This Guidance demonstrates how to improve the accuracy of the Customer Lifetime Value (CLV) metric by combining the data from both historical and proprietary databases, unifying operational and real-time data, and delivering that data through a powerful business intelligence service. Using the scenario of a financial institution, this Guidance demonstrates how to leverage data from various sources, such as transaction systems, enterprise resource planning (ERP), customer clickstream data, as well as data from customer relationship management (CRM) software. A machine learning model can then be trained on those results and predict a CLV. The results are displayed in interactive dashboards to visualize customer profiles, revenue, and lifetime value, empowering users to unlock actionable insights.

Please note: [Disclaimer]

Architecture Diagram

[Architecture diagram description]

Download the architecture diagram PDF 

Well-Architected Pillars

The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

  • AWS CloudTrail and Amazon CloudWatch can be configured with this Guidance to ensure continuous monitoring and auditing of workloads, vital for maintaining high operational standards in any industry. CloudTrail provides governance, compliance, and operational auditing, while CloudWatch offers monitoring and observability.

    Read the Operational Excellence whitepaper 
  • AWS Key Management Service (AWS KMS) has native data encryption integration with the services used in this Guidance. Whereas AWS Identity and Access Management (IAM) helps to both define minimum permissions for accessing every resource and reduce data exposure. Essential for safeguarding data and operations, these services offer robust security layers, crucial across industries.

    Read the Security whitepaper 
  • Amazon S3 and Elastic Load Balancing (ELB) help to ensure workloads are performing their intended functions correctly, consistently, while recovering quickly from failure. Specifically, Amazon S3 ensures data durability and availability, while ELB enhances application fault tolerance. Reliability is fundamental in this Guidance, and these services work in tandem to help ensure data integrity and consistent application performance.

    Read the Reliability whitepaper 
  • Aurora is an AWS managed relational database engine, offering a high-performance, distributed storage system, ideal for data processing. Amazon Redshift is a fast, simple, cost-effective data warehouse service, enabling efficient large dataset handling through parallel query processing. These services are selected for their superior data processing capabilities, crucial for handling diverse datasets in a performance-focused environment.

    Read the Performance Efficiency whitepaper 
  • The services configured throughout this Guidance are pivotal for cost management, offering scalable solutions that align with the financial objectives of any industry by optimizing resources and minimizing unnecessary expenses. For example, AWS DMS is a service that helps you migrate databases with minimal downtime, facilitating cost-effective data replication. A serverless architecture, particularly with AWS DMS, ensures operational cost efficiency through resource auto-scaling and serverless deployment.

    Read the Cost Optimization whitepaper 
  • This Guidance provides end users with insights on customer lifetime value. It allows bank advisors and marketing teams to target only the right customers and reduce the carbon footprint of all the customer engagement channels (like emails and printed proposals). It does this sustainability with the help of Amazon S3 that offers scalable storage, which reduces resource usage, and AWS Lambda, an event-driven service that optimizes computing resources. Selected for their efficient resource management, these services support eco-friendly goals by reducing the carbon footprint across industries.

    Read the Sustainability whitepaper 

Implementation Resources

The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.

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This [blog post/e-book/Guidance/sample code] demonstrates how [insert short description].


The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.

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