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PUBLISHER: Frost & Sullivan | PRODUCT CODE: 1953305

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PUBLISHER: Frost & Sullivan | PRODUCT CODE: 1953305

AI Analytics: Powering Decision Intelligence for the CX Industry

PUBLISHED:
PAGES: 35 Pages
DELIVERY TIME: 1-2 business days
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Improving CX Performance

One of the most powerful applications of AI technology in CX today is AI-powered analytics. The introduction of AI analytics can supercharge contact center business intelligence, transforming it from a tool that tells a story of the past to an instrument that informs the future. AI analytics enable a range of new capabilities, including:

Automated insight generation that autonomously analyzes large, complex datasets to identify patterns, trends, and anomalies, all of which are capabilities that previously required a data scientist to implement.

Predictive and prescriptive analytics through machine and deep learning which enables BI tools to move beyond populating dashboards to delivering forecasts, risk assessments, and actionable insights that drive proactive decision making.

Natural language querying and conversational analytics that enable teams to interact with BI systems using natural language, democratizing access to business insights and decision intelligence.

Real-time data analysis that continuously processes and analyzes streaming data, providing real time insights that enhance operational agility and responsiveness to changing market or customer conditions.

Advanced customer and sentiment analysis that can process customer interaction data (e.g., voice, text, chat) to extract customer sentiment, intent, and experience signals enabling more comprehensive and nuanced insights from customer behavior.

This study explores important contact center applications and reveals how AI analytics has transformed from a reporting layer to become the operational nervous system of the modern contact center, connecting customer experience optimization, virtual agent performance, and human agent enablement into a unified intelligence fabric. Contact center application analysis includes:

AI Analytics as a Driver for Identified CX Priorities

Addressing Customer Frustrations with Virtual Agents

Addressing Customer Frustrations with Live Voice

Using AI Analytics to Inform Improving Agent Experience Priorities

Addressing Omnichannel Integration Challenges with AI Analytics

Leveraging AI Analytics for Contact Center Outsourcing Decisions

Additionally, this study outlines the AI analytics capabilities of 22 contact center software vendors, plotting their solutions on an AI analytics capabilities matrix.

Product Code: KBAE-76

Table of Contents

Research Objectives and Methodology

  • Research Objectives and Methodology of Customer Survey
  • Respondent Profile

Key Findings

AI Analytics in CX

  • AI Analytics Introduction
  • AI Analytics vs. Traditional BI
  • Generative AI as a Key Enabler of AI Analytics
  • AI Analytics Tech Stack
  • AI Analytics in CX Use Cases
  • Strategic CX Benefits From AI Analytics

AI Analytics Impact on CX

  • AI Analytics as a Driver for Identified CX Priorities
  • Addressing Customer Frustrations with Virtual Agents
  • Addressing Customer Frustrations with Live Voice
  • Using AI Analytics to Inform Improving Agent Experience Priorities
  • Addressing Omnichannel Integration Challenges with AI Analytics
  • Leveraging AI Analytics for Contact Center Outsourcing Decisions

AI Analytics in CX Capabilities Matrix

  • AI Analytics in CX Capabilities Matrix

Appendix

  • Growth Opportunities Fuel the Growth Pipeline Engine(TM)
  • Why Is It Increasingly Difficult to Grow?

The Strategic Imperative 8(TM)

Next Steps

  • Benefits and Impacts of Growth Opportunities
  • Next Steps

List of Exhibits

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