AI in Customer Relationship Management Market Size, Share & Trends Analysis Report By Type (Machine Learning, Natural Language Processing (NLP), Image and Speech Recognition, Predictive Analytics, Chatbots and Virtual Assistants), By Application, By Industry, By Deployment Model, By Region, And By Segment Forecasts, 2024-2031

Report Id: 2745 Pages: 180 Last Updated: 25 September 2024 Format: PDF / PPT / Excel / Power BI
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Segmentation of AI in Customer Relationship Management Market-

AI in Customer Relationship Management Market By Type-

  • Machine Learning
  • Natural Language Processing (NLP)
  • Image and Speech Recognition
  • Predictive Analytics
  • Chatbots and Virtual Assistants

ai in customer relationship management

AI in Customer Relationship Management Market By Application-

  • Sales Automation
  • Customer Service and Support
  • Marketing Personalization
  • Customer Data Analysis
  • Lead Scoring and Qualification

AI in Customer Relationship Management Market By Industry-

  • Retail and E-commerce
  • Banking and Finance
  • Healthcare
  • Telecommunications
  • Travel and Hospitality

AI in Customer Relationship Management Market By Deployment Model-

  • Cloud-based AI-CRM
  • On-premises AI-CRM
  • Hybrid AI-CRM

AI in Customer Relationship Management Market By Region-

North America-

  • The US
  • Canada
  • Mexico

Europe-

  • Germany
  • The UK
  • France
  • Italy
  • Spain
  • Rest of Europe

Asia-Pacific-

  • China
  • Japan
  • India
  • South Korea
  • South East Asia
  • Rest of Asia Pacific

Latin America-

  • Brazil
  • Argentina
  • Rest of Latin America

 Middle East & Africa-

  • GCC Countries
  • South Africa
  • Rest of Middle East and Africa

Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions

Chapter 2. Executive Summary

Chapter 3. Global AI in Customer Relationship Management Market Snapshot

Chapter 4. Global AI in Customer Relationship Management Market Variables, Trends & Scope

4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis

Chapter 5. Market Segmentation 1: by Industry Estimates & Trend Analysis
5.1. by Industry & Market Share, 2019 & 2031
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Industry:

5.2.1. Retail and E-commerce
5.2.2. Banking and Finance
5.2.3. Healthcare
5.2.4. Telecommunications
5.2.5. Travel and Hospitality

Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2019 & 2031
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Application:

6.2.1. Sales Automation
6.2.2. Customer Service and Support
6.2.3. Marketing Personalization
6.2.4. Customer Data Analysis
6.2.5. Lead Scoring and Qualification

Chapter 7. Market Segmentation 3: by Type Estimates & Trend Analysis
7.1. by Type & Market Share, 2019 & 2031
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Type:
7.2.1. Machine Learning
7.2.2. Natural Language Processing (NLP)
7.2.3. Image and Speech Recognition
7.2.4. Predictive Analytics
7.2.5. Chatbots and Virtual Assistants

Chapter 8. Market Segmentation 4: by Deployment Model Estimates & Trend Analysis
8.1. By Deployment Model & Market Share, 2019 & 2031
8.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following By Deployment Model:
8.2.1. Cloud-based AI-CRM
8.2.2. On-premises AI-CRM
8.2.3. Hybrid AI-CRM

Chapter 9. AI in Customer Relationship Management Market Segmentation 5: Regional Estimates & Trend Analysis

9.1. North America
9.1.1. North America AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Industry, 2024-2031
9.1.2. North America AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.1.3. North America AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
9.1.4. North America AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Deployment Model, 2024-2031
9.1.5. North America AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

9.2. Europe
9.2.1. Europe AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Industry, 2024-2031
9.2.2. Europe AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.2.3. Europe AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
9.2.4. Europe AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Deployment Model, 2024-2031
9.2.5. Europe AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

9.3. Asia Pacific
9.3.1. Asia Pacific AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Industry, 2024-2031
9.3.2. Asia Pacific AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.3.3. Asia-Pacific Thermal Cyclers Asia-Pacific AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
9.3.4. Asia-Pacific AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Deployment Model, 2024-2031
9.3.5. Asia Pacific AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

9.4. Latin America
9.4.1. Latin America AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Industry, 2024-2031
9.4.2. Latin America AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.4.3. Latin America AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
9.4.4. Latin America AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Deployment Model, 2024-2031
9.4.5. Latin America AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

9.5. Middle East & Africa
9.5.1. Middle East & Africa AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Industry, 2024-2031
9.5.2. Middle East & Africa AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
9.5.3. Middle East & Africa AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
9.5.4. Middle East & Africa AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by Deployment Model, 2024-2031
9.5.5. Middle East & Africa AI in Customer Relationship Management Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

Chapter 10. Competitive Landscape
10.1. Major Mergers and Acquisitions/Strategic Alliances
10.2. Company Profiles

