Dataset Description:

• The dataset represents retail transactional data. It contains information about customers, their purchases, products, and transaction details. The data includes various attributes such as customer ID, name, email, phone, address, city, state, zipcode, country, age, gender, income, customer segment, last purchase date, total purchases, amount spent, product category, product brand, product type, feedback, shipping method, payment method, and order status.

Key Points:

Customer Information:

• Includes customer details like ID, name, email, phone, address, city, state, zipcode, country, age, and gender. Customer segments are categorized into Premium, Regular, and New. ##Transaction Details:
• Transaction-specific data such as transaction ID, last purchase date, total purchases, amount spent, total purchase amount, feedback, shipping method, payment method, and order status. ##Product Information:
• Contains product-related details such as product category, brand, and type. Products are categorized into electronics, clothing, grocery, books, and home decor. ##Geographic Information:
• Contains location details including city, state, and country. Available for various countries including USA, UK, Canada, Australia, and Germany. ##Temporal Information:
• Last purchase date is provided along with separate columns for year, month, date, and time. Allows analysis based on temporal patterns and trends. ##Data Quality:
• Some rows contain null values, and others are duplicates, which may need to be handled during data preprocessing. Null values are randomly distributed across rows. Duplicate rows are available at different parts of the dataset. ##Potential Analysis:
• Customer segmentation analysis based on demographics, purchase behavior, and feedback. Sales trend analysis over time to identify peak seasons or trends. Product performance analysis to determine popular categories, brands, or types. Geographic analysis to understand regional preferences and trends. Payment and shipping method analysis to optimize services. Customer satisfaction analysis based on feedback and order status. ##Data Preprocessing:
• Handling null values and duplicates. Parsing and formatting temporal data. Encoding categorical variables. Scaling numerical variables if required. Splitting data into training and testing sets for modeling.

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Retail Analytics Market Size 2025-2029

The retail analytics market size is forecast to increase by USD 28.47 billion, at a CAGR of 29.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing volume and complexity of data generated by retail businesses. This data deluge offers valuable insights for retailers, enabling them to optimize operations, enhance customer experience, and make data-driven decisions. However, this trend also presents challenges. One of the most pressing issues is the increasing adoption of Artificial Intelligence (AI) in the retail sector. While AI brings numerous benefits, such as personalized marketing and improved supply chain management, it also raises privacy and security concerns among customers.
Retailers must address these concerns through transparent data handling practices and robust security measures to maintain customer trust and loyalty. Navigating these challenges requires a strategic approach, with a focus on data security, customer privacy, and effective implementation of AI technologies. Companies that successfully harness the power of retail analytics while addressing these challenges will gain a competitive edge in the market.

What will be the Size of the Retail Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the constant need for businesses to gain insights from their data and adapt to shifting consumer behaviors. Entities such as text analytics, data quality, price optimization, customer journey mapping, mobile analytics, time series analysis, regression analysis, social media analytics, data mining, historical data analysis, and data cleansing are integral components of this dynamic landscape. Text analytics uncovers hidden patterns and trends in unstructured data, while data quality ensures the accuracy and consistency of information. Price optimization leverages historical data to determine optimal pricing strategies, and customer journey mapping provides insights into the customer experience.

Mobile analytics caters to the growing number of mobile shoppers, and time series analysis identifies trends and patterns over time. Regression analysis uncovers relationships between variables, social media analytics monitors brand sentiment, and data mining uncovers hidden patterns and correlations. Historical data analysis informs strategic decision-making, and data cleansing prepares data for analysis. Customer feedback analysis provides valuable insights into customer satisfaction, and association rule mining uncovers relationships between customer behaviors and purchases. Predictive analytics anticipates future trends, real-time analytics delivers insights in real-time, and market basket analysis uncovers relationships between products. Data security safeguards sensitive information, machine learning (ML) and artificial intelligence (AI) enhance data analysis capabilities, and cloud-based analytics offers flexibility and scalability.

