고객 경험 CX 위한 인공 지능

2021-11-22

출판:Dash Network 출판년월: 2021년 11월

・Artificial Intelligence for CX Applications:
Case Studies, Market Drivers, Market Barriers, and Best Practices for the Adoption of AI Within CX Platforms and Applications
고객 경험 CX 위한 인공 지능:CX 플랫폼 및 용도에서의 AI 채용을 위한 사례 연구, 시장 촉진 요인, 성장 억제 요인, 성공 사례

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견적문의의뢰  리포트목차

ダッシュネットワーク(Dash Network)「고객 경험 CX 위한 인공 지능:CX 플랫폼 및 용도에서의 AI 채용을 위한 사례 연구, 시장 촉진 요인, 성장 억제 요인, 성공 사례 – Artificial Intelligence for CX Applications: Case Studies, Market Drivers, Market Barriers, and Best Practices for the Adoption of AI Within CX Platforms and Applications」는 고객 경험(CX/고객 체험) 플랫폼, 용도, 프로그램의 채용과 이용을 둘러싼 시장 성장 촉진 요인과 억제 요인, 인공지능(AI)의 일반적인 유스 케이스 카테고리, CX 개선을 위한 AI 이용 에 관한 상세 설명을 포함한 대표적인 사례 연구 등에 주목하고 있습니다。

주요 게시물

  • 이그제큐티브 요약
  • 시장 동인
    고객 대면 자동화 및 비서에 대한 수요 증가
    백엔드 자동화 및 지능형 분석에 대한 수요 증가
    데이터 기반 통찰력 및 고객 여정에 대한 수요 증가
    더 깊은 고객 참여로 더 많은 가치 확인
  • 시장 장벽
    제한된 범위 또는 데이터 품질
    CX 과제와 AI 솔루션 간의 조정 부족
    제한된 데이터 거버넌스 정책 및 개인정보 보호 문제
    알고리즘 관리 문제
  • 규제 문제
    AI 알고리즘 규제
    주 데이터 개인 정보 보호법
    주 데이터 보안법
    국가 위반 통지법
  • 사례 연구
    고객 대면 사례 연구
    넷플릭스 추천 엔진
    N26: AI를 사용하여 가상 비서 전원 공급
    Kiwi.com: 여행 서비스를 위한 AI 도우미

    백오피스 사례 연구
    UPS: AI를 사용하여 물류 개선
    Cresta: AI 기반 코칭

    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

    Case Studies

    • Netflix
    • N26
    • Kiwi.com
    • UPS
    • Cresta

    Key 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 End-Use Markets
    • Retailers
    • Consumer goods firms
    • Online/offline retailers
    • B2B and services firms
    • Media and entertainment companies
    • Transportation companies
    • Hospitality companies
    • Telecommunications companies
    • Healthcare companies
    • First/Third-party marketers
    • Data privacy specialists
    • Software vendors
    • Shipping companies
    • Logistics companies

    Technologies

    • Artificial Intelligence (AI)
    • Machine Learning (ML)
    • Deep Learning (DL)
    • Natural Language Processing (NLP)
    • Computer Vision (CV)

    Geographies

    • North America
    • Europe
    • Asia Pacific
    • Latin America
    • Middle East and Africa

    목차

    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

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