Key Market Overview:
Retail Analytics Market size was worth USD 6.12 Billion in 2022, accounting for a CAGR of 17.5% during the forecast period (2023-2030), and the market is projected to be worth USD 26.13 Billion by 2030.
Retail analytics is a field that utilizes data analysis tools and techniques to gain insights into the performance of a retail business. The main aim of retail analytics is to assist retailers in making better business decisions, enhancing their operations, and increasing their profitability. There are several types of retail analytics, including sales analytics, which focuses on analyzing sales data to understand trends, patterns, and customer behavior. Retailers use this information to optimize pricing, promotions, and product assortments.
In January 2020, Placer.ai, a location data analytics company, secured USD 12 million in funding to drive research and development of new features for its platform and expand its presence in the United States. This investment reflects the growing importance of retail analytics in the market, as companies seek to gain deeper insights into customer behavior and optimize their retail operations. Placer.ai’s innovative technology leverages location data to provide retailers with real-time insights into foot traffic, store performance, and customer behavior, enabling them to make data-driven decisions to improve their retail operations. With the growing demand for retail analytics, Placer.ai’s funding will allow the company to enhance its platform and expand its services to meet the needs of its clients in the retail industry. This investment is expected to drive growth and development in the retail analytics market in the coming years.
Furthermore, inventory analytics focuses on analyzing inventory data to improve stock management, reduce costs, and minimize inventory-related losses. Customer analytics analyzes customer data to understand buying patterns, preferences, and behavior. Retailers use this information to personalize their marketing efforts and create more targeted campaigns. Finally, marketing analytics involves analyzing marketing data to understand the effectiveness of marketing campaigns and optimize marketing spend. Overall, retail analytics is an essential tool for any retailer looking to improve their business operations and profitability.
As more consumers shift to online shopping, retailers need to analyze large volumes of data to stay competitive. Retail analytics helps e-commerce retailers understand customer behavior, optimize pricing, and improve supply chain management. Furthermore, customers today expect personalized marketing experiences that are tailored to their specific needs and preferences. Retail analytics helps retailers analyze customer data to create more targeted and effective marketing campaigns. Additionally, with the rise of big data, retailers have access to vast amounts of data that provides valuable insights into customer behavior, trends, and preferences. Retail analytics helps retailers make sense of this data and turn it into actionable insights. The above mentioned factors propel the market growth of the market.
Implementing retail analytics solutions is expensive, especially for small and medium-sized retailers. The cost of acquiring and implementing the required hardware, software, and IT infrastructure is a significant barrier to adoption. Furthermore, retailers need to handle sensitive customer data, including personal and financial information. This creates data privacy and security concerns, especially with the increasing number of data breaches and cyber threats. The above-mentioned factors hamper the market growth.
The integration of retail analytics with IoT and smart devices offers the potential for a more connected and personalized shopping experience. Retailers can leverage this technology to track customer behavior in real-time, optimize store layouts, and personalize marketing efforts. Additionally, AI and machine learning can be used to analyze large volumes of data quickly, providing valuable insights to retailers. These insights can help retailers optimize pricing, improve supply chain management, and enhance the overall customer experience. As a result, the use of these technologies has the potential to revolutionize the retail industry and provide significant opportunities for growth and development in the future.
For instance, in May 2020, Simons, a prominent Canadian fashion retailer, implemented AI technology in its retail merchandising strategy through a partnership with Retalon, an AI and predictive analysis firm. This collaboration is expected to help Simons accurately forecast demand and generate shipments, allocate products more efficiently, and calculate replenishment needs with greater precision. The integration of AI in the retail merchandising process is intended to improve efficiency and accuracy, allowing Simons to optimize inventory management and enhance the overall customer experience.
Retail Analytics Report Coverage:
|Report Attributes||Report Details|
|Market Size in 2030 (USD Billion)||26.13 Billion|
|By Type||Predictive Analytics, Descriptive Analytics, Diagnostic Analytics, and Prescriptive Analytics|
|By Component||Software and Services|
|By Deployment||Cloud-based and On-premises|
|By Application||Customer Analytics, Merchandising Analytics, Supply Chain Analytics, Store Operations Analytics, Sales Forecasting, and Demand Forecasting|
|By End-user||Large Enterprises and Small and Medium-Sized Enterprises (SMEs)|
|By Region||North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa|
|Key Players||Microsoft Corporation, Teledyne FLIR LLC, HCL Technologies Limited (HCL Enterprise), IBM Corporation, SAP SE, Oracle Corporation, Qliktech International AB (Qlik), Wipro Limited, Nielsen Consumer LLC, and Fractal Analytics Inc.|
Based on product type, the market is categorized into predictive analytics, descriptive analytics, diagnostic analytics, and prescriptive analytics. The predictive analysis holds the largest market share in the year 2022. Predictive analytics uses historical data, machine learning, and other techniques to forecast future trends and behaviors. It helps retailers make more informed decisions about inventory management, pricing strategies, and marketing campaigns, among other things.
