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市场调查报告书
商品编码
1852006

欧洲零售业分析：市场占有率分析、产业趋势、统计数据和成长预测（2025-2030 年）

Europe Retail Analytics - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 120 Pages | 商品交期: 2-3个工作天内

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简介目录

预计到 2025 年，欧洲零售分析市场规模将达到 32.3 亿美元，到 2030 年将达到 68.1 亿美元，预测期内复合年增长率将达到 16.1%。

欧洲零售分析-市场-IMG1

持续的数位市场改革、不断上涨的能源价格以及欧盟《数位市场法案》正推动零售商转向先进的数据平台，以提高营运利润率并确保合规性。云端平台透过实现按需扩展、整合分散数据和降低资本支出，为此转型提供了支援。随着零售商大力推动即时货架监控，边缘分析技术正逐渐扩展到大卖场，而人工智慧驱动的动态定价则有助于在通膨环境下保障毛利率。各国趋势仍存在差异：德国目前在支出方面领先，但义大利、西班牙以及中东欧国家也正在快速成长。资料科学人才缺口的扩大和日益严格的隐私保护可能会限制部分营运商采用云端平台的速度。

欧洲零售分析市场趋势与洞察

数据主导的个人化行销可提高店内转换率

部署云端基础建议引擎的零售商将店内行为数据输入到与分析网路购物路径相同的决策中心，从而实现货架和收银台的即时提案。早期采用者报告转换率提升了 15% 至 25%，使他们能够将预算从全面促销转向精准激励。成功的专案依赖以奖励为核心的设计架构，该架构会标记使用者同意、汇总使用者画像并居住本地资料。需求主要集中在德国、法国和英国，这些国家拥有高密度的消费者和成熟的会员集点卡，提供了丰富的资料流。供应商简化了传统 POS 资料和电商日誌的身份解析流程，推动了大部分成长。由此产生的个人化循环支持企业从价格战转向体验主导型差异化。

人工智慧定价引擎优化通膨环境下的欧洲利润率

2024年初，欧元区食品通膨率飙升至15%，食品和一般商品的利润空间受到挤压。即使投入成本持续波动，采用演算法每小时调整SKU价格的零售财务部门也面临3%至5%的利润率下降。欧洲中央银行观察到，企业在高通膨时期会更频繁地调整价格，这强化了能够自动进行弹性建模的分析平台的商业性逻辑。动态引擎整合了竞争对手数据、能源额外费用和即时需求，以提案最佳货架价格和降价时机。由于来自折扣店的激烈竞争，德国和义大利的零售商率先采取行动，其他零售商也纷纷效法。促销活动期间，推理工作负载激增，需要扩展且不出现硬体瓶颈，促使云端技术成为主流。

零售资料科学人才短缺

四分之三的德国零售商表示，2024年数据职位的招募将会延迟，法国和英国的情况也类似。金融科技和深度科技垂直领域的竞争推高了薪资水平，超出了许多零售商的承受能力。供应商正在透过整合自动化模型建置和自然语言查询层来应对这项挑战，使品类经理无需编写程式码即可进行预测。託管服务也在兴起，供应商以订阅模式提供资料科学家服务。中期来看，欧盟资助的技能再培训倡议或许能够缓解这项限制，但在此之前，平台易用性仍是欧洲零售分析市场的关键采购标准。

细分市场分析

到2024年，云端选项将占零售支出的58.1%，证实了大多数零售商倾向于将分析工作负载的运算和储存层外包。随着企业将大量报告、人工智慧训练和即时事件流管道迁移到外部部署，预计到2030年，以云端驱动的欧洲零售分析市场规模将以18.2%的复合年增长率成长。这一成长势头很大程度上是由週期性促销活动推动的，例如黑色星期五、双十一和自有品牌忠诚度计划，这些活动给传统数据中心带来了压力。 SAP 2025年第一季的欧洲、中东和非洲地区云端收入年增30%，反映了这一转变。虽然在拥有严格智慧财产权控制的时尚和奢侈品行业，本地丛集仍在运作，但随着机密运算晶片增强公共云端的安全态势，其份额将会下降。混合架构弥合了这一差距，它允许店内摄影机在本地处理影像，同时将聚合指标上传到中央资料湖，从而支援GDPR本地化和低延迟决策。

可扩展性是一项短期优势，但成本控制是其根本。面临能源价格上涨风险的零售商发现，将运算资源迁移异地可以有效抵消电力波动的影响，因为超大规模资料中心营运商会对其电力合约进行长期对冲。这种财务可预测性引起了财务主管的共鸣，他们既要保护息税折旧摊提前利润 (EBITDA)，又要将资金再投资于业务成长。供应商之间的竞争正在转向附加价值服务（垂直化资料模型、预训练定价演算法、面向商品销售人员的一键式仪錶板）。未来五年，这些差异化因素将影响欧洲零售分析市场云端部分的收入格局。

