This dataset provides detailed insights into retail sales, featuring a range of factors that influence sales performance. It includes records on sales revenue, units sold, discount percentages, marketing spend, and the impact of seasonal trends and holidays.

Key Features:

• Sales Revenue (USD): Total revenue generated from sales.
• Units Sold: Quantity of items sold.
• Discount Percentage: The percentage discount applied to products.
• Marketing Spend (USD): Budget allocated to marketing efforts.
• Store ID: Identifier for the retail store.
• Product Category: The category to which the product belongs (e.g., Electronics, Clothing).
• Date: The date when the sale occurred.
• Store Location: Geographic location of the store.
• Day of the Week: Day when the sale took place.
• Holiday Effect: Indicator of whether the sale happened during a holiday period.

Use Cases:

• Predictive Modeling: Build models to forecast future sales based on historical data.
• Marketing Analysis: Evaluate the effectiveness of marketing spend and discount strategies.
• Seasonal Trend Analysis: Examine how different seasons and holidays impact sales.
• Revenue Optimization: Identify strategies to optimize pricing and marketing for increased revenue.

Notes:

This dataset is synthetic and generated for analysis purposes. It reflects typical retail sales patterns and is designed to support a wide range of data science and business analytics projects.

The dataset is about a retail sales dataset containing information about store sales for various products over time.

The specific variables include: Store: Unique identifier for the store location Date: Calendar date of the sales data Product: Name of the product being sold Weekly Sales: Total number of units sold for the product in a week Inventory Level: Number of units of the product currently in stock at the store Temperature: Average temperature for the week at the store location Past Promotion of Product (in lac): Total value (in lakhs) of any past promotions for the product during the week (1 lac = 100,000) Demand Forecast: Predicted number of units to be sold for the product in the next week (provided for baseline model comparison)

This dataset can be used for various analytical purposes related to retail sales and inventory management, including:

Demand forecasting: By analyzing historical sales data, temperature, past promotions, and other relevant factors, you can build models to predict future demand for products. This information can be used to optimize inventory levels and prevent stock outs or overstocking. Promotion analysis: You can compare sales data during promotional periods with non-promotional periods to assess the effectiveness of different promotions and identify products that respond well to promotions. Product analysis: By analyzing sales data across different stores and time periods, you can identify which products are most popular and in which locations. This information can be used to inform product placement, marketing strategies, and assortment planning. Store performance analysis: You can compare sales performance across different stores to identify top-performing stores and understand factors contributing to their success. This information can be used to identify areas for improvement in underperforming stores.

By utilizing this dataset for these analytical purposes, retail organizations can gain valuable insights into their sales patterns, customer behavior, and inventory management practices. This information can be used to make data-driven decisions that improve sales performance, profitability, and customer satisfaction.

Retail Sales in the United States increased 0.10 percent in April of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

Snapshot img

Retail Analytics Market Size 2024-2028

The retail analytics market size is forecast to increase by USD 21.6 billion at a CAGR of 28.1% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing volume and complexity of data generated by retail businesses. With the rise of artificial intelligence (AI) and machine learning in the retail sector, inventory management is becoming more efficient through the use of inventory robots and automated data analytics. Big Data is playing a crucial role in enabling retailers to gain insights into customer behavior and preferences, leading to personalized marketing strategies. However, the adoption of advanced technologies like AI and Big Data also brings challenges, including privacy and security concerns.
Blockchain technology is emerging as a potential solution to address data security issues, ensuring the integrity and transparency of retail data. Overall, the retails analytics market is poised for continued growth, with innovative technologies and trends shaping the future of retail.

