Protecting E-commerce Revenue with Proactive Incident Detection and Response

Your online store generates the most revenue during peak shopping events, and these periods carry the highest operational risk. Because performance issues can affect sales and customer experience during high-traffic periods, fast detection and response is critical.

The National Retail Federation reports that 202.9 million consumers shopped during the five-day Thanksgiving through Cyber Monday weekend in 2025. When checkout slows or a product catalog becomes unresponsive under such a high level of traffic, every second matters.

In this blog, you'll learn how to prepare your e-commerce workloads for peak events, which alarms to configure for each tier of your architecture, and how AWS Incident Detection and Response (AWS IDR) engineers coordinate the response when issues arise.

Why E-Commerce Disruptions Hit Harder

Disruptions such as traffic surges, database connection exhaustion, and cache failures affect e-commerce businesses differently than they affect other industries.

Revenue loss is immediate: Unlike subscription-based services, you can't recover lost sales after a peak event ends. When a checkout outage happens during a flash sale, transactions are lost.

Customer trust erodes quickly: Shoppers expect reliability. A checkout failure, slow page load, or unresponsive search drives customers to competitors. Research from Google shows that 53% of mobile users abandon sites that take longer than 3 seconds to load.

Failures cascade across services: Modern e-commerce architectures rely on interconnected microservices. An issue in the product catalog service can cause issues with the shopping cart, checkout, and order fulfillment systems.

Alarm volume overwhelms operations teams: During a major event, operations teams can receive dozens of alarms across multiple services simultaneously. Identifying the root cause and coordinating the response becomes difficult without a structured approach.

Here's a common scenario: It's Black Friday, and a store experiences record-breaking traffic. The product catalog service struggles under the unexpected load, causing slow page loads and timeouts. Customers can't add items to their carts or complete checkout.

AWS IDR gives you all-day incident management for your critical workload. The following section explains how you can use AWS IDR to protect your revenue during peak events.

Solution Overview

AWS IDR helps you prepare for and respond to e-commerce disruptions through the following capabilities:

• All day monitoring: AWS Incident Management Engineers monitor your workloads every day and respond within a 5-minute commitment.
• Customized runbooks: You work with the AWS IDR team to build response procedures that are tailored to your specific failure scenarios, such as traffic surges and database exhaustion.
• Validation exercises: During the onboarding of your critical workload to AWS IDR, you conduct GameDay exercises with the AWS IDR team to validate your alarm flows, response plans, and escalation contacts.
• Continuous improvement: After each incident, the AWS IDR team reviews what happened and if needed, updates your runbooks and adjusts alarm configurations.

To understand how to implement these capabilities, the next section walks through a typical e-commerce architecture.

How A Typical E-Commerce Architecture Works

Knowing your architecture helps you understand where issues can occur and why monitoring matters:

• Frontend: A fast-loading application that's hosted on Amazon CloudFront and Amazon Simple Storage Service (Amazon S3) delivers a global low-latency browsing experience.
• Backend services: Critical operations, including product catalog, user accounts, order processing, and shopping cart management, run as containerized services (applications that are packaged with their dependencies) on Amazon Elastic Container Service (Amazon ECS) with AWS Fargate.
• Data storage: Amazon Aurora manages transactional data. Amazon DynamoDB stores product catalogs and user sessions. Amazon ElastiCache retrieves frequently accessed data, such as product details and session state.
• Payment processing: Secure integration with third-party payment gateways uses AWS Key Management Service (AWS KMS) for encryption of sensitive customer information.
• Order fulfillment: Amazon Simple Queue Service (Amazon SQS) queues dissociate order processing from fulfillment, and APIs connect your store with external logistics providers.
• Monitoring: Amazon CloudWatch tracks performance metrics across your frontend, application, data, and messaging tiers, triggering alarms when thresholds are exceeded.

Figure 1 illustrates how these components work together and where AWS IDR monitoring integrates with your architecture.

