가격 최적화 소프트웨어 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the price optimization software market size is projected to expand from USD 1.68 billion in 2025 and USD 1.95 billion in 2026 to USD 4.17 billion by 2031, registering a CAGR of 16.06% between 2026 and 2031.

This report is Segmented by Deployment Model (Cloud, On-Premise, Hybrid), End-Use Industry (Retail and ECommerce, Manufacturing, and More), Pricing Strategy Type (AI-Driven Dynamic Pricing, and More), Organization Size (Large Enterprises, Small and Medium Enterprises), and Geography (North America, Europe, Asia-Pacific, and More). The Market Forecasts are Provided in Terms of Value (USD).

Global Price Optimization Software Market Trends and Insights

AI-Powered Real-Time Dynamic Pricing Accelerates Omnichannel Retail Growth

Retailers now run machine-learning models that ingest competitor prices, inventory levels, weather forecasts, and micro-behavioral signals to adjust prices many times per day. Walmart filed a March 2026 patent describing neural-network systems that change shelf and online prices simultaneously, while Kroger disclosed that AI pricing lifted gross margin even though same-store sales were flat. Feedvisor reports more than 46 million price updates annually on Amazon and Walmart marketplaces, proving that continuous optimization outperforms weekly rule-based changes. Reinforcement-learning engines now surface non-linear elasticities, such as device-specific cart-abandonment tendencies, that spreadsheets cannot detect. Synchronizing electronic shelf labels with marketplace listings prevents showrooming arbitrage and protects brand integrity across channels.

Cloud-Native SaaS Models Slash Total Cost of Ownership and Speed Implementations

Subscription delivery eliminates capital expenditure and dedicated infrastructure, making the price optimization software market accessible to firms beyond the Fortune 500. Pricefx Copilot, launched in January 2026, integrates with SAP, Salesforce, and Microsoft Dynamics via pre-built APIs, cutting deployment time to 8-12 weeks and reducing lifetime ownership costs by around 40% compared with on-premise builds. Zilliant's November 2025 Pricing Plus bundles optimization, deal guidance, and CPQ into a single license, letting mid-market manufacturers avoid hiring specialist analysts. Vendavo's AI Pricing Assistant, released April 2026, rides SAP Business Technology Platform, ensuring upgrade-safe connectivity for SAP-centric enterprises. Vendors reserve advanced AI only for SaaS tiers, further tipping adoption toward cloud.

Poor Master-Data Quality and Siloed Systems Hinder ROI

Pricing models rely on granular cost, competitor, and customer data, yet many firms store products, customers, and discounts in incompatible ERP and CRM silos. The Federal Trade Commission's January 2025 surveillance-pricing study found that intermediaries must often reconcile incomplete feeds manually, eroding the speed advantage of automation. Model N surveys show 85% of executives cite data quality as the top barrier to value realization. Missing SKU-level costs, rebate-adjusted profitability, or localized competitor prices can extend implementation timelines by up to six months and require cross-functional governance that organizations frequently lack.

Other drivers and restraints analyzed in the detailed report include:

1. Inflationary Margin Pressure Makes Algorithmic Pricing a Board Priority
2. End-to-End CPQ and eCommerce Integration Unlocks Revenue Synergies
3. Cultural Resistance to Algorithmic Price Changes Slows Deployment

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Cloud deployments held 63.7% of the price optimization software market share in 2025, and the segment is expected to grow at a 17.1% CAGR through 2031. The price optimization software market size advantage originates from subscription economics, automatic upgrades, and the ability to roll out generative AI modules without on-premise hardware. Vendors increasingly gate-cutting-edge functions behind SaaS licenses, making parity in on-premise environments cost-prohibitive.

Hybrid architectures preserve sensitive data on private servers while running machine-learning training in the cloud, a pattern Vendavo supports via SAP Business Technology Platform. On-premise installations will persist in defense and highly regulated finance, but sovereign-cloud expansions in Europe and Asia-Pacific are softening data-residency objections. Collectively, these trends fortify the cloud's pathway to an even larger share of the price optimization software market.

AI-driven dynamic engines commanded 47.4% of the 2025 value, reflecting retailer and marketplace adoption of reinforcement-learning approaches. However, rule-based guardrails remain essential in sectors with margin floors or regulatory ceilings, ensuring compliance while algorithms explore elasticities. Usage-based and subscription-hybrid approaches are projected to post the fastest expansion at 17.9% CAGR, signaling a structural shift as vendors align revenue with actual utilization.

The price optimization software market size tied to usage billing will grow as more SaaS and API businesses migrate from flat-fee licenses to consumption-based models. Walmart's patent blends neural forecasts with hard constraints, highlighting that best-practice architectures merge AI agility with business rules. Hybrid governance frameworks allow B2B manufacturers to leverage predictive guidance while respecting negotiated contract limits, maintaining trust while boosting realized margins.

Geography Analysis

North America contributed 36.6% of 2025 revenue, sustained by early adoption and the presence of leading vendors. PROS reported USD 76 million in subscription revenue during Q3 2025, underscoring the region's tilt to SaaS pros.com. Federal Trade Commission and Department of Justice investigations into algorithmic rent coordination and surveillance pricing are increasing compliance costs, forcing vendors to embed explainability and audit trails. Despite regulatory friction, continuous investment in AI and cloud infrastructure keeps the United States an innovation nucleus for the price optimization software market.

