Retail Analytics Market Forecasts By Leading Key Players: Adobe Systems, Teradata, IBM Corporation, Angoss Software Corporation, Tableau Software, Oracle Corporation
Retail Analytics Market has been rapidly gaining momentum in recent times, subject to the escalating requirement of thorough insights to efficiently handle and forecast future performance. Retail analytics undoubtedly help suppliers to predict consumer needs, facilitating retailers to set appropriate costs for the right products at the right time, at the right location.
In addition, data analytics help in effectively devising product pricing strategies and integrating the same in mainstream retailing operations, thereby increasing their demand and inherently providing a boost to the overall retail analytics industry. Applying retail analytics in coining pricing strategies has of late become the need of the hour, given the rise in the complexity witnessed across the retail industry.
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Top Key Players:
Major Key Players in retail analytics market such as Adobe Systems, Teradata, IBM Corporation, Angoss Software Corporation, Tableau Software, and Oracle Corporation have made significant contributions to the retail analytics market in recent times
Market share calculation in this report is not done based on companies covered in it. The calculation of retail analytics market share is done based on a regional approach and countries covered. On request, additional companies can be covered as well as regional data for particular country / countries can be provided.
Regional Segmentations: -
The Asia Pacific retail analytics market is expected to witness significant growth during the forecast timeframe, owing to the expansion of organized retail sector. In India, the exponential increase of e-commerce industry size and smartphones to buy consumer goods has prompted the need for data-driven business intelligence solutions.
This has attracted big players such as HCL and IBM, in addition to the emergence of regional players including Manthan Systems, BRIDGEi2i Analytics Solutions, and Fractal Analytics.
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Partial Chapter of the Table of Content
Chapter 1. Methodology and Scope
1.1.1. Initial data exploration
1.1.2. Statistical model and forecast
1.1.3. Industry insights and validation
1.1.4. Definition and forecast parameters
1.2. Data Sources
Chapter 2. Executive Summary
2.1. Retail analytics industry 3600 synopses, 2013 - 2024
2.1.1. Business trends
2.1.2. Regional trends
2.1.3. Solution trends
2.1.4. Function trends
2.1.5. Deployment trends
Chapter 3. Retail Analytics Industry Insights
3.1. Industry segmentation
3.2. Industry landscape, 2013 - 2024
3.3. Industry ecosystem analysis
3.3.1. Vendor matrix
3.4. Technology landscape
3.4.1. Advanced Analytics
3.4.4. Trade Promotion Optimization (TPO)
3.4.5. Optimization & collaboration
3.5. Retail industry outlook
3.5.1. North America
3.5.3. Asia Pacific
3.5.4. Latin America
3.6. Regulatory landscape
3.7. Industry impact forces
3.7.1. Growth drivers
184.108.40.206. Growing e-commerce industry in China and India
220.127.116.11. Increasing penetration of big data in the retail industry in the U.S.
18.104.22.168. Growing proliferation of smartphones in Asia Pacific
22.214.171.124. Increasing competition among retailers and growing need for differentiation in North America
126.96.36.199. New generation of highly informed and demanding customers in Europe
188.8.131.52. Growing need for analytics maturity in Asia Pacific
184.108.40.206. Growing adoption of ecommerce in retail in Latin America
220.127.116.11. Expanding retail sector in Middle East and Africa
3.7.2. Industry pitfalls & challenges
18.104.22.168. Lack of technical expertise
22.214.171.124. Data privacy concerns
3.8. Growth potential analysis
3.8.1. Business models
3.9. Porter's analysis
3.9.1. Supplier power
3.9.2. Buyer power
3.9.3. Threat of new entrants
3.9.4. Threat of substitutes
3.9.5. Internal rivalry
3.10. Competitive landscape, 2016
3.10.1. Strategy dashboard
3.11. PESTEL analysis
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Growing demand for customized products and constantly changing consumer requirements is expected to drive the supply chain analytics software demand. Lack of inventory space has compelled retailers gather insights from the supply chain data to optimize operations and reduce costs. Reporting and visualization tools are anticipated to witness increase in demand, owing to the growing need to analyze data in an easy-to-read format in lesser time. Smart visual insights and implementation artificial intelligent dramatically reduces the time to insight and arrive at decision. Companies are inclined towards developing such tools that make data access and visualization easy contributing towards the retail analytics market growth.
The demand for on-premise solutions is expected to gain momentum from the small and unorganized retailers which is expected to drive the retail analytics market. These solutions are ideal for the organization with limited customer foot-fall and limited geographical presence. Increasing competition among the solution providers has compelled them to introduce customized on-premise solutions that cater to specific demands of the retailers.
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