Newly-released Retail Analytics Market industry analysis report by Future Market Insights shows that global sales of Retail Analytics Market in 2022 were held at US$ 9,300 million. With 17.5%, the projected market growth during 2023 to 2033 is expected to be higher than the historical growth. In 2023, the market is estimated to surpass a valuation of US$ 10,797.4 million in 2023, and reach US$ 55,247.6 million by 2033.
| Attribute | Details |
|---|---|
| Global Retail Analytics Market Size (2023) | US$ 10,797.4 million |
| Global Retail Analytics Market Size (2033) | US$ 55,247.6 million |
| Global Retail Analytics Market CAGR (2023 to 2033) | 17.5% |
| United States Retail Analytics Market Size (2033) | US$ 16.8 billion |
| United States Retail Analytics Market CAGR (2023 to 2033) | 17.6% |
| Key Companies Covered |
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As per the Retail Analytics Market industry research by Future Market Insights - a market research and competitive intelligence provider, historically, from 2018 to 2022, the market value of the Retail Analytics Market increased at around 15.6% CAGR, wherein, countries such as the United States, United Kingdom, China, Japan, and South Korea held a significant share in the global market. With an absolute dollar opportunity of US$ 37.7 billion during 2023 to 2033, the market is projected to reach a valuation of US$ 47 billion by 2033.
Retail analytics is the most common way of tracking business data, for example, stock levels, buyer conduct, and marketing projections. This incorporates giving experiences to comprehend and streamline the retail business' inventory network, buyer conduct, deal patterns, functional cycles, and general execution. With the present high client expectations for retail, organizations should meet those rising necessities with customized omnichannel offers, effective cycles, and speedy changes to upcoming patterns - all of which require retail analytics.
Retailers should have the option to precisely target and expect client needs to offer the perfect items at the ideal cost brilliantly which needs analytics. Analytics can assist retailers with pursuing the right marketing choices, further develop their business processes, and convey better overall client experiences by uncovering regions for development and advancement. There are some areas of the retail business that can gain profit from analytics. It tends to be utilized to give an exhaustive perspective on the business and evaluate the effectiveness of business processes. For instance, a retailer can utilize prescient investigation to change stock in light of client buying patterns and diminish squandering and related costs.
Retail analytics can further develop advertising strategies. It can assist with focusing on clients by deciding the ideal client in view of data accumulated on current and past clients' area, age, inclinations, buying designs, and other key elements. Customized promotion in the retail business is turning out to be more ordinary and requires a profound comprehension of individual client inclinations. With retail investigation, organizations can foster methodologies zeroed in on unambiguous clients. Retail analytics can be utilized to foresee purchaser necessities and business upgrades to acquire an upper hand. Examining deals data can assist retailers with distinguishing arising patterns and client needs.
The rising need for price optimization strategy is driving the development of the retail analytics market. Clients today are shrewd, knowing how to get the most value for the money. They think about costs online while shopping in stores, have applications that give markdown codes, and are dedicated to retailers who offer the most benefit for their cash. Thus, a sound and rising main concern require areas of strength for an enhancement approach.
In the retail business, evaluating analytics permits organizations to set ideal valuing for specific items, seasons, and stores by breaking down missed deals, stock turn, selling patterns, and different elements. Estimating investigation likewise affects stock, permitting them to more readily deal with their stock in view of stock, request, and occasional varieties.
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The retail sector is finding it difficult to get back into business after being in a complete lockdown for months. Like other client-driven sectors, retail also flourishes with client conduct and commitment, and is battling to stay aware of lockdown-prompted changes in client conduct. Alongside falling deals, retail is confronting an information shortage. This information is typically the way to guarantee an improved client experience.
With the evaporating of such critical data in light of deals, the retail area has lost admittance to pertinent bits of knowledge to support client dependability plans, AI-driven items, and administration suggestions. It has additionally impacted systems for promoting and business choices. A wide range of retail associations has been impacted by this absence of information, whether free or chain, blocks, concrete or internet business or start-up or laid out substances.
The global retail and customer merchandise has been adjusting beyond anyone's expectation. Undertakings and clients have begun understanding that computerized change is tied in with adopting an information-driven strategy for each part of their business to make an upper hand. For instance, for a retailer, computerized change may be tied in with giving continuous best proposals while clients are in actual stores or enhancing stock to give a superior on the web and in-store insight.
