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Mostly Used By Retail, Manufacturing, Healthcare, Legal, Finance Industries

OpenAI Application for Contact Center Analytics

Contact center analytics is the process of collecting, measuring, and analyzing data from contact center interactions. This data can be used to improve customer satisfaction, reduce costs, and improve agent productivity. We are using Azure's speech to text service for automatic Contact Center Analytics, form filling and entity extraction with OpenAl to machine readable records.

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How It Works?

We are leveraging OpenAI's ML models and Azure's Speech to Text service to build advanced Contact Center Analytics. This system is also eligible in form filling and entity extraction to machine readable records.

 

Data Collection & Preparation

In this phase, we leverage tools like Azure Data Factory, Azure Blob Storage, Azure Databricks or Azure Synapse Analytics to store, manage and transform the data collected. In contact center analytics, data collection typically includes information such as call volume, call duration, wait times, call abandonment rates, agent performance metrics (e.g., average handling time, first call resolution), customer satisfaction ratings, feedback from surveys or customer interactions, and data from self-service channels like IVR or chatbots. Collecting and analyzing this data helps organizations gain insights into contact center operations, agent performance, customer experience, and operational efficiency.

Model Training Using OpenAI

For Model Training, we have used OpenAI. OpenAI's model development for Contact Center analytics facilitates advanced analysis of customer interactions and sentiment, empowering organizations to optimize agent performance, enhance customer satisfaction, and improve operational efficiency. By leveraging OpenAI's language models, developers can extract valuable insights from vast amounts of data, driving data-driven decision-making and delivering superior contact center experiences.

Model Deployment

We prefer Azure Services like Azure Functions, Azure Kubernetes Services (AKS), or Azure App Services to deploy the Contact Center Analytics model, but the same model will also be able to deploy on other cloud platforms like AWS Services e.g; Amazon SageMaker, Amazon Elastic Compute Cloud (EC2), Amazon S3 (Simple Storage Service) AWS Lambda, for GCP Services like Google Compute Engine, Google Cloud Functions, Google Cloud AI Platform can be used, HuggingFace etc.

What We Offer

ThirdEye Data builds advanced Contact Center Analysis for enterprises to improve customer satisfaction, Reducing costs and increase agents productivity.

OpneAI Integration

Leveraging OpenAI's ML models and NLP capabilities to identify patterns and trends in customer behavior, sentiments, and profiling users.

Data Transformation

Taking care of the data collection, cleaning, transforming, and preparing the call center and social media data at scale to train the ML model.

Model Development

Develop custom machine learning model including feature selection, algorithm and techniques to provide deep and accurate insight.

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We have prepared advanced Call Center Analytics systems which are ready to be deployed.

Customer Feedbacks

All the reviews are independently verified by Clutch.

ThirdEye Data brought in their best to support us.

Kumar Mankala Advisor of AI/ML Program & Products, SCE

They went above and beyond to actualize the our ambitions.

Matt Singer CEO, EcoTrak

Their project management and communication were top-notch.

Andriy Romanyuk Head of R&D, Glas Trösch Group

Frequently Asked Questions

Before embracing the contact center analytics, businesses may have some common asks. We are trying to answer them here.

We are using a variety of tools and technologies for contact center analytics, including CRM software, call recording software, OpenAI's ML models, Azure's Speech to Text Service and analytics platforms.

We are ensuring the security of our contact center data by using encryption, firewalls, and access controls.

We are using contact center analytics to improve your customer service by identifying areas where customer satisfaction can be improved, providing agents with real-time insights into customer interactions, and tracking the effectiveness of your marketing campaigns.

We are using contact center analytics to reduce costs by identifying opportunities to automate tasks or improve efficiency.

We are using contact center analytics to improve agent productivity by providing agents with real-time insights into customer interactions and by tracking the performance of individual agents.

There are a few risks associated with contact center analytics, including:

  • Data security: Contact center data can be sensitive, so it is important to take steps to protect it from unauthorized access.
  • Privacy: Contact center data can also contain personal information, so it is important to be mindful of privacy laws and regulations.
  • Bias: Contact center analytics can be biased if the data is not collected or analyzed properly.
  • Accuracy: The accuracy of OpenAI and Azure Speech to Text services can vary depending on the quality of the audio recordings.

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