Business intelligence (BI) and open-source analytics provide comprehensive data analysis solutions, while inventory management and supply chain optimization streamline operations. Data governance ensures data is used ethically and effectively, and loyalty programs and A/B testing optimize customer engagement and retention. Seasonality analysis accounts for seasonal trends, and trend analysis identifies emerging trends. Data integration connects disparate data sources, and clickstream analysis tracks user behavior on websites. In the ever-changing retail landscape, these entities are seamlessly integrated into retail analytics solutions, enabling businesses to stay competitive and adapt to evolving market dynamics.

How is this Retail Analytics Industry segmented?

The retail analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  In-store operation
  Customer management
  Supply chain management
  Marketing and merchandizing
  Others


Component

  Software
  Services


Deployment

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The in-store operation segment is estimated to witness significant growth during the forecast period. In the realm of retail, the in-store operation segment of the market plays a pivotal role in optimizing brick-and-mortar retail operations. This segment encompasses various data analytics applications within phys

This dataset contains a list of sales and movement data by item and department appended monthly.

It is rich in information that can be leveraged for various data science applications. For instance, analyzing this dataset can offer insights into consumer behavior, such as preferences for specific types of beverages (e.g., wine, beer) during different times of the year. Furthermore, the dataset can be used to identify trends in sales and transfers, highlighting seasonal effects or the impact of certain suppliers on the market.

One could start with exploratory data analysis (EDA) to understand the basic distribution of sales and transfers across different item types and suppliers. Time series analysis can provide insights into seasonal trends and sales forecasts. Cluster analysis might reveal groups of suppliers or items with similar sales patterns, which can be useful for targeted marketing and inventory management.

Retail Analytics Market is Segmented by Solutions (Software and Services), Deployment (Cloud, On-Premises, Hybrid), Function (Customer Management, Supply Chain Management, Marketing and Merchandising - Pricing/Yield, Other Functions - Order Management), Retail Format (Brick-And-Mortar Stores, Pure-Play E-Commerce, Omnichannel Retailers), Geography (North America, South America, Europe, Asia-Pacific, Middle East and Africa).

🔍 Total Sales: Achieved $456,000 in revenue across 1,000 transactions, with an average transaction value of $456.00.

👥 Customer Demographics:

Average Age: 41.39 years Gender Distribution: 51% male, 49% female Most active age groups: 31-40 & 41-50 years 🏷️ Product Performance:

Top Categories: Electronics and Clothing led the sales, each contributing $160,000, followed by Beauty products with $140,000. Quantity Sold: Clothing topped the charts with 894 units sold. 📈 Sales Trends: Identified key sales peaks, especially in May 2023, indicating the success of targeted promotional strategies.

Why This Matters:

Understanding these metrics allows for better-targeted marketing, efficient inventory management, and strategic planning to capitalize on peak sales periods. This project demonstrates the power of data-driven decision-making in retail!

💡 Takeaway: Power BI continues to be a game-changer in visualizing and interpreting complex data, helping businesses to not just see numbers but to translate them into actionable insights.

I’m always looking forward to new challenges and projects that push my skills further. If you're interested in diving into the details or discussing data insights, feel free to reach out!

PowerBI #DataAnalysis #RetailSales #DataVisualization #BusinessIntelligence #DataDriven

Retail Analytics Market Overview

Retail analytics involves collecting and analyzing data from various sources in retail operations. It helps retailers make informed decisions to improve their business performance, optimize inventory, and enhance customer experience.

By analyzing sales trends, customer behavior, and inventory levels, retailers can make better decisions about pricing, marketing, and supply chain management. This data-driven approach also aids in fraud detection, competitive analysis, and improving overall store layout and merchandising. Ultimately, retail analytics empowers retailers to stay competitive and profitable in today's dynamic market.

Market Overview: The global Big Data Analytics in Retail market is expected to grow exponentially, reaching a value of 10190 million USD by 2033, exhibiting a robust CAGR during the forecast period. This growth is attributed to several key factors, including the increasing adoption of digital technologies, the proliferation of data-driven decision-making, and the growing need for personalized customer experiences. Additionally, the market is segmented into software and service, platform, and application, with merchandising and in-store analytics, marketing and customer analytics, and supply chain analytics being the major application areas. Key Trends and Market Dynamics: The Big Data Analytics in Retail market is witnessing a surge in the adoption of cloud-based solutions, as they offer scalability, cost-effectiveness, and real-time data processing capabilities. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) is enhancing the accuracy and efficiency of data analysis, enabling retailers to gain actionable insights. However, concerns over data security and privacy, as well as the lack of skilled professionals, pose potential challenges to the market growth. Nonetheless, the increasing demand for personalized marketing campaigns, supply chain optimization, and improved customer engagement is expected to fuel market expansion in the years to come.