Figure 1: E-commerce workload architecture with AWS IDR monitoring

The next section covers which alarms to configure for each tier.

Configuring Your Alarm Strategy

AWS IDR uses a two-phase observability model. During onboarding, you focus on business-outcome metrics—the key performance indicators (KPIs) that directly reflect the customer experience, such as checkout success rate and orders per minute. After onboarding, you add in infrastructure metrics to provide deeper diagnostic insight. This approach makes sure that alarms are triggered based on customer impact, not just on resource utilization.

The following tables outline the recommended Amazon CloudWatch alarms for each tier that align with AWS IDR alarming best practices. Start with business-outcome metrics and frontend alarms, then expand to the application and data tiers as you tune your thresholds.

Note: The following thresholds are starting points. Adjust them based on your traffic patterns, business scale, and risk tolerance.

Frontend Tier

The frontend is the first point of contact for customers. Alarms here detect issues that directly affect page load times and the browsing experience.

CloudFront (CloudFront AWS IDR Alarming Best Practices)

Alarm	Metric	Statistic	Period	Threshold	Rationale
High 5xx Error Rate	5xxErrorRate (percentage of requests that return server errors)	Average	5 min	> 5% for 3 datapoints	Origin errors that affect page loads
Origin Latency	OriginLatency (time for origin to respond)	p99 (99th percentile)	5 min	> 3000 ms for 3 datapoints	Slow origin responses that cause page load delays
Cache Hit Rate Drop	CacheHitRate (percentage of requests served from cache)	Average	5 min	< 80% for 3 datapoints	Cache invalidation that increases load on origin servers

In the load balancer layer, the following alarms detect issues between CloudFront and your backend services.

Elastic Load Balancing (ELB AWS IDR Alarming Best Practices)

Alarm	Metric	Statistic	Period	Threshold	Rationale
ELB 5xx Errors	HTTPCode_ELB_5XX_Count (server errors from load balancer)	Sum	5 min	> 100 for 3 datapoints	Backend failures that affect checkout and cart
Target 5xx Errors	HTTPCode_Target_5XX_Count (server errors from application)	Sum	5 min	> 50 for 3 datapoints	Application errors in microservices
High Latency	TargetResponseTime (time for application to respond)	p99 (99th percentile)	5 min	> 5 s for 3 datapoints	Slow responses that cause cart abandonment
Unhealthy Targets	UnHealthyHostCount (number of failing targets)	Maximum	1 min	> 0 for 3 datapoints	Service instances that fail health checks

Application Tier

Behind the load balancer, the application tier runs the core business logic. Alarms here detect resource pressure that can degrade checkout, search, and cart operations.

Amazon ECS on AWS Fargate (Amazon ECS AWS IDR Alarming Best Practices)

Alarm	Metric	Statistic	Period	Threshold	Rationale
High CPU	CPUUtilization (processor usage percentage)	Average	5 min	> 80% for 3 datapoints	Microservices under heavy load during flash sales
High Memory	MemoryUtilization (memory usage percentage)	Average	5 min	> 80% for 3 datapoints	Memory pressure that risks task failures on checkout or search
Running Task Count Drop	RunningTaskCount (number of active tasks)	Minimum	1 min	< desired count for 3 datapoints	Tasks that crash or fail to start during peak traffic

Data Tier

The data tier stores transactions, sessions, and product information. Alarms in the data tier detect capacity limits and latency issues before they affect customers.

