This Guidance demonstrates how you can implement a personalization engine to derive and deliver the right guest experience when your guest most needs it. By aggregating data from various guest touchpoints, you can determine preferences and make tailored recommendations for room type, amenities, dining, and activities to delight each guest. Personalization can enhance your guest's satisfaction and loyalty, while incentivizing high-value ancillary purchases, strengthening your brand image.
Please note: [Disclaimer]
[Architecture diagram description]
Use Amazon Kinesis Data Streams to ingest real-time reservation events (new, modified, and canceled) from the central reservation system (CRS) to AWS.
Optionally, enrich your guest data with demographics, Foursquare Places, and attribution data from third-party data marketplaces using AWS Data Exchange.
Store all raw ingested JSON data (reservation, stay and folio, loyalty, demographics, Foursquare Places, and clickstream event data) on Amazon Simple Storage Service (Amazon S3).
Use AWS Entity Resolution to resolve a guest’s identity across all interactions, and merge this data into a unified profile with their preferences.
Create a preference recommendation model, based on the complete 360 degree view of guest data, using Amazon Personalize.
Recommend amenities, dining, and activities to guests on your website and mobile app based on their preferences by using API Gateway.
Activate campaigns on Amazon Pinpoint by configuring a personalized campaign from Amazon Personalize based on guest preferences, then send tailored emails, SMS, and push notifications.
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.
This Guidance uses API Gateway integrated with Amazon CloudWatch to provide monitoring capabilities so that you can track API calls and latency. This Guidance integrates with AWS CloudTrail for API request logging. These features improve observability of API operations and their usage. You can also set up CloudWatch alarms to notify you of errors or threshold breaches in near real-time.
This Guidance lets you use AWS Identity and Access Management (IAM) to strengthen data security and access control through granular permissions. By using IAM to grant least-privilege permissions, you can make sure that users have access only to the specific resources they need.
This Guidance uses Amazon Pinpoint, which provides built-in scalability and redundancy so that you can reliably deliver SMS, email, and push notification messages to users. Amazon Pinpoint is a fully managed service that delivers reliable communication with users even during peak times.
This Guidance uses DynamoDB, which provides fast, consistent, single-digit millisecond latency. It is also a fully managed NoSQL database that provides automatic scaling (through its on-demand mode) and replication across Availability Zones, facilitating high-performance efficiency and low latency. This makes it ideal for a high volume of users trying to access the website and application.
This Guidance uses Amazon Personalize, which charges based on the number of requests to your endpoints, so you only pay for recommendations that are generated. It also automatically scales resources so you don’t over-provision capacity. Additionally, as a fully managed service, Amazon Personalize removes the need for you to manage machine learning infrastructure. It handles capacity planning, model training, and hosting, so you don’t have to provision resources up front.
This Guidance uses Lambda, which lets you run code without provisioning or managing servers. Its automatic scalability and its reuse of implementation environments optimizes resource usage. This minimizes energy usage because Lambda runs only the necessary compute for your workloads. Additionally, you save more energy if you use Lambda functions powered by AWS Graviton.
A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment.
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.
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.