This statistic shows the value of the retail analytics market worldwide in 2016, with a forecast from 2017 to 2022. The global retail analytics market was valued at **** billion U.S. dollars in 2016, and was forecast to reach about *** billion dollars by 2022.

The APAC retail analytics market, valued at $9.28 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 14.43% from 2025 to 2033. This surge is driven by the increasing adoption of data-driven decision-making strategies among retailers in the region. E-commerce expansion, the rising need for personalized customer experiences, and the proliferation of advanced technologies like AI and machine learning are key catalysts. The market is segmented by deployment mode (on-premise and on-demand), type (solutions and services), module type (strategy, marketing, financial management, store operations, merchandising, supply chain, and others), business type (SMEs and large-scale organizations), and geography (China, India, Japan, and South Korea). The on-demand segment is witnessing faster growth due to its scalability and cost-effectiveness. Services, particularly integration, support, and consulting, are in high demand as retailers need assistance in implementing and leveraging these analytics solutions. Large-scale organizations are currently the major consumers, however, the SME segment is poised for significant growth, driven by increasing affordability and accessibility of cloud-based solutions. While data privacy concerns and the complexity of integrating various data sources pose challenges, the overall market outlook remains highly positive, fueled by continuous technological advancements and growing digitalization across the APAC retail landscape. China and India, with their vast retail markets and rapidly evolving technological infrastructure, are expected to be the leading contributors to market expansion. The competitive landscape is dynamic, with a mix of established players like IBM, SAP, and Oracle, alongside specialized retail analytics vendors such as Qlik, Tableau, and Retail Next. These companies are focusing on innovation in areas such as predictive analytics, customer segmentation, and supply chain optimization to capture market share. Strategic partnerships, mergers and acquisitions, and the development of comprehensive, integrated platforms are becoming increasingly important competitive strategies. The success of companies in this space hinges on their ability to provide robust, user-friendly solutions that offer actionable insights and effectively address the specific needs of retailers across various segments and geographies. Future growth will likely be driven by the increased adoption of advanced analytics techniques, such as real-time analytics and sentiment analysis, and the integration of these analytics with other retail technologies, such as CRM and POS systems. This report provides a comprehensive analysis of the APAC Retail Analytics Market, covering the period 2019-2033. It delves into the market's size, growth drivers, challenges, and future trends, offering invaluable insights for businesses operating or planning to enter this dynamic sector. The study's base year is 2025, with estimations for 2025 and forecasts extending to 2033, utilizing historical data from 2019-2024. Key players like Qlik Technologies Inc, IBM Corporation, Adobe Systems Incorporated, SAP SE, and others are profiled. This report is essential for investors, retailers, and analytics providers seeking to navigate the complexities of this rapidly evolving market. Recent developments include: August 2022: Maxis invested in ComeBy, a Malaysia-based retail analytics startup, to bolster innovation and digitalization within the retail industry. ComeBy offers brick-and-mortar retailers valuable insights into individual shopper preferences before reaching the checkout counter. The company asserts that its approach, which combines both active and passive tracking, enhances customer engagement and optimizes in-store sales, as well as remarketing and merchandising efforts., June 2022: Amazon introduced a groundbreaking analytics tool that empowers consumer packaged goods (CPG) companies to monitor consumer interest in their products within Amazon Go and Amazon Fresh stores, known for their frictionless checkout technology. The new service, named Store Analytics, provides suppliers with "aggregated and anonymous insights" regarding customer interactions with their products, utilizing data collected by Amazon's innovative Walk Out and Dash Cart systems.. Key drivers for this market are: Increased Emphasis on Predictive Analysis, Sustained increase in volume of data; Growing demand for sales forecasting. Potential restraints include: Lack of general awareness and expertise in emerging regions, Standardization and Integration issues. Notable trends are: Solutions Segment is Anticipated to Hold Major Market Share.