Advanced change in retail can help client maintenance and fulfillment by offering clients the administrations and items they need. The fourth modern transformation (Industry 4.0) is characterized by arising advances that obscure lines between the computerized and actual universes. Joined with strong investigation instruments, including situation examination, prescient learning calculations and representation, admittance to information is changing the way that organizations perform.
Organizations can now gather tremendous informational indexes from actual offices and resources continuously, execute progressed examinations to produce new experiences, and pursue more successful choices. The computerized upset is changing how items are planned, created, and conveyed to clients. It offers sgnificant ramifications for the retail esteem chain.
North America is expected to continue its dominance in the market with a projected CAGR of 17.6%. The retail analytics market has been witnessing an expansion in the number of next-generation purchasers, as well as an ascent in the utilization of social and versatile stages for purchasing. Due to these contemplations, shippers are using an abundance of psychographic information and investigation innovations to gather granular information and dig further into buyer requirements and inclinations. Retailers settle on shrewd promoting choices in view of information obtained through different web-based entertainment advancements, for example, offers and informing, that straightforwardly appeal to clients.
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United States held the largest share of the global market and is expected to reach a valuation of US$ 16.8 billion by the end of the forecast period. Conventional improvement options for physical store expansions have been rendered inactive with the boom in the internet. The way merchandising analytics is dealt with has changed because of online stages, provincial combinations, and overall market development.
Retailers entered the market because of furious competition from online platforms, which gave a clearer picture of combination, valuing, advancements, obtaining, renewal, and in-store arranging and execution. This has thusly expanded the development of the retail analytics market in the country.
Market revenue through Retail Analytics Software hold the highest revenue share and is predicted to increase at a CAGR of 17.4% during the forecast period. The capacity of the software to give detailed analytical information on key performance indicators of the business is expected to drive the market in the future years. Furthermore, the Software segment is reliably engaged in experimentation and development which works with the consolidation of new advancements that tackle issues related to retail business effectively. Such factors are expected to drive segment growth during the forecast period
The Retail Analytics industry is fiercely competitive, and top competitors are continually implementing new strategies to obtain market domination. Key players of the industry are Microsoft, IBM, Oracle, Salesforce, SAP, AWS, SAS Institute, Qlik, Manthan, Bridgei2i, MicroStrategy, Teradata, HCL, Fujitsu, Domo, Google, FLIR Systems, Information Builders, and 1010Data.
Some of the recent developments of key players in the Retail Analytics Market are as follows:
Similarly, recent developments related to companies offering Retail Analytics Market have been tracked by the team at Future Market Insights, which are available in the full report.
The retail analytics market CAGR for 2033 is 17.5%.
The market is estimated to reach US$ 55,247.6 billion by 2033.
Microsoft, IBM, and Oracle are key market players.
The market is estimated to secure a valuation of US$ 10,797.4 billion in 2023.
North America holds a significant share of the market.
