This dataset contains comprehensive sales data that can be used for analysis, visualization, and modeling. It includes key attributes such as:

order_id: Unique identifier for each order. product: Name of the product sold. quantity_ordered: Quantity of the product purchased in each transaction. price_each: Price of a single unit of the product. order_date: Date and time when the order was placed. purchase_address: Full address of the purchase, including street, city, and state.

Potential Use Cases Sales Analysis: Identify trends in product performance and seasonal demand. Revenue Insights: Analyze total and per-unit revenue across products or cities. Geographical Analysis: Discover top-performing cities and regions. Time-Based Trends: Analyze monthly sales trends and patterns. Machine Learning Applications: Build predictive models for sales forecasting or customer segmentation.

This is a relational database schema for a sales and order management system, designed to track customers, employees, products, orders, and payments. Below is a detailed breakdown of each table and their relationships:

1. productlines Table (Product Categories)

• Represents different product categories.
• Primary Key: productLine
• Attributes:
  • textDescription: A short description of the product line.
  • htmlDescription: A detailed HTML-based description.
  • image: Associated image (if applicable).
• Relationships:
  • One-to-Many with products: Each product belongs to one productLine.

2. products Table (Product Information)

• Stores details of individual products.
• Primary Key: productCode
• Attributes:
  • productName: Name of the product.
  • productLine: Foreign key linking to productlines.
  • productScale, productVendor, productDescription: Additional product details.
  • quantityInStock: Number of units available.
  • buyPrice: Cost price per unit.
  • MSRP: Manufacturer's Suggested Retail Price.
• Relationships:
  • Many-to-One with productlines (each product belongs to one category).
  • One-to-Many with orderdetails (a product can be part of many orders).

3. orderdetails Table (Line Items in an Order)

• Stores details of each product within an order.
• Composite Primary Key: (orderNumber, productCode)
• Attributes:
  • quantityOrdered: Number of units in the order.
  • priceEach: Price per unit.
  • orderLineNumber: The sequence number in the order.
• Relationships:
  • Many-to-One with orders (each order has multiple products).
  • Many-to-One with products (each product can appear in multiple orders).

4. orders Table (Customer Orders)

• Represents customer orders.
• Primary Key: orderNumber
• Attributes:
  • orderDate: Date when the order was placed.
  • requiredDate: Expected delivery date.
  • shippedDate: Actual shipping date (can be NULL if not shipped).
  • status: Order status (e.g., "Shipped", "In Process", "Cancelled").
  • comments: Additional remarks about the order.
  • customerNumber: Foreign key linking to customers.
• Relationships:
  • One-to-Many with orderdetails (an order contains multiple products).
  • Many-to-One with customers (each order is placed by one customer).

5. customers Table (Customer Details)

• Stores customer information.
• Primary Key: customerNumber
• Attributes:
  • customerName: Name of the customer.
  • contactLastName, contactFirstName: Contact person.
  • phone: Contact number.
  • addressLine1, addressLine2, city, state, postalCode, country: Address details.
  • salesRepEmployeeNumber: Foreign key linking to employees, representing the sales representative.
  • creditLimit: Maximum credit limit assigned to the customer.
• Relationships:
  • One-to-Many with orders (a customer can place multiple orders).
  • One-to-Many with payments (a customer can make multiple payments).
  • Many-to-One with employees (each customer has a sales representative).

6. payments Table (Customer Payments)

• Stores payment transactions.
• Composite Primary Key: (customerNumber, checkNumber)
• Attributes:
  • paymentDate: Date of payment.
  • amount: Payment amount.
• Relationships:
  • Many-to-One with customers (each payment is linked to a customer).

7. employees Table (Employee Information)

• Stores details of employees, including reporting hierarchy.
• Primary Key: employeeNumber
• Attributes:
  • lastName, firstName: Employee's name.
  • extension, email: Contact details.
  • officeCode: Foreign key linking to offices, representing the employee's office.
  • reportsTo: References another employeeNumber, establishing a hierarchy.
  • jobTitle: Employee’s role (e.g., "Sales Rep", "Manager").
• Relationships:
  • Many-to-One with offices (each employee works in one office).
  • One-to-Many with employees (self-referential, representing reporting structure).
  • One-to-Many with customers (each employee manages multiple customers).