The big data analytics in retail market reached a market size of USD 4.56 Billion in 2020 and is expected to reach a market size of USD 20.82 Billion by 2028, at a CAGR of 21.2%. Big data analytics in retail industry report classifies global market by share, trend, and on the basis of component, dep...

The global Retail Analytics Services market, valued at $4042.9 million in 2025, is poised for significant growth over the forecast period (2025-2033). While the provided CAGR is missing, a conservative estimate, considering the increasing adoption of data-driven strategies within the retail sector and advancements in analytics technologies, would place it between 10% and 15%. This growth is fueled by several key drivers. The increasing need for retailers to optimize pricing, enhance supply chain efficiency, and personalize customer experiences is driving the demand for sophisticated analytics solutions. The rise of e-commerce and omnichannel retailing further exacerbates the need for real-time data analysis and insights to understand consumer behavior and preferences across various touchpoints. Furthermore, the proliferation of big data and the development of advanced analytical tools, such as AI and machine learning, are enabling retailers to extract more valuable insights from their data, leading to improved decision-making and operational efficiency. Market segmentation reveals strong demand across both SMEs and large enterprises, with merchandising, pricing, and performance analysis being the most sought-after services. Leading players like IBM, Oracle, and Microsoft are shaping the market landscape through continuous innovation and strategic partnerships, while specialized analytics providers cater to niche needs. However, factors such as the high cost of implementation and the need for skilled personnel to manage and interpret complex analytics outputs could pose challenges to market expansion. The geographical distribution of the market shows a strong presence in North America and Europe, driven by the advanced retail infrastructure and high adoption of digital technologies. However, the Asia-Pacific region is expected to witness significant growth, fueled by rapid economic development and the expanding e-commerce sector in countries like China and India. The competitive landscape is characterized by a mix of large technology companies offering comprehensive solutions and specialized analytics providers focusing on specific retail segments. The future success of players in this market will depend on their ability to provide customized solutions tailored to specific retail needs, leverage cutting-edge technologies, and demonstrate a clear return on investment for their clients. Ongoing innovations in areas such as predictive analytics, customer journey mapping, and fraud detection will continue to shape market trends and propel the growth of the Retail Analytics Services sector.

The global retail analytics market size was worth more than USD 8.63 billion in 2024 and is poised to witness a CAGR of over 20.3%, crossing USD 95.38 billion revenue by 2037. Cloud segment is forecast to achieve majority share by 2037, due to surge in global spending on cloud services infrastructure.

The Big Data Analytics in Retail market is experiencing robust growth, projected to reach $6.38 billion in 2025 and expanding at a Compound Annual Growth Rate (CAGR) of 21.20% from 2025 to 2033. This significant expansion is driven by the increasing need for retailers to leverage data for improved decision-making, personalized customer experiences, and optimized supply chains. Key drivers include the proliferation of e-commerce, the rise of omnichannel strategies, the increasing availability of affordable and powerful data analytics tools, and the growing need for real-time insights. Trends like the adoption of artificial intelligence (AI) and machine learning (ML) for predictive analytics, the use of cloud-based solutions for scalability and cost-effectiveness, and the focus on data security and privacy are shaping market dynamics. While challenges remain, such as the complexity of integrating diverse data sources and the need for skilled data analysts, the overall outlook remains highly positive. The market is segmented by application (merchandising and supply chain analytics, social media analytics, customer analytics, operational intelligence, and others) and business type (small and medium enterprises and large-scale organizations). Leading players like SAP, Oracle, Qlik, and Salesforce are actively investing in innovative solutions to cater to this growing demand, fueling further market growth. The geographical distribution shows a strong presence in North America and Europe, with the Asia-Pacific region exhibiting high growth potential. The forecast period (2025-2033) suggests sustained market expansion, driven by continuous technological advancements and the expanding adoption of big data analytics across various retail segments. The ability to gain actionable insights from customer behavior, inventory management, and supply chain efficiency will continue to be a key differentiator for retailers. Market penetration will likely increase significantly in emerging economies, where the adoption rate of digital technologies is rising rapidly. Competitive pressures will lead to innovation in the provision of analytics services, likely resulting in more cost-effective and user-friendly solutions. Strategic partnerships and mergers and acquisitions will likely continue to shape the market landscape. Recent developments include: September 2022 - Coresight Research, a global provider of research, data, events, and advisory services for consumer-facing retail technology and real estate companies and investors, acquired Alternative Data Analytics, a leading data strategy, and insights firm. This acquisition will significantly increase data capabilities and further extend expertise in data-driven research., August 2022 - Global Measurement and Data Analytics company Nielsen and Microsoft launched a new enterprise data solution to accelerate innovation in retail using Artificial Intelligence data analytics to create scalable, high-performance data environments.. Key drivers for this market are: Increased Emphasis on Predictive Analytics, Merchandising and Supply Chain Analytics Segment Expected to Hold Significant Share. Potential restraints include: Increased Emphasis on Predictive Analytics, Merchandising and Supply Chain Analytics Segment Expected to Hold Significant Share. Notable trends are: Merchandising and Supply Chain Analytics Segment Expected to Hold Significant Share.

