In 2025, holiday retail sales in the United States were forecast to reach over************* U.S. dollars. This figure was given as a conservative estimate; retail sales over the holiday season were projected to be between ****** billion U.S. dollars and *******billion U.S. dollars in 2025. Holiday retail sales have risen substantially since the turn of the century, reaching approximately 416 billion U.S. dollars in 2002. Holiday retail sales are a fraction of total retail sales in the United States, which were around ************** U.S. dollars in 2023. Holiday season e-commerce is also on the rise, with increasing numbers of retailers and consumers going digital. What makes up the winter holiday season in the United States? The winter holiday season includes shopping occasions such as Thanksgiving weekend - which is made up of Black Friday, Small Business Saturday, and Cyber Monday, Super Saturday – the last Saturday before Christmas, and Christmas itself. Thanksgiving weekend is a very popular time for consumers to partake in holiday shopping. In 2022, over *** million U.S. consumers shopped on Black Friday. Leading companies in U.S. retail The domestic retail market in the United States is very competitive, with many companies recording substantial retail sales. 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.

According to a survey carried out during the coronavirus pandemic in the United States in 2020, some 60 percent of respondents planned to start their holiday shopping in October or earlier. In comparison to previous years' habits, shoppers are starting to make holiday purchases earlier in 2020.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

In the United States, holiday season online retail sales grew by 8.6 percent in 2024 compared to the previous year. Forecasts suggested that this growth would decrease in 2025 to 5.3 percent. The new normal in holiday shopping In 2020, the COVID-19 pandemic prompted many U.S. consumers to do their holiday shopping online. A year later, although the situation once again allows for physical shopping, e-commerce is still gaining relevance. According to estimates, holiday season online retail sales in the United States were to reach new heights in 2024, amounting to 241 billion dollars. As in previous years, Cyber Monday and Black Friday would remain the most relevant holiday shopping days in 2025, expected to generate approximately 14 billion and 12 billion U.S. dollars in sales, respectively. A preference for online With Cyber Monday expected to generate 2.5 billion dollars more than Black Friday in 2025, it comes as no surprise that most holiday shoppers reported that their preferred type of retailer for holiday gifts were online-only retailers. Over six in ten consumers prefered to buy holiday gifts from e-commerce only merchants, while department stores only only preferred by about 24 percent of seasonal shoppers.

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.

In 2022, shoppers in Sweden planned to do roughly ** percent of their holiday shopping on Black Friday. Around ** percent of Finnish consumers' holiday shopping was planned for Black Friday that same year.

This is a dataset about Christmas sales and trends. It contains features, such as PaymentType, TotalPrice, Events, Weather, PromotionApplied, ProductNames, Category, etc.

Columns Description

Date (Date of the transaction, format: YYYY-MM-DD) Time (Time of the transaction, format: HH:MM:SS) CustomerID (Unique identifier for each customer) Age (Age of the customer) Gender (Gender of the customer: Male, Female, Other) Location (City or town where the purchase was made) StoreID (Unique identifier for the store, if applicable) OnlineOrderFlag (Boolean: True if online, False if in-store) ProductID (Unique identifier for the product) ProductName (Name of the product) Category (Category of the product, e.g., Electronics, Clothing, Toys, Food, Decorations) Quantity (Number of items purchased in the transaction) UnitPrice (Price per unit of the product) TotalPrice (Total price for the product, calculated as Quantity * UnitPrice) PaymentType (Type of payment, e.g., Credit Card, Debit Card, Cash, Online Payment) PromotionApplied (Boolean: True if any promotion was applied, False otherwise) DiscountAmount (The amount of discount, if any) GiftWrap (Boolean: True if the product was gift-wrapped, False otherwise) ShippingMethod (Method of shipping, e.g., Standard, Express, Overnight, if online) DeliveryTime (Number of days taken for delivery, if online) Weather (General weather condition on the day of purchase, e.g., Snowy, Rainy, Sunny) Event (Special events on the purchase day, e.g., Christmas Market, Black Friday) CustomerSatisfaction (Customer satisfaction rating, on a scale of 1-5) ReturnFlag (Boolean: True if the product was returned, False otherwise)

Acknowledgments: The dataset was made available for the Onyx Data Challenge for December 2023.

