What’s New in Retail from re:Invent 2021

It’s been two years since AWS hosted an in person re:Invent conference. Although everyone had masks on, the excitement was palpable. I had a great time meeting with customers in a face-to-face setting again with dinners and parties. It was really fun. And with so much news coming out of the event, I’m sure it was hard to keep up with all of the information. In this post, I’ll cover announcements and use cases that are relevant for retailers.

Connect physical locations

Wi-Fi connectivity in physical retail stores has a bad reputation. It goes down often, has spotty connectivity, and can get overloaded with too many devices. AWS Private 5G is designed to help retailers overcome these challenges. It’s a fully managed cellular network that customers can run in stores, distribution centers, and factories with better security and coverage to handle many more devices. AWS Outpost servers is another physical location announcement. These are smaller form factor (1U and 2U) servers that can run applications on premises while being connected to the same control plane as your cloud workloads. This solution will be particularly useful for standardizing IT architectures, as well as for software procurement and deployment to on-premises locations. Furthermore, since the management, visualization, and monitoring of many different environments are becoming increasingly complex, we released AWS Cloud WAN. It’s a wide area networking (WAN) service that provides a central dashboard and control plane for an enterprise WAN. And if you’ve been looking at digital twin use cases for factories, distribution centers, or stores, you’ll want to check out AWS IoT TwinMaker, a service that makes it easier to build digital twins.

Increase customer engagement

Amazon Personalize Intelligent User Segmentation now lets you automatically identify high propensity users, which means you no longer need to develop and maintain an extensive and brittle rule catalog. We’ve also released new recommenders that are part of Amazon Personalize and have been optimized for the retail industry. Simply select the retail-specific use cases that you need from a library of recommenders within Amazon Personalize, such as “most viewed,” “best sellers,” “frequently bought together,” “customers who viewed X also viewed Y,” and “recommended for you.”

Stop managing clusters

We’ve heard customers tell us over an over that they don’t want to manage clusters of servers. Instead, they want to focus on innovating within the platforms. This is why we announced several new serverless offerings. Amazon EMR Serverless removes a lot of the management overhead associated with EMR clusters, thereby allowing developers to focus on big data code rather than the underlying infrastructure. Amazon Redshift now has a serverless option, in addition to Apache Kafka with Amazon MSK Serverless. For customers who’ve been looking at the trade-offs between Amazon SageMaker-hosted and Amazon Lambda-hosted models for inference, we introduced Amazon SageMaker Serverless Inference, a pay-per-use fully managed machine learning (ML) inference service.

Accelerate application development

AWS continues to focus on quicker, easier application development. Amazon Lex Automated Chatbot Designer automates chatbot design using conversation transcripts. Meanwhile, with AWS Amplify Studio, developers can accelerate UI development with minimal coding. And with DDoS mitigation for your applications with AWS Shield Advanced, you can enable application layer (layer 7) protection.

Provide better customer support

Contact center agents can improve productivity and customer support with ML-powered call summaries in Amazon Connect. AWS also released a unified agent application to improve the agent experience and customer interactions.

Democratize and accelerate machine learning

To enable your business teams to build ML models to generate predictions without relying on engineering and data science teams, Amazon SageMaker Canvas is a visual, no-code ML solution. And anyone who’s built an ML-model knows data wrangling, feature extraction, and labeling can easily take up 80% of the data science process. To accelerate data labeling, we developed Amazon SageMaker Ground Truth Plus, a new turnkey service that lets you create high-quality training datasets. Moreover, the service can reduce costs by as much as 40%. Finally, for more advanced analytics teams that focus on deep learning models, check out our latest generation of AWS Trainium, a purpose built chip for ML training that’s the most cost-effective model training in the cloud.

We also added natural language query processing to Amazon QuickSight with Amazon QuickSight Q. You can ask questions in natural language and receive accurate answers with relevant visualizations that offer more insights from your data. Imagine asking the application, “What are the northwest regional sales from yesterday for stores over 20,000 sq. ft.?” and getting an instant visualization?

Optimize data access patterns and costs

For ecommerce use cases, we see many customers collecting data from various systems to build materialized views to speed information retrieval times for real time applications—think “user profile” or “order history”. We recently released AWS Glue Elastic Views, a fully managed service to build materialized views. And, now with the announcement of Automated Materialized Views (AutoMV), we’ve added a similar capability to Amazon Redshift. Many retail customers are using Amazon DynamoDB as storage for things that are only queried periodically, such as order history. Therefore, we now support Standard-Infrequent Access (DynamoDB Standard-IA) table class, which can provide 60% savings for infrequence data accessed.

For retailers who want to monetize data or standardize data publishing and subscribing, check out AWS Data Exchange for Amazon Redshift. This service lets you make datasets available from your data warehouse to customers or internal teams via AWS Data Exchange. All billing and subscription details are managed by the service.

Modernize the mainframe

For customers who want to migrate their mainframe to the cloud, we’ve released AWS Mainframe Modernization, a fully managed platform to help customers along their modernization journey.

Impact enterprise goals

We updated the Well-Architected Framework to add a sustainability pillar, which provides guidance to customers about best practices to reach sustainability goals, a common board-level topic with retailers these days. Furthermore, you can add your own retail-specific custom lenses and questions to ensure a consistent and relevant approach to architecture reviews across the enterprise.

If you’d like to discuss how these new solution and service announcements can help your retail organization, AWS is here to help. Contact your account team today to get started.