在激烈的市场竞争中，零售商将重点放在客户忠诚度维繫和个人化促销上，而竞争考察将确保2024年销售额成长29.6%。该模组的预测性细分引擎和购买路径归因模型仍将是全通路策略的基础。然而，预计到2030年，供应履约将以17.3%的复合年增长率实现最快成长。汉高透过数据主导的能源优化，在2024年实现了400万欧元的成本节约，展现了切实可见的投资报酬率。

策略与规划仪錶板将商品行销、财务和营运的关键绩效指标 (KPI) 汇总成董事会层级的记分卡，构成变革管理的核心。商品行销和品类优化模组利用人工智慧模拟购物篮亲和性，并推荐季节性商品组合更新。随着工会零售商寻求自动化重复性审核，门市营运分析（尤其是用于损耗控制的电脑视觉技术）正蓬勃发展。财务管理模组（包括即时毛利率追踪）仍然至关重要，尤其是在食品零售领域，每天都会推出数十种新产品。这些细分领域展示了欧洲零售分析产业如何在收入成长和成本控制方面创造价值。

欧洲零售分析市场按部署类型（本地部署、云端部署、混合部署）、模组类型（策略与规划、行销与客户洞察、其他）、公司规模（中小企业、大型企业）、零售业态（实体店、电商、全通路零售）和国家/地区进行细分。市场预测以美元计价。

其他福利：

  • Excel格式的市场预测（ME）表
  • 3个月的分析师支持

目录

第一章 引言

  • 研究假设和市场定义
  • 调查范围

第二章调查方法

第三章执行摘要

第四章 市场情势

  • 市场概览
  • 市场驱动因素
    • 数据主导的个人化提升店内转换率
    • 人工智慧定价引擎优化通膨环境下的欧洲利润率
    • 边缘分析技术在即时货架监测中的兴起
    • 统一商务要求对客户进行单一视图管理。
    • 欧盟数位市场法将使零售商拥有第一方数据
    • 采用能源效率分析来遏制不断上涨的能源费用。
  • 市场限制
    • 零售资料科学人才短缺
    • GDPR 和电子隐私法规提高了资料隐私保护水平
    • 传统POS系统碎片化阻碍了资料集成
    • 冻结对中小型零售企业的资本投资
  • 价值链分析
  • 监管环境
  • 技术展望
  • 波特五力分析
    • 新进入者的威胁
    • 买方的议价能力
    • 供应商的议价能力
    • 替代品的威胁
    • 竞争对手之间的竞争
  • 宏观经济因素如何影响市场

第五章 市场规模与成长预测

  • 按实现类型
    • 本地部署
    • 云
    • 杂交种
  • 按模组类型
    • 策略与规划
    • 行销和客户洞察
    • 财务管理
    • 门市营运和防损
    • 商品行销及品类优化
    • 供应炼和履约
  • 按企业规模
    • 小型企业
    • 大公司
  • 按零售业态
    • 实体店面
    • 电子商务
    • 全通路零售
  • 按国家/地区
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 其他欧洲地区

第六章 竞争情势

  • 市场集中度
  • 策略趋势
  • 市占率分析
  • 公司简介
    • SAP SE
    • Oracle Corporation
    • IBM Corporation
    • SAS Institute Inc.
    • Microsoft Corporation
    • QlikTech International AB
    • Tableau Software LLC
    • MicroStrategy Incorporated
    • Zoho Corporation Pvt. Ltd.
    • Alteryx Inc.
    • RetailNext Inc.
    • Blue Yonder GmbH
    • NielsenIQ
    • ThoughtSpot Inc.
    • Sisense Ltd.
    • Domo Inc.
    • Looker Data Sciences（Google）
    • Snowflake Inc.
    • Databricks Inc.
    • C3.ai Inc.

第七章 市场机会与未来展望

简介目录
Product Code: 53590

The Europe retail analytics market size stood at USD 3.23 billion in 2025 and is projected to reach USD 6.81 billion by 2030, registering a 16.1% CAGR during the forecast period.

Europe Retail Analytics - Market - IMG1

Ongoing digital-market reforms, elevated energy prices, and the EU Digital Markets Act are steering retailers toward advanced data platforms that improve operating margins and regulatory compliance. Cloud platforms underpin this transition because they scale on demand, integrate disparate data types, and cut capital expenditure. Edge analytics is spreading through hypermarkets as retailers push real-time shelf monitoring, while AI-enabled dynamic pricing helps defend gross margins in an inflationary environment. Country dynamics remain heterogeneous: Germany drives spending today, but Italy, Spain, and several Central and Eastern European countries are expanding faster. A widening gap in data-science talent and stricter privacy enforcement could restrain the pace of deployments for some operators.

