In 2020, there were 41,977 firms in the United States with sales amounting to less than 5,000 U.S. dollars. Comparatively, there were more than 500,000 firms in the country with sales between 50,000 and 99,999 U.S. dollars.

This table includes current ratio, debt to equity ratio, interest coverage ratio, debt ratio, revenue to equity ratio, revenue to closing inventory ratio, current debt to equity, net profit to equity, net fixed assets to equity, gross margin, return on total assets, collection period for accounts receivable. Incorporated businesses only. Values are averages in current dollars unless otherwise stated.

This dataset represents the percent change in net revenue of open small businesses in North Carolina counties calculated as a seven-day moving average, which is seasonally adjusted and indexed to January 4-31 2020. The data obtained from the Opportunity Insights data repository in Github includes the daily percentage change in net revenue of open small businesses compared to January 2020 levels. The data is then aggregated as a rolling 7-day average. Twenty-two of North Carolina’s 100 counties are represented in this dataset, none of which are non-CBSA (outside of both metropolitan and micropolitan areas). Additionally, only one county is identified as having high pre-existing unemployment, and two as having lower median income. As a result, the dataset disproportionately represents relatively prosperous metropolitan centers and does not represent other regions of the state.

In 2024, the average profit made by SMEs in the UK amounted to approximately 11,000 British pounds, with SMEs that employed between 50 and 249 people making a median profit of around 243,000 pounds.

This table includes total number of businesses and total revenue (all incorporated statuses); sales of goods and services, and other revenues (incorporated businesses only). Values are averages in current dollars unless otherwise stated.

Average yearly sales and revenue growth for small and medium enterprises in 2020 by region, CMA level, North American Industry Classification System (NAICS), demographics, age of business, employment size, rate of growth, etc.

Nonemployer Statistics is an annual series that provides statistics on U.S. businesses with no paid employees or payroll, are subject to federal income taxes, and have receipts of $1,000 or more ($1 or more for the Construction sector). This program is authorized by the United States Code, Titles 13 and 26. Also, the collection provides data for approximately 450 North American Industry Classification System (NAICS) industries at the national, state, county, metropolitan statistical area, and combined statistical area geography levels. The majority of NAICS industries are included with some exceptions as follows: crop and animal production; investment funds, trusts, and other financial vehicles; management of companies and enterprises; and public administration. Data are also presented by Legal Form of Organization (LFO) (U.S. and state only) as filed with the Internal Revenue Service (IRS). Most nonemployers are self-employed individuals operating unincorporated businesses (known as sole proprietorships), which may or may not be the owner's principal source of income. Nonemployers Statistics features nonemployers in several arts-related industries and occupations, including the following: Arts, entertainment, and recreation (NAICS Code 71) Performing arts companies Spectator sports Promoters of performing arts, sports, and similar events Independent artists, writers, and performers Museums, historical sites, and similar institutions Amusement parks and arcades Professional, scientific, and technical services (NAICS Code 54) Architectural services Landscape architectural services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, and musical instrument stores Sewing, needlework, and piece goods stores Book stores Art dealers Nonemployer Statistics data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The data are processed through various automated and analytical review to eliminate employers from the tabulation, correct and complete data items, remove anomalies, and validate geography coding and industry classification. Prior to publication, the noise infusion method is applied to protect individual businesses from disclosure. Noise infusion was first applied to Nonemployer Statistics in 2005. Prior to 2005, data were suppressed using the complementary cell suppression method. For more information on the coverage and methods used in Nonemployer Statistics, refer to NES Methodology. The majority of all business establishments in the United States are nonemployers, yet these firms average less than 4 percent of all sales and receipts nationally. Due to their small economic impact, these firms are excluded from most other Census Bureau business statistics (the primary exception being the Survey of Business Owners). The Nonemployers Statistics series is the primary resource available to study the scope and activities of nonemployers at a detailed geographic level. For complementary statistics on the firms that do have paid employees, refer to the County Business Patterns. Additional sources of data on small businesses include the Economic Census, and the Statistics of U.S. Businesses. The annual Nonemployer Statistics data are available approximately 18 months after each reference year. Data for years since 2002 are published via comma-delimited format (csv) for spreadsheet or database use, and in the American FactFinder (AFF). For help accessing the data, please refer to the Data User Guide.

