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市場調查報告書
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2021744

人工智慧市場價格最佳化預測（至2034年）：全球組件、定價策略、技術、功能、應用、最終用戶和區域分析

AI in Pricing Optimization Market Forecasts to 2034 - Global Analysis By Component (Software, and Services), Pricing Strategy, Technology, Functionality, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球價格優​​化人工智慧市場預計將在 2026 年達到 35 億美元，並在預測期內以 24.5% 的複合年成長率成長，到 2034 年達到 205 億美元。

人工智慧在價格最佳化領域運用先進的演算法和數據驅動模型，為產品或服務制定最有效的定價策略。它分析客戶行為、市場需求、競爭對手定價和歷史銷售數據等因素，即時提案最優價格。透過利用機器學習和預測分析等技術，企業可以最大限度地提高收入、提升利潤率、增強競爭力，並動態適應不斷變化的市場環境。

對動態和即時定價策略的需求日益成長

傳統的靜態定價模式已不足以實現收益最大化和保持競爭力。人工智慧驅動的價格最佳化使企業能夠即時分析數百萬個資料點，包括購買歷史、季節性因素和競爭對手動態，並自動同步調整數千個SKU的價格。這項功能在價格彈性較高的電商、旅遊和零售業尤為重要。透過實施人工智慧驅動的動態定價，企業可以將利潤率提高5%至15%，減少缺貨情況，並即時回應市場變化。全通路零售的日益普及和對個人化客戶體驗日益成長的需求，進一步加速了對即時定價解決方案的需求，推動了全球市場的擴張。

實施和資料整合成本高昂

許多中小型企業難以籌集資金來實施這些解決方案，尤其是在缺乏與傳統IT系統無縫整合所需的API和資料標準化的情況下。此外，訓練人工智慧模型需要大量乾淨的歷史交易數據，但由於部門壁壘，這些數據往往無法取得或分散不全。諸如GDPR和CCPA等資料隱私法規進一步加劇了跨境定價策略的複雜性。對於擁有複雜產品目錄和多個銷售管道的企業而言，建立準確的價格彈性模型可能需要數月的調整。這些技術和財務障礙限制了市場滲透，尤其是在數位轉型尚處於發展階段的地區。

個性化全通路定價模式的成長

現代消費者期望無論網路商店、行動應用程式或實體店，都能獲得一致且個人化的價格。人工智慧能夠實現細分定價，根據個人會員等級、瀏覽行為和購買頻率最佳化優惠，同時又不疏遠其他客戶。此外，基於訂閱的定價最佳化工具降低了中小企業的進入門檻。因果模型和提升模型的整合使零售商能夠在推出促銷活動之前模擬各種「假設」情境。隨著無頭商務和即時競價平台的日益普及，人工智慧定價引擎可以直接整合到結帳流程中。製造商也正在採用這些工具進行B2B動態報價。這個不斷擴大的目標市場涵蓋零售、旅遊、電信和醫療保健等行業，為人工智慧定價供應商帶來了巨大的成長機會。

模型偏差和定價缺乏透明度

如果使用不完整或不具代表性的歷史資料訓練人工智慧價格最佳化模型，則可能無意中引入偏差。這可能導致不公平的定價行為，違反消費者保護法。此外，深度學習模式的「黑箱」特性使得企業難以向客戶和監管機構解釋價格變動，這可能會損害品牌信任。競爭對手也可能逆向工程定價規則，引發價格戰和共謀的風險。如果沒有健全的管治結構和可解釋的人工智慧技術，企業將面臨法律調查和聲譽損害。這些透明度方面的挑戰限制了人工智慧在保險、醫療保健和銀行等高度監管行業的應用，因為這些行業需要對定價決策做出清晰的解釋。