8. offices Table (Office Locations)

• Represents company office locations.
• Primary Key: officeCode
• Attributes:
  • city, state, country: Location details.
  • phone: Office contact number.
  • addressLine1, addressLine2, postalCode, territory: Address details.
• Relationships:
  • One-to-Many with employees (each office has multiple employees).

Conclusion

This schema provides a well-structured design for managing a sales and order system, covering: ✅ Product inventory
✅ Order and payment tracking
✅ Customer and employee management
✅ Office locations and hierarchical reporting

****Attribute information:****

Row ID: A unique identifier for each row in the table Order ID: The identifier for each sales order Order Date: The date the order was placed Ship Date: The date the order was shipped Delivery Duration: The amount of time it took to deliver the order Ship Mode: The shipping method used for the order Customer ID: The identifier for the customer who placed the order Customer Name: The name of the customer who placed the order Country: The customer's country City: The customer's city State: The customer's state Postal Code: The customer's postal code Region: The customer's region Product ID: The identifier for the product that was ordered Category: The category of the product that was ordered (e.g., furniture, office supplies, technology) Sub-Category - This attribute likely refers to a subcategory within a larger product category (e.g., Tables within Furniture). (Bookcases - Chairs - Labels - Tables - Storage - Furnishings - Art - Phones - Binders - Appliances - Paper - Others). Product Name - This attribute specifies the name of the product sold. (Bush Somerset Collection Bookcase - Hon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back - Self-Adhesive Address Labels for Typewriters by Universal - Bretford CP4500 Series Slim Rectangular Table - Others).

Sales - This attribute shows the total sales amount for each product. Values are listed in currency format Quantity - This attribute specifies the number of units sold for each product. Integer values. Discount - This attribute indicates the discount offered on the product. Discount Value - This attribute shows the total discount amount applied to the product. Profit - This attribute shows the profit earned on the sale of each product. COGS - This attribute likely refers to each product's Cost of Goods Sold. COGS = Sales - Profit

Nothing ever becomes real till it is experienced.

-John Keats

While we don't know the context in which John Keats mentioned this, we are sure about its implication in data science. While you would have enjoyed and gained exposure to real world problems in this challenge, here is another opportunity to get your hand dirty with this practice problem.

Problem Statement :

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.

Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.

Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

Data :

We have 14204 samples in data set.

Variable Description

Item Identifier: A code provided for the item of sale

Item Weight: Weight of item

Item Fat Content: A categorical column of how much fat is present in the item: ‘Low Fat’, ‘Regular’, ‘low fat’, ‘LF’, ‘reg’

Item Visibility: Numeric value for how visible the item is

Item Type: What category does the item belong to: ‘Dairy’, ‘Soft Drinks’, ‘Meat’, ‘Fruits and Vegetables’, ‘Household’, ‘Baking Goods’, ‘Snack Foods’, ‘Frozen Foods’, ‘Breakfast’, ’Health and Hygiene’, ‘Hard Drinks’, ‘Canned’, ‘Breads’, ‘Starchy Foods’, ‘Others’, ‘Seafood’.

Item MRP: The MRP price of item

Outlet Identifier: Which outlet was the item sold. This will be categorical column

Outlet Establishment Year: Which year was the outlet established

Outlet Size: A categorical column to explain size of outlet: ‘Medium’, ‘High’, ‘Small’.

Outlet Location Type: A categorical column to describe the location of the outlet: ‘Tier 1’, ‘Tier 2’, ‘Tier 3’

Outlet Type: Categorical column for type of outlet: ‘Supermarket Type1’, ‘Supermarket Type2’, ‘Supermarket Type3’, ‘Grocery Store’

Item Outlet Sales: The number of sales for an item.

Evaluation Metric:

We will use the Root Mean Square Error value to judge your response

Analysis of ‘Big Mart Sales’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/akashdeepkuila/big-mart-sales on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

The data scientists at Big Mart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet.

