Big Data Analytics in Retail Market Size Expected to Reach $25,560 million by 2028
The global ðð¢ð ðƒðšððš ð€ð§ðšð¥ð²ðð¢ðœð¬ ð¢ð§ ð‘ðžððšð¢ð¥ ðŒðšð«ð¤ðžð 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. Rise in spending on big
data analytics tools, increase in need to deliver personalized customer
experience to increase sales, surge in adoption of customer-centric strategies,
and rise in awareness regarding benefits of big data analytics in retail are
major factors that fuel growth of the big data analytics in retail market. In
addition, rise in growth of the e-commerce sector also propels growth of this
market. However, issues in collecting and collating data from disparate systems
are expected to hinder the big data analytics in retail market growth. On the
contrary, integration of new technologies such as machine learning and AI in
big data analytics in retail is expected to provide lucrative opportunities for
the market growth in the coming years.
Big data analytics in retail helps in 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, finance and fixed asset management
and more.
although the on-premise big data analytics in
retail deployment is considerable in Europe,
penetration and availability of cloud for mass users are expected to open-up
significant opportunities for growth of the big data analytics in retail
market. Low operational costs associated with cloud-based big data analytics in
retail is expected to influence various medium & small sized enterprises to
implement cloud enabled big data analytics in retail and extend support for
growth of big data analytics in retail. Further, retail data analytics brings
value to decision-making and provides actionable insights, giving retail
companies competitive advantages and enabling them to chart cost structures
more efficiently.
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.
Based on region, the market is studied across
regions including Asia-Pacific, North America, Europe, and LAMEA. The region
across North America held the largest market share in 2020, holding nearly
two-fifths of the total share, and is expected to dominate in terms of revenue
by 2028. Simultaneously, the Asia-Pacific region is estimated to exhibit the
largest CAGR of 27.4% during the forecast period.
Based on application, the supply chain operations management
segment accounted for the highest share in 2020, holding nearly one-third of
the global big data analytics in retail market, and is expected to maintain its
lead throughout the forecast period. However, the customer analytics segment is
estimated to cite the highest CAGR of 26.3% from 2021 to 2028.
Rise in expenditure on big data analytics tools,
surge in need to deliver personalized customer experience to increase sales,
and growth of e-commerce sector drive the growth of the global ðð¢ð ðƒðšððš ð€ð§ðšð¥ð²ðð¢ðœð¬ ð¢ð§ ð‘ðžððšð¢ð¥ ðŒðšð«ð¤ðžð.
However, collecting and collating the data from disparate systems hamper the
market growth. Moreover, integration of new technologies such as IoT, AI and
machine learning in big data analytics in retail and growing demand of
predictive analytics in retail expected to usher a plethora of opportunities in
the future.
ð“ð¡ðž ð¤ðžð² ð©ð¥ðšð²ðžð«ð¬ in this space are ðŽð‘ð€ð‚ð‹ð„ ð‚ðŽð‘ððŽð‘ð€ð“ðˆðŽð, ð€ðƒðŽðð„ ðˆðð‚., ð“ðˆðð‚ðŽ ð’ðŽð…ð“ð–ð€ð‘ð„ ðˆðð‚., ð’ð€ð’ ðˆðð’ð“ðˆð“ð”ð“ð„ ðˆðð‚., ð“ð€ðð‹ð„ð€ð” ð’ðŽð…ð“ð–ð€ð‘ð„, ð’ð€ð ð’ð„, ð’ðˆð’ð„ðð’ð„ ðˆðð‚., ð“ð„ð‘ð€ðƒð€ð“ð€ ð‚ðŽð‘ððŽð‘ð€ð“ðˆðŽð, ðˆðð“ð„ð‘ðð€ð“ðˆðŽðð€ð‹ ðð”ð’ðˆðð„ð’ð’ ðŒð€ð‚ð‡ðˆðð„ð’ ð‚ðŽð‘ððŽð‘ð€ð“ðˆðŽð, ð‚ðˆð’ð‚ðŽ ð’ð˜ð’ð“ð„ðŒð’, ðˆðð‚.
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