리테일 애널리틱스 : 시장 점유율 분석, 산업 동향, 통계, 성장 예측(2025-2030년)

The retail analytics market size is estimated at USD 6.60 billion in 2025 and is projected to reach USD 8.12 billion by 2030, registering a 4.23% CAGR across the period.

Broader omnichannel commerce, surging data volumes, and rapid adoption of AI-driven decision engines are encouraging retailers to embed analytics into day-to-day processes. Cloud delivery models are cutting ownership costs and trimming deployment cycles, which allows mid-tier chains to access capabilities once limited to global leaders. Real-time personalization, prescriptive inventory tools, and advanced promotion optimisation are lifting conversion rates and order profitability even as margins tighten. Competitive advantage is shifting toward platforms that integrate predictive, prescriptive, and generative capabilities, signaling that analytics has moved from a discretionary spend to a fundamental retail requirement.

Global Retail Analytics Market Trends and Insights

Omnichannel data-volume explosion

Retailers now ingest vast pools of transaction, behavioural, and social signals that dwarf prior years and mandate scalable analytics platforms. The pace of data growth is prompting investments in unified data lakes that consolidate store, e-commerce, and third-party feeds to support near real-time reporting. Operators that master rapid pattern recognition improve pricing agility, sharpen demand forecasts, and raise inventory turns, which collectively push gross-margin lift. Managing this surge also accelerates cloud demand because elastic infrastructure prevents performance bottlenecks during peak events. Consequently, analytic maturity becomes a barometer for commercial resilience.

AI/ML advances enabling real-time prescriptive insights

Machine-learning-powered demand sensing is reducing supply-chain errors and missed sales opportunities, while computer vision applications extend intelligence to the sales floor. Retailers that embed AI into recommendation engines capture new cross-sell revenue streams and improve lifetime customer value as output moves from descriptive dashboards to automated decisions. Multimodal models that blend language, vision, and structured data produce unified intelligence layers that break down functional silos. These shifts are redefining the talent profiles retailers seek and are reshaping vendor selection criteria, with emphasis now on pre-built, explainable models.

Data-privacy regulation and compliance costs

Tightening privacy laws such as GDPR and CCPA compel retailers to re-engineer data pipelines to uphold consent and minimisation rules. Modernisation budgets now allocate growing shares to security tooling that encrypts and governs sensitive information, diverting funds from analytics innovation. Cross-border retailers face complex compliance matrices that elongate deployment cycles for global rollouts. Privacy-by-design frameworks help reduce regulatory exposure but also limit data granularity, which may curb the scope of advanced analytics in high-stringency markets. The interplay between protection mandates and analytics ambitions, therefore, remains a key strategic balancing act.

Other drivers and restraints analyzed in the detailed report include:

1. Uptake of cloud analytics lowering TCO
2. Demand for hyper-personalised shopping journeys
3. Legacy IT stacks and analytics skills gap

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

Segment Analysis

Cloud deployments captured 62.00% of projects in 2024 and will grow at a 9.60% CAGR to 2030. Subscription pricing minimises capital commitments, and elastic compute equips retailers to handle promotional peaks without performance dips. Although some chains retain sensitive data on-site to satisfy sovereignty and latency requirements, hybrid architectures that blend local data storage with cloud compute are emerging as a strategic middle ground. These hybrid designs reduce round-trip times for mission-critical workloads such as fraud detection while leveraging cloud economics for analytics model training. As a result, deployment choices increasingly hinge on workload characteristics rather than a blanket cloud-first or on-premises stance.

Pure-play e-commerce retailers captured 60.00% of deployments in 2024 and will continue to outgrow other formats at a 9.20% CAGR. Their digital-native architectures enable rapid adoption of predictive models without legacy integration hurdles, allowing them to refine experiences at every click. Brick-and-mortar operators are bridging the data gap by adding sensors, RFID, and computer vision to convert in-store behaviour into actionable data. Omnichannel chains adopt unified commerce analytics that merge online and store insights to improve cross-channel stock allocation and fulfilment routing. These varied priorities illustrate that business model dictates analytics roadmaps, yet all formats converge on the need for faster, more granular insights.

Retail Analytics Market is Segmented by Solutions (Software and Services), Deployment (Cloud, On-Premises, Hybrid), Function (Customer Management, Supply Chain Management, Marketing and Merchandising - Pricing/Yield, Other Functions - Order Management), Retail Format (Brick-And-Mortar Stores, Pure-Play E-Commerce, Omnichannel Retailers), Geography (North America, South America, Europe, Asia-Pacific, Middle East and Africa).

Geography Analysis

North America led the retail analytics market with 38.20% revenue share in 2024, supported by robust cloud infrastructure, deep e-commerce penetration, and proximity to leading analytics vendors. United States retailers plan to raise technology budgets in 2025, directing the largest allocations to customer-data platforms and AI-based decision engines. Canada trails slightly yet invests heavily in customer analytics to differentiate in a mature market, while Mexico emphasises supply-chain optimisation to address logistics volatility.

