A slow product page or failing payment system can cost a sale. During peak season, these problems don’t stay contained. A delay in one system can affect several others, making it harder to find the cause once traffic is at its highest.
You can’t prevent every system failure, but you can be prepared for when it happens. Start with a load-testing plan built around the promotional calendar, historical traffic data and expected demand. Test the journeys that matter most to shoppers, from finding a product to receiving an order confirmation. Include the systems behind those journeys, particularly payment services, inventory integrations and fulfillment readiness.
Pressure-test at a minimum of 150-200 percent of forecast peak demand as a planning target, with higher levels where previous spikes or major campaigns justify them. Sudden surges and sustained traffic place different demands on technology, so both need attention. AWS in particular recommends regular performance testing, capacity planning and recovery exercises ahead of peak retail events. Influencer-driven demand can be less predictable than planned promotions, so ensuring readiness on a rolling basis can make sure you don’t miss any viral moments. Checkout deserves particular scrutiny. Baymard reports an average documented cart abandonment rate of about 70 percent, with reasons for abandonment ranging from complex checkout process to website errors and payment issues. Even thorough testing can’t predict every problem. During live operations, teams need to know when performance is deteriorating and whether a failure in one service is affecting another.
The right platform can give IT teams the visibility they need to prevent problems in the first place. Sapient Sustain, our AI-powered platform for IT operations, connects application performance with incident history to help teams spot problems earlier and investigate recurring failures. During peak season, that context matters when a hiccup in one system begins affecting another.
Teams can use this information to set warning thresholds for page speed and checkout success, decide who should respond when performance slips and prepare rollback plans for changes that cause problems.