Big Data Analytics in Retail Market Expected to Reach $25.56 Billion by 2028

According to a recent report published by Allied Market Research, titled, “Big Data Analytics in Retail Market by component, deployment, enterprise size, and application: global opportunity analysis and industry forecast, 2020–2027,” 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 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.

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.

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..

The current estimation of 2027 is projected to be higher than pre-COVID-19 estimates. The COVID-19 outbreak has high impact on growth of the market. As the COVID-19 pandemic continues to grow around the world, retail companies are leveraging big data analytics to analyze business continuity scenarios and planning sustainable strategies according to disruptive retail industry trends. The big data analytics in retail industry is majorly witnessing trends such as personalization of services, omni channel experience, predictive analytics demand, and dynamic pricing models. Furthermore, consumers are inclined toward predictive analytics to make their enterprise future ready. This is attributed to the fact that predictive analytics help in optimizing performance & reliability of computing resources to investigate, assess, and address financial risks. As per a survey by Drenser Advisory, in April 2020, almost 48% companies kept their data analytics spending same as 2019 and around 40% respondents said they are increasing their data analytics budget than that of 2019.

Key Findings Of The Study

• By component, in 2020 the software segment dominated the Big Data Analytics in Retail Market size. However, the services segment is expected to exhibit significant growth during the forecast period.
• On the basis of deployment type, the on-premise segment accounted for the highest revenue in 2020, however, the cloud segment is expected to witness highest growth rate during the forecast period.
• On the basis of application, the supply chain operations management segment accounted for the highest revenue in 2019, however, the customer analytics segment is expected to witness highest growth rate during the forecast period.
• Region wise, the Big Data Analytics in Retail Market was dominated by North America. However, Asia-Pacific is expected to witness significant growth in the upcoming years. 

Some of the key players profiled in the report include Alteryx Inc., IBM, Microsoft, Microstrategy Inc., Oracle Corporation, Qlik Technologies Inc., RetailNext, SAP SE, SAS institute, and Teradata. This study includes market trends, Big Data Analytics in Retail Market analysis, and future estimations to determine imminent investment pockets.