This statistic shows the retail sales value in Saudi Arabia in 2018, with estimates from 2019 to 2025. In 2018, the retail sales value amounted to 123.9 billion U.S. dollars. It was estimated that the retail sales value would grow until 2025, reaching around 168.9 billion U.S. dollars.

Retail Sales in the United States increased 0.20 percent in February 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.

The historical sales dataset for this research is obtained from a Bangladeshi retailer. The dataset covers a period of 1826 days and includes daily sales data for a particular product from 01 January 2013 to 31 December 2017. The raw sales data has 2 columns: the first column contains timestamps, while the remaining column reflects the quantity sold.

Based on a forecast, retail sales revenues in Germany will amount to over 773 billion euros in 2025. Figures are expected to increase annually. This timeline shows the retail sales revenue development in Germany from 2011 to 2025.

Retail Sales in the United States increased 3.10 percent in February of 2025 over the same month in the previous year. This dataset provides - United States Retail Sales YoY - actual values, historical data, forecast, chart, statistics, economic calendar and news.

According to the data, Walmart's net sales were forecast to be around 547 billion U.S. dollars in 2021, following the upsurge in 2020 that was driven by COVID-19. From 2021 onwards, Walmart's net sales were forecast to increase with each consecutive year. By 2026, it was forecast that Walmart's net sales would grow to 675.2 billion U.S. dollars, which includes store-based and e-commerce net sales.

In 2020, global retail sales fell by 2.9 percent as a result of the COVID-19 pandemic, bouncing back in 2021 with a growth of 9.7 percent Global retail sales were projected to amount to around 27.3 trillion U.S. dollars by 2022, up from approximately 23.7 trillion U.S. dollars in 2020.

American retailers worldwide
As a result of globalization and various trade agreements between markets and countries, many retailers are capable of doing business on a global scale. Many of the world’s leading retailers are American companies. Walmart and Amazon are examples of such American retailers. The success of U.S. retailers can also be seen through their performance in online retail.

Retail in the U.S.
The domestic retail market in the United States is a lucrative market, in which many companies compete. Walmart, a retail chain offering low prices and a wide selection of products, is the leading retailer in the United States. Amazon, The Kroger Co., Costco, and Target are a selection of other leading U.S. retailers.

A first estimate of retail sales in value and volume terms for Great Britain, seasonally and non-seasonally adjusted.

This dataset contains one .csv file that can be used as a new benchmark data for the solving of real-world sales forecasting problem. All data are real and obtained experimentally in production environment in one of the biggest retail company in Bosnia and Herzegovina. The available data in this dataset are in period from 2010 to 2018.

Dataset

This dataset was created by Pratyusha Kar

Released under CC0: Public Domain

Contents

Retail Sales in Taiwan decreased 20.20 percent in February of 2025 over the previous month. This dataset provides the latest reported value for - Taiwan Retail Sales MoM - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Envestnet®| Yodlee®'s Consumer Purchase 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?

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.

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: AnalyticsB2C 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

https://raw.githubusercontent.com/Masterx-AI/Project_Retail_Analysis_with_Walmart/main/Wallmart1.jpg" alt="">

Description:

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Acknowledgements

The dataset is taken from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

From 2022 to 2027, the United States is expected to remain the largest market for Amazon, with retail sales forecast to reach 759 billion U.S. dollars in 2027. Germany, Amazon's second largest market, is set to reach 147 billion dollars by 2027, according to April 2022 estimates.

Dataset

This dataset was created by Chase Carlson

Released under MIT

Contents

Retail Sales in Poland decreased 0.50 percent in February of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Poland Retail Sales YoY - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

According to Cognitive Market Research, the global Artificial Intelligence in Retail market size is USD 4951.2 million in 2023and will expand at a compound annual growth rate (CAGR) of 39.50% from 2023 to 2030.

Enhanced customer personalization to provide viable market output
Demand for online remains higher in Artificial Intelligence in the Retail market.
The machine learning and deep learning category held the highest Artificial Intelligence in Retail market revenue share in 2023.
North American Artificial Intelligence In Retail will continue to lead, whereas the Asia-Pacific Artificial Intelligence In Retail market will experience the most substantial growth until 2030.

Enhanced Customer Personalization to Provide Viable Market Output

A primary driver of Artificial Intelligence in the Retail market is the pursuit of enhanced customer personalization. A.I. algorithms analyze vast datasets of customer behaviors, preferences, and purchase history to deliver highly personalized shopping experiences. Retailers leverage this insight to offer tailored product recommendations, targeted marketing campaigns, and personalized promotions. The drive for superior customer personalization not only enhances customer satisfaction but also increases engagement and boosts sales. This focus on individualized interactions through A.I. applications is a key driver shaping the dynamic landscape of A.I. in the retail market.

