Recapping re:Invent 2020: What’s New in CPG, Part 2

I’ve said it before, and I’ll say it again: AWS re:Invent 2020 was a huge success! At our annual technology conference back in December, we learned about many advances in AWS technologies. I posted a couple of weeks ago covering some of the new foundational technologies that can help CPGs “Make” production processes more streamlined with innovative, data-driven machine learning (ML). In this blog, I’ll cover the new products that AWS leaders announced at re:Invent 2020 to help CPGs “Move and Market” their products, in addition to driving business insights.

Demand Forecasting (Move)

Amazon Forecast Weather Index is a great enhancement to Amazon Forecast, an AWS Managed Service that uses pre-configured ML to deliver highly accurate forecasts – the very same technology used at Amazon.com. This new functionality automatically includes local weather information in the demand forecasting process to increase forecasting accuracy and improve demand forecasts for seasonal products.

Amazon Location Service makes it easy to add private, secure location data to applications. You can add functionality, like maps, routing, geofencing, and tracking, to your applications as well. CPG use cases include track-and-trace applications, supply chain coordination and optimization, and intelligent routing.

Digital User Engagement (Market)

Amazon Neptune ML is a new feature of Amazon Neptune, a managed graph database service that makes it easy to build and run applications that use highly connected datasets. The service uses ML to make faster, more accurate predictions using graph data – with +50% better predictions than non-graph methods. CPG use cases include enhancing consumer 360, improving digital user engagement (e.g., tracking the customer’s journey and recommending next steps for user engagement or product discounts), and product recommendations.

With Amazon Connect, we released several features to improve digital user engagement, including:

• Amazon Connect Wisdom – Uses ML to search connected repositories (i.e., FAQs, Wikis, etc.) so service agents can find answers to customer questions faster.
• Contact Lens for Amazon Connect – Tracks customer conversations to ensure compliance and better understand sentiments and trends.
• Amazon Connect Customer Profiles – Gives contact center agents a more unified view of customer profiles by integrating customer data from multiple applications.

Business Insights

These two new services can help CPGs reduce the cycle time to aggregate and prepare data for ML and analytics. Since neither solution requires you to write code, you can gain insights from your data quicker and easier.

• Amazon SageMaker Data Wrangler is a visual tool that speeds the time to prepare data for ML from weeks to minutes. With a single interface, you can select, cleanse, explore, and visualize insights from your data.
• AWS Glue DataBrew is a visual data preparation tool with over 250 pre-built transformations to help data scientists and analysts clean and normalize data for analytics and ML.

Amazon QuickSight Q is a new capability of Amazon QuickSight, a highly scalable, serverless ML-powered business intelligence (BI) service. Amazon QuickSight Q uses natural language processing (NLP) to instantly answer business questions, saving weeks of effort for your BI teams, since they don’t need to build data models and front-end dashboards. Users can type simple questions, such as “What is our year-over-year growth rate?” and get answers with visualizations.

Scaling and Migrations

Amazon Aurora Serverless v2 is the next version of Amazon Aurora Serverless. It can scale to hundreds of thousands of transactions in a fraction of a second, delivering as much as 90% cost savings compared to provisioning servers for peak capacity. It provides an on-demand, automatically scaling deployment and is a great fit for highly variable and unpredictable workloads, which many CPGs experienced during the early days of the pandemic.

Babelfish for Aurora PostgreSQL is a new translation layer for the PostgreSQL-compatible edition of Amazon Aurora. It enables Aurora to understand T-SQL commands, Microsoft’s proprietary SQL dialect, from applications written for Microsoft SQL Server. This feature makes it easier, faster, and more cost effective to modify and migrate applications running on SQL Server (2014 or newer) to Aurora.

Building on Amazon’s Consumer Business Experience

Many of the re:Invent 2020 product announcements involve ML. Indeed, automated machine learning (AutoML) was a common thread and key takeaway for the CPG industry. AutoML is the use of predefined ML algorithms to improve operations, revenue, customer engagement, and business insights, without requiring ML expertise, to help facilitate “Make. Move. Market.” business activities.

These new services provide a unique, differentiated value to our CPG customers, leveraging our deep expertise and understanding of this massive market segment. Learn more about AWS for CPG, and get your questions answered here.