AWS Architecture Blog

Category: Amazon Personalize

Reference architecture for near real-time recommendations

Architecting near real-time personalized recommendations with Amazon Personalize

Delivering personalized customer experiences enables organizations to improve business outcomes such as acquiring and retaining customers, increasing engagement, driving efficiencies, and improving discoverability. Developing an in-house personalization solution can take a lot of time, which increases the time it takes for your business to launch new features and user experiences. In this post, we show […]

Digital shopping experience architecture

Amazon Personalize customer outreach on your ecommerce platform

In the past, brick-and-mortar retailers leveraged native marketing and advertisement channels to engage with consumers. They have promoted their products and services through TV commercials, and magazine and newspaper ads. Many of them have started using social media and digital advertisements. Although marketing approaches are beginning to modernize and expand to digital channels, businesses still […]

Figure 1. Enterprise customer engagement channels and corresponding AWS services

Architecting Cross-channel Intelligent Customer Engagements

Recently, we have had customers express the desire to build “omni-channels.” These omni-channels provide a centralized overview of digital engagement channels that help you better understand your customers and offer a more personalized experience. Many companies have tried or are trying to implement an omni-channel strategy. However, because most existing channels are built on different platforms and […]

Amazon Personalize: from datasets to a recommendation API

Automating Recommendation Engine Training with Amazon Personalize and AWS Glue

Customers from startups to enterprises observe increased revenue when personalizing customer interactions. Still, many companies are not yet leveraging the power of personalization, or, are relying solely on rule-based strategies. Those strategies are effort-intensive to maintain and not effective. Common reasons for not launching machine learning (ML) based personalization projects include: the complexity of aggregating […]