Using this model, Big Mart will try to understand the properties of products and outlets which play a key role in increasing sales.

Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

Content

The dataset provides the product details and the outlet information of the products purchased with their sales value split into a train set (8523) and a test (5681) set. Train file: CSV containing the item outlet information with sales value Test file: CSV containing item outlet combinations for which sales need to be forecasted

Variable Description

• ProductID : unique product ID
• Weight : weight of products
• FatContent : specifies whether the product is low on fat or not
• Visibility : percentage of total display area of all products in a store allocated to the particular product
• ProductType : the category to which the product belongs
• MRP : Maximum Retail Price (listed price) of the products
• OutletID : unique store ID
• EstablishmentYear : year of establishment of the outlets
• OutletSize : the size of the store in terms of ground area covered
• LocationType : the type of city in which the store is located
• OutletType : specifies whether the outlet is just a grocery store or some sort of supermarket
• OutletSales : (target variable) sales of the product in the particular store

Inspiration

Sales of a given product at a retail store can depend both on store attributes as well as product attributes. The dataset is ideal to explore and build a data science model to predict the future sales.

--- Original source retains full ownership of the source dataset ---

NYC Real Estate Sales, January 2016 – September 2025 (DuckDB Format) This dataset contains records of real estate transactions in New York City from January 2016 through September 2025, stored as a single DuckDB database file. It includes details such as sale date, price, property address, borough, and other relevant attributes. The data is suitable for property market analysis, data science projects, and urban studies research. Data for each borough is in a database table named as such, eg Manhattan data is in the manhattan table, Staten Island data in the staten_island table, etc.

Big Mart Data have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The description about the features are given below

       Variable Description

Item_Identifier :Unique product ID Item_Weight :Weight of product Item_Fat_Content :Whether the product is low fat or not Item_Visibility : The % of total display area of all products in a store allocated to the particular product Item_Type :The category to which the product belongs Item_MRP :Maximum Retail Price (list price) of the product Outlet_Identifier :Unique store ID Outlet_Establishment_Year :The year in which store was established Outlet_Size :The size of the store in terms of ground area covered Outlet_Location_Type :The type of city in which the store is located Outlet_Type : Whether the outlet is just a grocery store or some sort of supermarket Item_Outlet_Sales :Sales of the product in the particular store. This is the outcome variable to be predicted.

Hong Kong DHL: DTI: Attributes: Sales Volume data was reported at 49.000 NA in Aug 2018. This records a decrease from the previous number of 51.000 NA for May 2018. Hong Kong DHL: DTI: Attributes: Sales Volume data is updated quarterly, averaging 48.000 NA from Nov 2014 (Median) to Aug 2018, with 16 observations. The data reached an all-time high of 55.000 NA in Nov 2014 and a record low of 37.000 NA in May 2016. Hong Kong DHL: DTI: Attributes: Sales Volume data remains active status in CEIC and is reported by Hong Kong Productivity Council. The data is categorized under Global Database’s Hong Kong – Table HK.S012: DHL Air Trade Leading Index Survey (DTI).

We seek to mitigate the challenges with web-scraped and off-the-shelf POI data, and provide tailored, complete, and manually verified datasets with Geolancer. Our goal is to help represent the physical world accurately for applications and services dependent on precise POI data, and offer a reliable basis for geospatial analysis and intelligence.

Our POI database is powered by our proprietary POI collection and verification platform, Geolancer, which provides manually verified, authentic, accurate, and up-to-date POI datasets.

Enrich your geospatial applications with a contextual layer of comprehensive and actionable information on landmarks, key features, business areas, and many more granular, on-demand attributes. We offer on-demand data collection and verification services that fit unique use cases and business requirements. Using our advanced data acquisition techniques, we build and offer tailormade POI datasets. Combined with our expertise in location data solutions, we can be a holistic data partner for our customers.