Aurora (Aurora AWS IDR Alarming Best Practices)

Alarm	Metric	Statistic	Period	Threshold	Rationale
High CPU	CPUUtilization (processor usage percentage)	Average	5 min	> 80% for 3 datapoints	Database under heavy query load from catalog and orders
Connection Exhaustion	DatabaseConnections (active database connections)	Maximum	1 min	> 80% of max for 3 datapoints	Risk of connection pool exhaustion during sustained peak
Read Latency	ReadLatency (time to complete read operations)	p99 (99th percentile)	5 min	> 20 ms for 3 datapoints	Slow reads that affect product catalog and search
Replica Lag	AuroraReplicaLag (delay between primary and replica)	Maximum	1 min	> 100 ms for 3 datapoints	Read replicas that fall behind, showing customers' stale inventory

DynamoDB (DynamoDB AWS IDR Alarming Best Practices)

Alarm	Metric	Statistic	Period	Threshold	Rationale
Read Throttling	ReadThrottleEvents (rejected read requests)	Sum	5 min	> 0 for 3 datapoints	Throttled cart and session reads
Write Throttling	WriteThrottleEvents (rejected write requests)	Sum	5 min	> 0 for 3 datapoints	Order writes throttled during checkout
System Errors	SystemErrors (service-side errors)	Sum	1 min	> 0 for 1 datapoint	Service-side errors that affect table availability

Amazon ElastiCache (Amazon ElastiCache AWS IDR Alarming Best Practices)

Alarm	Metric	Statistic	Period	Threshold	Rationale
High Engine CPU	EngineCPUUtilization (cache engine processor usage)	Average	5 min	> 80% for 3 datapoints	Cache under heavy load from session and product lookups
Low Cache Hit Rate	CacheHitRate (percentage of successful cache lookups)	Average	5 min	< 80% for 3 datapoints	Cache misses that increase database load
High Evictions	Evictions (items removed from cache because of memory pressure)	Sum	5 min	> 1000 for 3 datapoints	Full cache that evicts frequently accessed product data

Messaging Tier

The messaging tier dissociates order processing from fulfillment. Alarms in this tier detect backlogs and processing failures in the order pipeline.

Amazon SQS (Amazon SQS AWS IDR Alarming Best Practices)

Alarm	Metric	Statistic	Period	Threshold	Rationale
Queue Depth Growing	ApproximateNumberOfMessagesVisible (messages waiting)	Maximum	5 min	> 10000 for 3 datapoints	Orders that back up because consumers can't keep up
Old Messages	ApproximateAgeOfOldestMessage (age of oldest message)	Maximum	5 min	> 300 s for 3 datapoints	Processing delays in order fulfillment
DLQ Messages	ApproximateNumberOfMessagesVisible on dead-letter queue	Sum	5 min	> 0 for 1 datapoint	Failed order processing that requires investigation

For the complete alarm configuration guidance for each service, see the [AWS IDR Alarming Best Practices collection](https://repost.aws/selections/KP6FA7iQgVSVeSNq1jAcjwxg/incident-detection-and-response-idr) on AWS re:Post.

The next section explains what happens when an alarm fires.

How AWS Incident Detection and Response Responds To E-Commerce Incidents

Monitoring And Response

AWS Incident Management Engineers monitor your onboarded workloads all day. When a CloudWatch alarm or a third-party application performance monitoring (APM) alert triggers, the AWS Incident Management Engineer assesses the situation within minutes. The engineer initiates a conference bridge, coordinates with your team and AWS service specialists, such as the Amazon ECS, Aurora, and DynamoDB teams, and manages the response from detection through resolution.

Preparing For A GameDay

AWS IDR conducts a GameDay exercise with you to validate your workload's readiness and the monitoring alarms. To participate, you must first complete the AWS IDR onboarding for your workloads.

After you onboarded, coordinate with the AWS IDR team at least 48 hours in advance. A typical session lasts 1–2 hours. The AWS IDR team provides AWS Command Line Interface (AWS CLI) commands to trigger test alarm states for each onboarded workload. During the GameDay exercise, you trigger test alarms, and the AWS IDR team validates the end-to-end flow—from alarm detection, ticket creation, and response within the committed timeframe, to bridge engagement, and runbook execution. AWS IDR and the customer’s teamlso verify that the appropriate escalation contacts can be reached during a real incident. GameDay exercises are included in AWS IDR onboarding at no additional cost.

