Dataset

This dataset was created by Anne Ezeh

Released under Apache 2.0

Contents

Small business transactions and revenue data aggregated from several credit card processors, collected by Womply and compiled by Opportunity Insights. Transactions and revenue are reported based on the ZIP code where the business is located. Data provided for CT (FIPS code 9), MA (25), NJ (34), NY (36), and RI (44). Data notes from Opportunity Insights: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division. Small businesses are defined as those with annual revenue below the Small Business Administration’s thresholds. Thresholds vary by 6 digit NAICS code ranging from a maximum number of employees between 100 to 1500 to be considered a small business depending on the industry. County-level and metro-level data and breakdowns by High/Middle/Low income ZIP codes have been temporarily removed since the August 21st 2020 update due to revisions in the structure of the raw data we receive. We hope to add them back to the OI Economic Tracker soon. More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

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.

Monthly dataset showing change in sales and jobs recorded by Xero, an online accounting software platform. This dataset is updated on a quarterly basis. These are official statistics in development. Source: Xero.

In the wake of COVID-19 and associated lockdowns, small businesses in every state saw a negative change in their revenues when compared to revenues before the pandemic began. Businesses in states like New York, New Jersey, and Michigan saw some of the ******* declines in revenues. Small businesses in more rural states such as South Dakota, Montana and Nebraska also saw their revenues ******, but **** dramatically then in aforementioned states.

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.

📊 Coffee Shop Financial Dataset (Synthetic, 2022–2023)

📝 Overview

This dataset simulates the financial records of a small-town coffee shop over a two-year period (Jan 2022 – Dec 2023).
It was designed for data science, bookkeeping, and analytics projects — including financial dashboards, revenue forecasting, and expense tracking.

The dataset contains 5 CSV files representing different business accounts:
1. checking_account_main.csv - Daily sales deposits (hot drinks, cold drinks, pastries, sandwiches) + operating expenses
2. checking_account_secondary.csv - Monthly transfers between accounts + payroll funding
3. credit_card_account.csv - Weekly credit card expenses (supplies, utilities, vendor charges) and payments
4. gusto_payroll.csv - Payroll data for 3 employees + 1 contractor
5. gusto_payroll_bc.csv - Payroll data for 3 full-time employees + 1 contractor + 1 seasonal employee, with actual tax breakdown for the province of British Columbia, Canada

📂 File Details

checking_account_main.csv

• date
• description
• category (Sales, Utilities, Rent, Supplies, etc.)
• amount (positive = inflow, negative = outflow)
• balance

checking_account_secondary.csv

• date
• description
• amount
• balance

credit_card_account.csv

• date
• vendor
• category (Supplies, Marketing, Utilities, etc.)
• amount (negative = charge, positive = payment)
• balance

gusto_payroll.csv

• date
• employee_id
• employee_name (Owner, Barista 1, Barista 2, Contractor)
• role (Owner, Barista, Manager, Contractor)
• gross_pay

gusto_payroll_bc.csv

This file simulates bi-weekly payroll data for a small coffee shop in British Columbia, Canada, covering January 2022 – December 2023.
It reflects realistic Canadian payroll structure with federal and provincial tax breakdowns, CPP, EI, and additional factors.

Columns: - date → Pay date (bi-weekly schedule)
- employee_id → Unique identifier for each employee
- employee_name → Owner, Barista 1, Barista 2, Manager, Contractor, plus a seasonal Barista (June–Aug 2022)
- role → Role within the coffee shop (Owner, Barista, Manager, Contractor)
- gross_pay → Total earnings before deductions (wages + tips + reimbursements)
- federal_tax → Federal income tax withheld
- provincial_tax → British Columbia income tax withheld
- cpp_employee → Employee CPP contribution
- ei_employee → Employee EI contribution
- other_deductions → Placeholder for possible deductions (e.g., garnishments, union dues)
- net_pay → Take-home pay after deductions
- tips → Declared tips (taxable, included in gross pay)
- travel_reimbursement → Non-taxable reimbursement for travel expenses (if applicable)
- cpp_employer → Employer portion of CPP contributions
- ei_employer → Employer portion of EI contributions

Notes: - Payroll data is synthetic but modeled on Canadian payroll rules (2022–2023 rates).
- A seasonal barista employee is included (employed June 1 – Aug 31, 2022).
- Travel reimbursements are non-taxable and recorded separately.
- This file allows users to practice payroll accounting, deductions analysis, and tax reconciliation.

📈 Business Context

• The coffee shop experiences higher sales September–February (holiday season & winter drinks).
• Sales dip March–June due to seasonality in a small town.
• Pastries are sourced from a local bakery, while sandwiches are made in-house.
• Payroll includes 3 employees (baristas, manager) and 1 independent contractor.

