Big Data Analytics in Retail Market: Size, Share, Growth, Analysis, Key Players, Revenue, Growth | Valuates Reports

Big Data Analytics in Retail Market Statistics 2028
The global big data analytics in retail market size was valued at US$ 4854 million in 2020 and is projected to reach US$ 25560 million by 2028 registering a CAGR of 23.1% from 2021 to 2028. Key drivers of big data analytics in the retail market include increased spending on big data analytics tools, a rise in the need to deliver a personalized customer experience to increase sales, and an increase in the growth of the eCommerce sector. Big data analytics is used in every stage of the retail industry to understand customer behavior, predict demand and optimize pricing.

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Other factors contributing to the growth of big data analytics in the retail market are the growing need for retaining customer loyalty, customer journey analytics, supply chain movement measurement, and the integration of advanced technologies such as machine learning AI. Big data analytics is also widely used to measure and predict forecast trends, optimize financial management and increase customer satisfaction. Wide adoption and usage are driving the growth of the market.
Big data analytics in retail market share analysis
Based on region, Asia-pacific is expected to hold the largest big data analytics in retail market share during the forecast period due to widespread adoption of cloud-based big data analytics in retail software, increase in internet penetration leading to more 4G connections, and rising e-commerce companies.
Based on deployment, the on-premise segment is expected to provide lucrative opportunities for growth in the big data analytics retail market share during the forecast period because on-premises software provides enhanced security features and easy deployment enabling wide-scale adoption in financial institutions and other data-sensitive organizations.

Based on components, the software segment is expected to hold the largest big data analytics in retail market share during the forecast period as this new big data analytics in retail software allows integration of AI and machine learning for better personalization allowing retail companies to enhance their customer experience and provide more enhanced recommendations to customers.

adoption of smartphones, increase in popularity of e-commerce companies, change in customer purchase patterns, and strong & growing competition among retail vendors in the region. These technologies have led to a great amount of data exchange on mobile and internet networks, and thus, enables enterprises to capture huge volumes of information about customer interactions. Further, many retail analytics vendors who have a strong presence in North America are expanding their business across Asia-Pacific, which creates lucrative opportunities for the big data analytics in retail market.
The key players operating in the global big data analytics in retail market analysis include Alteryx Inc., IBM, Microsoft, Microstrategy Inc., Oracle Corporation, Qlik Technologies Inc., RetailNext, SAP SE, SAS institute, and Teradata.

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Key Benefits for Stakeholders
• This study includes the analytical depiction of the global big data analytics in retail market forecast and trends to determine the imminent investment pockets.
• The report presents information related to key drivers, restraints, and big data analytics in retail market opportunity.
• The current market size is quantitatively analyzed from 2019 to 2027 to highlight the financial competency of the big data analytics in retail industry.
• Porter's five forces analysis illustrates the potency of buyers & suppliers in the market
Big Data Analytics in Retail Market Segments
Big Data Analytics in Retail Market by Component
• Software
• Services

Big Data Analytics in Retail Market by Deployment
• On-premise
• Cloud

Big Data Analytics in Retail Market by Enterprise Size
• Large Enterprises
• Small & Medium Enterprises (SMEs)

Big Data Analytics in Retail Market by Application
• Sales and marketing analytics
• Supply chain operations management
• Merchandising analytics
• Customer analytics
• Others

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