The size of the Retail Analytics Market was valued at USD 2.45 Billion in 2024 and is projected to reach USD 6.42 Billion by 2033, with an expected CAGR of 14.76% during the forecast period. The retail analytics market has emerged as a critical component for businesses aiming to stay competitive in a rapidly evolving landscape. By leveraging advanced technologies such as artificial intelligence, machine learning, and big data, retail analytics enables companies to gain actionable insights into customer behavior, inventory management, pricing strategies, and market trends. This market has witnessed significant growth due to the increasing adoption of e-commerce, omnichannel retailing, and digital transformation initiatives. Retailers are using analytics to optimize operations, enhance customer experience, and improve decision-making processes. The integration of predictive analytics and real-time data has further strengthened its role in identifying opportunities and addressing challenges such as supply chain disruptions and fluctuating consumer demands. As personalization becomes a key focus for modern retail, businesses are leveraging analytics to create targeted marketing campaigns and improve customer retention. Additionally, the growing use of cloud-based solutions and data visualization tools has simplified the deployment of analytics across small and large enterprises alike. With advancements in technology and a surge in consumer data, the retail analytics market is poised for sustained growth, reshaping how businesses operate in the retail sector. Recent developments include: October 2022: MRI Software announced the acquisition of Springboard, which is a provider of shopper traffic counts and AI-powered analytics to landlords, government authorities in the UK, and retailers., January 2023: Tech Mahindra announced a strategic alliance with Retalon; this will allow Retail and CPG companies to acquire greater consumer insights, increase operational efficiency and make improved decisions., June 2022: A leading provider of store-focused retail analytic techniques, Retail Insights expanded its collaboration with Kroger to help the loyalty of grocers for being fresh and in stock both in-store and online.. Key drivers for this market are: Increasing competition and need for customer-centricity

Proliferation of data from multiple sources

Advancements in data analytics technologies

Growing focus on supply chain optimization

Government initiatives to support digital transformation. Potential restraints include: Data privacy and security concerns

Lack of skilled professionals

Complexity of data integration and analysis

Cost of implementation and maintenance. Notable trends are: Edge Computing: Enables real-time data analysis at the point of sale

Natural Language Processing (NLP): Allows retailers to analyze unstructured data from customer interactions

Computer Vision: Used for image and video analysis to enhance customer experience

Blockchain: Provides secure and transparent data management.

The global Retail Analytics Services market, valued at $2832 million in 2025, is poised for significant growth, exhibiting a Compound Annual Growth Rate (CAGR) of 5.8% from 2025 to 2033. This expansion is driven by the increasing adoption of data-driven decision-making within the retail sector. Businesses, both large enterprises and SMEs, are increasingly leveraging retail analytics to optimize merchandising strategies, refine pricing models, enhance operational performance, and improve yield management. The demand for advanced analytics capabilities, including predictive modeling and AI-powered insights, is a key driver, enabling retailers to personalize customer experiences, optimize inventory management, and improve supply chain efficiency. The market segmentation reflects this diverse application, with merchandising, pricing, and performance analysis representing core segments, complemented by specialized offerings like yield analysis and other emerging analytics solutions. Leading technology providers such as IBM, Oracle, Microsoft, and specialized retail analytics firms are actively contributing to market growth through innovative solutions and strategic partnerships. The geographic distribution is broad, with North America and Europe currently holding significant market share, though Asia-Pacific is expected to experience substantial growth fueled by increasing digitalization and e-commerce penetration. The competitive landscape is characterized by a mix of established technology giants and specialized retail analytics vendors. While established players offer comprehensive solutions integrated within their broader enterprise software portfolios, specialized firms provide niche expertise and tailored solutions. This competitive dynamic ensures innovation and caters to the diverse needs of retailers across various scales and segments. Future market growth will likely be shaped by advancements in big data technologies, cloud computing, and the increasing adoption of artificial intelligence and machine learning within retail analytics platforms. The continued focus on enhancing customer experience, improving operational efficiency, and gaining a competitive edge will drive further adoption of sophisticated retail analytics solutions in the coming years.

