Big Data Analytics in Retail Market Insights - 2027
The global big data analytics in retail market size was valued at $4,854 million in 2020, and is projected to reach $25,560 million by 2028, registering a CAGR of 23.1% from 2021 to 2028. Big data analytics in retail enables detecting customer behavior, discovering customer shopping patterns and trends, improving quality of customer service, and achieving better customer retention and satisfaction. It can be used by retailers for customer segmentation, customer loyalty analysis, pricing analysis, cross selling, supply chain management, demand forecasting, market basket analysis, and finance and fixed asset management.
In accordance with several interviews that were conducted of top level CXOs, adoption of big data analytics in retail software has increased over time to propel decision-making capability of organizations and to improve business insights of retail companies. In addition, ability of big data analytics in retail software to provide different opportunities for business and gain new insights to run business efficiently is increasing its popularity among end users.
Segment review
The big data analytics in retail market is segmented on the basis of component, deployment, organization size, application, and region. By component, the market is categorized into software and services. On the basis of deployment, it is classified into on-premise and cloud. As per organization size, market is divided into large enterprises and small & medium sized enterprises (SMEs). Depending on application, it is divided into sales & marketing analytics, supply chain operations management, merchandising analytics, customer analytics, and others. By region, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
In 2019, the global big data analytics in retail market share was dominated by the software segment, and is expected to maintain its dominance in the upcoming years the software segment includes different big data analytics tools and platforms for storing, managing, and analyzing valuable information collected form large data sets in retail companies. These solutions help organizations leverage best return from their data, either by making better decisions or bringing in more revenue. Retail companies are presently focused on traditional descriptive and exploratory analytics to automated decision making driven by advanced analytics and machine learning. These new big data analytics in retail software are improving personalization at a transformational scale by allowing retail companies to enhance customer experience and provide more customized recommendations to customers. Thus, integration of advanced technologies such as AI is expected to boost growth of this segment in the coming years.
By deployment, the on-premise deployment model for big data analytics in retail enables installation of software and permits applications to run on systems present in premises of an organization instead of putting on server space or cloud. These types of software offer enhanced security features, which drive their adoption in largescale financial institutions and other data sensitive organizations, where security is priority. On-premise-based software is known for better maintenance of servers and continuous system facilitates implementation of these big data analytics in retail. In addition, on-premise deployment mode is considered widely useful in large enterprises as it involves a significant investment to implement and organizations need to purchase interconnected servers as well as software to manage the system. Furthermore, better security of data as compared to cloud-based software promotes its adoption among organizations.
Asia-Pacific is one of the fastest growing regions, owing to adoption of cloud-enabled big data analytics in retail software are expected to witness growth in this region, owing to increase in popularity of fast internet connectivity including 4G connections, growing
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 report focuses on growth prospects, restraints, and big data analytics in retail market analysis. The study provides Porter’s five forces analysis of the internet advertising industry to understand impact of various factors such as bargaining power of suppliers, competitive intensity of competitors, threat of new entrants, threat of substitutes, and bargaining power of buyers on the big data analytics in retail market trends.
Impact of COVID-19 on Big Data Analytics in Retail Market (Pre and Post Analysis):
Size of the big data analytics in retail market is estimated to grow from 5,955 million in 2021, and is projected to reach $25,560 million by 2028, at a CAGR of 23.1%. The current estimation of 2028 is projected to be higher than pre-COVID-19 estimates. COVID-19 pandemic has bought a positive impact on the big data analytics in retail market, achieving a growth rate of 3–5% in the 2021. The global big data in retail analytics market witnessed significant growth in the recent past, and is expected to exhibit similar trend in the coming years. Although the retail sector has witnessed decline in growth rate, retail companies are still focused on studying customer trends and analyzing future market dynamics. Thus, retail companies are expected to continue their investments on big data analytics. Retail data analytics can help companies stay ahead of shopper trends by applying customer analytics in retail to uncover, interpret, and act on meaningful data insights, including in-store and online shopper patterns. In addition, strong awareness about data analytics benefits, unaffected data analytics budget by enterprises, and need to analyze risks has increased demand for predictive analytics at a significant rate, which, in turn, supports growth of the market. Economically, it generated $4,437.3 million in 2019, and is expected to reach $17,851.7 in 2027.
Top Impacting Factors
Increase in spending on big data analytics tools, rise in need to deliver personalized customer experience to increase sales, increase in growth of e-commerce sector, growth in demand for predictive analytics in retail, and integration of new technologies such as IoT, AI, and machine learning in big data analytics in retail are the major factors that propel the market growth. However, issues in collecting and collating data from disparate systems are expected to hamper the market growth during the forecast period.
By Component
Service segment is projected as one of the most lucrative segments.