Europe Retail Analytics Market Trends and Insights

Data-driven personalization lifts in-store conversion

Retailers deploying cloud-based recommendation engines now feed in-store behavior into the same decision hubs that analyze web journeys, enabling real-time offers at the shelf or check-out. Early adopters report conversion uplifts between 15% and 25%, which in turn drives budget reallocation from blanket promotions to targeted incentives. Successful programs rely on privacy-by-design architectures that tag consent, aggregate profiles, and maintain local data residence. Demand concentrates in Germany, France, and the United Kingdom, where high shopper density and loyalty-card maturity provide rich data streams. Vendors that simplify identity resolution across legacy point-of-sale feeds and e-commerce logs capture most of the growth. The resulting personalization loop supports the broader shift toward experience-led differentiation rather than price competition.

AI-powered pricing engines optimise margins in inflationary Europe

Food and general merchandise margins came under pressure when euro-area food inflation peaked at 15% in early 2024. Retail finance departments that embraced algorithms capable of re-pricing SKUs hourly saw 3-5% margin relief even as input costs remained volatile. The European Central Bank confirms that firms alter prices more frequently during high-inflation periods, reinforcing the commercial logic for analytics platforms that automate elasticity modelling. Dynamic engines ingest competitor crawls, energy surcharges, and real-time demand to suggest optimal shelf prices and markdown cadences. Retailers in Germany and Italy moved fastest because of intense discounter competition, setting a regional template others now follow. Cloud deployment dominates because inference workloads spike during promotion events and must scale without hardware bottlenecks.

Shortage of retail data-science talent pool

Three-quarters of German retailers reported hiring delays for data roles in 2024, and similar patterns surfaced in France and the United Kingdom. Competition from fintech and deep-tech verticals raises salary benchmarks beyond what many retailers can absorb. Vendors respond by embedding automated model-building and natural-language query layers so that category managers can run forecasts without writing code. Managed-service engagements gain traction as well, with providers offering fractional data scientists on subscription. Over the medium term, EU-funded reskilling initiatives could ease constraints, but until then, platform usability remains a key purchase criterion inside the European retail analytics market.

Other drivers and restraints analyzed in the detailed report include:

  1. Proliferation of edge analytics for real-time shelf monitoring
  2. Unified commerce mandates single view of customer
  3. Data-privacy tightening under GDPR and forthcoming ePrivacy Regulation

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

Segment Analysis

Cloud options drew 58.1% of spending in 2024, confirming that most retailers prefer outsourcing compute and storage layers for analytics workloads. The Europe retail analytics market size attributed to cloud is forecast to climb at an 18.2% CAGR until 2030 as enterprises migrate batch reporting, AI training and real-time event streaming pipelines off-premise. Much of the momentum comes from periodic promotional surges-Black Friday, Singles' Day, or private-label loyalty drives-that stress legacy data centers. SAP's EMEA cloud revenue jumped 30% year-over-year in Q1 2025, mirroring this migration trend. On-premise clusters still run in fashion and luxury houses with stringent intellectual-property controls, yet their share will shrink as confidential-computing chips harden public-cloud security postures. Hybrid architectures bridge the gap where in-store cameras process images locally but upload aggregated metrics to central lakes, supporting GDPR locality and low-latency decisions.

Scalability explains the near-term advantage, but cost discipline sustains it. Retailers exposed to energy spikes discovered that moving compute off-site neutralizes power volatility, because hyperscalers hedge electricity contracts longer term. That financial predictability resonates with finance chiefs tasked to safeguard EBITDA while reinvesting in growth. Vendor competition now shifts to value-added services-verticalized data models, pre-trained pricing algorithms, and one-click dashboards for merchandisers. These differentiators will shape revenue capture inside the cloud portion of the Europe retail analytics market over the next five years.

Marketing and Customer Insights secured 29.6% revenue in 2024 because retailers target loyalty retention and individualized promotions amid relentless competition. The module's predictive-segmentation engines and path-to-purchase attribution models remain foundational for omnichannel strategies. Yet, Supply-Chain and Fulfilment is expected to clock a 17.3% CAGR to 2030, the fastest among all modules, as retailers reengineer networks for resilience. Henkel's EUR 4 million saving through data-driven energy optimization in 2024 showcase tangible ROI.

