AWS Machine Learning Blog

Category: Amazon Athena

Shopper Sentiment: Analyzing in-store customer experience

Retailers have been using in-store video to analyze customer behaviors and demographics for many years.  Separate systems are commonly used for different tasks.  For example, one system would count the number of customers moving through a store, in which part of the store those customers linger and near which products.  Another system will hold the store layout, whilst yet […]

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Run SQL queries from your SageMaker notebooks using Amazon Athena

The volume, velocity and variety of data has been ever increasing since the advent of the internet. The problem many enterprises face is managing this “big data” and trying to make sense out of it to yield the most desirable outcome. Siloes in enterprises, continuous ingestion of data in numerous formats, and the ever-changing technology […]

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Detect sentiment from customer reviews using Amazon Comprehend

In today’s world, public content has never been more relevant. Data from customer reviews is being used as a tool to gain insight into consumption-related decisions as the understanding of its associated sentiment grants businesses invaluable market awareness and the ability to proactively address issues early. Sentiment analysis uses a process to computationally determine whether […]

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Build a social media dashboard using machine learning and BI services

In this blog post we’ll show you how you can use Amazon Translate, Amazon Comprehend, Amazon Kinesis, Amazon Athena, and Amazon QuickSight to build a natural-language-processing (NLP)-powered social media dashboard for tweets. Social media interactions between organizations and customers deepen brand awareness. These conversations are a low-cost way to acquire leads, improve website traffic, develop […]

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Serverless Unsupervised Machine Learning with AWS Glue and Amazon Athena

Have you ever had the need to segment a data set based on some of its attributes? K-means is one of the most common machine learning algorithms used to segment data. The algorithm works by separating data into different groups, called clusters. Each sample is assigned a cluster so that the samples assigned to the […]

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Capture and Analyze Customer Demographic Data Using Amazon Rekognition & Amazon Athena

Millions of customers shop in brick and mortar stores every day. Currently, most of these retailers have no efficient way to identify these shoppers and understand their purchasing behavior. They rely on third-party market research firms to provide customer demographic and purchase preference information.

This blog post walks you how you can use AWS services to identify purchasing behavior of your customers. We show you:

How retailers can use captured images in real time.
How Amazon Rekognition can be used to retrieve face attributes like age range, emotions, gender, etc.
How you can use Amazon Athena and Amazon QuickSight to analyze the face attributes.
How you can create unique insights and learn about customer emotions and demographics.
How to implement serverless architecture using AWS managed services.

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