新冠疫情的影響：

新冠疫情大大加速了人工智慧在價格最佳化領域的應用，這主要歸因於供應鏈的不穩定性和消費者支出模式的不可預測性變化。封鎖措施迫使零售商、航空公司和飯店徹底放棄了傳統的定價模式。那些已經實施人工智慧動態定價的企業能夠更好地管理庫存，應對需求的急劇下降，並抓住必需品需求的有限成長。然而，預算限制在2020年初延緩了許多新應用的實施。疫情後，電子商務和非接觸式支付的快速發展永久提升了對即時定價資訊的需求。隨著企業專注於恢復利潤率和加強業務永續營運，對人工智慧定價工具的投資強勁復甦，其中基於雲端的解決方案由於遠距辦公的柔軟性而呈現出尤為顯著的成長。

在預測期內，軟體領域預計將佔據最大佔有率。

在預測期內，軟體領域預計將佔據最大的市場佔有率。該領域包括價格最佳化平台、收益管理系統和分析工具，這些都是任何人工智慧定價解決方案的核心。對演算法提案、需求預測和競爭情報的迫切需求推動了這一主導地位。機器學習和雲端原生架構的持續進步正在推動軟體的功能提升和應用普及。

在預測期內，動態定價細分市場預計將呈現最高的複合年成長率。

在預測期內，動態定價細分市場預計將呈現最高的成長率。動態定價利用即時數據，例如需求波動、競爭對手定價和存量基準，自動調整價格，每天多次甚至每分鐘都可能進行調整。這種策略正擴大被價格敏感型產業所採用，例如電子商務、叫車、機票銷售和飯店預訂。隨著強化學習模型的發展，系統現在無需人工干預即可測試和學習最佳定價策略。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率。這主要歸功於IBM、微軟、Google和AWS等主要人工智慧技術供應商，以及PROS和Vendavo等領先的價格最佳化供應商。該地區成熟的電子商務和零售業，包括亞馬遜和沃爾瑪，正在大力投資人工智慧定價。此外，早期採用雲端為創業投資的分析技術以及創投對人工智慧Start-Ups的強勁投入，也促進了人工智慧技術的廣泛應用。完善的數位基礎設施和對個人化定價的積極嘗試，進一步鞏固了北美的市場主導地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於中國、印度和東南亞電子商務的快速擴張，以及智慧型手機普及率的提高和數位支付方式的日益普及。阿里巴巴、Flipkart 和 Shopee 等本土平台的崛起，推動了對人工智慧驅動的動態個人化定價的需求。新加坡、日本和韓國政府正在加大對人工智慧研究的投入，並致力於零售技術的現代化。在全部區域，中小企業在數位轉型過程中，正迅速採用價格合理的雲端價格最佳化工具。

免費客製化服務優勢：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 主要參與者（最多3家公司）的SWOT分析
  • 區域分類
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章：執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球價格優​​化中的人工智慧市場：按組件分類

  • 軟體
    • 價格最佳化平台
    • 收益管理系統
    • 分析和預測工具
  • 服務
    • 諮詢服務
    • 整合與部署
    • 支援與維護

第6章：全球價格優​​化中的人工智慧市場：定價策略

  • 動態定價
  • 定價策略基於競爭對手
  • 基於價值的定價
  • 成本定價
  • 捆綁定價
  • 促銷價格

第7章：全球價格優​​化中的人工智慧市場：按技術分類

  • 監督學習模型
  • 強化學習模型
  • 深度學習模型
  • 因果模型和提升模型
  • 預測分析模型

第8章：全球價格優​​化中的人工智慧市場：功能性

  • 動態價格調整
  • 個性化定價
  • 基於細分市場的定價
  • 情境模擬和假設分析
  • 全通路定價最佳化

第9章：全球人工智慧價格最佳化市場：按應用領域分類

  • 價格最佳化和建議
  • 收益管理
  • 需求預測
  • 價格彈性分析
  • 競爭定價情報
  • 最佳化促銷和折扣
  • 其他用途

第10章：全球人工智慧價格最佳化市場：按最終用戶分類

  • 零售
  • 電子商務
  • 旅遊與飯店
  • 生產/分銷
  • 銀行、金融服務、保險
  • 電訊
  • 衛生保健
  • 能源公用事業

第11章：全球價格優​​化人工智慧市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • PROS Holdings, Inc.
  • Pricefx
  • Zilliant, Inc.
  • Vendavo, Inc.
  • SAP SE
  • Oracle Corporation
  • IBM Corporation
  • SAS Institute Inc.
  • Accenture
  • Wipro Limited
  • Competera Limited
  • Revionics, Inc.
  • Blue Yonder
  • Omnia Retail
  • Wiser Solutions, Inc.
Product Code: SMRC35021