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

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In today's competitive retail landscape, businesses are leveraging advanced analytics to optimize customer experience, enhance loyalty programs, and improve product performance. Omnichannel customer experience, sales velocity, and geospatial analytics are key areas of focus for retailers seeking to increase customer retention. Trend forecasting and customer sentiment analysis help retailers stay ahead of the curve, while market basket analysis and purchase history analysis provide valuable insights into customer behavior. Retailers are also adopting retail analytics software and consulting services to gain a deeper understanding of customer engagement strategies, supply chain transparency, and business process automation. Predictive modeling, location-based marketing, and social listening are essential tools for data-driven decision making.
Retail technology adoption continues to evolve, with blockchain in retail and automated reporting streamlining operations. Personalized recommendations, cross-selling and upselling, average transaction value, and return rate analysis are essential metrics for retailers looking to boost revenue. Ethical sourcing and retail sustainability are becoming increasingly important, with data science playing a crucial role in ensuring transparency and accountability. Retail analytics consulting firms offer valuable expertise in customer behavior tracking, data visualization tools, and mobile point-of-sale (mPOS) systems. By harnessing the power of data and analytics, retailers can gain a competitive edge and deliver a more engaging and personalized shopping experience.

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 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

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


Component

  Software
  Services


Geography

  North America

    US


  Europe

    Germany
    UK


  Middle East and Africa



  APAC

    China
    India


  South America



  Rest of World (ROW)

By Application Insights

The in-store operation segment is estimated to witness significant growth during the forecast period.

In-store operations within the retail sector increasingly rely on data analytics to optimize various aspects of brick-and-mortar retailing. This segment of the market encompasses the analysis of data generated within physical retail environments, with the primary objective of enhancing customer experiences, increasing operational efficiency, and driving business success. Retailers recognize the importance of delivering exceptional customer experiences to attract and retain shoppers. By analyzing in-store data, retailers gain insights into customer behavior, preferences, and pain points, enabling them to customize the shopping journey and ensure a seamless experience. Data-driven decision-making is a key focus for retailers, who use analytics to optimize various in-store processes.

This includes data integration from multiple sources, such as point-of-sale (POS) systems, customer feedback analysis, foot traffic analysis, and store layout design. By leveraging these insights, retailers can allocate resources more effectively, streamline operations, and improve overall performance. Machine learning algorithms and predictive analytics play a crucial role in in-store operations, allowing retailers to anticipate trends, optimize pricing strategies, and implement effective loss prevention measures. Additionally, real

📜 Dataset Overview

This dataset contains 3,400 records of fashion retail sales, capturing various details about customer purchases, including item details, purchase amounts, ratings, and payment methods. It is useful for analyzing customer buying behavior, product popularity, and payment preferences.

📂 Dataset Details

🔍 Key Insights

• The dataset contains 3,400 transactions.
• Missing values are present in:
  • Purchase Amount (USD): 650 missing values
  • Review Rating: 324 missing values
• Payment Method includes multiple categories, allowing analysis of payment trends.
• Date Purchase is in DD-MM-YYYY format, which can be useful for time-series analysis.
• The dataset can help analyze sales trends, customer preferences, and payment behaviors in the fashion retail industry.

📊 Potential Use Cases

• Sales Analysis: Understanding which fashion items are selling the most.
• Customer Insights: Analyzing purchase behaviors and spending patterns.
• Trend Forecasting: Identifying seasonal trends in fashion retail.
• Payment Method Preferences: Understanding how customers prefer to pay.

