Category: Analytics

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.

Your users have questions and you have answers, but you need a better way for your users to ask their questions and get the right answers. They often call your help desk, or post to your support forum, but over time this adds stress and cost to your organization. Could a chat bot add value for your customers? Interestingly, a recent poll shows that 44% of people would rather talk to a chat bot than to a human! In this post we provide a sample solution, called QnABot (pronounced “Q and A Bot”). The QnABot uses Amazon Lex and Amazon Alexa to provide a conversational interface for your “Questions and Answers.” This allows your users to ask their questions and get quick and relevant answers.

If you generate machine learning (ML) models, you know that the key challenge is exporting and importing them into other frameworks to separate model generation and prediction. Many applications use PMML (Predictive Model Markup Language) to move ML models from one framework to another. PMML is an XML representation of a data mining model. In […]