New York, Jan. 16, 2024 (GLOBE NEWSWIRE) -- Introduction and Definition of the Market Retail analytics refers to the systematic analysis of data generated within the retail industry to extract valuable insights that can be applied to enhance decision-making. This process involves the utilization of advanced analytics tools and technologies to examine consumer behavior, improve inventory management, and optimize operational efficiency. By harnessing data from various sources such as customer demographics, point-of-sale transactions, and online interactions, retailers can gain a more comprehensive understanding of market trends, consumer preferences, and performance metrics.
Embracing a data-driven approach empowers businesses to improve decision-making processes, streamline operations, and provide personalized shopping experiences for their customers.The global retail analytics market is forecast to expand at a CAGR of 10.9% and thereby increase from a value of US$17.4 Billion in 2023, to US$36 Billion by the end of 2030.The global retail analytics market is influenced by a range of factors. Firstly, the rapid growth of e-commerce has led to a massive influx of data, compelling retailers to leverage analytics for gaining a competitive edge. Additionally, the increasing demand for tailored customer experiences has driven the adoption of analytics tools to better understand and anticipate consumer preferences.
Furthermore, the need for effective inventory management and optimization of supply chains has fueled the adoption of analytics solutions to reduce costs and enhance overall efficiency. Retailers are increasingly recognizing the pivotal role of data-driven insights in achieving a competitive advantage. Consequently, the retail analytics market is expected to witness continuous expansion, driven by the ongoing pursuit of operational efficiency and customer-centric strategies.
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Retail Analytics Market Report Scope:
| Report Coverage | Details | |
| Market Revenue 2023 | US$17.4 Bn | |
| Estimated Revenue 2030 | US$36.0 Bn | |
| Growth Rate – CAGR | 10.9% | |
| Forecast Period | 2023-2030 | |
| No. of Pages | 237 Pages | |
Key Market Segments Covered |
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Regions Covered |
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Key Companies Profiled |
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Market Dynamics:
Drivers: Accelerating Digital Transformation for Enhanced Customer Experience and Retail Operations
The global retail and consumer goods sector are swiftly adapting to digitalization, a transformation that emphasizes data-driven strategies to gain a competitive edge. This shift entails real-time offers for in-store customers, optimized inventory management for improved online and offline experiences, and a focus on customer satisfaction and retention. The convergence of digital and physical realms in the fourth industrial revolution, combined with advanced analytics tools, is revolutionizing data utilization. Real-time data collection and analysis empower companies to gain fresh insights and make more informed decisions, reshaping product design, development, and delivery across the retail value chain.
Restraint: Technology Adoption Challenges in the Unorganized Retail Sector
While larger retailers harness technology for various aspects like billing, inventory management, and supply chain, the unorganized retail sector faces challenges in technology adoption. Budget constraints, coupled with the hidden costs of maintaining legacy systems, limit their capacity for innovation and digital transformation. The sector, particularly in rural and remote areas, is hindered by high capital costs and limited access to funding, making it difficult to compete with well-funded offline and e-commerce competitors.
Opportunity: Data Analytics Proliferation Amid COVID-19
Organizations are rapidly establishing analytics capabilities to navigate the challenges posed by COVID-19 and prepare for an uncertain future. Data and analytics are pivotal in predicting and reacting to shifting consumer behaviors during and after the pandemic. With increasing market competitiveness, data analytics is integrated throughout the retail process to track emerging products, forecast sales, predict demand, and optimize product placements. Personalized marketing campaigns based on customer data and preferences are facilitated through analytics, enhancing customer engagement and satisfaction.
Challenge: Data Deficiency Post-COVID-19
The retail sector, like others heavily reliant on customer behavior, has been profoundly impacted by the COVID-19 pandemic. Lockdown-induced changes in consumer behavior have disrupted sales and created a data deficit, affecting the ability to sustain customer loyalty programs, AI-driven recommendations, marketing strategies, and business decisions. Independent or chain retailers, brick-and-mortar or e-commerce businesses, startups, and established entities alike have grappled with the loss of crucial data.
