Azure

Utilizing Textual content Analytics in name facilities

Azure Cognitive Providers gives Textual content Analytics APIs that simplify extracting info from textual content information utilizing pure language processing and machine studying. These APIs wrap pre-built language processing capabilities, for instance, sentiment evaluation, key phrase extraction, entity recognition, and language detection.

Utilizing Textual content Analytics, companies can draw deeper insights from interactions with their prospects. These insights can be utilized to create administration reviews, automate enterprise processes, for aggressive evaluation, and extra. One space that may present such insights is recorded customer support calls which may present the required information to:

  • Measure and enhance buyer satisfaction
  • Monitor name middle and agent efficiency
  • Look into efficiency of assorted service areas

On this weblog, we are going to have a look at how we will acquire insights from these recorded buyer calls utilizing Azure Cognitive Providers.

Utilizing a mix of those providers, resembling Textual content Analytics and Speech APIs, we will extract info from the content material of buyer and agent conversations. We will then visualize the outcomes and search for traits and patterns.

Diagram showing how combination of Cognitive Services can extract information

The sequence is as follows:

  • Utilizing Azure Speech APIs, we will convert the recorded calls to textual content. With the textual content transcriptions in hand, we will then run Textual content Analytics APIs to realize extra perception into the content material of the conversations.
  • The sentiment evaluation API gives info on the general sentiment of the textual content in three classes constructive, impartial, and unfavorable. At every flip of the dialog between the agent and buyer, we will:
    • See how the client sentiment is bettering, staying the identical, or declining.
    • Consider the decision, the agent, or both for his or her effectiveness in dealing with buyer complaints throughout completely different instances.
    • See when an agent is persistently capable of flip unfavorable conversations into constructive or vice versa and determine alternatives for coaching.
  • Utilizing the important thing phrase extraction API, we will extract the important thing phrases within the dialog. This information, together with the detected sentiment, can assign classes to a set of key phrases through the name. With this information in hand, we will:
    • See which phrases carry unfavorable or constructive sentiment.
    • Consider shifts in sentiment over time or throughout product and repair bulletins.

Table showing overall sentiment in three text categories

  • Utilizing the entity recognition API, we will extract entities resembling individual, group, location, date time, and extra. We will use this information, for instance, to:
    • Tie the decision sentiment to particular occasions resembling product launches or retailer openings in an space.
    • Use buyer mentions of opponents for aggressive intelligence and evaluation.
  • Lastly, Energy BI might help visualize the insights and talk the patterns and traits to drive to motion.

Power BI graph visualizing the insights and communicating the patterns and trends

Utilizing the Azure Cognitive Providers Textual content Analytics, we will acquire deeper insights into buyer interactions and transcend easy buyer surveys into the content material of their conversations.

A pattern code implementation of the above workflow might be discovered on GitHub.

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