Growth of E-Commerce Sector
With big data analytics in retail software, retailers can improve performance of their online stores to generate more revenue. Utilizing website analytics, clickstream data and heatmaps studies, retailers can optimize product landing pages to ensure better engagement and conversion rates. Personalized product recommendations and offers based on historic web footprints of customers increase chances of click throughs and sales. Items can be promoted by inspecting data points such as product browsing activity by region, user feedback and reviews, saved wish lists, or items in abandoned shopping carts. Further, presently, customers are more connected than ever before, owing to proliferation of smartphones. Thus, customers can access any information to consumer products using channels such as mobile, social media, and e-commerce sites. Thus, to understand buying decisions of customers, companies are utilizing customer journey analytics. This, in turn, drives growth of the big data analytics in retail market.
Increase in spending on big data analytics tools
Retailers across the globe are increasingly adopting big data technologies to generate more value and data driven decision making. Companies are leveraging data generated to improve customer facing experiences, employee productivity, operational improvement, and product innovation. As per a survey of 100 U.S. retailers published by Microsoft, 33% retailers invested in analytics capabilities in 2017, and 29% respondents were planning to invest in big data analytics in 2018. Mostly these retailers were seeking to utilize these tools for forecasting, personalization, marketing, price optimization, and merchandizing. As per a study conducted by Forbes in 2018, customer analytics, operational analytics, and fraud & compliance are some of the top use cases for big data in the retail industry. It has emerged as the most important information system for CEOs and they are now viewing data and analytics software as directly contributing to organizational profitability. Thus, retail companies are increasingly focused on adopting big data analytics software and embedding it in existing workflows of organizations.
By Region
Asia-Pacific is projected as one of the most significant region.
Key Benefits For Stakeholders
- The study provides an in-depth analysis of the big data analytics in retail market along with current trends and future estimations to elucidate imminent investment pockets.
- Information about key drivers, restrains, and opportunities and their impact analysis on the market size is provided in the report.
- Porter’s five forces analysis illustrates the potency of buyers and suppliers operating in the industry.
- The quantitative analysis of big data in retail storage market for the period 2020-2028 is provided to determine the market potential.
Big Data Analytics in Retail Market Report Highlights
| Aspects | Details |
 By Component |
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 By Deployment |
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 By Organization Size |
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 By Application |
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 By Region |
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 Key Market Players | TERADATA CORPORATION, ADOBE INC., TABLEAU SOFTWARE, SAS INSTITUTE INC., INTERNATIONAL BUSINESS MACHINES CORPORATION, SISENSE INC., ORACLE CORPORATION, TIBCO SOFTWARE INC., SAP SE, CISCO SYSTEMS, INC. |
Analyst Review
According to CXOs of major companies, the big data in retail market is experiencing a rapid growth, as retailers across the globe are increasingly adopting big data technologies to generate more value and data driven decision making. Companies are leveraging data generated to improve customer facing experiences, employee productivity, operational improvement, and product innovation. As per a survey of 100 U.S. retailers published by Microsoft, 33% retailers invested in analytics capabilities in 2018 and 29% respondents were planning to invest in big data analytics in 2019. Mostly these retailers were seeking to utilize these tools for forecasting, personalization, marketing, price optimization, and merchandizing. As per a study conducted by Forbes in 2017, customer analytics, operational analytics, and fraud & compliance are some of the top use cases for big data in the retail industry. It has emerged as most important information system for CEOs and they are now viewing data and analytics software as directly contributing to organizational profitability. Thus, retail companies are increasingly focused on adopting big data analytics software and embedding it in existing workflows of organizations.
According to CXOs of leading companies, the big data in retail market is experiencing a colossal shift. With big data analytics in retail software, retailers can improve performance of their online stores to generate more revenue. In addition, by utilizing website analytics, clickstream data, and heat map studies, retailers can optimize product landing pages to ensure better engagement and conversion rates. Personalized product recommendations and offers based on historic web footprints of customers increase chances of click through and sales. Items can be promoted by inspecting data points such as product browsing activity by region, user feedback and reviews, saved wish lists, or items in abandoned shopping carts. Further, presently, customers are more connected than ever before, owing to proliferation of smartphones. Thus, customers can access any information to consumer products using channels such as mobile, social media, and e-commerce sites. Thus, to understand buying decisions of customers, companies are utilizing customer journey analytics. This, in turn, drives growth of the big data analytics in retail market.
The big data in retail market is competitive and comprises a number of regional and global vendors competing based on factors such as cost of solutions & services, reliability, efficiency of products, and support services. The market is concentrated with major players consuming 30–45% of the share. The degree of concentration is expected to remain same during the forecast period. Furthermore, as IoT becomes more significant, increasing number retailers are starting to equip their stores with sensors that can sense when a nearby consumer has the store’s app installed on their smart device. From this, retailers are able to send timely offers to influence a shopper’s decision to purchase their products or introduce customers to their new products. Thus, owing to such advantages of big data analytics in retail software, the market is expected to grow during the forecast period.
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