This table includes total expenses, cost of sales (direct expenses), wages and benefits, purchases, materials and sub-contracts, opening inventory, closing inventory, operating expenses (indirect expenses), labour and commissions, amortization and depletion, repairs and maintenance, utilities and telephone and telecommunication, rent, interest and bank charges, advertising and promotion, delivery and shipping and warehouse, insurance, other indirect expenses, net profit or loss. All incorporation statuses. Values are averages in current dollars unless otherwise stated.

This data set is of certified small businesses (SBF), where the ownership and control is race and gender neutral. This dataset includes businesses that are small as defined by the Office of Supplier Diversity based on a three-year average of either or both Full Time Equivalent employees (FTEs) and/or a three-year average of gross revenue. This data set is updated daily and is searchable and exportable at this link: https://osd.delaware.gov/Home/OSD. The eligibility and size for an SBF certified business is viewable at: https://business.delaware.gov/osd where you can review the application and eligibility requirements. The Office of Supplier Diversity's mission is to assist the entire supplier diversity community of minority, women, veteran, service disabled veteran, and individuals with disabilities owned businesses as well as small businesses of a unique size in competing for the provision of commodities, services, and construction to State departments, agencies, authorities, school districts, higher educat

In 2023, WhatsApp Business was estimated to have an average revenue per user (ARPU) of approximately **** U.S. dollars worldwide. Australia and Oceania was estimated to be the global region with the highest ARPU for WhatsApp Business, with **** U.S. dollars in revenues per user on the platform. In 2023, it is estimated that WhatsApp Business generated around over ***** million U.S. dollars in revenues worldwide, with Asia and Europe generating the largest share of revenues.WhatsApp Business was launched in 2018 and is available to download for small businesses and companies. Regular WhatsApp users do not need an additional app, but can connect instantly with company representatives or automated chatbots from their regular app interface.

This table includes total number of businesses and total revenue (all incorporated statuses); sales of goods and services, and other revenues (incorporated businesses only). Values are averages in current dollars unless otherwise stated.

Dataset Overview

This dataset contains 2,000 rows of data from coffee shops, offering detailed insights into factors that influence daily revenue. It includes key operational and environmental variables that provide a comprehensive view of how business activities and external conditions affect sales performance. Designed for use in predictive analytics and business optimization, this dataset is a valuable resource for anyone looking to understand the relationship between customer behavior, operational decisions, and revenue generation in the food and beverage industry.

Columns & Variables

The dataset features a variety of columns that capture the operational details of coffee shops, including customer activity, store operations, and external factors such as marketing spend and location foot traffic.

1. Number of Customers Per Day

   • The total number of customers visiting the coffee shop on any given day.
   • Range: 50 - 500 customers.

2. Average Order Value ($)

   • The average dollar amount spent by each customer during their visit.
   • Range: $2.50 - $10.00.

3. Operating Hours Per Day

   • The total number of hours the coffee shop is open for business each day.
   • Range: 6 - 18 hours.

4. Number of Employees

   • The number of employees working on a given day. This can influence service speed, customer satisfaction, and ultimately, sales.
   • Range: 2 - 15 employees.

5. Marketing Spend Per Day ($)

   • The amount of money spent on marketing campaigns or promotions on any given day.
   • Range: $10 - $500 per day.

6. Location Foot Traffic (people/hour)

   • The number of people passing by the coffee shop per hour, a variable indicative of the shop's location and its potential to attract customers.
   • Range: 50 - 1000 people per hour.

Target Variable

• Daily Revenue ($)
  • This is the dependent variable representing the total revenue generated by the coffee shop each day.
  • It is calculated as a combination of customer visits, average spending, and other operational factors like marketing spend and staff availability.
  • Range: $200 - $10,000 per day.