10.2.1. Salesforce
10.2.2. Microsoft Corporation
10.2.3. IBM Corporation
10.2.4. Oracle Corporation
10.2.5. SAP SE
10.2.6. Adobe Inc.
10.2.7. Pegasystems Inc.
10.2.8. HubSpot Inc.
10.2.9. Zendesk Inc.
10.2.10. Freshworks Inc.
10.2.11. Genesys Telecommunications Laboratories, Inc.
10.2.12. Zoho Corporation
10.2.13. SugarCRM Inc.
10.2.14. Insightly Inc.
10.2.15. Infusionsoft by Keap
10.2.16. Nimble LLC
10.2.17. Act-On Software Inc.
10.2.18. Copper Inc.
10.2.19. Agile CRM Inc.
10.2.20. Apptivo Inc.
10.2.21. EngageBay Inc.
10.2.22. Ontraport Inc.
10.2.23. Really Simple Systems Ltd.
10.2.24. Soffront Software Inc.
10.2.25. Maximizer Services Inc.

Research Design and Approach

This study employed a multi-step, mixed-method research approach that integrates:

  • Secondary research
  • Primary research
  • Data triangulation
  • Hybrid top-down and bottom-up modelling
  • Forecasting and scenario analysis

This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.

Secondary Research

Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.

Sources Consulted

Secondary data for the market study was gathered from multiple credible sources, including:

  • Government databases, regulatory bodies, and public institutions
  • International organizations (WHO, OECD, IMF, World Bank, etc.)
  • Commercial and paid databases
  • Industry associations, trade publications, and technical journals
  • Company annual reports, investor presentations, press releases, and SEC filings
  • Academic research papers, patents, and scientific literature
  • Previous market research publications and syndicated reports

These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.

Secondary Research

Primary Research

Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.

Stakeholders Interviewed

Primary interviews for this study involved:

  • Manufacturers and suppliers in the market value chain
  • Distributors, channel partners, and integrators
  • End-users / customers (e.g., hospitals, labs, enterprises, consumers, etc., depending on the market)
  • Industry experts, technology specialists, consultants, and regulatory professionals
  • Senior executives (CEOs, CTOs, VPs, Directors) and product managers

Interview Process

Interviews were conducted via:

  • Structured and semi-structured questionnaires
  • Telephonic and video interactions
  • Email correspondences
  • Expert consultation sessions

Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.

Data Processing, Normalization, and Validation

All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.

The data validation process included:

  • Standardization of units (currency conversions, volume units, inflation adjustments)
  • Cross-verification of data points across multiple secondary sources
  • Normalization of inconsistent datasets
  • Identification and resolution of data gaps
  • Outlier detection and removal through algorithmic and manual checks
  • Plausibility and coherence checks across segments and geographies

This ensured that the dataset used for modelling was clean, robust, and reliable.

Market Size Estimation and Data Triangulation

Bottom-Up Approach

The bottom-up approach involved aggregating segment-level data, such as:

  • Company revenues
  • Product-level sales
  • Installed base/usage volumes
  • Adoption and penetration rates
  • Pricing analysis

This method was primarily used when detailed micro-level market data were available.

Bottom Up Approach

Top-Down Approach

The top-down approach used macro-level indicators:

  • Parent market benchmarks
  • Global/regional industry trends
  • Economic indicators (GDP, demographics, spending patterns)
  • Penetration and usage ratios

This approach was used for segments where granular data were limited or inconsistent.

Hybrid Triangulation Approach

To ensure accuracy, a triangulated hybrid model was used. This included:

  • Reconciling top-down and bottom-up estimates
  • Cross-checking revenues, volumes, and pricing assumptions
  • Incorporating expert insights to validate segment splits and adoption rates

This multi-angle validation yielded the final market size.

Forecasting Framework and Scenario Modelling

Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.

Forecasting Methods

  • Time-series modelling
  • S-curve and diffusion models (for emerging technologies)
  • Driver-based forecasting (GDP, disposable income, adoption rates, regulatory changes)
  • Price elasticity models
  • Market maturity and lifecycle-based projections

Scenario Analysis

Given inherent uncertainties, three scenarios were constructed:

  • Base-Case Scenario: Expected trajectory under current conditions
  • Optimistic Scenario: High adoption, favourable regulation, strong economic tailwinds
  • Conservative Scenario: Slow adoption, regulatory delays, economic constraints

Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.

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Frequently Asked Questions

The AI in Customer Relationship Management Market Size is valued at USD 14.8 billion in 2023 and is predicted to reach USD 138.6 billion by the year 2

The AI in Customer Relationship Management Market is expected to grow at a 32.5% CAGR during the forecast period for 2024-2031.

Salesforce Inc., Microsoft Corporation, Oracle Corporation, IBM Corporation, SAP SE, Zendesk Inc., Adobe Inc., C3.ai Inc., Zoho Corporation, Genesys,

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