As ecommerce continues to grow, organizations often implement systems to identify significant anomalies in user traffic. Traditionally, these systems rely on static alerts or manual oversight, which can be insufficient for detecting minor anomalies in real-time. Recognizing these slight deviations in traffic—such as variations in page visits or order completions—enables businesses to respond swiftly, thus mitigating potential negative impacts on key performance indicators (KPIs). In this post, we will present a sophisticated artificial intelligence and machine learning (AI/ML) approach utilizing AWS services to automatically detect both major and minor anomalies based on historical and current traffic data.
Understanding the Variability of Ecommerce Traffic
Ecommerce traffic is subject to fluctuations influenced by various factors such as seasonality, time of day, and specific dates. For instance, websites often see a surge in activity during weekday evenings compared to mornings, with weekends typically drawing even more visitors. In contrast, peak shopping days like Black Friday and Cyber Monday disrupt these regular patterns. Consequently, the dynamic nature of ecommerce traffic complicates the near-real-time detection of minor anomalies. Crafting a solution that intelligently identifies even the slightest deviations in user traffic based on historical data is crucial.
Components of the Anomaly Detection Framework
Our anomaly detection architecture comprises three key elements:
- The ecommerce application utilized by customers
- A platform for data ingestion, transformation, and storage
- Anomaly detection and notification system
This architecture automates data ingestion and anomaly detection while providing a user-friendly interface for interacting with and managing anomalies based on severity.
The Ecommerce Application
The online purchasing journey involves several user actions, including:
- Searching for and viewing products on the Product Display Page (PDP)
- Adding items to the shopping cart
- Completing purchases on the checkout page
Traffic data from these interactions is aggregated into time-based segments, which serve as essential points for analyzing traffic patterns.
Data Ingestion and Transformation Platform
Ecommerce applications generate diverse data types at varying volumes, necessitating a robust streaming platform for continuous data ingestion. This data must also be transformed and stored for subsequent analysis and machine learning. For this purpose, we utilize Amazon Kinesis Data Streams for data ingestion, along with Amazon Kinesis Data Firehose and AWS Lambda for data transformation, with storage in Amazon Simple Storage Service (S3).
Real-Time Anomaly Detection and Notification
Once data is prepared, it is essential to analyze it for anomalies in near-real time. This requires notifying the appropriate teams to take corrective actions as needed. We employ Amazon Lookout for Metrics and Amazon Simple Notification Service (SNS) to fulfill these requirements. Lookout for Metrics utilizes machine learning to identify and diagnose anomalies in traffic patterns, adjusting over time with feedback to enhance accuracy. Additionally, it integrates seamlessly with Amazon SNS, enabling notifications via SMS, mobile push, or email.
Monitoring Ecommerce Traffic with Lookout for Metrics
As illustrated in our architecture diagram, user traffic and interactions with the ecommerce application are captured over time and ingested into Kinesis Data Streams. Data is transformed and stored in S3, where we create a detector in Lookout for Metrics linked to the S3 bucket. This integration allows automatic data ingestion into the created detector. Once activated, Lookout for Metrics starts monitoring the data for anomalies in near real-time and provides customizable severity thresholds to help minimize false positives. Furthermore, it can publish notifications to an SNS Topic, allowing users to subscribe for alerts regarding any anomalies.
In conclusion, we have explored the challenges of detecting minor anomalies in ecommerce traffic in real-time and demonstrated the services available, such as Lookout for Metrics, that can assist in monitoring these anomalies. Implementing this architecture can enhance your ability to detect anomalies promptly, thus reducing any adverse effects on your business KPIs. For related insights, please check out this blog post and this authoritative resource. Additionally, for those interested in opportunities in this field, consider visiting this excellent resource.
Location: Amazon IXD – VGT2, 6401 E Howdy Wells Ave, Las Vegas, NV 89115.
Leave a Reply