In May 2025, mail order and online retail sales in Germany were **** percent higher in real terms than in the same period of the previous year. In nominal terms, retail sales in the same month also increased, by **** percent compared to the same period in the previous year. The difference reflects the higher price level in the retail sector, although the upward price trend slowed significantly over the course of the past year.

Comprehensive dataset tracking Black Friday online order volumes, revenue, mobile vs desktop sales share, product category performance, and year-over-year growth metrics from 2020-2024, compiled from Adobe Analytics, Salesforce Commerce Cloud, and National Retail Federation sources covering over 1 trillion retail site visits.

The revenue in the online food delivery e-commerce market in Turkey was modeled to be ************ U.S. dollars in 2024. Following a continuous upward trend, the revenue has risen by ************ U.S. dollars since 2017. Between 2024 and 2030, the revenue will rise by ************ U.S. dollars, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Online Food Delivery.

The average revenue per user in the online food delivery e-commerce market in Poland was modeled to be ***** U.S. dollars in 2024. Between 2017 and 2024, the average revenue per user rose by ***** U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The average revenue per user will steadily rise by ***** U.S. dollars over the period from 2024 to 2030, reflecting a clear upward trend.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Online Food Delivery.

Global Online Ordering Systems market size 2025 was XX Million. Online Ordering Systems Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

DoorDash began life as a paloaltodelivery.com and at launch the four founders ran the entire operation, which included building the app, receiving orders and delivering them. It instilled a culture...

🏢 USA Online Shopping Companies Dataset (2024) This dataset showcases a snapshot of the top-performing online shopping companies in the United States as of 2024. It includes crucial business insights such as company names, their respective industries, annual revenues, growth rates, employee counts, and headquarters locations.

📦 Dataset Overview: Year Covered: 2024

Total Companies: N (replace with actual count)

Columns Included:

Rank – Position based on revenue or market performance

Name – Name of the company

Industry – Type of e-commerce business (e.g., Electronics, Fashion, Retail)

Revenue (USD millions) – Annual revenue in millions of USD

Revenue Growth – Percentage growth compared to the previous year

Employees – Number of employees in the company

Headquarters – City and state where the company is based

💡 Use Cases: Market research and industry analysis

Business intelligence dashboards

Growth trend modeling in e-commerce

Employment and revenue correlation studies

Geographic distribution of successful shopping companies

This dataset is ideal for analysts, students, and professionals aiming to explore the structure and performance of the leading online shopping companies in the USA.

The revenue change in the online food delivery market in Poland was modeled to stand at ***** percent in 2024. Between 2018 and 2024, the revenue growth rose by **** percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend. The revenue growth is forecast to decline by **** percentage points from 2024 to 2030, fluctuating as it trends downward.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Online Food Delivery.

Global Restaurant Online Ordering System market size 2025 was XX Million. Restaurant Online Ordering System Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

The revenue is forecast to experience significant growth in all segments in 2029. Comparing the two different segments for the year 2029, the segment 'Restaurant Delivery' leads the ranking with ****** million U.S. dollars. Contrastingly, 'Platform Delivery' is ranked last, with ***** million U.S. dollars. Their difference, compared to Restaurant Delivery, lies at ****** million U.S. dollars.

Key Food Delivery StatisticsTop Food Delivery AppsFood Delivery Revenue by CountryProjected Food Delivery Market SizeFood Delivery Users by AppUS Food Delivery Market ShareFood Delivery Downloads by...

Global B2C Online Ordering Market is segmented by Application (Retail_ Food Service_ Healthcare_ Grocery_ Entertainment), Type (E-commerce Platforms_ Mobile Ordering Apps_ Cloud Ordering Solutions_ Payment Systems_ Delivery Services), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Historical Black Friday revenue data showing 11-year trends in U.S. online and retail sales with year-over-year growth analysis and market insights.

Global B2C Online Ordering market size 2025 was XX Million. B2C Online Ordering Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

1) Data Introduction • The Shopper's Behavior and Revenue Dataset contains more than 12,300 pieces of information about online shopping customers' purchasing behavior and revenue, including customer purchasing patterns, product reviews, discounts, and payment methods.

2) Data Utilization (1) Shopper's Behavior and Revenue Dataset has characteristics that: • This dataset includes a variety of variables related to your shopping behavior, including demographics, purchase history, products and categories, purchase frequency, review ratings, discounts, and promotion usage. • Provides information that can analyze e-commerce customer behavior from multiple angles, such as whether to purchase (Revenue), visitor type, traffic type, browser, operating system, region, and weekend visitation. (2) Shopper's Behavior and Revenue Dataset can be used to: • Customer Segmentation and Target Marketing: You can analyze customer behavior patterns and characteristics to establish customized marketing strategies, and use them to request reviews and induce repurchases. • Forecast and Sales Analysis: By analyzing purchase conversion rate, review impact, discount effect, etc., you can contribute to increased sales and improved customer satisfaction.

📹 Project Video available on YouTube - https://youtu.be/dQwnyCEZ-UU

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

It is a sales data of a restaurant company operating in multiple cities in the world. It contains information about individual sales transactions, customer demographics, and product details. The data is structured in a tabular format, with each row representing a single record and each column representing a specific attribute. This dataset can be commonly used for business intelligence, sales forecasting, and customer behaviour analysis.

Using this dataset, we answered multiple questions with Python in our Project.

Q.1) Most Preferred Payment Method ?

Q.2) Most Selling Product - By Quantity & By Revenue ?

Q.3) Which city had maximum revenue , or , Which Manager earned maximum revenue ?

Q.4) Date wise revenue.

Q.5) Average Revenue.

Q.6) Average Revenue of November & December month.

Q.7) Standard Deviation of Revenue and Quantity ?

Q.8) Variance of Revenue and Quantity ?

Q.9) Is revenue increasing or decreasing over time?

Q.10) Average 'Quantity Sold' & 'Average Revenue' for each product ?

These are the main Features/Columns available in the dataset :

1) Order ID: A unique identifier for each sales order. This can be used to track individual transactions.

2) Order Date: The date when the order was placed. This column is essential for time-series analysis, such as identifying sales trends over time or seasonality.

3) Product: The name or type of the product sold. This column is crucial for analyzing sales performance by product category.

4) Price : The unit price of the product. This, along with 'Quantity Ordered', is used to calculate the total price of each order.

5) Quantity : The number of units of the product sold in a single order. This is a key metric for calculating revenue and understanding sales volume.

6) Purchase Type : The order was made online or in-store or drive-thru.

7) Payment Method : How the payment for the order was done.

8) Manager : Name of the manager of the store.

9) City : The location of the store. This can be used for geographical analysis of sales, such as identifying top-performing regions or optimizing logistics.

Global Online On Demand Food Delivery Services market size 2021 was recorded $115.607 Billion whereas by the end of 2025 it will reach $161.4 Billion. According to the author, by 2033 Online On Demand Food Delivery Services market size will become $314.586. Online On Demand Food Delivery Services market will be growing at a CAGR of 8.7% during 2025 to 2033.

Global Online Food Ordering Market is segmented by Application (Delivery_ Takeaway), Type (E-commerce_ Food Tech), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Global Online Food Ordering System market size 2025 was XX Million. Online Food Ordering System Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Global Online Shopping Guide Platform market size 2021 was recorded $9348.64 Million whereas by the end of 2025 it will reach $13032 Million. According to the author, by 2033 Online Shopping Guide Platform market size will become $25324.3. Online Shopping Guide Platform market will be growing at a CAGR of 8.659% during 2025 to 2033.