Data Distribution & Insights

The dataset spans a wide variety of operational scenarios, from small neighborhood coffee shops with limited traffic to larger, high-traffic locations with extensive marketing budgets. This variety allows for exploring different predictive modeling strategies. Key insights that can be derived from the data include:

• The effect of marketing spend on daily revenue.
• The correlation between customer count and daily sales.
• The relationship between staffing levels and revenue generation.
• The influence of foot traffic and operating hours on customer behavior.

Use Cases & Applications

The dataset offers a wide range of applications, especially in predictive analytics, business optimization, and forecasting:

• Predictive Modeling: Use machine learning models such as regression, decision trees, or neural networks to predict daily revenue based on operational data.
• Business Strategy Development: Analyze how changes in marketing spend, staff numbers, or operating hours can optimize revenue and improve efficiency.
• Customer Insights: Identify patterns in customer behavior related to shop operations and external factors like foot traffic and marketing campaigns.
• Resource Allocation: Determine optimal staffing levels and marketing budgets based on predicted sales, improving overall profitability.

Real-World Applications in the Food & Beverage Industry

For coffee shop owners, managers, and analysts in the food and beverage industry, this dataset provides an essential tool for refining daily operations and boosting profitability. Insights gained from this data can help:

• Optimize Marketing Campaigns: Evaluate the effectiveness of daily or seasonal marketing campaigns on revenue.
• Staff Scheduling: Predict busy days and ensure that the right number of employees are scheduled to maximize efficiency.
• Revenue Forecasting: Provide accurate revenue projections that can assist with financial planning and decision-making.
• Operational Efficiency: Discover the most profitable operating hours and adjust business hours accordingly.

This dataset is also ideal for aspiring data scientists and machine learning practitioners looking to apply their skills to real-world business problems in the food and beverage sector.

Conclusion

The Coffee Shop Revenue Prediction Dataset is a versatile and comprehensive resource for understanding the dynamics of daily sales performance in coffee shops. With a focus on key operational factors, it is perfect for building predictive models, ...

This table includes percent of profitable businesses; total revenue, total expenses, and net profit (profitable businesses); total revenue, total expenses, and net loss (non-profitable businesses). All businesses only. Values are averages in current dollars unless otherwise stated.

This dataset was generated from a public earning's call (press release article). And used to generate examples of the way real humans would speak regarding the matters in the article, within real world scenarios. Here they are below:

Here are the linguistic variations for each of the queries in the dataset, based on the example article provided:

Here are five examples related to strong average loan growth in US Personal Banking (#5):

1. Mortgage Loans: An increase in demand for mortgage loans contributed to the strong average loan growth in US Personal Banking. Customers taking advantage of low interest rates led to a surge in mortgage applications and approvals.

2. Auto Loans: Robust consumer spending and increased car sales led to higher demand for auto loans, contributing to the strong loan growth in US Personal Banking. Customers seeking financing options for purchasing vehicles played a significant role in this growth.

3. Personal Loans: The availability of personal loans with favorable terms and competitive interest rates attracted borrowers, resulting in strong average loan growth in US Personal Banking. Customers availed personal loans for various purposes such as home improvements, debt consolidation, or financing other personal expenses.

4. Small Business Loans: US Personal Banking also witnessed strong loan growth due to increased lending to small businesses. As entrepreneurs and small business owners sought capital for expansion, equipment purchases, or working capital, the demand for small business loans rose, contributing to the growth.

5. Student Loans: The higher education sector continued to rely on student loans to finance tuition fees and related expenses. With the increasing cost of education, a rise in student loan applications and approvals contributed to the strong average loan growth in US Personal Banking.

General Queries Query: "What was the revenue for Personal Banking and Wealth Management (PBWM) in the last quarter?"