1. Executive Summary 1.1. Global Market Outlook 1.2. Demand-side Trends 1.3. Supply-side Trends 1.4. Technology Roadmap Analysis 1.5. Analysis and Recommendations 2. Market Overview 2.1. Market Coverage / Taxonomy 2.2. Market Definition / Scope / Limitations 3. Market Background 3.1. Market Dynamics 3.1.1. Drivers 3.1.2. Restraints 3.1.3. Opportunity 3.1.4. Trends 3.2. Scenario Forecast 3.2.1. Demand in Optimistic Scenario 3.2.2. Demand in Likely Scenario 3.2.3. Demand in Conservative Scenario 3.3. Opportunity Map Analysis 3.4. Investment Feasibility Matrix 3.5. PESTLE and Porter’s Analysis 3.6. Regulatory Landscape 3.6.1. By Key Regions 3.6.2. By Key Countries 3.7. Regional Parent Market Outlook 4. Global Market Analysis 2018 to 2022 and Forecast, 2023 to 2033 4.1. Historical Market Size Value (US$ Million) Analysis, 2018 to 2022 4.2. Current and Future Market Size Value (US$ Million) Projections, 2023 to 2033 4.2.1. Y-o-Y Growth Trend Analysis 4.2.2. Absolute $ Opportunity Analysis 5. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Solution 5.1. Introduction / Key Findings 5.2. Historical Market Size Value (US$ Million) Analysis By Solution, 2018 to 2022 5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Solution, 2023 to 2033 5.3.1. Software 5.3.2. Service 5.3.2.1. Training & Consulting 5.3.2.2. Integration and Deployment 5.3.2.3. Managed Services 5.4. Y-o-Y Growth Trend Analysis By Solution, 2018 to 2022 5.5. Absolute $ Opportunity Analysis By Solution, 2023 to 2033 6. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Function 6.1. Introduction / Key Findings 6.2. Historical Market Size Value (US$ Million) Analysis By Function, 2018 to 2022 6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Function, 2023 to 2033 6.3.1. Customer Management 6.3.2. Merchandising 6.3.3. Store Operations 6.3.4. Supply Chain 6.3.5. Strategy & Planning 6.4. Y-o-Y Growth Trend Analysis By Function, 2018 to 2022 6.5. Absolute $ Opportunity Analysis By Function, 2023 to 2033 7. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Enterprise Size 7.1. Introduction / Key Findings 7.2. Historical Market Size Value (US$ Million) Analysis by Enterprise Size, 2018 to 2022 7.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast by Enterprise Size, 2023 to 2033 7.3.1. SMEs 7.3.2. Large Enterprises 7.4. Y-o-Y Growth Trend Analysis by Enterprise Size, 2018 to 2022 7.5. Absolute $ Opportunity Analysis by Enterprise Size, 2023 to 2033 8. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Deployment Model 8.1. Introduction / Key Findings 8.2. Historical Market Size Value (US$ Million) Analysis By Deployment Model, 2018 to 2022 8.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Deployment Model, 2023 to 2033 8.3.1. On-Premise 8.3.2. Cloud 8.4. Y-o-Y Growth Trend Analysis By Deployment Model, 2018 to 2022 8.5. Absolute $ Opportunity Analysis By Deployment Model, 2023 to 2033 9. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Field Crowdsourcing 9.1. Introduction / Key Findings 9.2. Historical Market Size Value (US$ Million) Analysis By Field Crowdsourcing, 2018 to 2022 9.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Field Crowdsourcing, 2023 to 2033 9.3.1. On-shelf availability 9.3.2. Documentation & Reporting 9.3.3. Promotion Campaign Management 9.3.4. Customer Insights 9.4. Y-o-Y Growth Trend Analysis By Field Crowdsourcing, 2018 to 2022 9.5. Absolute $ Opportunity Analysis By Field Crowdsourcing, 2023 to 2033 10. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Region 10.1. Introduction 10.2. Historical Market Size Value (US$ Million) Analysis By Region, 2018 to 2022 10.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2023 to 2033 10.3.1. North America 10.3.2. Latin America 10.3.3. Western Europe 10.3.4. Eastern Europe 10.3.5. South Asia and Pacific 10.3.6. East Asia 10.3.7. Middle East and Africa 10.4. Market Attractiveness Analysis By Region 11. North America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 11.