Europe combines strong demand with stringent governance. The European Commission confirmed in July 2025 that multiple cartel probes involve algorithmic pricing, and the UK Competition and Markets Authority has added a chief technologist to police digital coordination. Simultaneously, the EU Carbon Border Adjustment Mechanism creates fresh use cases for suppliers calculating carbon-inclusive landed costs. Vendors offering transparent, auditable models with carbon adjustments gain a competitive foothold across the bloc. Sovereign-cloud rollouts and data-localization frameworks are tempering objections to SaaS adoption, gradually tipping more deals toward public-cloud deployment.

Asia-Pacific is the fastest growing, forecast to expand at a 16.9% CAGR through 2031. E-commerce platforms in China and India update prices several times per hour to manage intense marketplace competition, driving demand for reinforcement-learning engines. Japan, South Korea, and ASEAN manufacturers deploy price optimization to manage global channels and volatile input costs. While data-protection regimes such as China's Personal Information Protection Law introduce transparency obligations, they remain less restrictive than European standards, giving vendors latitude to scale rapidly. Emerging economies in Southeast Asia, coupled with cross-border marketplace expansion, position the territory as the foremost growth engine for the price optimization software market.

1. PROS Holdings, Inc.
2. Pricefx AG
3. Vendavo, Inc.
4. Zilliant, Inc.
5. Revionics LLC
6. Competera Limited
7. Vistaar Technologies, Inc.
8. Omnia Retail B.V.
9. Quicklizard Ltd.
10. Feedvisor Ltd.
11. Open Pricer SAS
12. Perfect Price Inc.
13. Periscope by McKinsey & Company, Inc.
14. Model N, Inc.
15. Aptos LLC (Revionics Division)
16. IBM Corporation (Watson Dynamic Pricing)
17. SAP SE (Price Management Module)
18. SPOSEA B.V.
19. Navetti AB (part of Vendavo)
20. Yieldigo a.s.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 AI-Powered Real-Time Dynamic Pricing Accelerates Omnichannel Retail Growth
  • 4.2.2 Cloud-Native SaaS Models Slash Total Cost of Ownership and Speed Implementations
  • 4.2.3 Inflationary Margin Pressure Makes Algorithmic Pricing a Board Priority
  • 4.2.4 End-to-End CPQ and eCommerce Integration Unlocks Revenue Synergies
  • 4.2.5 GenAI-Enabled Price Narratives Enhance Sales Adoption and Negotiation Outcomes
  • 4.2.6 Carbon-Adjusted Pricing Algorithms Gain Traction in ESG-Sensitive Sectors
• 4.3 Market Restraints
  • 4.3.1 Poor Master-Data Quality and Siloed Systems Hinder ROI
  • 4.3.2 Cultural Resistance to Algorithmic Price Changes Slows Deployment
  • 4.3.3 Algorithmic Collusion Concerns Trigger Antitrust Scrutiny and Self-Regulation
  • 4.3.4 Edge-Case Model Failures in Volatile Demand Windows Erode Executive Trust
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Industry Value and Supply-Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Threat of New Entrants
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Bargaining Power of Suppliers
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Deployment Model
  • 5.1.1 Cloud
  • 5.1.2 On-Premise
  • 5.1.3 Hybrid
• 5.2 By End-Use Industry
  • 5.2.1 Retail and eCommerce
  • 5.2.2 Manufacturing
  • 5.2.3 Transportation and Logistics
  • 5.2.4 Financial Services
  • 5.2.5 Other End-Use Industries
• 5.3 By Pricing Strategy Type
  • 5.3.1 AI-Driven Dynamic Pricing
  • 5.3.2 Rule-Based Dynamic Pricing
  • 5.3.3 Markdown Optimization
  • 5.3.4 Promotion Optimization
• 5.4 By Organization Size
  • 5.4.1 Large Enterprises
  • 5.4.2 Small and Medium Enterprises (SMEs)
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 Europe
    • 5.5.2.1 Germany
    • 5.5.2.2 United Kingdom
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Rest of Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 Japan
    • 5.5.3.3 India
    • 5.5.3.4 South Korea
    • 5.5.3.5 Australia
    • 5.5.3.6 Rest of Asia-Pacific
  • 5.5.4 Middle East and Africa
    • 5.5.4.1 Middle East
      • 5.5.4.1.1 Saudi Arabia
      • 5.5.4.1.2 United Arab Emirates
      • 5.5.4.1.3 Rest of Middle East
    • 5.5.4.2 Africa
      • 5.5.4.2.1 South Africa
      • 5.5.4.2.2 Egypt
      • 5.5.4.2.3 Rest of Africa
  • 5.5.5 South America
    • 5.5.5.1 Brazil
    • 5.5.5.2 Argentina
    • 5.5.5.3 Rest of South America

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 PROS Holdings, Inc.
  • 6.4.2 Pricefx AG
  • 6.4.3 Vendavo, Inc.
  • 6.4.4 Zilliant, Inc.
  • 6.4.5 Revionics LLC
  • 6.4.6 Competera Limited
  • 6.4.7 Vistaar Technologies, Inc.
  • 6.4.8 Omnia Retail B.V.
  • 6.4.9 Quicklizard Ltd.
  • 6.4.10 Feedvisor Ltd.
  • 6.4.11 Open Pricer SAS
  • 6.4.12 Perfect Price Inc.
  • 6.4.13 Periscope by McKinsey & Company, Inc.
  • 6.4.14 Model N, Inc.
  • 6.4.15 Aptos LLC (Revionics Division)
  • 6.4.16 IBM Corporation (Watson Dynamic Pricing)
  • 6.4.17 SAP SE (Price Management Module)
  • 6.4.18 SPOSEA B.V.
  • 6.4.19 Navetti AB (part of Vendavo)
  • 6.4.20 Yieldigo a.s.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-space and Unmet-Need Assessment