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Analyst Assessment:
In light of the ongoing evolution within the retail analytics sector, top executives (CXOs) are diligently assessing the opportunities and obstacles presented by this emerging technology. The retail analytics industry has experienced rapid growth in response to the immense data generated by consumers both in brick-and-mortar stores and online shopping platforms, driving the demand for advanced analytics and decision-making solutions. CXOs are carefully weighing the potential advantages that retail analytics can bring, including an enhanced customer experience, heightened operational efficiency, and cost optimization.
Furthermore, retail analytics holds the promise of conferring a competitive edge, a vital asset in the fiercely competitive retail landscape. These factors are anticipated to create attractive growth prospects in the market in the foreseeable future. Moreover, by scrutinizing data related to inventory levels, supply chain performance, and workforce management, CXOs can pinpoint areas ripe for improvement, streamline processes, and curtail expenses. Additionally, retail analytics empowers businesses to optimize resource allocation, identify operational bottlenecks, and augment overall operational efficiency.
Nevertheless, CXOs are not oblivious to the challenges associated with retail analytics, foremost among them being data quality and integration. These primary concerns revolve around ensuring the reliability and uniformity of data, making it a complex task for organizations to harmonize and cleanse data for analytical purposes. Furthermore, the effective implementation and utilization of retail analytics necessitate a skilled workforce proficient in data analysis, statistical modeling, and data visualization—a potential impediment to market growth. Additionally, successful retail analytics often hinges on a robust technological infrastructure capable of handling massive data volumes and executing complex analytical tasks. CXOs must carefully align solutions and services with advanced technological capabilities, considering factors like scalability, reliability, and security.
By proactively addressing these challenges, CXOs can unlock the full potential of retail analytics, reshaping their retail operations, generating value, and securing a competitive edge within their industry. For example, in March 2022, Snowflake introduced a data cloud specifically tailored to the retail sector, designed to tackle the challenges confronting the industry.
This innovative retail data cloud resolves these challenges by consolidating industry-specific data sets and multiple partner solutions onto a unified platform.
Retail analytics market research typically involves segmenting the market to better understand its various aspects, including customer behavior, trends, and the competitive landscape. The segmentation of the retail analytics market can vary depending on the specific research goals and the level of granularity required. However, here are some common segments used in retail analytics market research:
Recent Developments:
1. Recent Acquisition
In September 2023, Oracle and Uber unveiled their collaborative effort, "Collect and Receive," a novel service integration within the Oracle Retail platform. This integration aims to enhance last-mile delivery services for both retailers and customers. Leveraging pre-integrated APIs and cloud platform technologies, the Oracle Retail Data Store enables retailers to seamlessly connect with Uber Direct, Uber's delivery solution. Through this joint solution, retailers can optimize their inventory management while expanding customer delivery options, including scheduled and same-day deliveries, order pickups, and convenient returns at nearby retail or postal facilities.
Impact on the Market: The upcoming integration of "Collect and Receive" between Oracle and Uber is poised to reshape the global retail industry significantly. With the aid of pre-integrated APIs and cloud technologies, this collaboration enhances retailers' last-mile delivery capabilities. It not only boosts operational efficiency in the retail sector but also enhances the overall customer experience by offering a broader range of delivery choices, such as scheduled and same-day deliveries, order pickups, and convenient returns. Simultaneously, it streamlines inventory management for retailers.
(Source: Oracle News)
2. New Strategic Partnership
In June 2023, Salesforce expanded its strategic partnership with Google to empower organizations in harnessing data and AI to optimize customer experiences, gain deeper insights into customer behavior, and execute more cost-effective campaigns across marketing, sales, service, and commerce. This partnership introduces two groundbreaking advancements in data and AI: real-time data sharing and enhanced predictive and generative AI capabilities. By leveraging their data and customized AI models, organizations can reduce costs, mitigate risks, and simplify the complexities of data synchronization across various platforms while enhancing their ability to anticipate and meet customer needs.
Impact on the Market: The extended collaboration between Salesforce and Google, focusing on data and AI utilization, is expected to have a substantial impact on the global market by elevating customer experiences. The integration of predictive and generative AI technologies, coupled with real-time data sharing, empowers enterprises to optimize processes, reduce expenses, and tackle the challenges associated with data synchronization across diverse platforms. This advancement enables businesses to gain a deeper understanding of consumer preferences and behaviors, facilitating the implementation of more targeted and efficient marketing, sales, service, and commerce initiatives on a global scale.