KEY FEATURES - Our proprietary, industry-leading manual verification platform Geolancer delivers up-to-date, authentic data points

• POI-as-a-Service with on-demand verification and collection in 170+ countries leveraging our network of 1M+ contributors

• Customise your feed by specific refresh rate, location, country, category, and brand based on your specific needs

• Data Noise Filtering Algorithms normalise and de-dupe POI data that is ready for analysis with minimal preparation

DATA QUALITY

Quadrant’s POI data are manually collected and verified by Geolancers. Our network of freelancers, maps cities and neighborhoods adding and updating POIs on our proprietary app Geolancer on their smartphone. Compared to other methods, this process guarantees accuracy and promises a healthy stream of POI data. This method of data collection also steers clear of infringement on users’ privacy and sale of their location data. These purpose-built apps do not store, collect, or share any data other than the physical location (without tying context back to an actual human being and their mobile device).

USE CASES

The main goal of POI data is to identify a place of interest, establish its accurate location, and help businesses understand the happenings around that place to make better, well-informed decisions. POI can be essential in assessing competition, improving operational efficiency, planning the expansion of your business, and more.

It can be used by businesses to power their apps and platforms for last-mile delivery, navigation, mapping, logistics, and more. Combined with mobility data, POI data can be employed by retail outlets to monitor traffic to one of their sites or of their competitors. Logistics businesses can save costs and improve customer experience with accurate address data. Real estate companies use POI data for site selection and project planning based on market potential. Governments can use POI data to enforce regulations, monitor public health and well-being, plan public infrastructure and services, and more. A few common and widespread use cases of POI data are:

• Navigation and mapping for digital marketplaces and apps.
• Logistics for online shopping, food delivery, last-mile delivery, and more.
• Improving operational efficiency for rideshare and transportation platforms.
• Demographic and human mobility studies for market consumption and competitive analysis.
• Market assessment, site selection, and business expansion.
• Disaster management and urban mapping for public welfare.
• Advertising and marketing deployment and ROI assessment.
• Real-estate mapping for online sales and renting platforms.About Geolancer

ABOUT GEOLANCER

Quadrant's POI-as-a-Service is powered by Geolancer, our industry-leading manual verification project. Geolancers, equipped with a smartphone running our proprietary app, manually add and verify POI data points, ensuring accuracy and authenticity. Geolancer helps data buyers acquire data with the update frequency suited for their specific use case.

Polygon geometry with attributes displaying sales tax domiciles in East Baton Rouge Parish, Louisiana.

Data Axle Reference Solutions, formerly ReferenceUSA, contains two directories--residential and business. Records include over 12 million U.S. businesses and 120 million U.S. residents. Businesses include private, public, and non-profit organizations, regardless of employee size or sales. This dataset of Data Axle’s business database provides 52 attributes about tens of millions of businesses across the United States for almost every business from the Fortune 500 down to mom-and-pop shops and work-from-home freelancers. The Data Axle business database is available in its entirety for the years 2017 to 2020. The data can be downloaded in a single commas separated values (.csv) file for each year of interest. This file is approximately 5 GB in size after de-compressing the .zip archive. To load the .csv in memory requires a minimum of 32 GB of RAM. To access the Data Axle data on low-memory systems, the .csv file for each year as been split into subsets by US Census defined geographic regions, as well as the more granular geographic divisions. The file census-regions-divisions.csv identifies the states and territories that belong to each region (5 regions plus territories) and divisions (9 divisions plus territories).

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4MM+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Global B2B Contact Database | 850M+ Verified LinkedIn Profiles | 95% Accuracy Stop guessing who still works where. Forager.ai delivers the world's most complete and dynamic B2B people intelligence—combining 850M+ LinkedIn profiles with verified mobile and email data, refreshed every 14 days to track role changes, promotions, and company moves.

