Text Analytics Engine: 2-Wk Proof of Concept (POC)

MAQ Software

Gain meaningful insights from text data collected from various customer interactions, social channels, and surveys.

Do you believe that voice of customer is the key to unlocking the best customer experience? Do you collect a lot of customer feedback and struggle to pull meaningful insights from feedback data?

Understanding what your customers are saying about your products, services, or events can be challenging. Especially if you have large volumes of text data over numerous channels. For customer satisfaction, you need to know what they’re saying, what their pain points are, and how to improve their experience.

You can manually collect and analyze text data from numerous sources. This process is time-consuming and prone to bias.

Our Text Analytics Engine uses advanced machine learning to generate insights, automate processes, and reduce the effects of bias. During our two-week Text Analytics POC, our team will work with you to understand your text data and define end-to-end Azure pipelines and data flows. Our team will enable you to implement the model with minimal effort, based on your unique requirements.

Deliverables

  • Detailed plan and architecture diagram of production setup, including estimates for:

    • Ingestion pipelines and data processing using ADF
    • Reusable Azure Databricks notebooks for data cleaning, model execution, and retraining.
    • ML operation using Azure ML Service, serverless architecture, Azure functions, Azure Databricks, and more (based on the use case)
  • POC Python notebooks for data cleaning, feature selection, and model execution using immediately available MAQ Text Analytics libraries for the identified use case

Agenda

Day 1-3

  • Connect with data SMEs from your organization and understand the nature of your data
  • Define problem statement for the use case
  • Sign up for necessary Azure subscriptions

Day 4-7

  • Create python notebooks with sample data in your subscription for data cleaning, feature selection, and model implementation

Day 8-10

  • Undergo model tuning, review, demo, and testing
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