About Dataset: Walmart Sales Forecast

This dataset focuses on predicting weekly store sales at Walmart by examining holiday effects, temporal patterns, and other influential factors. The goal is to enable efficient stock planning, revenue calculations, and strategic decision-making by understanding patterns related to seasonal sales fluctuations. This machine learning model is developed based on resources from : https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/overview/evaluation .

Dataset Overview

1. Test Data Contains 115,064 rows with information: Store, Department, Date, IsHoliday. "IsHoliday" indicates whether the week includes a special holiday. Holidays tend to show higher average sales than non-holiday periods.

2. Train Data Also contains 115,064 rows with Store, Department, Date, IsHoliday, Weekly Sales. Weekly sales are the recorded weekly sales for specific departments at certain stores.

3. Features Data Consists of 8,190 rows with variables such as Temperature, Fuel Price, CPI, Unemployment, Markdown 1-5, IsHoliday * Temperature: Average temperature (Fahrenheit) in a region. * Fuel Price: Can impact consumer spending and sales. * Markdowns 1-5: Promotional markdowns (missing values marked as NA). * CPI: Consumer Price Index (reflects inflation/deflation). * Unemployment: Unemployment rate in a region that affects consumer spending.

4.Store Data Includes details about Walmart stores such as store numbers, store types, and store sizes. Walmart has 45 stores categorized into 3 types: * Type A: Sizes from 39.690 to 219.622 * Type B: Sizes from 34.875 to 140.167 * Type C: Sizes from 39.690 to 42.988 The target variables for prediction are weekly sales, is holiday, and date. The other features are explored to identify patterns and generate insights to build accurate prediction models.

Modeling Objective

The goal is to predict the impact of holidays on weekly store sales. To achieve this, a Time Series modeling approach was applied using variables such as date, weekly sales, is holiday, lag features, rolling averages, and XGBoost. The evaluation metric used was Weighted Mean Absolute Error (WMAE), which emphasizes periods of higher significance, such as holidays.

Final Model Metrics: * Weighted Mean Absolute Error = 211 * Error rate relative to average weekly sales = ~1.32%.

The low error percentage highlights the model's accuracy in forecasting weekly sales and assessing seasonal fluctuations.

Insights

• The analysis provides actionable insights by identifying holiday effects on sales trends.
• This supports better stock planning, strategic financial planning, and risk management.

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Based on Statista estimates, the total value of retail sales over the holiday shopping season is forecast to reach 973 billion U.S. dollars in 2024. This marks a 4.8 percent increase on the previous year's value, which accounted to about 929 billion U.S. dollars.

This dataset is synthetically generated to simulate real-world retail transactions during Valentine's Day. While the data is not derived from actual customer records, it has been carefully crafted to reflect realistic purchasing behaviors, trends, and patterns observed in the retail industry during this holiday season. The goal of creating this synthetic dataset is to provide a comprehensive and versatile resource for data analysis, visualization, and exploration without compromising privacy or confidentiality.

In Germany, shoppers predominantly plan to pick physical stores when shopping for groceries and beverages during the 2019 holiday season. According to the results of a consumer survey conducted on holiday shopping patterns, shoppers expressed an intention to go to online channels rather than physical stores for their holiday spending on travel, as well as when shopping for kitchen appliances and consumer electronics.

Holiday Parcel Delivery Market Outlook

According to our latest research, the global holiday parcel delivery market size reached USD 112.8 billion in 2024, driven by the exponential surge in e-commerce and the increasing culture of gifting during festive seasons. The market is projected to grow at a robust CAGR of 7.4% from 2025 to 2033, reaching an estimated USD 215.6 billion by 2033. This expansion is primarily fueled by the rapid adoption of digital platforms, evolving consumer expectations for faster deliveries, and the proliferation of cross-border e-commerce. The sector’s impressive growth trajectory is a testament to the ongoing transformation of global retail and logistics landscapes, as per our latest research findings.

