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AIML20: Extracting Value from Text and Audio to Inform Business Strategy

A part of the Developer's Guide to AI series

In this workshop we introduce you to Microsoft Azure's Cognitive Services, Azure blob storage, LUIS, Logic Apps, and PowerBI.

Setup Requirements

  • Azure subscription
  • Python, C#, Ruby, or Go
  • Git

Setup

Create Azure resources

Create Logic App

LUIS

PowerBI

Case study

Tailwind Traders is a general appliance and hardware store available in several countries. However, Tailwind Traders has seen an overwhelming increased amount of support tickets regarding their services and products.

The business teams want to store, analyse and extract insights from their text and audio data to make better product backlog decisions and reduce their support tickets.Tailwind Traders applies Cognitive Services Text Analytics to improve the efficiency of analyzing support tickets. We use Power BI to aggregate their findings to implement improvements.

Demo 1: The first demo will go over the difference between unstructured data and structured data. We will explain why using Blob Storage was a good option in this scenario. In this demo, the use of the Azure Storage Explorer will be shown as an easy and applicable tool to utilize when dealing with different storage types. Finally, we will cover how to retrieve data from the blob and parse that data.

Demo 2: The data is retrieved from blob storage and analyzed using the Azure text analytics service, including sentiment analysis and key phrase extraction. We also show how LUIS can be very effective when working with support tickets and emails by training LUIS to understand support tickets and categorize each one.

Demo 3: Now we want to optimize Tailwind Traders' response to new customer support requests and extract insights from the data. We set up Logic Apps to send users an automated email tailored based on users' requests. Finally, we show how to use Power BI to leverage new insights to drive business decisions.

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