The Big Data Analytics in Retail market is experiencing robust growth, driven by the increasing need for retailers to understand consumer behavior, optimize pricing strategies, and personalize the shopping experience. The market, estimated at $50 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. This expansion is fueled by several key trends: the proliferation of connected devices generating vast amounts of data, advancements in artificial intelligence (AI) and machine learning (ML) algorithms enabling sophisticated data analysis, and the growing adoption of cloud-based solutions for scalable and cost-effective data storage and processing. Retailers are increasingly leveraging big data analytics to improve supply chain efficiency, reduce waste, enhance customer loyalty programs, and gain a competitive edge through data-driven decision making. While data security and privacy concerns represent a significant restraint, the overall market outlook remains positive, driven by the continuous innovation in analytics technologies and the increasing willingness of retailers to invest in data-driven strategies for improved business outcomes. The competitive landscape is characterized by a mix of established technology vendors like IBM, SAP, Microsoft, Oracle, and SAS, alongside specialized analytics providers such as Tableau Software, Qlik Technologies, and RetailNext. These companies are actively investing in research and development to improve their offerings and cater to the evolving needs of the retail industry. The market is segmented geographically, with North America and Europe currently holding significant market share, but regions like Asia-Pacific are witnessing rapid growth due to increasing digitalization and e-commerce adoption. The continued development of sophisticated analytics tools, integration of omnichannel data sources, and the rise of real-time analytics will further propel market growth in the coming years, creating opportunities for both established players and emerging technology companies.

Analysis of ‘Retail Case Study Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/darpan25bajaj/retail-case-study-data on 13 November 2021.

--- Dataset description provided by original source is as follows ---

Analytics in Retail:

With the retail market getting more and more competitive by the day, there has never been anything more important than the ability for optimizing service business processes when trying to satisfy the expectations of customers. Channelizing and managing data with the aim of working in favor of the customer as well as generating profits is very significant for survival.

Ideally, a retailer’s customer data reflects the company’s success in reaching and nurturing its customers. Retailers built reports summarizing customer behavior using metrics such as conversion rate, average order value, recency of purchase and total amount spent in recent transactions. These measurements provided general insight into the behavioral tendencies of customers.

Customer intelligence is the practice of determining and delivering data-driven insights into past and predicted future customer behavior.To be effective, customer intelligence must combine raw transactional and behavioral data to generate derived measures. In a nutshell, for big retail players all over the world, data analytics is applied more these days at all stages of the retail process – taking track of popular products that are emerging, doing forecasts of sales and future demand via predictive simulation, optimizing placements of products and offers through heat-mapping of customers and many others.

About the Data

A Retail store is required to analyze the day-to-day transactions and keep a track of its customers spread across various locations along with their purchases/returns across various categories.

What can be done with the data?

Create a report and display the calculated metrics, reports and inferences.

Data Schema

This book has three sheets (Customer, Transaction, Product Hierarchy):

• Customer: Customer information including demographics
• Transaction: Transaction of customers
• Product Hierarchy: Product information

--- Original source retains full ownership of the source dataset ---

Introduction

According to Retail Analytics Statistics, Retail analytics involves collecting and analyzing data to make informed business decisions in the retail sector. It helps retailers enhance the customer experience, optimize inventory, and improve pricing strategies. Key components include data collection, descriptive, predictive, and prescriptive analytics, as well as customer and store analytics.