Why Talent & Sales Teams Rely on Forager ✅ Passive Candidate Goldmine Over 40% of profiles include verified personal emails and mobile numbers, essential for reaching:

Executives who mask work contact details

Job-hoppers before they update LinkedIn

Hard-to-reach specialists (e.g. AI engineers, compliance leads)

✅ 100% Ethical Sourcing Fully compliant

Your Swiss Army Knife for Hiring & Sales Every Profile Includes:

Verified work & personal emails + mobile numbers

Full career history (With durations)

Rich skills matrix (endorsed & self-reported)

Education and certifications

Team size & current management scope

Trusted Across Industries:

🔹 Recruitment Agencies Source Python developers with consistent GitHub activity

Discover passive CFOs open to PE-backed ventures

🔹 Sales Teams Target CMOs within their first 90 days on the job

Track buyers engaging with competitors’ ads (via intent data)

🔹 VC & PE Firms Build accurate org charts for due diligence

Identify leadership teams preparing to exit post-acquisition

🔹 HR Tech & SaaS Platforms Enrich ATS/CRM systems through API

Power diversity dashboards with gender & ethnicity insights

Enterprise-Ready Delivery ATS/CRM Integrations: Greenhouse, Bullhorn, Salesforce

Real-Time API: Lookups with sub-300ms latency

Snowflake Sync: Daily refreshed tables with SCD2 history

Compliance Hub: Auto-delete & opt-out workflows across systems

LinkedIn Database | Verified B2B Contacts | Recruitment Intelligence | Sales Lead Database | Talent Mapping | Executive Contact Data | GDPR-Compliant Profiles | Passive Candidate Sourcing | People Enrichment API | Skills-Based Hiring

A global database of Direct Marketing Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future. Leverage up-to-date audience targeting population trends for market research, audience targeting, and sales territory mapping.

Self-hosted marketing population dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The Demographic Data is standardized, unified, and ready to use.

Use cases for the Global Consumer Behavior Database (Direct Marketing Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Audience targeting

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Demographic data export methodology

Our population data packages are offered in CSV format. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our Consumer databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

The world has digitized rapidly, especially since the advent of the internet. Banks, financial institutions, hospitals, insurance companies, and e-commerce platforms rely heavily on databases to manage customer accounts, transactions, and sensitive financial data. With the advancements in the technology sector, the database monitoring software market is poised to be valued at a staggering US$ 2.40 billion in 2024.

Category-wise Insights

Country-wise Insights

This dataset spans a period of thirteen months and captures detailed information about retail transactions. Each record in the dataset represents a specific sale and includes key attributes such as invoice number, stock code, product description, quantity, invoice date, unit price, customer ID, and the country where the customer is located. This data provides a comprehensive view of the sales activities over the specified time frame.

Sales Training Market size was valued at USD 10.32 Billion in 2024 and is projected to reach USD 18.95 Billion by 2032, growing at a CAGR 8% from 2026 to 2032.Global Sales Training Market DriversLeveraging CRM Services for Enhanced Sales Effectiveness: A significant driver for the Sales Training Market comes from the widespread adoption and sophisticated utilization of Customer Relationship Management (CRM) Services. These services are fundamentally designed to meticulously manage customer interactions and relationships, acting as the central hub for customer data analysis, personalized communication strategies, and the implementation of effective loyalty programs. For sales teams, CRM systems are invaluable for understanding customer preferences, tracking engagement history, and identifying opportunities for upselling or cross-selling. Consequently, sales training programs are increasingly focused on equipping sales professionals with the expertise to leverage CRM tools effectively, enabling them to enhance customer satisfaction, improve retention rates, and ultimately optimize the entire sales funnel. This emphasis on data-driven, customer-centric selling directly fuels the demand for advanced sales training.Bolstering Trust and Security with Authentication Services: While seemingly indirect, the rising importance of Authentication Services significantly influences the Sales Training Market, particularly in sectors dealing with sensitive transactions or digital products. These services are critical for ensuring secure user identification and verification through methods such as OTP-based logins, biometric verification, and two-factor authentication.

Sales Data contains information about the sale of freehold and leasehold properties within Western Australia. This dataset is derived from; information from Transfer of Land documents registered at Landgate subject to the Transfer of Land Act 1943 for each of the last 3 sales of the property, and known property attribute information at the time of last sale, that was gathered subject to the Valuation of Land Act 1977. This dataset reflects information about the last three sales of property dating back to 1988. As the information is gathered from 2 different sources of stored data, that have been captured to service the requirements of independent Legislation, data contained in this dataset is subject to anomalies and may not necessarily meet the intended purpose of the user. © Western Australian Land Information Authority. Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions.