Correlated Metrics For Faster Root Cause Identification

During a traffic surge, operations teams might receive dozens of CloudWatch alarms that fire simultaneously across multiple services. AWS IDR helps reduce the time it takes to troubleshoot by correlating business outcome metrics with infrastructure metrics. For example, a spike in the cart abandonment rate alongside elevated latency in the product catalog service helps pinpoint the root cause within minutes rather than hours.

Continuous Improvement Through Post-Incident Reviews

After resolving an event, AWS IDR team conducts a review to strengthen future resiliency. A typical review produces recommendations, such as implementing predictive auto-scaling for Amazon ECS based on historical traffic patterns, adding Amazon API Gateway caching to reduce load on the catalog service during peak periods, and updating alarm thresholds based on what the team observed during the event. The team might also recommend that you run controlled failure injection tests, also known as chaos engineering, to confirm that auto-scaling and failover mechanisms work as expected. These recommendations feed directly into updated runbooks and alarm configurations, creating a cycle of continuous improvement.

Real-world scenarios

Flash Sale Traffic Surge

An e-commerce customer launches a 48-hour flash sale. Within the first hour, traffic spikes to 15 times the normal volume. A CloudWatch alarm triggers when Application Load Balancer 5xx errors exceed the configured threshold. The AWS Incident Management Engineers engage within minutes, open a conference bridge with the customer’s operations team and AWS support specialists, and identify that the Auto Scaling group has reached its maximum capacity. The team increases the limits and pre-warms additional capacity for the remainder of the sales period. The store returns to normal operation within 20 minutes. Post-incident, the team updates alarm thresholds and adds scaling policy alarms.

Holiday Database Connection Exhaustion

During the holiday shopping season, sustained high traffic over two weeks gradually exhausts database connections. A CloudWatch alarm on Aurora Database triggers before the customer notices degradation. The AWS Incident Management Engineer proactively engages the customer’s team and coordinates with the AWS support team. The engineer applies a connection pooling adjustment as an immediate workaround while the team right-sizes the database instance and adds connection monitoring alarms for early warning.

Conclusion

To protect your e-commerce revenue during peak events, start by implementing business outcome alarms—order rate, checkout success rate, and page load time—and frontend alarms—CloudFront 5xx errors and Application Load Balancer latency. Then, use the tables in this blog as a starting point to expand to application and data tiers. For detailed implementation guidance, see the documentation for CloudFront, Amazon ECS, Aurora, and DynamoDB.

If you're an AWS Enterprise Support or AWS Unified Operations customer, then contact your account team to assess whether your workloads qualify for AWS IDR onboarding. To prepare, document your critical workloads, identify your key business outcome metrics, and map your escalation contacts.

If you don't have an Enterprise Support plan, then you can still implement the alarm strategy described in this article. Start by configuring the CloudWatch alarms from the tables in this blog, and then use the AWS Well-Architected Framework Reliability Pillar to assess your architecture’s resiliency. You can also explore AWS Countdown Premium for planned peak event support.

To learn more about how AWS Support plans and offerings can help, see AWS Support. To get started with AWS IDR, see What is AWS Incident Detection and Response?

Further reading

• Conquering Peak Retail Events with AWS
• Improve Application Resiliency Using AWS Incident Detection and Response
• AWS Observability Best Practices
• Guidance for Web Store on AWS

About the authors

Adebimpe Babsalaam is an Incident Management Engineer at Amazon Web Services (AWS). Adebimpe plays a critical role in managing and coordinating incident response efforts to ensure the reliability and availability of AWS services for customers.

Wole Modupe is a Senior Technical Account Manager at Amazon Web Services (AWS), part of the AWS Enterprise Support organization. Wole works closely with AWS enterprise customers to help them optimize their cloud environments, resolve technical challenges, and achieve their business objectives.