The Asia-Pacific (APAC) retail analytics market, valued at $9.28 billion in 2025, is poised for significant growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 14.43% from 2025 to 2033. This robust expansion is driven by several key factors. The increasing adoption of omnichannel strategies by retailers necessitates sophisticated analytics to understand customer behavior across various touchpoints. Furthermore, the burgeoning e-commerce sector in APAC, particularly in rapidly developing economies like India and China, fuels the demand for data-driven insights to optimize pricing, inventory management, and targeted marketing campaigns. Advanced analytics solutions, including predictive modeling and AI-powered tools, are becoming increasingly crucial for retailers to personalize customer experiences, improve operational efficiency, and gain a competitive edge in a dynamic market. The market is segmented by business type (SMEs and large organizations), deployment mode (on-premise and on-demand), solution type (analytics, visualization tools, data management, etc.), service type (integration, support & consulting), and module type (strategy & planning, marketing, financial management, store operations, merchandising, and supply chain management). The presence of established players like IBM, SAP, and Oracle, alongside emerging technology providers, creates a competitive landscape fostering innovation and accessibility to these solutions. The growth trajectory of the APAC retail analytics market is influenced by several trends. The increasing availability of big data and improved data infrastructure allows for more comprehensive and accurate analysis. Retailers are also focusing on enhancing customer experience through personalized recommendations and targeted promotions, pushing the demand for advanced analytics capabilities. However, challenges such as data security concerns, the need for skilled analytics professionals, and the high cost of implementation can act as restraints. Despite these challenges, the long-term growth prospects remain strong due to the increasing digitalization of retail, the growing adoption of cloud-based solutions, and the continuous development of more sophisticated analytics tools. The market's future will be shaped by the ongoing integration of artificial intelligence and machine learning to improve predictive capabilities and automate decision-making processes within retail operations across the APAC region. China and India are expected to be key growth drivers within the region due to their large and rapidly evolving retail landscapes. This comprehensive report provides an in-depth analysis of the Asia-Pacific (APAC) retail analytics industry, offering invaluable insights for industry professionals, investors, and strategic decision-makers. The report covers the period 2019-2033, with a focus on 2025, utilizing a robust methodology to forecast market growth and identify key trends. The APAC retail analytics market is poised for significant expansion, driven by technological advancements, evolving consumer preferences, and increasing adoption of data-driven strategies by retailers of all sizes. This report unravels the complexities of this dynamic market, providing actionable insights and strategic recommendations. The market size in 2025 is estimated at xx Million, with a projected value of xx Million by 2033. 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.

Envestnet®| Yodlee®'s Consumer Spending Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: Analytics B2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis.

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Small specialty retail stores are influenced by broad macroeconomic variables rather than product-specific trends. Still, individual segments do respond to specific shifts in consumer preferences. In recent years, rising per capita disposable income has sustained demand throughout the retail sector. A recovery from the pandemic boosted consumer spending and encouraged consumers to return to brick-and-mortar stores. Specialty retailers were relatively unaffected by pandemic declines as high-income consumers and tobacco users, two significant markets for the industry, continued to spend. Competition from online and big-box retailers has risen, putting downward pressure on profit. More stores are expanding their online platforms to boost consumer reach and provide additional revenue streams. Rising operational costs have contributed to a slight dip in profit. Revenue for small specialty retailers is expected to swell at a CAGR of 4.0% to $68.4 billion through the end of 2025, including a hike of 2.0% in 2025 alone. Despite intensifying competition from discount department stores and online retailers, specialty retail stores have relied on serving a particular niche to remain successful. Big-box stores offer a one-stop shopping experience with lower prices for similar products. External competition has driven underperforming retailers to exit the industry, leaving nonemployers and small retail stores with low barriers to entry. Still, revenue gains have prompted the emergence of many new specialty retailers seeking to capitalize on the trend of shopping locally and broader sustainability trends. Small retailers have maintained a strong customer base by offering a unique in-store experience and high-quality products. Moving forward, small specialty retailers will continue expanding, albeit slower than in the previous five-year period. A gain in consumer spending and consumer confidence compounded by growing environmental awareness will support specialty retail store sales. Ongoing competition from large-scale retailers and declining smoking rates will mitigate specialty retailers' expansion. More consumers view consumer products, particularly luxury and nostalgic items, as sound investment options. Stores can benefit from this trend by stocking high-end goods that appeal to these consumers, focusing on popular brands. Revenue is expected to expand at a CAGR of 1.4% to $73.3 billion through the end of 2030.