One of the most significant growth factors for the holiday parcel delivery market is the remarkable rise in online shopping, especially during peak holiday periods such as Christmas, Diwali, Lunar New Year, and Black Friday. E-commerce giants and small retailers alike are leveraging advanced logistics networks to meet the surging demand for timely parcel deliveries. Consumers now expect not only convenience but also rapid fulfillment of their online purchases, prompting parcel delivery providers to innovate their service offerings. The introduction of real-time tracking, flexible delivery windows, and enhanced last-mile delivery solutions are crucial advancements that have helped the industry keep pace with changing customer preferences. Additionally, the integration of artificial intelligence and data analytics is allowing companies to optimize routes, reduce delivery times, and minimize costs, further enhancing the overall customer experience.

Another key driver in the holiday parcel delivery market is the expansion of cross-border e-commerce, which has opened new avenues for both retailers and logistics providers. International shopping events, facilitated by seamless online payment systems and efficient customs clearance processes, have made it easier for consumers to purchase goods from overseas. This trend has necessitated the development of robust global logistics networks capable of handling large volumes of parcels with speed and reliability. Furthermore, the increasing penetration of smartphones and internet connectivity, particularly in emerging economies, has broadened the customer base for online retailers, thereby boosting the demand for holiday parcel delivery services. Companies are responding by forging strategic partnerships, investing in advanced warehousing solutions, and deploying technology-driven delivery models to enhance their global reach.

Sustainability is also emerging as a pivotal growth factor in the holiday parcel delivery market. With growing awareness about climate change and environmental impact, both consumers and companies are seeking greener logistics solutions. Parcel delivery providers are investing in electric vehicles, eco-friendly packaging, and carbon offset programs to reduce their environmental footprint. Additionally, regulatory frameworks in regions such as Europe and North America are encouraging the adoption of sustainable practices. These initiatives not only help companies comply with environmental standards but also appeal to environmentally conscious consumers, providing a competitive edge in a crowded market. The emphasis on sustainability is expected to shape the future trajectory of the holiday parcel delivery sector, as stakeholders increasingly prioritize responsible business practices.

From a regional perspective, Asia Pacific is emerging as the fastest-growing market for holiday parcel delivery, owing to its large and digitally savvy population, rapid urbanization, and burgeoning e-commerce ecosystem. North America and Europe, while mature, continue to witness steady growth due to high consumer spending during holidays and the presence of established logistics infrastructures. Meanwhile, Latin America and the Middle East & Africa are showing promising potential, driven by improving digital penetration and rising disposable incomes. Each region presents unique challenges and opportunities, from navigating complex regulatory environments to addressing infrastructure gaps. Nonetheless, the global outlook for the holiday parcel delivery market remains highly optimistic, with innovation and customer-centricity at the forefront of industry evolution.

Service Type Analysis

The holiday parcel delivery market is segmented by service type into st

During the 2025 holiday season, 47 percent of American shoppers planned to buy exclusively or mostly online. Only 9 percent of shoppers intend to buy everything in stores, while 24 percent plan to divide their holiday purchases equally between online and offline purchase channels.

Snapshot img

The online retail market share in the US is expected to increase to USD 460.13 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 11.64%.

The report extensively covers online retail market in the US segmentation by the following:

Product - Apparel, footwear, and accessories, consumer electronics and electricals, food and grocery, home furniture and furnishing, and others
Device - Smartphones and tablets and PCs

The US online retail market report offers information on several market vendors, including Amazon.com Inc., Apple Inc., Best Buy Co. Inc., Costco Wholesale Corp., eBay Inc., Kroger Co., Target Corp., The Home Depot Inc., Walmart Inc., and Wayfair Inc. among others.