According to Stratistics MRC, the Global AI in Pricing Optimization Market is accounted for $3.5 billion in 2026 and is expected to reach $20.5 billion by 2034 growing at a CAGR of 24.5% during the forecast period. AI in pricing optimization is the use of advanced algorithms and data-driven models to determine the most effective pricing strategies for products or services. It analyzes factors such as customer behavior, market demand, competitor pricing, and historical sales data to recommend optimal prices in real time. By leveraging techniques like machine learning and predictive analytics, it helps businesses maximize revenue, improve profit margins, and enhance competitiveness while adapting dynamically to changing market conditions.

Market Dynamics:

Driver:

Increasing demand for dynamic and real-time pricing strategies

Traditional static pricing models are no longer sufficient to maximize revenue or maintain competitiveness. AI-powered pricing optimization enables companies to analyze millions of data points in real time including purchase history, seasonality, and competitor moves to automatically adjust prices across thousands of SKUs simultaneously. This capability is particularly critical in e-commerce, travel, and retail sectors where price elasticity is high. By implementing AI-driven dynamic pricing, organizations can increase profit margins by 5-15%, reduce stockouts, and respond instantly to market shifts. The growing adoption of omnichannel retail and the need for personalized customer experiences further accelerate demand for real-time pricing solutions, driving global market expansion.

Restraint:

High implementation and data integration costs

Many mid-sized and smaller enterprises struggle to afford these solutions, especially when legacy IT systems lack APIs or data standardization needed for seamless integration. Additionally, training AI models demands large volumes of clean, historical transaction data-often unavailable or fragmented across siloed departments. Data privacy regulations such as GDPR and CCPA further complicate cross-border pricing strategies. For organizations with complex product catalogs or multiple sales channels, achieving accurate price elasticity models can take months of calibration. These technical and financial barriers limit market penetration, particularly in developing regions where digital transformation is still maturing.

Opportunity:

Growth of personalized and omnichannel pricing models

Modern consumers expect consistent yet personalized prices across online stores, mobile apps, and physical locations. AI enables segmentation-based pricing where offers are tailored to individual loyalty status, browsing behavior, or purchase frequency without alienating other customers. Furthermore, subscription-based pricing optimization tools are lowering entry barriers for small businesses. The integration of causal and uplift models allows retailers to simulate "what-if" scenarios before launching promotions. As headless commerce and real-time bidding platforms gain traction, AI pricing engines can be embedded directly into checkout flows. Manufacturers are also adopting these tools for B2B dynamic quoting. This expanding addressable market across retail, travel, telecom, and healthcare creates substantial growth opportunities for AI pricing vendors.

Threat:

Model bias and lack of pricing transparency

AI-driven pricing optimization models can inadvertently introduce bias if trained on incomplete or unrepresentative historical data, leading to unfair pricing practices that may violate consumer protection laws. Additionally, the "black box" nature of deep learning models makes it difficult for businesses to explain price changes to customers or regulators, potentially damaging brand trust. Competitors may also reverse-engineer pricing rules, leading to price wars or collusion risks. Without robust governance frameworks and explainable AI techniques, companies face legal scrutiny and reputational damage. These transparency challenges limit adoption in highly regulated industries such as insurance, healthcare, and banking, where pricing decisions require clear justifications.

Covid-19 Impact:

The COVID-19 pandemic dramatically accelerated the adoption of AI in pricing optimization as supply chains became unstable and consumer spending patterns shifted unpredictably. Lockdowns forced retailers, airlines, and hotels to abandon historical pricing models entirely. Companies that deployed AI-driven dynamic pricing were better able to manage inventory, adjust for sudden demand collapses, and capture limited surges in essential goods. However, budget constraints delayed many new implementations in early 2020. Post-pandemic, the rapid growth of e-commerce and contactless payments has permanently increased the need for real-time pricing intelligence. As businesses focus on margin recovery and operational resilience, investment in AI pricing tools has rebounded strongly, with cloud-based solutions seeing particular growth due to remote work flexibility.

