Power hyper-personalized decisions with AI that understands your customers.

Product Description

Smart Recommendation Engine (SRE) is a cloud-based AI and machine learning service that delivers real-time, hyper-personalized recommendations across digital channels. Built on AWS Personalize and enhanced with Strata’s proprietary models and real-time decisioning engine (RTDM), SRE transforms customer interaction data into actionable recommendations—maximizing relevance, increasing conversion, and boosting profitability.

Business Problems Solved

  • Reliance on static business rules limits personalization and campaign performance.
  • Limited understanding of customer behavior and purchase intent.
  • Inefficient offer targeting leads to low conversion and high churn.
  • Manual, resource-intensive campaign management.
  • Inability to monitor campaign effectiveness and optimize in real time.

How It Works

SRE ingests customer interaction data, product attributes, and user demographics to build and train models using Amazon Personalize. Recommendations are generated and re-ranked in real time using business criteria such as profitability or conversion propensity. Results are delivered via API or batch to campaign platforms. The models are retrained periodically, monitored with Amazon CloudWatch, and optimized using a full MLOps pipeline.

Use Cases

  • Personalized product or content recommendations on ecommerce and media platforms.
  • Optimized top-up or bundle offers in telco retention campaigns.
  • Dynamic website personalization based on real-time user behavior.
  • Segmented promotions targeting high-value customer cohorts.

Key Benefits

  • +2.4pp conversion uplift and +14.5% revenue boost in real implementations.
  • Personalization at scale powered by AWS AI/ML services.
  • Campaign performance insights via automated monitoring dashboards.
  • Increased customer satisfaction and loyalty through relevant interactions.
  • Reduced operational cost by automating model training and recommendation delivery.

Implementation Options

  • Real-time inference: Recommendations delivered instantly via API calls for high-frequency use cases (e.g., web personalization, telco recharge offers).
  • Batch inference: Recommendations generated at regular intervals for use in email campaigns, customer segmentation, or dashboard-driven targeting.


AWS Services Used: 

Amazon Personalize – Core service for personalized recommendation models.

Amazon S3 – Secure and scalable data storage for training and inference datasets.

AWS Glue – ETL pipelines to prepare and transform user, item, and interaction data.

AWS Lambda & Sagemaker– Serverless automation of training, scoring, and model updates.

Amazon CloudWatch – Monitoring and logging for model and pipeline performance.

Amazon DynamoDB – (optional, for real-time use) Used to cache user profiles, interaction history, or top recommendations with low latency.
AWS Step Functions – (optional, for orchestrating complex training and deployment workflows in both real-time and batch scenarios).