Business challenge

The e-commerce platform had reached a critical point in its digital growth journey. For years, it relied on static, search-based recommendations and manual merchandising rules that offered the same product suggestions to every visitor. The technology worked — but only up to a point.

As traffic and product catalogs grew, the cracks began to show. The system couldn’t adapt to changing user behavior, and every update required manual intervention. Different environments ran on disconnected infrastructure making performance unpredictable and maintenance costly.

Marketing teams were eager to deliver personalized, real-time experiences, but the data was scattered across systems, making it impossible to understand how users interacted or what they truly wanted. Developers were forced to maintain brittle integrations instead of innovating.

The business needed to evolve: to understand every click, view, and purchase in real time — and to transform that insight into tailored product recommendations that could drive engagement, conversion, and customer loyalty at scale.

Solution

Cloud Softway designed a cloud-native personalization platform on AWS, centered around Amazon Personalize, to deliver intelligent, data-driven recommendations at scale.
The platform was architected to ingest real-time customer behavior, train and retrain recommendation models automatically, and serve low-latency results directly to the website and mobile app.

High-Level Architecture

  1. Event Streaming Layer:

    • User interactions (product views, clicks, cart actions, purchases) are captured via a lightweight API layer built on Amazon API Gateway.
    • These events stream into Amazon Kinesis Data Streams, then batched and delivered to Amazon S3 using Kinesis Data Firehose for durable storage and downstream processing.

  2. Data Storage and Processing:

    • Amazon S3 serves as the central data lake for all behavioral, catalog, and user metadata.
    • Data is pre-processed using AWS Lambda or AWS Glue jobs for format validation, timestamp normalization, and event schema alignment.

  3. Personalization Engine:

    • Amazon Personalize consumes S3 datasets through defined schemas (users.csv, items.csv, interactions.csv).
    • A dataset group is created to manage multiple recommendation models (recipes) such as:
      • USER_PERSONALIZATION for personalized recommendations
      • RELATED_ITEMS for product detail page suggestions
      • PERSONALIZED_RANKING for reordering search results based on user affinity
    • Event trackers are configured for real-time ingestion, enabling continuous model updates without full retraining.
    • Models are deployed as Personalize Campaigns, with endpoints exposed for inference.

  4. Serving Layer:

    • AWS Lambda functions query Amazon Personalize’s runtime API to fetch recommendations in milliseconds.
    • Results are served to front-end clients through API Gateway endpoints, integrated seamlessly into existing website and mobile app components.
    • The design ensures zero code change in the frontend during the POC phase.

  5. Security, Monitoring, and Governance:

    • All data flows are encrypted in transit (TLS 1.2) and at rest (KMS encryption).
    • IAM least privilege and multi-account guardrails enforce isolation between environments.
    • CloudWatch metrics, CloudTrail auditing, and AWS Config provide full operational visibility.
    • Infrastructure is defined using AWS CloudFormation, ensuring repeatable, version-controlled deployments.

Key Features / Implementation Highlights

  • Real-time event ingestion and enrichment:
    Live user actions streamed through Kinesis enable near real-time updates to recommendation models.

  • Modular ML architecture:
    Multiple Amazon Personalize recipes (User-Personalization, Related-Items, Personalized-Ranking) deployed as independent campaigns, allowing flexible experimentation across use cases.

  • Automated pipelines:
    End-to-end CI/CD using CodePipeline and CodeBuild for provisioning, data schema updates, and campaign retraining.

  • Model observability:
    Integrated evaluation metrics (coverage, precision, HR@K) monitored via CloudWatch dashboards to measure model accuracy over time.

  • Seamless integration with existing systems:
    APIs abstracted via Lambda allow the front-end to consume recommendations with minimal change, supporting both web and mobile clients.

Key implementation highlights

  • Personalization that learns instantly:
    Recommendations update in real time as users browse, delivering truly adaptive experiences.

  • Improved engagement and conversions:
    Anticipated 10–15% uplift in click-through rate and 8–12% increase in average order value, based on early test cohorts.

  • Operational efficiency:
    Fully managed ML eliminates infrastructure overhead — no custom model training pipelines or GPU provisioning required.

  • Scalable by design:
    Auto-scaling ECS services and serverless components ensure consistent latency during peak events like Black Friday or Ramadan campaigns.

  • Data-driven decision making:
    Continuous feedback loops allow business teams to iterate on placement, design, and strategy backed by measurable performance metrics.

Business impact

  • Reduced request failure rate from approximately 60% to almost zero through resilient scaling patterns.
  • Improved scalability and fault tolerance, ensuring consistent performance during peak traffic.
  • Decreased deployment time from hours to minutes with automated pipelines.
  • Delivered significant cost savings by eliminating inefficient scaling and optimising resource utilisation.
  • Elevated customer experience with faster response times and stable operations.

Industry relevance

Ideal for e-commerce, retail, and digital marketplaces seeking to:

  • Replace static recommendations with adaptive ML-based personalization.
  • Reduce operational complexity in managing recommendation engines.
  • Integrate intelligence into existing apps with minimal engineering effort.