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period. This segment includes pricing optimization platforms, revenue management systems, and analytics tools that form the core of any AI pricing solution. The essential need for algorithmic price recommendation, demand forecasting, and competitive intelligence drives this dominance. Ongoing advancements in machine learning and cloud-native architectures increase software capabilities and adoption.

The dynamic pricing segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the dynamic pricing segment is predicted to witness the highest growth rate. Dynamic pricing uses real-time data including demand fluctuations, competitor pricing, and inventory levels to automatically adjust prices multiple times per day or even per minute. This strategy is increasingly adopted in e-commerce, ride-hailing, airline ticketing, and hotel booking industries where price sensitivity is high. The development of reinforcement learning models allows systems to test and learn optimal pricing policies without manual intervention.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by the presence of major AI technology providers such as IBM, Microsoft, Google, and AWS, along with leading pricing optimization vendors like PROS and Vendavo. The region's mature e-commerce and retail sectors, including Amazon and Walmart, heavily invest in AI-driven pricing. Additionally, early adoption of cloud-based analytics and strong venture capital funding for AI startups contribute to high penetration rates. The well-developed digital infrastructure and willingness to experiment with personalized pricing further solidify North America's dominant position.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid e-commerce expansion in China, India, and Southeast Asia, along with increasing smartphone penetration and digital payment adoption. The rise of local platforms like Alibaba, Flipkart, and Shopee drives demand for AI-based dynamic and personalized pricing. Governments in Singapore, Japan, and South Korea are investing in AI research and retail technology modernization. As small and medium enterprises across the region digitize their operations, affordable cloud-based pricing optimization tools see rapid adoption.

Key players in the market

Some of the key players in AI in Pricing Optimization Market include PROS Holdings, Inc., Pricefx, Zilliant, Inc., Vendavo, Inc., SAP SE, Oracle Corporation, IBM Corporation, SAS Institute Inc., Accenture, Wipro Limited, Competera Limited, Revionics, Inc., Blue Yonder, Omnia Retail, and Wiser Solutions, Inc.

Key Developments:

In April 2026, IBM announced a strategic collaboration with Arm to develop new dual-architecture hardware that helps enterprises run future AI and data intensive workloads with greater flexibility, reliability, and security. IBM's leadership in system design, from silicon to software and security, has helped enterprises adopt emerging technologies with the scale and reliability required for mission-critical workloads.

In March 2026, Oracle announced the latest updates to Oracle AI Agent Studio for Fusion Applications, a complete development platform for building, connecting, and running AI automation and agentic applications. The latest updates to Oracle AI Agent Studio include a new agentic applications builder as well as new capabilities that support workflow orchestration, content intelligence, contextual memory, and ROI measurement.

Components Covered:

  • Software
  • Services

Pricing Strategies Covered:

  • Dynamic Pricing
  • Competitive-Based Pricing
  • Value-Based Pricing
  • Cost-Plus Pricing
  • Bundle Pricing
  • Promotional Pricing

Technologies Covered:

  • Supervised Learning Models
  • Reinforcement Learning Models
  • Deep Learning Models
  • Causal & Uplift Models
  • Predictive Analytics Models

Functionalities Covered:

  • Dynamic Price Adjustment
  • Personalized Pricing
  • Segmentation-Based Pricing
  • Scenario Simulation & What-if Analysis
  • Omnichannel Price Optimization

Applications Covered:

  • Price Optimization & Recommendation
  • Revenue Management
  • Demand Forecasting
  • Price Elasticity Analysis
  • Competitive Price Intelligence
  • Promotion & Discount Optimization
  • Other Applications

End Users Covered:

  • Retail
  • E-commerce
  • Travel & Hospitality
  • Manufacturing & Distribution
  • Banking, Financial Services, Insurance
  • Telecommunications
  • Healthcare
  • Energy & Utilities

