The retail analytics market is set to witness staggering growth by 2027 on account of growing prevalence of e-commerce across the globe. Moreover, favorable shift in consumer preferences is expected to further support market growth over the forecast period.

Retail analytics refers to the process of delivering analytical data on inventory levels, supply chain movement, consumer demand, sales, and other factors, which are important for marketing and procurement choices. Demand and supply data analytics can be utilized to keep procurement levels up to date as well as make marketing decisions.

Recently, surging prominence of social media and online marketplaces has unveiled massive opportunities for product penetration as retail analytics seamlessly analyzes commonly purchased items, preferences, and consumer demographics. As a result, there is an upsurge in the demand for accurate retail insights among companies worldwide to hone their marketing strategies, which has bolstered industry adoption.

Notably, many key industry players are engaging in strategic partnerships and acquisitions among other lucrative business plans to gain a competitive edge in the market, thereby propelling business growth. For instance, in July 2021, Salesforce, a leading CRM firm, completed the acquisition of Slack Technologies, a US-based software company. The acquisition was purposed to help Salesforce build an effective digital platform for various employees, customers, and partners to connect with each other.

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To cite another instance, in May 2021, 1010data, a key provider in the retail analytics sphere, inked a partnership agreement with Fractal, a major AI and analytics technology company, to provide improved consumer behavior and market trend analytics. The alliance was focused on enabling retail and consumer packaged goods (CPG) organizations to help customers identify current and future trends by leveraging strong expertise in strategy and digital transformation.

Similarly, in May 2021, Qlik Technologies, a data analytics solutions provider, announced the expansion of its strategic partnership with Databricks, an American software company, with support for introducing Databricks' secure data sharing platform, Delta Sharing. With the deal, joint customers could safely share relevant analytical insights through cloud and the platform of their preference.

The retail analytics market has been categorized based on function, enterprise size, solution, field crowdsourcing, deployment model, and region. As per function, the market has further been differentiated into merchandising, strategy & planning, customer management, store operations, and supply chain management. The strategy & planning function segment is slated to grow at a CAGR of approximately 15% through the study timeline owing to increased demand for business growth strategies based on data analytics among retailers.

In terms of solution, the market has been segmented into service and software. The service segment has further been bifurcated into integration and deployment, managed service, and training & consulting. The training & consulting segment held a sizeable market share of about 20% in 2020 and is expected to observe notable growth by 2027 due to mounting demand for analytics consultation services.

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With respect to field crowdsourcing, the market has been divided into documentation and reporting, customer insights, on-shelf availability, and promotion campaign management. The documentation and reporting segment is anticipated to amass substantial gains by 2027, expanding at a CAGR of around 15% over the forecast period owing to escalating demand for retail reporting services.

From the regional perspective, the Middle East and Africa retail analytics market is projected to grow considerably at over 20% CAGR through the estimated timeline driven by the robust development of the retail industry in the region.

Chapter 1 Methodology & Scope

• 1.1 Scope and definition
• 1.2 Methodology & forecast parameters
• 1.3 COVID-19 impact
  • 1.3.1 North America
  • 1.3.2 Europe
  • 1.3.3 Asia Pacific
  • 1.3.4 Latin America
  • 1.3.5 MEA
• 1.4 Regional trends
  • 1.4.1 North America
  • 1.4.2 Europe
  • 1.4.3 Asia Pacific
  • 1.4.4 Latin America
  • 1.4.5 MEA
• 1.5 Data Sources
  • 1.5.1 Secondary
    • 1.5.1.1 Paid sources
    • 1.5.1.2 Public sources
  • 1.5.2 Primary

Chapter 2 Executive Summary

• 2.1 Retail analytics industry 360 degree synopsis, 2016 - 2027
• 2.2 Business trends
  • 2.2.1 Total Addressable Market (TAM)
• 2.3 Regional trends
• 2.4 Function trends
• 2.5 Solution trends
• 2.6 Enterprise size trends
• 2.7 Deployment model trends
• 2.8 Field crowdsourcing trends

Chapter 3 Retail Analytics Industry Insights

• 3.1 Introduction
• 3.2 Industry segmentation
• 3.3 Impact of COVID-19 outbres
  • 3.3.1 Global outlook
  • 3.3.2 Regional outlook
    • 3.3.2.1 North America
    • 3.3.2.2 Europe
    • 3.3.2.3 Asia Pacific
    • 3.3.2.4 Latin America
    • 3.3.2.5 MEA
  • 3.3.3 Industry value chain
    • 3.3.3.1 Retail analytics provider
    • 3.3.3.2 Marketing & distribution channel
  • 3.3.4 Competitive landscape
    • 3.3.4.1 Strategy
    • 3.3.4.2 Distribution network
    • 3.3.4.3 Business growth
• 3.4 Industry ecosystem analysis
  • 3.4.1 Component manufactrer
  • 3.4.2 Thirrd party servic provider
  • 3.4.3 Platform provider
  • 3.4.4 Vendor matrix
• 3.5 Technology & innovation landscape
  • 3.5.1 Advanced Analytics
  • 3.5.2 Transactional
  • 3.5.3 Collaborative
  • 3.5.4 Trade Promotion Optimization (TPO)
  • 3.5.5 Optimization & collaboration
  • 3.5.6 Single photon technology
• 3.6 Retail industry outlook
  • 3.6.1 North America
  • 3.6.2 Europe
  • 3.6.3 Asia Pacific
  • 3.6.4 Latin America
  • 3.6.5 Middle East & Africa
• 3.7 Regulatory landscape
  • 3.7.1 North America
    • 3.7.1.1 Health Insurance Portability and Accountability Act (HIPAA) of 1996 (U.S)
    • 3.7.1.2 Standards on Privacy and Web Analytics (Canada)
  • 3.7.2 Europe
    • 3.7.2.1 General Data Protection Regulation (EU)
    • 3.7.2.2 Exception of Text & Data Mining (TDM) in Copyright in Digital
    • 3.7.2.3 Single Market (DSM Draft Directive EU)
  • 3.7.3 APAC
    • 3.7.3.1 Information Security Technology Personal Information Security Specification GB/T 35273 2017 (China)
  • 3.7.4 Latin America
    • 3.7.4.1 The General Data Protection Law (Brazil)
• 3.8 Use of retail analytics for improving the effectiveness of in-store promotions/merchandising
• 3.9 Investment portfolio
• 3.10 Industry impact forces
  • 3.10.1 Growth drivers
    • 3.10.1.1 Growing e-commerce industry in China and India
    • 3.10.1.2 Increasing penetration of big data in the retail industry in the U.S.
    • 3.10.1.3 Growing proliferation of smartphones in Asia Pacific
    • 3.10.1.4 Increasing competition among retailers and increasing need for differentiation in North America
    • 3.10.1.5 New generation of highly informed and demanding customers in Europe
    • 3.10.1.6 Growing need for analytics maturity in Asia Pacific
    • 3.10.1.7 Growing adoption of e-commerce in Latin America
    • 3.10.1.8 Expanding retail sector in Middle East & Africa
  • 3.10.2 Industry pitfall & challenges
    • 3.10.2.1 Lack of technical expertise
    • 3.10.2.2 Data privacy concerns
• 3.11 Growth potential analysis
  • 3.11.1 Business model
• 3.12 Porter's analysis
  • 3.12.1 Supplier power
  • 3.12.2 Buyer power
  • 3.12.3 Threat of new entrant
  • 3.12.4 Threat of substitution
• 3.13 PESTEL analysis