Residential market value estimates and most recent sales values for owned properties at a parcel level within Fairfax County as of the VALID_TO date in the attribute table. For methodology and a data dictionary please view the IPLS data dictionary

Access B2B Contact Data for North American Small Business Owners with Success.ai—your go-to provider for verified, high-quality business datasets. This dataset is tailored for businesses, agencies, and professionals seeking direct access to decision-makers within the small business ecosystem across North America. With over 170 million professional profiles, it’s an unparalleled resource for powering your marketing, sales, and lead generation efforts.

Key Features of the Dataset:

Verified Contact Details

Includes accurate and up-to-date email addresses and phone numbers to ensure you reach your targets reliably.

AI-validated for 99% accuracy, eliminating errors and reducing wasted efforts.

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Comprehensive data points include job titles, skills, work experience, and education to enable precise segmentation and targeting.

Enriched with insights into decision-making roles, helping you connect directly with small business owners, CEOs, and other key stakeholders.

Business-Specific Information

Covers essential details such as industry, company size, location, and more, enabling you to tailor your campaigns effectively. Ideal for profiling and understanding the unique needs of small businesses.

Continuously Updated Data

Our dataset is maintained and updated regularly to ensure relevance and accuracy in fast-changing market conditions. New business contacts are added frequently, helping you stay ahead of the competition.

Why Choose Success.ai?

At Success.ai, we understand the critical importance of high-quality data for your business success. Here’s why our dataset stands out:

Tailored for Small Business Engagement Focused specifically on North American small business owners, this dataset is an invaluable resource for building relationships with SMEs (Small and Medium Enterprises). Whether you’re targeting startups, local businesses, or established small enterprises, our dataset has you covered.

Comprehensive Coverage Across North America Spanning the United States, Canada, and Mexico, our dataset ensures wide-reaching access to verified small business contacts in the region.

Categories Tailored to Your Needs Includes highly relevant categories such as Small Business Contact Data, CEO Contact Data, B2B Contact Data, and Email Address Data to match your marketing and sales strategies.

Customizable and Flexible Choose from a wide range of filtering options to create datasets that meet your exact specifications, including filtering by industry, company size, geographic location, and more.

Best Price Guaranteed We pride ourselves on offering the most competitive rates without compromising on quality. When you partner with Success.ai, you receive superior data at the best value.

Seamless Integration Delivered in formats that integrate effortlessly with your CRM, marketing automation, or sales platforms, so you can start acting on the data immediately.

Use Cases: This dataset empowers you to:

Drive Sales Growth: Build and refine your sales pipeline by connecting directly with decision-makers in small businesses. Optimize Marketing Campaigns: Launch highly targeted email and phone outreach campaigns with verified contact data. Expand Your Network: Leverage the dataset to build relationships with small business owners and other key figures within the B2B landscape. Improve Data Accuracy: Enhance your existing databases with verified, enriched contact information, reducing bounce rates and increasing ROI. Industries Served: Whether you're in B2B SaaS, digital marketing, consulting, or any field requiring accurate and targeted contact data, this dataset serves industries of all kinds. It is especially useful for professionals focused on:

Lead Generation Business Development Market Research Sales Outreach Customer Acquisition What’s Included in the Dataset: Each profile provides:

Full Name Verified Email Address Phone Number (where available) Job Title Company Name Industry Company Size Location Skills and Professional Experience Education Background With over 170 million profiles, you can tap into a wealth of opportunities to expand your reach and grow your business.

Why High-Quality Contact Data Matters: Accurate, verified contact data is the foundation of any successful B2B strategy. Reaching small business owners and decision-makers directly ensures your message lands where it matters most, reducing costs and improving the effectiveness of your campaigns. By choosing Success.ai, you ensure that every contact in your pipeline is a genuine opportunity.

Partner with Success.ai for Better Data, Better Results: Success.ai is committed to delivering premium-quality B2B data solutions at scale. With our small business owner dataset, you can unlock the potential of North America's dynamic small business market.

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