The retail analytics market, valued at $6.33 billion in 2025, is projected to experience robust growth, driven by the increasing need for data-driven decision-making within the retail sector. This growth is fueled by several key factors. Firstly, the rising adoption of omnichannel strategies necessitates sophisticated analytics to understand customer behavior across multiple touchpoints. Secondly, advancements in artificial intelligence (AI) and machine learning (ML) are empowering retailers to leverage predictive analytics for inventory optimization, personalized marketing, and improved supply chain efficiency. Furthermore, the proliferation of big data from various sources, including point-of-sale systems, customer relationship management (CRM) databases, and social media, provides rich insights for enhancing operational processes and customer experiences. The market's growth is segmented across various solutions (software and services), deployment models (cloud and on-premise), and functional areas (customer management, in-store analytics, supply chain management, and marketing). While the cloud deployment model is experiencing significant traction due to its scalability and cost-effectiveness, on-premise solutions continue to hold relevance for enterprises with stringent data security requirements. Leading players such as SAP, IBM, Salesforce, and Oracle are actively investing in R&D and strategic acquisitions to consolidate their market positions and cater to the evolving needs of retailers. The projected Compound Annual Growth Rate (CAGR) of 4.23% from 2025 to 2033 indicates a steady expansion of the retail analytics market. However, challenges such as data security concerns, the need for skilled analytics professionals, and the high initial investment costs for implementing sophisticated analytics solutions may act as potential restraints. Nevertheless, the overall market outlook remains positive, driven by the increasing recognition of the strategic importance of data analytics in achieving competitive advantage and improving profitability in a dynamic retail landscape. Geographic expansion, particularly in rapidly developing economies in Asia-Pacific and Latin America, presents significant growth opportunities for market players. Companies are increasingly focusing on developing integrated solutions that combine various analytical capabilities to address the diverse needs of retailers across different segments and geographies. 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: Increasing Volumes of Data and Technological Advancements in AI and AR/VR, Increasing E-retail Sales. Notable trends are: In-store Operation Hold Major Share.

Analysis of ‘🏦 US Retail Sales Per Capita by Store Type’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/us-retail-sales-per-capita-by-store-type-2000-20e on 13 February 2022.

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

About this dataset

I have added a column on the right that shows the compound annual growth rate (CGR) of per capita spending from 2000 to 2015.

source:

This dataset was created by Gary Hoover and contains around 0 samples along with Unnamed: 15, Unnamed: 9, technical information and other features such as: - Unnamed: 18 - Unnamed: 12 - and more.

How to use this dataset

• Analyze Unnamed: 4 in relation to Unnamed: 10
• Study the influence of Unnamed: 14 on Unnamed: 1
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Gary Hoover

Start A New Notebook!

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

In-Store Analytics Market Valuation – 2024-2031

In-Store Analytics Market was valued at USD 1532.7 Million in 2024 and is projected to reach USD 5213.2 Billion By 2031, growing at a CAGR of 18.24% during the forecast period 2024 to 2031.

In-Store Analytics Market: Definition/ Overview

In-store analytics refers to the collection, measurement, and analysis of data related to customer behavior and store operations within a retail environment. Utilizing technologies such as sensors, cameras, and data analytics platforms, it provides insights into how customers navigate the store, their interaction with products, and overall shopping patterns. This data helps retailers understand shopper preferences, optimize store layouts, and enhance the shopping experience.

In-store analytics is applied to various aspects of retail operations. For example, it can optimize store layouts by analyzing foot traffic patterns to place high-demand products in strategic locations. Retailers also use it to monitor real-time inventory levels, ensuring popular items are stocked appropriately and reducing out-of-stock scenarios. Additionally, the data helps in personalizing marketing efforts by tracking customer behavior and tailoring promotions to increase engagement and sales.

In 2020, the market of predictive analytics in department stores was forecast to reach 9.2 billion U.S. dollars globally, a 27 percent of compound annual growth rate since 2015 when the value reached 2.7 billion U.S. dollars. Predictive analytics assist retailers in making better informed decisions about stocking and product ordering.

Success.ai delivers unparalleled access to Retail Store Data for Asia’s retail and e-commerce sectors, encompassing subcategories such as ecommerce data, ecommerce merchant data, ecommerce market data, and company data. Whether you’re targeting emerging markets or established players, our solutions provide the tools to connect with decision-makers, analyze market trends, and drive strategic growth. With continuously updated datasets and AI-validated accuracy, Success.ai ensures your data is always relevant and reliable.

