市場調查報告書
商品編碼
1099306

CX 人工智能

Artificial Intelligence for CX Applications

出版日期: | 出版商: Dash Network LLC | 英文 29 Pages, 7 Charts, Tables, and Figures | 訂單完成後即時交付

價格
  • 全貌
  • 簡介
  • 目錄
簡介

本報告詳細介紹了在 CX 平台、應用程序和程序中採用和使用 AI 的市場驅動因素和限制,常見的 AI 用例類別以及使用 AI 來改進 CX。它側重於一些典型案例研究。它還詳細介紹了當前的人工智能法規,這些法規通常側重於正確收集和使用個人信息。


目錄

  1. 執行摘要
    1. 簡介
    2. 市場促進因素
    3. 市場限制
    4. Dash 研究洞察
  2. 市場概況
    1. 簡介
      1. 用例類別
        1. 智能洞察
        2. 預測
        3. 首選項
        4. 建議
        5. 自動化
    2. 人工智能的商業整合
    3. 市場驅動力
      1. 客戶服務自動化和對助理的需求不斷增長
      2. 對後端自動化和智能分析的需求不斷增加
      3. 以數據為依據的洞察力和對客戶旅程日益增長的需求
      4. 在更深入的客戶參與中看到更多價值
    4. 市場限制
      1. 數據范圍或質量有限
      2. CX 挑戰和 AI 解決方案之間缺乏一致性
      3. 有限的數據治理政策和隱私問題
      4. 算法管理問題
    5. 監管問題
      1. AI算法監管
      2. 數據隱私法
      3. 數據安全法
      4. 違反通知法
  3. 案例研究
    1. 客戶案例研究
      1. Netflix 推薦引擎
      2. N26:用人工智能驅動虛擬助手
      3. Kiwi.com:旅遊服務AI助手
    2. 後台案例研究
      1. UPS:使用人工智能改善物流
      2. Cresta:AI 主導的輔導
  4. 最佳實踐
    1. 培養以數據為中心的文化
    2. 消除數據孤島
    3. 使用人工智能來支持和增強人類的努力
  5. 首字母縮寫詞列表
  6. 目錄
  7. 圖表
  8. 附錄
    1. 調查範圍
    2. 來源和研究方法
    3. 版權聲明
目錄

Artificial intelligence (AI) has become nearly ubiquitous across a wide range of industries and use cases, and within the CX discipline, AI functionality is no different; AI is increasingly being integrated or incorporated into CX platforms and applications. AI functionality is being integrated or incorporated into CX platforms and applications, with low- or no-code interfaces that allow CX, marketing, and sales professionals with little data science or computer coding experience to manipulate data and tune algorithms to serve several different functions. Many organizations have already seen the benefit of deploying AI across customer-facing functions and in back-office systems to support applications including the generation of intelligent insights, predictions, customer preferences, next-best-action recommendations, and the support of higher levels of automation.

AI heavily relies on the capture, organization, and activation of customer data, processing the data and capturing various aspects of interactions with customers. As more data is captured and processed, more complex algorithms or combinations of algorithms can be deployed, resulting in greater value and a greater return on investment (ROI).

This Dash Research report focuses on the market drivers and barriers surrounding the adoption and use of AI in CX platforms, applications, and programs, the general use case categories for AI, and several representative case studies detailing the use of AI to improve CX. The report also details current AI regulations, which generally focus on the proper collection and use of personal information.

Key Questions Addressed:

  • How are companies using AI to support their CX initiatives?
  • What are the key drivers of AI adoption for CX applications and platforms?
  • What are the key functions within CX that AI can support or enable?
  • What barriers exist that may hinder the adoption of AI within CX platforms or applications?
  • What are the key underlying technologies used in AI?
  • What are the relevant regulatory issues of which CX professionals using AI should be aware?
  • What are some examples of AI being utilized in the real world?

Who Needs This Report?

  • CX practitioners
  • Marketing/sales managers
  • C-suite and strategy directors
  • IT integration specialists
  • Logistics specialists
  • Contact center managers
  • Investor community

Table of Contents

  1. Executive Summary
    1. Introduction
    2. Market drivers
    3. Market barriers
    4. Dash Research insights
  2. Market Overview
    1. Introduction
      1. Use case categories
        1. Intelligent insights
        2. Predictions
        3. Preferences
        4. Recommendations
        5. Automation
    2. Commercial integration of AI
    3. Market drivers
      1. Increasing demand for customer-facing automation and assistants
      2. Higher demand for backend automation and intelligent analysis
      3. Growing appetite for data-led insights and customer journeys
      4. More value seen with deeper customer engagement
    4. Market barriers
      1. Limited scope or quality of data
      2. Lack of alignment between CX challenges and AI solutions
      3. Limited data governance policies and privacy concerns
      4. Algorithm management issues
    5. Regulatory issues
      1. AI algorithm regulation
      2. State data privacy laws
      3. State data security laws
      4. State breach notification laws
  3. Case Studies
    1. Customer-facing case studies
      1. Netflix Recommendation Engine
      2. N26: Using AI to power a virtual assistant
      3. Kiwi.com: AI assistants for travel services
    2. Back-office case studies
      1. UPS: Using AI to improve logistics
      2. Cresta: AI-driven coaching
  4. Best Practices
    1. Develop a data-centric culture
    2. Eliminate data silos
    3. Use AI to support and augment human efforts
  5. Acronym and Abbreviation List
  6. Table of Contents
  7. Table of Figures
  8. Appendix
    1. Scope of study
    2. Sources and methodology
    3. Copyright notice

List of Figures

  • AI Maturity and Data Integration Depth
  • Predictive Modeling Using Machine Learning
  • A Typical Online/Offline Customer Journey Map
  • Data Observability
  • Netflix Recommendation Engine
  • N26 AI Assistant
  • UPS ORION System