This online retail market in the US research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches.

What will the Online Retail Market Size in the US be During the Forecast Period?

Download the Free Report Sample to Unlock the Online Retail Market Size in the US for the Forecast Period and Other Important Statistics

Online Retail Market in the US: Key Drivers, Trends, and Challenges

The growing seasonal and holiday sales is notably driving the online retail market growth in the US, although factors such as transportation and logistics may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the online retail industry in the US. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key US Online Retail Market Driver

The growing seasonal and holiday sales is one of the key drivers supporting the US online retail market growth. For instance, from November 1 to December 24, e-commerce sales in the US increased by 11% in 2021, when compared to a massive 47.2% growth in the holiday season of 2020. E-commerce sales made up 20.9 % of total retail sales in the holiday season of 2021, slightly higher than 20.6 percent in 2020. Thanksgiving, Black Friday, and Cyber Monday are the days that see a high amount of online shopping. Apparel, footwear and accessories, consumer electronics, computer hardware, and toys are the largest gaining product categories during the holiday season. Consumers in the US spent $204.5 billion online in November and December 2021, up 8.6% over the same period in 2020. Such exciting sales and offers are driving the market growth.

Key US Online Retail Market Trend

Omni-channel retailing is one of the key US online retail market trends fueling the market growth. It is rapidly becoming the norm for many retailers in the US. It offers consumers the option to shop online and pick up the merchandise from the store nearest to their location on the same day. Retailers are observing a high web influence on their in-store sales. For instance, Best Buy is integrating its offline and online stores to boost revenues. As a part of its omnichannel strategy, the retailer is utilizing physical stores as distribution centers for online purchases. According to Best Buy, 40% of its online shoppers prefer picking up their purchases from physical stores. Best Buy also challenges online and discount retailers with its match-to-price strategy, claiming to offer gadgets at or below the price offered by competitors. Such strategies are expected to boost market growth during the forecast period.

Key US Online Retail Market Challenge

Transportation and logistics are some of the factors hindering the US online retail market growth. Product procurement or sourcing, shipment of ordered items, and delivery to customers are the three major processes where the intervention of transportation and logistics come into the picture. All these processes require a high investment of both time and money, which challenges the efficiency and effectiveness of retailers and their costing strategies. The higher cost incurred from transportation and logistics reduces the margin of retailers, and most of the time, retailers are unable to break even. Between rising fuel prices, driver shortages, as well as a governmental and societal push for increased digitization and sustainability, transport and logistics will continue to be under a lot of pressure. Such factors will negatively impact the market growth during the forecast period.

This online retail market in the US analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2022-2026.

Who are the Major Online Retail Market Vendors in the US?

The report analyzes the market’s competitive landscape and offers information on several market vendors, includi

In 2024, most U.S. shoppers preferred to shop **************** for the holidays. ***************** was only forecast to dominate ****** channels during the weekend after Thanksgiving and after Christmas until the New Year.

Travel was brought to a standstill by COVID-19, threatening the UK economy. With restrictions now lifted, let's review some emerging trends in the UK tourism industry.

Success.ai’s Retail Data for the Retail Sector in Asia enables businesses to navigate dynamic consumer markets, evolving retail landscapes, and rapidly changing consumer behavior across the region. Leveraging over 170 million verified professional profiles and 30 million company profiles, this dataset delivers comprehensive firmographic details, verified contact information, and decision-maker insights for retailers ranging from boutique shops and e-commerce platforms to large department store chains and multinational franchises.

Whether you’re launching new products, entering emerging markets, or optimizing supply chain strategies, Success.ai’s continuously updated and AI-validated data ensures you engage the right stakeholders at the right time, all backed by our Best Price Guarantee.

Why Choose Success.ai’s Retail Data in Asia?

1. Comprehensive Company Information

   • Access verified work emails, phone numbers, and LinkedIn profiles of retail decision-makers, buyers, and merchandising managers across Asia.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and minimizing wasted outreach efforts.