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 11.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 11.2.1. By Country 11.2.1.1. The USA 11.2.1.2. Canada 11.2.2. By Solution 11.2.3. By Function 11.2.4. By Enterprise Size 11.2.5. By Deployment Model 11.2.6. By Field Crowdsourcing 11.3. Market Attractiveness Analysis 11.3.1. By Country 11.3.2. By Solution 11.3.3. By Function 11.3.4. By Enterprise Size 11.3.5. By Deployment Model 11.3.6. By Field Crowdsourcing 11.4. Key Takeaways 12. Latin America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 12.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 12.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 12.2.1. By Country 12.2.1.1. Brazil 12.2.1.2. Mexico 12.2.1.3. Rest of Latin America 12.2.2. By Solution 12.2.3. By Function 12.2.4. By Enterprise Size 12.2.5. By Deployment Model 12.2.6. By Field Crowdsourcing 12.3. Market Attractiveness Analysis 12.3.1. By Country 12.3.2. By Solution 12.3.3. By Function 12.3.4. By Enterprise Size 12.3.5. By Deployment Model 12.3.6. By Field Crowdsourcing 12.4. Key Takeaways 13. Western Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 13.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 13.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 13.2.1. By Country 13.2.1.1. Germany 13.2.1.2. United Kingdom 13.2.1.3. France 13.2.1.4. Spain 13.2.1.5. Italy 13.2.1.6. Rest of Western Europe 13.2.2. By Solution 13.2.3. By Function 13.2.4. By Enterprise Size 13.2.5. By Deployment Model 13.2.6. By Field Crowdsourcing 13.3. Market Attractiveness Analysis 13.3.1. By Country 13.3.2. By Solution 13.3.3. By Function 13.3.4. By Enterprise Size 13.3.5. By Deployment Model 13.3.6. By Field Crowdsourcing 13.4. Key Takeaways 14. Eastern Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 14.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 14.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 14.2.1. By Country 14.2.1.1. Poland 14.2.1.2. Russia 14.2.1.3. Czech Republic 14.2.1.4. Romania 14.2.1.5. Rest of Eastern Europe 14.2.2. By Solution 14.2.3. By Function 14.2.4. By Enterprise Size 14.2.5. By Deployment Model 14.2.6. By Field Crowdsourcing 14.3. Market Attractiveness Analysis 14.3.1. By Country 14.3.2. By Solution 14.3.3. By Function 14.3.4. By Enterprise Size 14.3.5. By Deployment Model 14.3.6. By Field Crowdsourcing 14.4. Key Takeaways 15. South Asia and Pacific Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 15.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 15.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 15.2.1. By Country 15.2.1.1. India 15.2.1.2. Bangladesh 15.2.1.3. Australia 15.2.1.4. New Zealand 15.2.1.5. Rest of South Asia and Pacific 15.2.2. By Solution 15.2.3. By Function 15.2.4. By Enterprise Size 15.2.5. By Deployment Model 15.2.6. By Field Crowdsourcing 15.3. Market Attractiveness Analysis 15.3.1. By Country 15.3.2. By Solution 15.3.3. By Function 15.3.4. By Enterprise Size 15.3.5. By Deployment Model 15.3.6. By Field Crowdsourcing 15.4. Key Takeaways 16. East Asia Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 16.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 16.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 16.2.1. By Country 16.2.1.1. China 16.2.1.2. Japan 16.2.1.3. South Korea 16.2.2. By Solution 16.2.3. By Function 16.2.4. By Enterprise Size 16.2.5. By Deployment Model 16.2.6. By Field Crowdsourcing 16.3. Market Attractiveness Analysis 16.3.1. By Country 16.3.2. By Solution 16.3.3. By Function 16.3.4. By Enterprise Size 16.3.5. By Deployment Model 16.3.6. By Field Crowdsourcing 16.4. Key Takeaways 17. Middle East and Africa Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country 17.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022 17.