Asia-Pacific is set to post the fastest CAGR at 6.30% through 2030, propelled by explosive e-commerce growth, increasing smartphone adoption, and ambitious digital transformation programs. China pioneers social-commerce analytics that integrate payment, delivery, and community engagement data, producing end-to-end insight loops. India's diverse consumer landscape encourages retailers to deploy predictive models that tailor assortments across urban and rural zones. Japan and South Korea emphasise store-centric analytics such as computer-vision footfall measurement to revitalise physical outlets.

Europe retains a meaningful footprint anchored by sophisticated retail systems in the United Kingdom, Germany, and France. Stringent privacy rules drive adoption of hybrid deployments that balance data residency with cloud scalability. Sustainability analytics is gaining traction as retailers use environmental metrics to guide sourcing and packaging decisions. Latin American growth concentrates in Brazil and Argentina, where analytics helps manage currency volatility and optimise localised promotions. Middle East and Africa remain smaller but active in markets such as Israel and the United Arab Emirates, where luxury retail and tourism accelerate the adoption of customer experience analytics.

1. SAP SE
2. IBM Corporation
3. Oracle Corporation
4. Salesforce Inc. (Tableau)
5. SAS Institute Inc.
6. QlikTech International AB
7. Microsoft Corp. (Power BI, Dynamics 365)
8. Amazon Web Services Inc. (QuickSight)
9. Google LLC (Looker)
10. Blue Yonder Inc.
11. Dunnhumby Ltd.
12. Teradata Corp.
13. RetailNext Inc.
14. Zebra Technologies Corp.
15. Altair Engineering Inc.
16. Alteryx Inc.
17. MicroStrategy Inc.
18. ThoughtSpot Inc.
19. Fujitsu Ltd.
20. Infor Inc.

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 Omnichannel data-volume explosion
  • 4.2.2 AI/ML advances for real-time prescriptive insights
  • 4.2.3 Uptake of cloud analytics lowering ownership costs
  • 4.2.4 Demand for hyper-personalised shopping journeys
• 4.3 Market Restraints
  • 4.3.1 Data-privacy regulation and compliance costs
  • 4.3.2 Legacy IT stacks and analytics skills gap
• 4.4 Supply Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Bargaining Power of Suppliers
  • 4.7.4 Threat of Substitute Products
  • 4.7.5 Intensity of Competitive Rivalry
• 4.8 Assessment of the Impact of Macroeconomic Trends on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Solution
  • 5.1.1 Software
  • 5.1.2 Services
• 5.2 By Deployment Model
  • 5.2.1 Cloud
  • 5.2.2 On-premise
  • 5.2.3 Hybrid
• 5.3 By Function
  • 5.3.1 Customer Management
  • 5.3.2 In-store Operation - Inventory Management
  • 5.3.3 In-store Operation - Performance Management
  • 5.3.4 Supply Chain Management
  • 5.3.5 Marketing and Merchandising - Pricing/Yield
  • 5.3.6 Other Functions - Transportation Management
  • 5.3.7 Other Functions - Order Management
• 5.4 By Retail Format
  • 5.4.1 Brick-and-Mortar Stores
  • 5.4.2 Pure-play E-commerce
  • 5.4.3 Omnichannel Retailers
• 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 United Kingdom
    • 5.5.2.2 Germany
    • 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 Rest of Asia-Pacific
  • 5.5.4 Middle East
    • 5.5.4.1 Israel
    • 5.5.4.2 Saudi Arabia
    • 5.5.4.3 United Arab Emirates
    • 5.5.4.4 Turkey
    • 5.5.4.5 Rest of Middle East
  • 5.5.5 Africa
    • 5.5.5.1 South Africa
    • 5.5.5.2 Egypt
    • 5.5.5.3 Rest of Africa
  • 5.5.6 South America
    • 5.5.6.1 Brazil
    • 5.5.6.2 Argentina
    • 5.5.6.3 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 for key companies, Products & Services, and Recent Developments)
  • 6.4.1 SAP SE
  • 6.4.2 IBM Corporation
  • 6.4.3 Oracle Corporation
  • 6.4.4 Salesforce Inc. (Tableau)
  • 6.4.5 SAS Institute Inc.
  • 6.4.6 QlikTech International AB
  • 6.4.7 Microsoft Corp. (Power BI, Dynamics 365)
  • 6.4.8 Amazon Web Services Inc. (QuickSight)
  • 6.4.9 Google LLC (Looker)
  • 6.4.10 Blue Yonder Inc.
  • 6.4.11 Dunnhumby Ltd.
  • 6.4.12 Teradata Corp.
  • 6.4.13 RetailNext Inc.
  • 6.4.14 Zebra Technologies Corp.
  • 6.4.15 Altair Engineering Inc.
  • 6.4.16 Alteryx Inc.
  • 6.4.17 MicroStrategy Inc.
  • 6.4.18 ThoughtSpot Inc.
  • 6.4.19 Fujitsu Ltd.
  • 6.4.20 Infor Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-space and Unmet-Need Assessment