affected, and how that might affect conversion rates—which affects our bottom line.”
– Nick Vecellio, Senior Manager of Monitoring Engineering, Wayfair
A smoothly running e-commerce site is a base requirement for successful website purchases. Yet bugs, slow page loads, and outages can occur unexpectedly, impacting customer experience. Identifying these hot spots requires deep insight into a website’s code performance, which neither business intelligence solutions nor tooling like Google Analytics provides. Datadog's code-level instrumentation can capture web store interactions in real time. This data can follow an individual customer’s path through the site, or can be aggregated into metrics for precisely scoped groups of site visitors to identify trends or compare the experience of different customer populations.
Seasonal promotions like Cyber Monday are frequently impacted by revenue-eroding website issues. These problems typically stem from unpredictable traffic that strains website infrastructure in “blind spots” for the website’s engineering team. Datadog allows for complete observability of a website’s code and infrastructure within minutes through more than 350 out-of-the-box integrations with commonly used technologies. Additionally, Datadog retains data for 15 months at full granularity. Analyzing this data with Datadog’s visualizations and machine learning–based forecasting algorithms allows retailers to predict future resource usage. Engineering teams using Datadog ultimately have “eyes on” every component powering their digital storefront while they prepare for and execute seasonal promotions.
As internet-enabled point-of-sale (POS) systems are deployed in retail locations, operations teams are being asked to oversee the performance of these Internet of Things (IoT) devices. Ensuring the reliability of these devices creates additional challenges: Hardware performance data needs to be collected from geographically disparate devices and analyzed jointly with the performance of the software running on that infrastructure. Datadog offers specialized handling for IoT devices, as well as the ability to correlate the performance data of IoT hardware with the KPIs of the web applications running on each device. This allows retailers to gain visibility across e-commerce and in-store digital assets, enabling centralized instrumentation for all touchpoints accessed by a customer.