Key Features of Success.ai's Retail Store Data for Retail & E-commerce in Asia:

Extensive Business Profiles: Access detailed profiles for 70M+ companies across Asia’s retail and e-commerce sectors. Profiles include firmographic data, revenue insights, employee counts, and operational scope.

Ecommerce Data: Gain insights into online marketplaces, customer demographics, and digital transaction patterns to refine your strategies.

Ecommerce Merchant Data: Understand vendor performance, supply chain metrics, and operational details to optimize partnerships.

Ecommerce Market Data: Analyze purchasing trends, regional preferences, and market demands to identify growth opportunities.

Contact Data for Decision-Makers: Reach key stakeholders, such as CEOs, marketing executives, and procurement managers. Verified contact details include work emails, phone numbers, and business addresses.

Real-Time Accuracy: AI-powered validation ensures a 99% accuracy rate, keeping your outreach efforts efficient and impactful.

Compliance and Ethics: All data is ethically sourced and fully compliant with GDPR and other regional data protection regulations.

Why Choose Success.ai for Retail Store Data?

Best Price Guarantee: We deliver industry-leading value with the most competitive pricing for comprehensive retail store data.

Customizable Solutions: Tailor your data to meet specific needs, such as targeting particular regions, industries, or company sizes.

Scalable Access: Our data solutions are built to grow with your business, supporting small startups to large-scale enterprises.

Seamless Integration: Effortlessly incorporate our data into your existing CRM, marketing, or analytics platforms.

Comprehensive Use Cases for Retail Store Data:

1. Market Entry and Expansion:

Identify potential partners, distributors, and clients to expand your footprint in Asia’s dynamic retail and e-commerce markets. Use detailed profiles to assess market opportunities and risks.

1. Personalized Marketing Campaigns:

Leverage ecommerce data and consumer insights to craft highly targeted campaigns. Connect directly with decision-makers for precise and effective communication.

1. Competitive Benchmarking:

Analyze competitors’ operations, market positioning, and consumer strategies to refine your business plans and gain a competitive edge.

1. Supplier and Vendor Selection:

Evaluate potential suppliers or vendors using ecommerce merchant data, including financial health, operational details, and contact data.

1. Customer Engagement and Retention:

Enhance customer loyalty programs and retention strategies by leveraging ecommerce market data and purchasing trends.

APIs to Amplify Your Results:

Enrichment API: Keep your CRM and analytics platforms up-to-date with real-time data enrichment, ensuring accurate and actionable company profiles.

Lead Generation API: Maximize your outreach with verified contact data for retail and e-commerce decision-makers. Ideal for driving targeted marketing and sales efforts.

Tailored Solutions for Industry Professionals:

Retailers: Expand your supply chain, identify new markets, and connect with key partners in the e-commerce ecosystem.

E-commerce Platforms: Optimize your vendor and partner selection with verified profiles and operational insights.

Marketing Agencies: Deliver highly personalized campaigns by leveraging detailed consumer data and decision-maker contacts.

Consultants: Provide data-driven recommendations to clients with access to comprehensive company data and market trends.

What Sets Success.ai Apart?

70M+ Business Profiles: Access an extensive and detailed database of companies across Asia’s retail and e-commerce sectors.

Global Compliance: All data is sourced ethically and adheres to international data privacy standards, including GDPR.

Real-Time Updates: Ensure your data remains accurate and relevant with our continuously updated datasets.

Dedicated Support: Our team of experts is available to help you maximize the value of our data solutions.

Empower Your Business with Success.ai:

Success.ai’s Retail Store Data for the retail and e-commerce sectors in Asia provides the insights and connections needed to thrive in this competitive market. Whether you’re entering a new region, launching a targeted campaign, or analyzing market trends, our data solutions ensure measurable success.

...