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI in Pricing Optimization Market, By Component

  • 5.1 Software
    • 5.1.1 Pricing Optimization Platforms
    • 5.1.2 Revenue Management Systems
    • 5.1.3 Analytics & Forecasting Tools
  • 5.2 Services
    • 5.2.1 Consulting Services
    • 5.2.2 Integration & Deployment
    • 5.2.3 Support & Maintenance

6 Global AI in Pricing Optimization Market, By Pricing Strategy

  • 6.1 Dynamic Pricing
  • 6.2 Competitive-Based Pricing
  • 6.3 Value-Based Pricing
  • 6.4 Cost-Plus Pricing
  • 6.5 Bundle Pricing
  • 6.6 Promotional Pricing

7 Global AI in Pricing Optimization Market, By Technology

  • 7.1 Supervised Learning Models
  • 7.2 Reinforcement Learning Models
  • 7.3 Deep Learning Models
  • 7.4 Causal & Uplift Models
  • 7.5 Predictive Analytics Models

8 Global AI in Pricing Optimization Market, By Functionality

  • 8.1 Dynamic Price Adjustment
  • 8.2 Personalized Pricing
  • 8.3 Segmentation-Based Pricing
  • 8.4 Scenario Simulation & What-if Analysis
  • 8.5 Omnichannel Price Optimization

9 Global AI in Pricing Optimization Market, By Application

  • 9.1 Price Optimization & Recommendation
  • 9.2 Revenue Management
  • 9.3 Demand Forecasting
  • 9.4 Price Elasticity Analysis
  • 9.5 Competitive Price Intelligence
  • 9.6 Promotion & Discount Optimization
  • 9.7 Other Applications

10 Global AI in Pricing Optimization Market, By End User

  • 10.1 Retail
  • 10.2 E-commerce
  • 10.3 Travel & Hospitality
  • 10.4 Manufacturing & Distribution
  • 10.5 Banking, Financial Services, Insurance
  • 10.6 Telecommunications
  • 10.7 Healthcare
  • 10.8 Energy & Utilities

11 Global AI in Pricing Optimization Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 PROS Holdings, Inc.
  • 14.2 Pricefx
  • 14.3 Zilliant, Inc.
  • 14.4 Vendavo, Inc.
  • 14.5 SAP SE
  • 14.6 Oracle Corporation
  • 14.7 IBM Corporation
  • 14.8 SAS Institute Inc.
  • 14.9 Accenture
  • 14.10 Wipro Limited
  • 14.11 Competera Limited
  • 14.12 Revionics, Inc.
  • 14.13 Blue Yonder
  • 14.14 Omnia Retail
  • 14.15 Wiser Solutions, Inc.