January 2023 - Microsoft and digital start-up AiFi worked together to offer Smart Store Analytics. It is a cloud-based tracking solution that helps merchants with operational and shopper insights for intelligent, cashierless stores.

Source-techcrunch.com/2023/01/10/aifi-microsoft-smart-store-analytics/

Improved Operational Efficiency to Propel Market Growth

Another pivotal driver is the quest for improved operational efficiency within the retail sector. A.I. technologies streamline various aspects of retail operations, from inventory management and demand forecasting to supply chain optimization and cashier-less checkout systems. By automating routine tasks and leveraging predictive analytics, retailers can enhance efficiency, reduce costs, and minimize errors. The pursuit of improved operational efficiency is a key motivator for retailers to invest in AI solutions, enabling them to stay competitive, adapt to dynamic market conditions, and meet the evolving demands of modern consumers in the highly competitive artificial intelligence (AI) retail market.

January 2023 - The EY Retail Intelligence solution, which is based on Microsoft Cloud, was introduced by the Fintech business EY to give customers a safe and efficient shopping experience. In order to deliver insightful information, this solution makes use of Microsoft Cloud for Retail and its technologies, which include image recognition, analytics, and artificial intelligence (A.I.).

Source-www.ey.com/en_gl/news/2023/01/ey-announces-launch-of-retail-solution-that-builds-on-the-microsoft-cloud-to-help-achieve-seamless-consumer-shopping-experiences

Market Dynamics of the Artificial Intelligence in the Retail market

Data Security Concerns to Restrict Market Growth

A prominent restraint in Artificial Intelligence in the Retail market is the pervasive concern over data security. As retailers increasingly rely on A.I. to process vast amounts of customer data for personalized experiences, there is a growing apprehension regarding the protection of sensitive information. The potential for data breaches and cyberattacks poses a significant challenge, as retailers must navigate the delicate balance between utilizing customer data for AI-driven initiatives and safeguarding it against potential security threats. Addressing these concerns is crucial to building and maintaining consumer trust in A.I. applications within the retail sector.

Impact of COVID–19 on the Artificial Intelligence in the Retail market

The COVID-19 pandemic significantly influenced artificial intelligence in the retail market, accelerating the adoption of A.I. technologies across the industry. With lockdowns, social distancing measures, and a surge in online shopping, retailers turned to A.I. to navigate the challenges posed by the pandemic. AI-powered solutions played a crucial role in optimizing supply chain management, predicting shifts in consumer behavior, and enhancing e-commerce experiences. Retailers lever...

BigMart Sales Prediction Challenge

BigMart, a leading retail chain, aims to enhance its sales strategy by analyzing historical sales data. The goal is to develop a predictive model that estimates the sales of various products across different outlets, helping BigMart understand the key factors influencing sales performance.

Problem Statement

BigMart has gathered sales data from 2013 for 1,559 products sold across 10 stores in different cities. Along with sales figures, various product and store attributes have been recorded. The objective is to build a machine learning model that can accurately forecast the sales of products at specific outlets.

By leveraging this predictive model, BigMart can gain insights into product and store characteristics that drive sales growth, enabling better business decisions.

Challenges

The dataset may contain missing values due to unreported data from certain stores, requiring appropriate data preprocessing techniques.

Dataset Overview

Train Dataset (8,523 records)

Includes both input features and the target variable (Item_Outlet_Sales).

• Product Features

  • Item_Identifier: Unique product ID
  • Item_Weight: Weight of the product
  • Item_Fat_Content: Fat level (low-fat or regular)
  • Item_Visibility: Percentage of display area allocated to the product
  • Item_Type: Category of the product
  • Item_MRP: Maximum Retail Price

• Store Features

  • Outlet_Identifier: Unique store ID
  • Outlet_Establishment_Year: Year the store was established
  • Outlet_Size: Store size (small, medium, large)
  • Outlet_Location_Type: City tier classification
  • Outlet_Type: Type of outlet (grocery store, supermarket, etc.)

• Target Variable

  • Item_Outlet_Sales: Sales of the product at a particular store (to be predicted)

Test Dataset (5,681 records)

Contains the same features as the train dataset except for Item_Outlet_Sales, which needs to be predicted.

Submission Format

Your model should generate a CSV file with the following columns:
- Item_Identifier: Unique product ID
- Outlet_Identifier: Unique store ID
- Item_Outlet_Sales: Predicted sales value

Reference

For more details, visit: Analytics Vidhya BigMart Sales III

Based on chain retail sale figures, Tesco was the leading retailer in the United Kingdom (UK) in 2021, with 66.1 billion British pounds. Amazon and Sainsbury's came in second and third in the ranking, with chain retail sales of 44 billion British pounds and 31 billion British pounds, respectively. Forecasts suggest that Amazon will take over the market in 2026 as the chain retail sales of the online marketplace giant is expected to reach over 84 billion British pounds.

Dataset

This dataset was created by Umesh Narkhede

Contents