2026 Global: Big Data Analytics In Retail Market-Competitive Review (2032) report
Description
The 2026 Global: Big Data Analytics In Retail Market-Competitive Review (2031) report features the global market size and projected growth/decline data for the period 2021 through 2032. The report primarily provides an examination of the business strategies for the ten largest global companies in the market and how their strategies differ.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for big data analytics in retail market by geography and historical trend. The scope of the report extends to sizing of the big data analytics in retail market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
Hashmato, RetailNext, eSite Analytics, Buxton, Sensormatic IQ, SiteZeus, Tango Analytics, Dor, FlyBuy, and Reonomy lead the Big Data Analytics in Retail Market with specialized platforms driving retail insights. These companies deliver real-time data on foot traffic, sales, customer behavior, and site selection, enabling retailers to optimize operations and boost profitability. Hashmato provides sales analytics for inventory and customer experience enhancements, while RetailNext offers foot traffic and behavior tracking for store optimization. eSite Analytics focuses on profitability through pricing and product placement analysis, and Buxton excels in site selection using demographics and trends. Sensormatic IQ monitors in-store performance with traffic data, SiteZeus applies AI for location predictions, and Tango Analytics manages full store lifecycles from selection to real estate.
Major players like SAS, IBM, Oracle, Microsoft, and Amazon extend big data capabilities into retail through scalable analytics and AI tools. SAS Viya platform supports data governance, ETL, and AI for retail forecasting and customer insights, serving industries including retail. IBM Watson delivers predictive analytics and machine learning for real-time decisions in retail supply chains. Oracle Analytics Cloud and Big Data Appliance handle vast datasets for inventory and demand prediction, while Microsoft Power BI and Azure Synapse integrate visualization with big data warehousing for retail dashboards. Amazon Web Services, via EMR, Redshift, Kinesis, and QuickSight, processes streaming retail data for personalized experiences and ETL efficiency.
Tredence, Tiger Analytics, and Databricks further strengthen retail analytics with industry-specific AI and unified platforms. Tredence's Retail.AI bridges analytics adoption gaps, embedding insights into processes for last-mile value in merchandising and loyalty. Tiger Analytics builds predictive models for CPG retail, automating workflows with AI to accelerate digital strategies. Databricks unifies data analytics on cloud platforms, empowering retail data science for machine learning and competitive outperformance. These firms collectively transform raw retail data into actionable strategies, reducing costs, enhancing personalization, and predicting trends amid growing market demands.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for big data analytics in retail market by geography and historical trend. The scope of the report extends to sizing of the big data analytics in retail market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
Hashmato, RetailNext, eSite Analytics, Buxton, Sensormatic IQ, SiteZeus, Tango Analytics, Dor, FlyBuy, and Reonomy lead the Big Data Analytics in Retail Market with specialized platforms driving retail insights. These companies deliver real-time data on foot traffic, sales, customer behavior, and site selection, enabling retailers to optimize operations and boost profitability. Hashmato provides sales analytics for inventory and customer experience enhancements, while RetailNext offers foot traffic and behavior tracking for store optimization. eSite Analytics focuses on profitability through pricing and product placement analysis, and Buxton excels in site selection using demographics and trends. Sensormatic IQ monitors in-store performance with traffic data, SiteZeus applies AI for location predictions, and Tango Analytics manages full store lifecycles from selection to real estate.
Major players like SAS, IBM, Oracle, Microsoft, and Amazon extend big data capabilities into retail through scalable analytics and AI tools. SAS Viya platform supports data governance, ETL, and AI for retail forecasting and customer insights, serving industries including retail. IBM Watson delivers predictive analytics and machine learning for real-time decisions in retail supply chains. Oracle Analytics Cloud and Big Data Appliance handle vast datasets for inventory and demand prediction, while Microsoft Power BI and Azure Synapse integrate visualization with big data warehousing for retail dashboards. Amazon Web Services, via EMR, Redshift, Kinesis, and QuickSight, processes streaming retail data for personalized experiences and ETL efficiency.
Tredence, Tiger Analytics, and Databricks further strengthen retail analytics with industry-specific AI and unified platforms. Tredence's Retail.AI bridges analytics adoption gaps, embedding insights into processes for last-mile value in merchandising and loyalty. Tiger Analytics builds predictive models for CPG retail, automating workflows with AI to accelerate digital strategies. Databricks unifies data analytics on cloud platforms, empowering retail data science for machine learning and competitive outperformance. These firms collectively transform raw retail data into actionable strategies, reducing costs, enhancing personalization, and predicting trends amid growing market demands.
Table of Contents
32 Pages
- 1.0 Scope of Report and Methodology
- 2.0 Market SWOT Analysis and Players
- 2.1 Market Definition
- 2.2 Market Segments
- 2.3 Market Strengths
- 2.4 Market Weaknesses
- 2.5 Market Threats
- 2.6 Market Opportunities
- 2.7 Major Players
- 3.0 Competitive Analysis
- 3.1 Market Player 1
- 3.2 Market Player 2
- 3.3 Market Player 3
- 3.4 Market Player 4
- 3.5 Market Player 5
- 3.6 Market Player 6
- 3.7 Market Player 7
- 3.8 Market Player 8
- 3.9 Market Player 9
- 3.10 Market Player 10
- 4.0 Comparative Business Strategies
- 4.1 Comparative Business Strategies of Player 1 and 2
- 4.2 Comparative Business Strategies of Player 1 and 3
- 4.3 Comparative Business Strategies of Player 1 and 4
- 4.4 Comparative Business Strategies of Player 2 and 3
- 4.5 Comparative Business Strategies of Player 2 and 4
- 4.6 Comparative Business Strategies of Player 3 and 4
- 5.0 Appendix
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