Chain Store Sales in the United States increased to 2967 USD Million in March from 2525 USD Million in February of 2025. This dataset provides the latest reported value for - United States Chain Store Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Retail Analytics Industry, valued at $6.33 million in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 4.23% during the forecast period from 2025 to 2033. This growth is driven by the increasing demand for data-driven decision-making in the retail sector, where solutions like software and services play crucial roles. The market segments by solution reveal that both software and services are essential, with software leading due to its scalability and ease of integration. In terms of deployment, the cloud segment is expected to grow faster than on-premise solutions, thanks to its flexibility and cost-effectiveness. Key functions within the market include customer management, in-store operations, supply chain management, and marketing and merchandising, with in-store operations, particularly inventory and performance management, being a significant driver of growth. Regionally, North America, with major markets in the United States and Canada, holds a significant share of the global Retail Analytics market, supported by advanced technological infrastructure and the presence of key players like SAS Institute Inc, IBM Corporation, and Oracle Corporation. Europe and Asia Pacific are also notable regions, with countries like Germany, the United Kingdom, China, and Japan contributing substantially to market growth. The competitive landscape includes major companies such as Hitachi Vantara LLC, QlikTech International AB, and Salesforce.com Inc (Tableau Software Inc), which are continuously innovating to meet the evolving needs of the retail sector. The market's growth is further influenced by trends such as the integration of artificial intelligence and machine learning into retail analytics solutions, enhancing predictive analytics capabilities and operational efficiency. 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.

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.

Snapshot img

E-Commerce Retail Market Size 2024-2028

The e-commerce retail market size is forecast to increase by USD 4061.3 billion at a CAGR of 11.2% between 2023 and 2028.

The market is experiencing growth, driven by the advent of personalized shopping experiences and the integration of Artificial Intelligence (AI) technologies. Consumers increasingly demand customized offerings, leading retailers to invest heavily in AI-powered solutions for product recommendations, inventory management, and customer service. However, this market is not without challenges. Strict regulatory policies related to compliance and customer protection continue to pose significant hurdles for retailers. Compliance with data privacy regulations, such as GDPR and CCPA, and ensuring secure payment gateways are essential for maintaining customer trust and avoiding hefty fines. Companies seeking to capitalize on this market's opportunities must prioritize investments in AI and personalization while navigating the complex regulatory landscape. Effective implementation of these strategies will enable retailers to differentiate themselves from competitors and thrive in the evolving the market.

What will be the Size of the E-Commerce Retail Market during the forecast period?

Request Free SampleThe market in the United States continues to experience growth, driven by increasing internet penetration and the convenience of online shopping. According to recent studies, retail e-commerce sales are projected to reach record levels, surpassing USD800 billion by 2025. This growth is fueled by several factors, including the proliferation of digital payment methods, such as mobile wallets and buy now, pay later options, and the integration of payment systems into e-commerce platforms for seamless transaction processing. Moreover, the market is witnessing a shift towards business-to-business (B2B) and cross-border e-commerce, as well as the adoption of advanced technologies like augmented reality and voice orders to enhance the shopping experience. The market is also witnessing a rise in direct selling through social media and marketplaces, with daily essentials, computer devices, and luxury items being popular categories. Inventory management and data security remain critical concerns for e-commerce retailers, with responsive websites and mobile applications becoming essential for reaching a wider customer base. The use of smartphones and tablet devices for online shopping continues to grow, making mobile technologies a significant trend in the market.

How is this E-Commerce Retail Industry segmented?

The e-commerce retail industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ProductApparel and accessoriesGroceriesFootwearPersonal and beauty careOthersModalityBusiness to business (B2B)Business to consumer (B2C)Consumer to consumer (C2C)GeographyAPACChinaIndiaJapanSouth KoreaNorth AmericaUSCanadaEuropeFranceGermanyItalyUKSouth AmericaMiddle East and Africa