Strategy and Planning dashboards knit KPIs from merchandising, finance, and operations into board-level scorecards, creating a change-management backbone. Merchandising and Category Optimization modules use AI to simulate basket affinities and recommend assortment refreshes every season. Store-operations analytics-especially computer vision for shrinkage-is picking up speed as unionized retailers look to automate repetitive audits. Financial Management modules, including real-time gross-margin tracking, remain essential, particularly in food retail, where dozens of commodity inputs move daily. Together, these sub-segments illustrate how the Europe retail analytics industry delivers value across both revenue growth and cost-containment objectives.

Europe Retail Analytics Market is Segmented by Mode of Deployment (On-Premise, Cloud, and Hybrid), Module Type (Strategy and Planning, Marketing and Customer Insights, and More), Business Size (Small and Medium Enterprises and Large Enterprises), Retail Format (Brick-And-Mortar, E-Commerce, and Omnichannel Retail), and Country. The Market Forecasts are Provided in Terms of Value (USD).

List of Companies Covered in this Report:

  1. SAP SE
  2. Oracle Corporation
  3. IBM Corporation
  4. SAS Institute Inc.
  5. Microsoft Corporation
  6. QlikTech International AB
  7. Tableau Software LLC
  8. MicroStrategy Incorporated
  9. Zoho Corporation Pvt. Ltd.
  10. Alteryx Inc.
  11. RetailNext Inc.
  12. Blue Yonder GmbH
  13. NielsenIQ
  14. ThoughtSpot Inc.
  15. Sisense Ltd.
  16. Domo Inc.
  17. Looker Data Sciences (Google)
  18. Snowflake Inc.
  19. Databricks Inc.
  20. C3.ai 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 Data-driven personalization lifts in-store conversion
    • 4.2.2 AI-powered pricing engines optimise margin in inflationary Europe
    • 4.2.3 Proliferation of edge analytics for real-time shelf monitoring
    • 4.2.4 Unified commerce mandates single view of customer
    • 4.2.5 EU Digital Markets Act pushing retailers to own first-party data
    • 4.2.6 Energy-efficiency analytics adopted to curb soaring utility bills
  • 4.3 Market Restraints
    • 4.3.1 Shortage of retail data-science talent pool
    • 4.3.2 Data-privacy tightening under GDPR and ePrivacy Regulation
    • 4.3.3 Legacy POS fragmentation impedes data integration
    • 4.3.4 Capital-expenditure freeze among SME retailers
  • 4.4 Value 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 Substitutes
    • 4.7.5 Intensity of Competitive Rivalry
  • 4.8 Impact of Macroeconomic Factors on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUES)

  • 5.1 By Mode of Deployment
    • 5.1.1 On-Premise
    • 5.1.2 Cloud
    • 5.1.3 Hybrid
  • 5.2 By Module Type
    • 5.2.1 Strategy and Planning
    • 5.2.2 Marketing and Customer Insights
    • 5.2.3 Financial Management
    • 5.2.4 Store Operations and Loss Prevention
    • 5.2.5 Merchandising and Category Optimisation
    • 5.2.6 Supply-Chain and Fulfilment
  • 5.3 By Business Size
    • 5.3.1 Small and Medium Enterprises
    • 5.3.2 Large Enterprises
  • 5.4 By Retail Format
    • 5.4.1 Brick-and-Mortar
    • 5.4.2 E-Commerce
    • 5.4.3 Omnichannel Retail
  • 5.5 By Country
    • 5.5.1 United Kingdom
    • 5.5.2 Germany
    • 5.5.3 France
    • 5.5.4 Italy
    • 5.5.5 Spain
    • 5.5.6 Rest of Europe

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, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
    • 6.4.1 SAP SE
    • 6.4.2 Oracle Corporation
    • 6.4.3 IBM Corporation
    • 6.4.4 SAS Institute Inc.
    • 6.4.5 Microsoft Corporation
    • 6.4.6 QlikTech International AB
    • 6.4.7 Tableau Software LLC
    • 6.4.8 MicroStrategy Incorporated
    • 6.4.9 Zoho Corporation Pvt. Ltd.
    • 6.4.10 Alteryx Inc.
    • 6.4.11 RetailNext Inc.
    • 6.4.12 Blue Yonder GmbH
    • 6.4.13 NielsenIQ
    • 6.4.14 ThoughtSpot Inc.
    • 6.4.15 Sisense Ltd.
    • 6.4.16 Domo Inc.
    • 6.4.17 Looker Data Sciences (Google)
    • 6.4.18 Snowflake Inc.
    • 6.4.19 Databricks Inc.
    • 6.4.20 C3.ai Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-space and Unmet-need Assessment