However, the prescriptive analytics is expected to witness the fastest CAGR over the forecast period. The prescriptive analytics takes predictive analytics a step further by providing specific recommendations for actions to take based on the insights generated. This uses a combination of data analysis, machine learning, and optimization algorithms to help retailers make data-driven decisions. Thereby this is the fastest growing segment.
Based on type, the retail analytics market is classified into software and services. The software segment holds the largest market share in the year 2022. The retail analytics software is a key component of any analytics program, providing retailers with the tools they need to collect, process, and analyze data to make informed decisions.
However, the services segment is anticipated to witness the fastest CAGR over the forecast period. As the demand for retail analytics solutions increases, retailers are seeking out third-party service providers to help them implement and use these solutions effectively. Retailers are increasingly realizing that they need expert guidance and support to get the most out of their retail analytics investments, leading to a growing demand for retail analytics services.
Based on deployment, the market is bifurcated into cloud-based and on-premises. The cloud-based segment holds the largest market share in the year 2022. This is due to the rise of big data and the need for real-time data analytics, more and more retailers are turning to cloud-based solutions to help them process and analyse large amounts of data quickly and efficiently. Additionally, cloud-based solutions offer the added benefit of being easily accessible from anywhere, which is particularly important for retailers with multiple locations or remote workforces.
However, the on premise segment is expected to witness the highest CAGR over the forecast period. As this is very helpful for the retailers who particularly need specific data security and customization needs.
Based on the application the market is classified into customer analytics, merchandising analytics, supply chain analytics, store operations analytics, sales forecasting, and demand forecasting. The customer analytics segment holds the largest market share in the year 2022. As this segment helps retailers better understand their customers and optimize their product assortments. The information from customer analytics is used to improve customer experiences, increase customer loyalty, and drive sales.
However, the supply chain analytics is expected to be the fastest growing segment over the forecast period. As supply chain analytics applications help retailers manage their supply chains more efficiently by optimizing inventory levels, improving vendor management, and reducing supply chain risks.
Based on the end-user the market is bifurcated into large enterprises and small and medium-sized enterprises (SMEs). The large enterprise segment holds the largest market share in the year 2022. This is due to their larger size and greater resources to invest in data analytics. However, small and medium-sized enterprises (SMEs) are the fastest-growing segment in the retail analytics market. This is due to the increasing availability of affordable cloud-based and software-as-a-service (SaaS) solutions, as well as the need for SMEs to remain competitive in a rapidly changing retail landscape. As SMEs look to optimize their operations and improve their customer experiences, many are turning to data analytics solutions to gain insights into customer behaviour, inventory management, and other aspects of their businesses.
Based on region, the Retail Analytics market is categorized into North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. North America holds the largest market share in the year 2022. This is due to the presence of several major retailers such as Walmart who utilize retail analytics solutions. Furthermore, the early adoption of data analytics solutions in the region also propels the market growth in this region.
However, Asia Pacific is expected to witness the fastest CAGR over the forecast period. As this region includes several fast-growing economies such as China, India, Japan, and Australia. Asia Pacific is the fastest-growing market for retail analytics due to the rapid expansion of the retail industry in the region and the increasing adoption of data analytics solutions.
Retail Analytics Market Competitive Landscape:
The market is highly competitive, with a large number of players operating at the global and regional levels. The players compete on various parameters such as price, quality, innovation, and customer service. They are constantly investing in research and development to improve their products and expand their market share. Mergers, acquisitions, and partnerships are also common strategies employed by companies to enhance their market position and expand their product portfolio. Key players in the market include-
- Microsoft Corporation
- Teledyne FLIR LLC
- HCL Technologies Limited (HCL Enterprise)
- IBM Corporation
- SAP SE
- Oracle Corporation
- Qliktech International AB (Qlik)
- Wipro Limited
- Nielsen Consumer LLC
- Fractal Analytics Inc.
- In June 2021, Wipro Limited announced a partnership with Levi Strauss & Co., a prominent clothing brand, aimed at enhancing the user experience and improving customer satisfaction through Wipro’s retail solutions and expertise. As part of the partnership, Levi Strauss & Co. is implementing Wipro’s AI-based retail tools across all of its channels to optimize its retail operations and deliver a more personalized customer experience. This collaboration is expected to leverage Wipro’s capabilities and Levi Strauss & Co.’s brand recognition to drive growth and competitiveness in the retail industry.
- In May 2021, Nielsen Consumer LLC has acquired Label Insight, a US-based data intelligence company focused on the fitness and wellness industry. The acquisition is expected to position Nielsen as a leading data provider for retail and industrial companies operating in the health and wellness sector. With the addition of Label Insight’s expertise and data offerings, Nielsen will be able to provide its clients with more robust insights into consumer behavior and trends in this growing market. This acquisition is a strategic move for Nielsen as it seeks to expand its capabilities and remain competitive in the dynamic and evolving retail industry.
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