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033 17.2.1. By Country 17.2.1.1. GCC Countries 17.2.1.2. South Africa 17.2.1.3. Israel 17.2.1.4. Rest of MEA 17.2.2. By Solution 17.2.3. By Function 17.2.4. By Enterprise Size 17.2.5. By Deployment Model 17.2.6. By Field Crowdsourcing 17.3. Market Attractiveness Analysis 17.3.1. By Country 17.3.2. By Solution 17.3.3. By Function 17.3.4. By Enterprise Size 17.3.5. By Deployment Model 17.3.6. By Field Crowdsourcing 17.4. Key Takeaways 18. Key Countries Market Analysis 18.1. USA 18.1.1. Pricing Analysis 18.1.2. Market Share Analysis, 2022 18.1.2.1. By Solution 18.1.2.2. By Function 18.1.2.3. By Enterprise Size 18.1.2.4. By Deployment Model 18.1.2.5. By Field Crowdsourcing 18.2. Canada 18.2.1. Pricing Analysis 18.2.2. Market Share Analysis, 2022 18.2.2.1. By Solution 18.2.2.2. By Function 18.2.2.3. By Enterprise Size 18.2.2.4. By Deployment Model 18.2.2.5. By Field Crowdsourcing 18.3. Brazil 18.3.1. Pricing Analysis 18.3.2. Market Share Analysis, 2022 18.3.2.1. By Solution 18.3.2.2. By Function 18.3.2.3. By Enterprise Size 18.3.2.4. By Deployment Model 18.3.2.5. By Field Crowdsourcing 18.4. Mexico 18.4.1. Pricing Analysis 18.4.2. Market Share Analysis, 2022 18.4.2.1. By Solution 18.4.2.2. By Function 18.4.2.3. By Enterprise Size 18.4.2.4. By Deployment Model 18.4.2.5. By Field Crowdsourcing 18.5. Germany 18.5.1. Pricing Analysis 18.5.2. Market Share Analysis, 2022 18.5.2.1. By Solution 18.5.2.2. By Function 18.5.2.3. By Enterprise Size 18.5.2.4. By Deployment Model 18.5.2.5. By Field Crowdsourcing 18.6. United Kingdom 18.6.1. Pricing Analysis 18.6.2. Market Share Analysis, 2022 18.6.2.1. By Solution 18.6.2.2. By Function 18.6.2.3. By Enterprise Size 18.6.2.4. By Deployment Model 18.6.2.5. By Field Crowdsourcing 18.7. France 18.7.1. Pricing Analysis 18.7.2. Market Share Analysis, 2022 18.7.2.1. By Solution 18.7.2.2. By Function 18.7.2.3. By Enterprise Size 18.7.2.4. By Deployment Model 18.7.2.5. By Field Crowdsourcing 18.8. Spain 18.8.1. Pricing Analysis 18.8.2. Market Share Analysis, 2022 18.8.2.1. By Solution 18.8.2.2. By Function 18.8.2.3. By Enterprise Size 18.8.2.4. By Deployment Model 18.8.2.5. By Field Crowdsourcing 18.9. Italy 18.9.1. Pricing Analysis 18.9.2. Market Share Analysis, 2022 18.9.2.1. By Solution 18.9.2.2. By Function 18.9.2.3. By Enterprise Size 18.9.2.4. By Deployment Model 18.9.2.5. By Field Crowdsourcing 18.10. Poland 18.10.1. Pricing Analysis 18.10.2. Market Share Analysis, 2022 18.10.2.1. By Solution 18.10.2.2. By Function 18.10.2.3. By Enterprise Size 18.10.2.4. By Deployment Model 18.10.2.5. By Field Crowdsourcing 18.11. Russia 18.11.1. Pricing Analysis 18.11.2. Market Share Analysis, 2022 18.11.2.1. By Solution 18.11.2.2. By Function 18.11.2.3. By Enterprise Size 18.11.2.4. By Deployment Model 18.11.2.5. By Field Crowdsourcing 18.12. Czech Republic 18.12.1. Pricing Analysis 18.12.2. Market Share Analysis, 2022 18.12.2.1. By Solution 18.12.2.2. By Function 18.12.2.3. By Enterprise Size 18.12.2.4. By Deployment Model 18.12.2.5. By Field Crowdsourcing 18.13. Romania 18.13.1. Pricing Analysis 18.13.2. Market Share Analysis, 2022 18.13.2.1. By Solution 18.13.2.2. By Function 18.13.2.3. By Enterprise Size 18.13.2.4. By Deployment Model 18.13.2.5. By Field Crowdsourcing 18.14. India 18.14.1. Pricing Analysis 18.14.2. Market Share Analysis, 2022 18.14.2.1. By Solution 18.14.2.2. By Function 18.14.2.3. By Enterprise Size 18.14.2.4. By Deployment Model 18.14.2.5. By Field Crowdsourcing 18.15. Bangladesh 18.15.1. Pricing Analysis 18.15.2. Market Share Analysis, 2022 18.15.2.1. By Solution 18.15.2.2. By Function 18.15.2.3. By Enterprise Size 18.15.2.4. By Deployment Model 18.15.2.5. By Field Crowdsourcing 18.16. Australia 18.16.1. Pricing Analysis 18.16.2. Market Share Analysis, 2022 18.16.2.1. By Solution 18.16.2.2. By Function 18.16.2.3. By Enterprise Size 18.16.2.4. By Deployment Model 18.16.2.5. By Field Crowdsourcing 18.17. New Zealand 