Challenges include data quality and privacy considerations. Future trends point to increased use of AI, IoT integration, personalization, and sustainability efforts. In summary, retail analytics is essential for retailers, driving operational efficiency and profitability while shaping the industry's future.

The global retail analytics market is set to experience USD 14.9 billion in 2025. The industry is expected to observe 17.5% CAGR from 2025 to 2035, reaching USD 68.9 billion by 2035.

Metrics	Values
Industry Size (2025E)	USD 14.9 billion
Industry Value (2035F)	USD 68.9 billion
CAGR (2025 to 2035)	17.5%

Contracts and Deals Analysis

Company	SAP SE
Contract/Development Details	SAP SE secured a contract to provide its retail solutions to a leading global supermarket chain. The partnership aims to enhance inventory management and customer personalization through advanced data analytics.
Date	February 2024
Contract Value (USD Mn)	Approximately USD 80 - USD 100
Estimated Renewal Period	3 - 5 years

Company	Oracle Corporation
Contract/Development Details	Oracle Corporation entered into an agreement with a major fashion retailer to implement its cloud-based platform. This initiative focuses on optimizing supply chain operations and improving sales forecasting accuracy.
Date	June 2024
Contract Value (USD Mn)	Approximately USD 60 - USD 80
Estimated Renewal Period	4 - 6 years

Company	IBM Corporation
Contract/Development Details	IBM Corporation was awarded a contract by a prominent e-commerce company to deploy its AI-driven tools. The objective is to enhance customer experience and boost conversion rates through personalized recommendations.
Date	September 2024
Contract Value (USD Mn)	Approximately USD 70 - USD 90
Estimated Renewal Period	3 - 5 years

Country-wise Analysis

Country	CAGR (2025 to 2035)
USA	9.1%
UK	8.8%
European Union	9.0%
Japan	8.9%
South Korea	9.3%

Competitive Outlook

Company Name	Estimated Market Share (%)
IBM	20-25%
Microsoft	15-20%
SAP SE	12-17%
Oracle Corporation	8-12%
SAS Institute Inc.	5-9%
Other Companies (combined)	20-30%

The retail analytics market is projected to reach $6.33 billion by 2033, exhibiting a CAGR of 4.23% during the forecast period. The rising need for retailers to gain insights into consumer behavior, optimize operations, and improve customer experiences is driving market growth. The increasing adoption of cloud-based solutions, big data analytics, and artificial intelligence (AI) is further fueling market expansion. Major market trends include the shift towards personalized marketing, the integration of omnichannel analytics, and the growing use of predictive analytics. Companies are increasingly investing in retail analytics solutions to enhance customer engagement, streamline supply chain management, and drive revenue growth. Key market players include SAS Institute Inc., IBM Corporation, Hitachi Vantara LLC, QlikTech International AB (Qlik), and Retail Next Inc. North America is the largest regional market, followed by Europe and Asia Pacific. The growing retail sector in emerging economies is expected to drive market growth in these regions in the coming years. Recent developments include: September 2023 - Priority Software acquired Retailsoft, a developer of innovative technology solutions for optimizing retail business efficiency and enhancing revenue growth. In addition, Priority is expanding the scope of its Retail Management Products and delivering significant value to Retailers by integrating Retailsoft's solutions. Retailsoft provides a dynamic platform with operational modules tailored to each organization's needs. These modules comprise work scheduling, communication tools, objective setting, and real-time access to POS data across all locations. Such features empower businesses with trend analysis, monitoring, and strategy optimization, facilitating data-driven decisions, sales goal setting, and fostering competition among branches., January 2023 - AiFi, a startup that aims to enable retailers to deploy autonomous shopping tech, partnered with Microsoft to launch a preview of a cloud service called Smart Store Analytics. It provides retailers using AiFi's technology with shopper and operational analytics for their fleets of "smart stores." With Smart Store Analytics, AiFi will handle store setup, logistics, and support, while Microsoft will deliver models for optimizing store payout, product recommendations, and inventory, among others.. Key drivers for this market are: Increasing Volumes of Data and Technological Advancements in AI and AR/VR, Increasing E-retail Sales. Potential restraints include: Lack of General Awareness and Expertise in Emerging Regions, Standardization and Integration Issues. Notable trends are: In-store Operation Hold Major Share.