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.
Principal Consultant
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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