18.17.1. Pricing Analysis 18.17.2. Market Share Analysis, 2022 18.17.2.1. By Solution 18.17.2.2. By Function 18.17.2.3. By Enterprise Size 18.17.2.4. By Deployment Model 18.17.2.5. By Field Crowdsourcing 18.18. China 18.18.1. Pricing Analysis 18.18.2. Market Share Analysis, 2022 18.18.2.1. By Solution 18.18.2.2. By Function 18.18.2.3. By Enterprise Size 18.18.2.4. By Deployment Model 18.18.2.5. By Field Crowdsourcing 18.19. Japan 18.19.1. Pricing Analysis 18.19.2. Market Share Analysis, 2022 18.19.2.1. By Solution 18.19.2.2. By Function 18.19.2.3. By Enterprise Size 18.19.2.4. By Deployment Model 18.19.2.5. By Field Crowdsourcing 18.20. South Korea 18.20.1. Pricing Analysis 18.20.2. Market Share Analysis, 2022 18.20.2.1. By Solution 18.20.2.2. By Function 18.20.2.3. By Enterprise Size 18.20.2.4. By Deployment Model 18.20.2.5. By Field Crowdsourcing 18.21. GCC Countries 18.21.1. Pricing Analysis 18.21.2. Market Share Analysis, 2022 18.21.2.1. By Solution 18.21.2.2. By Function 18.21.2.3. By Enterprise Size 18.21.2.4. By Deployment Model 18.21.2.5. By Field Crowdsourcing 18.22. South Africa 18.22.1. Pricing Analysis 18.22.2. Market Share Analysis, 2022 18.22.2.1. By Solution 18.22.2.2. By Function 18.22.2.3. By Enterprise Size 18.22.2.4. By Deployment Model 18.22.2.5. By Field Crowdsourcing 18.23. Israel 18.23.1. Pricing Analysis 18.23.2. Market Share Analysis, 2022 18.23.2.1. By Solution 18.23.2.2. By Function 18.23.2.3. By Enterprise Size 18.23.2.4. By Deployment Model 18.23.2.5. By Field Crowdsourcing 19. Market Structure Analysis 19.1. Competition Dashboard 19.2. Competition Benchmarking 19.3. Market Share Analysis of Top Players 19.3.1. By Regional 19.3.2. By Solution 19.3.3. By Function 19.3.4. By Enterprise Size 19.3.5. By Deployment Model 19.3.6. By Field Crowdsourcing 20. Competition Analysis 20.1. Competition Deep Dive 20.1.1. Microsoft 20.1.1.1. Overview 20.1.1.2. Product Portfolio 20.1.1.3. Profitability by Market Segments 20.1.1.4. Sales Footprint 20.1.1.5. Strategy Overview 20.1.1.5.1. Marketing Strategy 20.1.2. IBM 20.1.2.1. Overview 20.1.2.2. Product Portfolio 20.1.2.3. Profitability by Market Segments 20.1.2.4. Sales Footprint 20.1.2.5. Strategy Overview 20.1.2.5.1. Marketing Strategy 20.1.3. Oracle 20.1.3.1. Overview 20.1.3.2. Product Portfolio 20.1.3.3. Profitability by Market Segments 20.1.3.4. Sales Footprint 20.1.3.5. Strategy Overview 20.1.3.5.1. Marketing Strategy 20.1.4. Salesforce 20.1.4.1. Overview 20.1.4.2. Product Portfolio 20.1.4.3. Profitability by Market Segments 20.1.4.4. Sales Footprint 20.1.4.5. Strategy Overview 20.1.4.5.1. Marketing Strategy 20.1.5. SAP 20.1.5.1. Overview 20.1.5.2. Product Portfolio 20.1.5.3. Profitability by Market Segments 20.1.5.4. Sales Footprint 20.1.5.5. Strategy Overview 20.1.5.5.1. Marketing Strategy 20.1.6. AWS 20.1.6.1. Overview 20.1.6.2. Product Portfolio 20.1.6.3. Profitability by Market Segments 20.1.6.4. Sales Footprint 20.1.6.5. Strategy Overview 20.1.6.5.1. Marketing Strategy 20.1.7. SAS Institute 20.1.7.1. Overview 20.1.7.2. Product Portfolio 20.1.7.3. Profitability by Market Segments 20.1.7.4. Sales Footprint 20.1.7.5. Strategy Overview 20.1.7.5.1. Marketing Strategy 20.1.8. Qlik 20.1.8.1. Overview 20.1.8.2. Product Portfolio 20.1.8.3. Profitability by Market Segments 20.1.8.4. Sales Footprint 20.1.8.5. Strategy Overview 20.1.8.5.1. Marketing Strategy 20.1.9. Manthan 20.1.9.1. Overview 20.1.9.2. Product Portfolio 20.1.9.3. Profitability by Market Segments 20.1.9.4. Sales Footprint 20.1.9.5. Strategy Overview 20.1.9.5.1. Marketing Strategy 20.1.10. Bridgei2i 20.1.10.1. Overview 20.1.10.2. Product Portfolio 20.1.10.3. Profitability by Market Segments 20.1.10.4. Sales Footprint 20.1.10.5. Strategy Overview 20.1.10.5.1. Marketing Strategy 21. Assumptions & Acronyms Used 22. Research Methodology
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Retail Analytics Market