🎯 Possible Use Cases

• Build a financial health dashboard
• Forecast revenue and expenses
• Create a profit & loss statement
• Test SQL queries for accounting workflows
• Explore data visualization with Python, R, or BI tools
• Educational projects for small business analytics

📜 License

This dataset is released under the MIT License, free to use for research, learning, or commercial purposes.

⭐ If you use this dataset in your project or notebook, please credit and share your work, it helps the community!

📷 Photo Credits: freepik

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.

SB: AZ: OP: Total Revenue: Do Not Know data was reported at 6.700 % in 11 Apr 2022. This records an increase from the previous number of 6.300 % for 04 Apr 2022. SB: AZ: OP: Total Revenue: Do Not Know data is updated weekly, averaging 7.600 % from Nov 2021 (Median) to 11 Apr 2022, with 18 observations. The data reached an all-time high of 10.900 % in 21 Mar 2022 and a record low of 4.600 % in 15 Nov 2021. SB: AZ: OP: Total Revenue: Do Not Know data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S: Small Business Pulse Survey: by State: West Region: Weekly, Beg Monday (Discontinued).

Value and volume of retail sales broken down by size of business

This survey shows an outlook of small business owners on their company's revenue development over the next 12 months in the United States from 2016 to 2020, by quarter. In the fourth quarter of 2020, about ** percent of small business owners stated their revenue would increase a little/a lot until the fourth quarter of 2021.

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.

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.

The SME data warehouse is based on existing administrative data sources from Statistics Canada and Canada Revenue Agency. Data covers tax year 2001 to tax year 2006. The SME Data Warehouse contains a complete, up to date and unduplicated list of all businesses in Canada based on Statistics Canada's Business Register for tax years 2001-2006. This product currently produces data for Small and Medium Sized Enterprises (SMEs). SMEs are defined as enterprises with less than 250 employees and less than $50 million in total revenue.

Small business transactions and revenue data aggregated from several credit card processors, collected by Womply and compiled by Opportunity Insights. Transactions and revenue are reported based on the ZIP code where the business is located.

Data provided for CT (FIPS code 9), MA (25), NJ (34), NY (36), and RI (44).

Data notes from Opportunity Insights: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division.

Small businesses are defined as those with annual revenue below the Small Business Administration’s thresholds. Thresholds vary by 6 digit NAICS code ranging from a maximum number of employees between 100 to 1500 to be considered a small business depending on the industry.

County-level and metro-level data and breakdowns by High/Middle/Low income ZIP codes have been temporarily removed since the August 21st 2020 update due to revisions in the structure of the raw data we receive. We hope to add them back to the OI Economic Tracker soon.

More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

SB: AK: OP: Total Revenue: 5,001-15,000 data was reported at 19.500 % in 21 Feb 2022. This records an increase from the previous number of 11.300 % for 03 Jan 2022. SB: AK: OP: Total Revenue: 5,001-15,000 data is updated weekly, averaging 18.800 % from Nov 2020 (Median) to 21 Feb 2022, with 19 observations. The data reached an all-time high of 23.600 % in 07 Dec 2020 and a record low of 10.200 % in 05 Apr 2021. SB: AK: OP: Total Revenue: 5,001-15,000 data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S: Small Business Pulse Survey: by State: West Region: Weekly, Beg Monday (Discontinued).

In the second quarter of 2024, 61 percent of surveyed small business owners indicated that the health of their business was in good shape. A further 24 percent of respondents said the health of their business was about average. At the end of 2019, small businesses in "very good" health peaked at 43 percent. By the end of 2020, this number fell to 25 percent in the wake of the COVID-19 pandemic.

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Comprehensive Use Cases for Small Business Contact Data:

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Refine your marketing strategy by leveraging verified contact details for small business owners. Execute highly personalized email, phone, and multi-channel campaigns with precision.

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Identify and connect with decision-makers in key industries. Use detailed profiles to enhance your sales outreach, close deals faster, and build long-term client relationships.

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Gain insights into small business trends, operational challenges, and industry benchmarks. Leverage this data for competitive analysis and market positioning.

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Foster partnerships with small businesses by identifying community leaders and entrepreneurial influencers in your target regions.

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Introduction

Small Business Statistics: To balance out every country's economy, small businesses are termed the backbone. Small businesses are types of corporations, partnerships, or sole proprietorships that are independently owned companies with fewer employees and lower revenue than large companies. Many small businesses are now utilising the internet and various digital tools to connect with customers, and many of them generate income by selling online.

This article includes several statistical analyses from different sources that will guide you in understanding the importance of small businesses' effectiveness in recent years.

Global Small Business Payroll Software market size 2025 was XX Million. Small Business Payroll Software Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.