2. Regional Focus on Asian Markets

   • Includes profiles of small specialty retailers, large department stores, convenience chains, online marketplaces, and luxury brands spanning regions like East Asia, Southeast Asia, and South Asia.
   • Understand region-specific consumer preferences, product trends, and competitive dynamics to guide targeted campaigns and market entries.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, store expansions, new franchise agreements, and shifts in inventory sourcing.
   • Stay aligned with evolving market conditions, shopper behaviors, and regulatory environments impacting the Asian retail sector.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and global privacy regulations, ensuring that your data usage remains compliant and your outreach respects personal boundaries.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with executives, buyers, store managers, and e-commerce directors shaping retail landscapes in Asia.
• 30M Company Profiles: Gain insights into brand portfolios, store counts, revenue ranges, and distribution networks.
• Firmographic & Demographic Data: Understand retail categories, merchandising strategies, supply chain partners, and consumer demographics influencing local markets.
• Verified Decision-Maker Contacts: Connect directly with key stakeholders responsible for purchasing decisions, vendor selection, category management, and brand partnerships.

Key Features of the Dataset:

1. Retail Decision-Maker Profiles
   • Identify and connect with CEOs, CFOs, category buyers, inventory planners, marketing directors, and store operations leaders.

2. Target professionals who determine product assortments, vendor negotiations, store layouts, pricing strategies, and promotional campaigns.

3. Advanced Filters for Precision Targeting

   • Filter by retail segment (fashion, electronics, groceries, cosmetics), country of operation, store format, or omnichannel strategies.
   • Tailor campaigns to align with unique cultural preferences, local consumer spending habits, and regulatory frameworks.

4. AI-Driven Enrichment

   • Profiles are enriched with actionable data, enabling personalized messaging, highlighting market-entry value propositions, and improving engagement outcomes in diverse Asian markets.

Strategic Use Cases:

1. Market Entry & Expansion

   • Identify suitable partners, franchisees, or distribution channels when entering new Asian markets.
   • Benchmark against established players, adapt offerings to local tastes, and secure placements in prime retail locations.

2. Supplier and Vendor Relations

3. Connect with procurement managers and inventory planners evaluating new suppliers or seeking innovative products.

4. Present packaging solutions, POS technology, or loyalty programs to retailers aiming to enhance the shopping experience.

5. Omnichannel and E-Commerce Growth

   • Engage e-commerce managers and digital marketing teams embracing online retail, click-and-collect services, and mobile payment integrations.
   • Align technology solutions with growing demand for contactless shopping, personalized recommendations, and seamless customer journeys.

6. Seasonal and Cultural Campaigns

   • Leverage local holidays, shopping festivals, and cultural events by reaching marketing directors and store managers who coordinate merchandise rotations, promotional deals, and experiential activations.
   • Adapt messaging to align with regional festivities and peak shopping periods.

Why Choose Success.ai?

1. Best Price Guarantee

2. Access top-quality verified data at competitive prices, ensuring strong ROI for product launches, brand expansions, and supply chain optimizations.

3. Sea...

The current microchip shortage spells trouble for holiday shoppers seeking a new video game console, but what about the gaming companies?

In the United States, electronics is forecast to be the most lucrative product category for retail e-commerce sales during the 2025 holiday season, expected to generate about 57.5 billion U.S. dollars. Apparel was forecast as the second highest earning category at 47.6 billion U.S. dollars.

During the 2025 holiday season, 60 percent of the budget designated to buy toys and hobby products and services is expected to be spent online in the United States. Most of the spending on electronics and accessories (58 percent) is also expected to happen online, while over 80 percent of food and beverage spending is planned in-store.

This statistic shows the change in holiday spending behavior among U.S. consumers in 2023. That year, about ** percent of shoppers in the United States claimed they would spend more on holiday purchases than they did one year before. Many of these survey respondents have said that inflation is the reason why they would be spending more than usual.