List of Tables

  • Table 1 Global AI in Pricing Optimization Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Pricing Optimization Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Pricing Optimization Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI in Pricing Optimization Market Outlook, By Pricing Optimization Platforms (2023-2034) ($MN)
  • Table 5 Global AI in Pricing Optimization Market Outlook, By Revenue Management Systems (2023-2034) ($MN)
  • Table 6 Global AI in Pricing Optimization Market Outlook, By Analytics & Forecasting Tools (2023-2034) ($MN)
  • Table 7 Global AI in Pricing Optimization Market Outlook, By Services (2023-2034) ($MN)
  • Table 8 Global AI in Pricing Optimization Market Outlook, By Consulting Services (2023-2034) ($MN)
  • Table 9 Global AI in Pricing Optimization Market Outlook, By Integration & Deployment (2023-2034) ($MN)
  • Table 10 Global AI in Pricing Optimization Market Outlook, By Support & Maintenance (2023-2034) ($MN)
  • Table 11 Global AI in Pricing Optimization Market Outlook, By Pricing Strategy (2023-2034) ($MN)
  • Table 12 Global AI in Pricing Optimization Market Outlook, By Dynamic Pricing (2023-2034) ($MN)
  • Table 13 Global AI in Pricing Optimization Market Outlook, By Competitive-Based Pricing (2023-2034) ($MN)
  • Table 14 Global AI in Pricing Optimization Market Outlook, By Value-Based Pricing (2023-2034) ($MN)
  • Table 15 Global AI in Pricing Optimization Market Outlook, By Cost-Plus Pricing (2023-2034) ($MN)
  • Table 16 Global AI in Pricing Optimization Market Outlook, By Bundle Pricing (2023-2034) ($MN)
  • Table 17 Global AI in Pricing Optimization Market Outlook, By Promotional Pricing (2023-2034) ($MN)
  • Table 18 Global AI in Pricing Optimization Market Outlook, By Technology (2023-2034) ($MN)
  • Table 19 Global AI in Pricing Optimization Market Outlook, By Supervised Learning Models (2023-2034) ($MN)
  • Table 20 Global AI in Pricing Optimization Market Outlook, By Reinforcement Learning Models (2023-2034) ($MN)
  • Table 21 Global AI in Pricing Optimization Market Outlook, By Deep Learning Models (2023-2034) ($MN)
  • Table 22 Global AI in Pricing Optimization Market Outlook, By Causal & Uplift Models (2023-2034) ($MN)
  • Table 23 Global AI in Pricing Optimization Market Outlook, By Predictive Analytics Models (2023-2034) ($MN)
  • Table 24 Global AI in Pricing Optimization Market Outlook, By Functionality (2023-2034) ($MN)
  • Table 25 Global AI in Pricing Optimization Market Outlook, By Dynamic Price Adjustment (2023-2034) ($MN)
  • Table 26 Global AI in Pricing Optimization Market Outlook, By Personalized Pricing (2023-2034) ($MN)
  • Table 27 Global AI in Pricing Optimization Market Outlook, By Segmentation-Based Pricing (2023-2034) ($MN)
  • Table 28 Global AI in Pricing Optimization Market Outlook, By Scenario Simulation & What-if Analysis (2023-2034) ($MN)
  • Table 29 Global AI in Pricing Optimization Market Outlook, By Omnichannel Price Optimization (2023-2034) ($MN)
  • Table 30 Global AI in Pricing Optimization Market Outlook, By Application (2023-2034) ($MN)
  • Table 31 Global AI in Pricing Optimization Market Outlook, By Price Optimization & Recommendation (2023-2034) ($MN)
  • Table 32 Global AI in Pricing Optimization Market Outlook, By Revenue Management (2023-2034) ($MN)
  • Table 33 Global AI in Pricing Optimization Market Outlook, By Demand Forecasting (2023-2034) ($MN)
  • Table 34 Global AI in Pricing Optimization Market Outlook, By Price Elasticity Analysis (2023-2034) ($MN)
  • Table 35 Global AI in Pricing Optimization Market Outlook, By Competitive Price Intelligence (2023-2034) ($MN)
  • Table 36 Global AI in Pricing Optimization Market Outlook, By Promotion & Discount Optimization (2023-2034) ($MN)
  • Table 37 Global AI in Pricing Optimization Market Outlook, By Other Applications (2023-2034) ($MN)
  • Table 38 Global AI in Pricing Optimization Market Outlook, By End User (2023-2034) ($MN)
  • Table 39 Global AI in Pricing Optimization Market Outlook, By Retail (2023-2034) ($MN)
  • Table 40 Global AI in Pricing Optimization Market Outlook, By E-commerce (2023-2034) ($MN)
  • Table 41 Global AI in Pricing Optimization Market Outlook, By Travel & Hospitality (2023-2034) ($MN)
  • Table 42 Global AI in Pricing Optimization Market Outlook, By Manufacturing & Distribution (2023-2034) ($MN)
  • Table 43 Global AI in Pricing Optimization Market Outlook, By Banking, Financial Services, Insurance (2023-2034) ($MN)
  • Table 44 Global AI in Pricing Optimization Market Outlook, By Telecommunications (2023-2034) ($MN)
  • Table 45 Global AI in Pricing Optimization Market Outlook, By Healthcare (2023-2034) ($MN)
  • Table 46 Global AI in Pricing Optimization Market Outlook, By Energy & Utilities (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.