Variation 1: "What were the PBWM revenues in the previous quarter?" Variation 2: "Can you provide the revenue figure for PBWM in the last quarter?" Variation 3: "How much revenue did PBWM generate in the last quarter?" Variation 4: "What was the total revenue for PBWM in the most recent quarter?" Variation 5: "Could you tell me the revenue earned by PBWM in the last quarter?" Query: "What were the revenue figures for different divisions under US Personal Banking?"

Variation 1: "Can you provide the revenue breakdown for various divisions within US Personal Banking?" Variation 2: "What were the revenues generated by the different divisions in US Personal Banking?" Variation 3: "How did the revenue distribution look across different divisions in US Personal Banking?" Variation 4: "What were the individual revenue figures for each division within US Personal Banking?" Variation 5: "Could you give me a breakdown of the revenues for different divisions in US Personal Banking?" Query: "How did operating expenses change for PBWM?"

Variation 1: "What was the change in operating expenses for PBWM?" Variation 2: "Were there any fluctuations in the operating expenses of PBWM?" Variation 3: "How did the operating expenses for PBWM evolve over the specified period?" Variation 4: "Can you provide insights into the changes in operating expenses for PBWM?" Variation 5: "What was the percentage change in operating expenses for PBWM?" Query: "What factors contributed to the increase in PBWM's cost of credit?"

Variation 1: "What were the drivers behind the rise in PBWM's cost of credit?" Variation 2: "Which factors influenced the increase in PBWM's cost of credit?" Variation 3: "Can you identify the elements that led to the higher cost of credit for PBWM?" Variation 4: "What were the contributing factors to the cost of credit escalation in PBWM?" Variation 5: "What were the key reasons behind the growth in PBWM's cost of credit?" Query: "What led to the decrease in PBWM's net income?"

Variation 1: "What were the factors responsible for the decline in PBWM's net income?" Variation 2: "Can you identify the causes of the reduction in PBWM's net income?" Variation 3: "What influenced the decrease in net income for PBWM?" Variation 4: "Were there specific drivers that contributed to the decline in PBWM's net income?" Variation 5: "What were the primary reasons behind the decrease in PBWM's net income?" These linguistic variations provide different ways to ask the same questions, allowing for a more diverse and robust training dataset for the chatbot.

Here are the extracted entities from the provided article:

Account Line Entities:

Revenues Operating expenses Cost of credit Net income Business Line Entities:

Personal Banking and Wealth Management (PBWM) Branded Cards Retail Services Retail Banking Global Wealth Management Markets Banking Investment Banking Corporate Lending...

Compared to before COVID-19, sales achieved through online channels increased among small and medium business-to-business companies. According to a study, in the UK, small and medium businesses (SMB) selling products and services to other companies generated ** percent of their revenue through e-commerce before the coronavirus, while the share rose to ** percent during the pandemic. Nevertheless, in the United States, the e-commerce share of revenue reported by small- and middle-sized firms was significantly lower than the average values shown by all the surveyed B2B sellers.

In 2023, small and medium-sized enterprises operating in Hungary generated almost *** trillion forints worth of revenue. Businesses with *** or more employees accounted for the largest share, nearly ** trillion forints.

This statistic shows the revenue of the industry “accounting, tax preparation, bookkeeping, and payroll services“ in the U.S. by segment from 2012 to 2017, with a forecast to 2024. It is projected that the revenue of accounting, tax preparation, bookkeeping, and payroll services in the U.S. will amount to approximately ***** billion U.S. Dollars by 2024.

This statistic displays the result of a survey on the revenue development forecast of small and medium-sized enterprises (SMEs) in selected Nordic countries in 2017. The heads of SMEs in Sweden were the most optimistic, with ** percent of company heads expecting a revenue growth of over *** percent in 2017. Similarly, roughly ** percent of company heads of Danish and Norwegian SMEs were looking forward to a revenue growth of *** to *** percent in each respective country. Average expectations of SMEs' revenue development in Finland were lower than in other surveyed Nordic countries. ** percent of the surveyed SME company heads in Finland did not expect a change in revenue for the year 2017.