case study

Driving Efficiency and Cost Savings Through Conversational AI for a Large Telecom Operator

Telecom

Business Impacts

Achieved a 52% improvement in containment over two years

Attained an impressive 44% overall containment rate

Deployed diverse NLU models capable of recognizing over 500 intents, including personalized GenAI-powered experiences

Doubled self-service rates within two years

Customer Key Facts

  • Country : United States of America
  • Industry : Telecom

Problem Context

Our client, a leading telecom provider in the US, serves retail, residential, and enterprise customers. They aimed to leverage generative AI-powered Google Contact Center AI to improve customer experience and cut contact center costs by automating over 250 million inbound queries across both chat and voice channels.

To ensure a successful transformation and maximize ROI, the client sought an outcome-based engagement model with incentives tied to results.

Challenges

  • Low containment rates on the IVR system, leading to inefficiencies and customer frustration
  • Outdated telephony platform hindering scalability and performance
  • Complex IVR flows across multiple use cases, complicating user interactions
  • Serving a diverse demographic with over four languages, adding complexity to language support and customer service

Technologies Used

DialogFlow CX

DialogFlow CX

Apigee

Apigee

Vertex AI Agent Builder

Vertex AI Agent Builder

Big Query

Big Query

Solution

Quantiphi developed a Generative AI-powered virtual agent using Dialogflow CX, seamlessly integrating it with the client’s existing contact center platform. This virtual agent was designed to handle and automate a wide range of customer queries across both chat and voice channels, significantly enhancing response accuracy and speed.

To ensure accountability and drive measurable results, Quantiphi adopted a unique outcome-based commercial model. Payments were directly tied to the continuous improvement and maintenance of containment rates, ensuring that the virtual agent not only met but sustained performance benchmarks over time. This model aligned incentives with the client’s goal of reducing contact center costs while enhancing customer experience.

Results

  • Achieved record-breaking containment rates across both voice and chat channels
  • The virtual agent successfully automated the majority of incoming queries, streamlining operations and improving efficiency on both chat and voice platforms

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