By Product Insights

The apparel and accessories segment is estimated to witness significant growth during the forecast period.The market for apparel and accessories is experiencing significant growth, driven by several key factors. Increasing financial institutions' support for online platforms, the trend toward business-to-business (B2B) and consumer-to-consumer (C2C) transactions, and the shift toward organized retail are major contributors to this expansion. The market for apparels and accessories, including footwear, is projected to reach substantial growth, especially in emerging markets. For instance, in India, the domestic lifestyle industry, which includes apparel, beauty, accessories, and footwear, is expected to reach USD210 billion by 2028. A significant driver of this growth is the Gen Z demographic, which is heavily influenced by social media trends and prefers the convenience of online shopping. This generation's preference for the latest fashion trends and willingness to spend on premium products makes them a crucial segment for e-commerce retailers. However, the market also faces challenges such as digital fraud and cybercrime, requiring digital infrastructure and cybersecurity measures. E-commerce platforms are incorporating security features, such as AI technologies, digital wallets, and payment integration, to ensure a safe and personalized shopping experience for consumers. The market is also witnessing the adoption of headless e-commerce, responsive websites, voice orders, and mobile applications to cater to the increasing use of tablet devices and smartphone devices for online shopping. Additionally, the market is seeing the emergence of cross-border e-commerce, daily essentials, and luxury items, requiring advanced inventory management

Retail Analytics Software Market size was valued at USD 7.5 Billion in 2024 and is projected to reach USD 31.2 Billion by 2031, growing at a CAGR of 17.25% during the forecast period 2024-2031.

Global Retail Analytics Software Market Drivers

The market drivers for the Retail Analytics Software Market can be influenced by various factors. These may include:

Growing E-Commerce Sector: The booming e-commerce industry is a significant driver for retail analytics software, as online retailers need robust tools to analyze vast amounts of data and derive actionable insights for improving customer satisfaction and operational efficiency. With the global e-commerce market expected to continue its rapid growth trajectory, the demand for analytics solutions will only intensify. Omnichannel Retailing: Retailers are increasingly adopting omnichannel strategies to provide a seamless shopping experience across various platforms, including online, offline, and mobile. Retail analytics software helps in synchronizing data from diverse channels, offering retailers actionable insights to enhance customer experiences and streamline operations, thus driving the market demand. Personalization and Customer-Centric Strategies: With the growing importance of personalized customer experiences, retailers are leveraging analytics software to gain in-depth understanding of customer behavior and preferences. Real-time analytics enables retailers to tailor offers, recommendations, and marketing campaigns, thereby improving customer loyalty and driving sales growth. Advancements in AI and Machine Learning: The integration of artificial intelligence and machine learning into retail analytics software offers advanced predictive analytics and automated insights. These technologies help retailers predict market trends, optimize inventory management, and enhance decision-making processes, making the software invaluable and boosting market growth. Increasing Use of IoT in Retail: The proliferation of IoT devices in retail, such as smart shelves, beacons, and connected POS systems, generates a vast amount of data. Retail analytics software is essential to process and analyze this data, providing valuable insights for inventory management, customer shopping patterns, and operational efficiencies, thereby driving the market upwards. Enhanced Fraud Detection: Retailers are adopting analytics software to combat fraud and reduce losses. Advanced analytics can detect unusual patterns and flag potential fraudulent activities in real time, enabling retailers to take immediate action. The growing need for robust fraud detection mechanisms is a strong driver for the retail analytics software market. Dynamic Pricing Strategies: Retailers are increasingly using analytics software to implement dynamic pricing strategies, adjusting prices based on factors such as demand, competitor pricing, and market conditions. This capability helps retailers maximize profits and market competitiveness, driving the adoption of retail analytics solutions. Data-Driven Inventory Management: Efficient inventory management is crucial for retail profitability. Retail analytics software provides critical insights into stock levels, turnover rates, and demand forecasting, helping retailers minimize stockouts and overstock situations. The push for more efficient inventory management systems fuels the demand for advanced analytics solutions in the retail sector. Increased Adoption of Cloud-Based Solutions: The trend towards cloud computing has made retail analytics software more accessible and scalable for businesses of all sizes. Cloud-based solutions offer flexibility, cost savings, and ease of integration with other systems, driving higher adoption rates among retailers and propelling market growth. Competitive Market Landscape: In a highly competitive retail environment, businesses strive for a competitive edge. Retail analytics software offers a strategic advantage by providing deep insights and detailed performance metrics, helping retailers to stay ahead of the competition. This competitive pressure compels more retailers to adopt analytics solutions, spurring market expansion.

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...