(Source: Salesforce News & Insights)
Top Key Players: Retail Analytics Market
The key players in the retail analytics market can vary over time as the industry evolves and new companies emerge. As of my last knowledge update in January 2022, here are some prominent companies that were significant players in the retail analytics market:
IBM: IBM offers a wide range of analytics solutions, including retail-specific analytics tools, to help retailers improve customer insights, optimize supply chains, and enhance overall operational efficiency.
SAP: SAP provides retail analytics solutions that assist retailers in areas such as inventory management, demand forecasting, and customer analytics.
Oracle: Oracle offers a suite of retail analytics solutions that enable retailers to make data-driven decisions, enhance customer experiences, and streamline their operations.
Salesforce: Salesforce provides customer relationship management (CRM) and analytics tools that help retailers gain insights into customer behavior and preferences, leading to improved marketing and sales strategies.
Adobe Analytics: Adobe Analytics offers a robust analytics platform for retailers, focusing on customer analytics, marketing optimization, and personalization.
SAS Institute: SAS offers advanced analytics and AI-driven solutions for retailers to optimize pricing, inventory management, and marketing campaigns.
Tableau (now part of Salesforce): Tableau provides data visualization and analytics tools that help retailers visualize and understand their data, making it easier to uncover actionable insights.
Qlik: Qlik's analytics platform helps retailers consolidate and analyze data from various sources, enabling better decision-making and operational efficiency.
Domo: Domo's cloud-based analytics platform offers retailers real-time data insights and visualization to improve decision-making and monitor business performance.
MicroStrategy: MicroStrategy provides analytics and business intelligence solutions to retailers for improved reporting, dashboards, and data-driven decision-making.
TIBCO Software: TIBCO offers analytics and data integration solutions that help retailers make sense of their data and improve operational efficiency.
Manthan: Manthan is a specialized provider of analytics and AI solutions tailored for the retail industry, focusing on areas such as customer analytics, merchandise analytics, and supply chain optimization.
Retail Analytics Market Research Segmentation
- By Deployment Type:
- On-Premises: Retail analytics solutions hosted and managed within the retailer's physical infrastructure.
- Cloud-Based: Retail analytics solutions hosted in the cloud, offering scalability and accessibility from anywhere.
- By Solution Type:
- Descriptive Analytics: Solutions that provide insights into past performance and historical data analysis.
- Predictive Analytics: Solutions that use historical data and algorithms to forecast future trends and customer behavior.
- Prescriptive Analytics: Solutions that recommend specific actions to optimize decision-making and business processes.
- By Application:
- Customer Analytics: Understanding customer behavior, preferences, and segmentation.
- Inventory Management: Optimizing stock levels, reducing carrying costs, and preventing stockouts.
- Pricing Optimization: Analyzing pricing strategies and competitive pricing intelligence.
- Supply Chain Analytics: Enhancing supply chain efficiency, monitoring supplier performance, and reducing supply chain costs.
- Store Operations: Improving store layout, staffing, and overall operational efficiency.
- By Retailer Type:
- Brick-and-Mortar Retailers: Traditional physical stores.
- E-commerce Retailers: Online retailers and marketplaces.
- Omnichannel Retailers: Retailers with both physical and online presence.
- By Region:
- North America
- Europe
- Asia-Pacific
- Latin America
- Middle East and Africa
- By Organization Size:
- Small and Medium-sized Enterprises (SMEs)
- Large Enterprises
- By End-User Industry:
- Apparel and Fashion
- Electronics and Appliances
- Grocery and Supermarkets
- Furniture and Home Decor
- Automotive
- Others
- By Data Source:
- Point-of-Sale (POS) Data
- E-commerce Data
- Social Media Data
- Customer Relationship Management (CRM) Data
- Loyalty Program Data
- Sensor Data (e.g., foot traffic, temperature)
- By Analytics Tools:
- Data Visualization Tools
- Predictive Modeling Tools
- Machine Learning Algorithms
- Business Intelligence (BI) Platforms
- By Vendor Type:
- Software Providers
- Service Providers
- Consulting Firms
These are some common segmentation criteria used in retail analytics market research. Depending on the specific research objectives, additional segments or subsegments may be defined to gain deeper insights into the market dynamics and trends.
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