McGRAW’s US B2B database delivers 80M+ verified contacts with 95%+ accuracy, backed by in-house call centers, social media validation, and market research. We provide real-time, reliable data for B2B outreach, lead generation, and market insights—ensuring precision, quality, and impact.

With 56 Million Businesses in the United States of America, Techsalerator has access to the highest B2B count of Data/ Business Data in the country.

Thanks to our unique tools and large data specialist team, we are able to select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all states and cities in the country : Example covered.

All states :

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho IllinoisIndiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri MontanaNebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon PennsylvaniaRhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

A few cities : New York City NY Los Angeles CA Chicago IL Houston TX Phoenix AZ Philadelphia PA San Antonio TX San Diego CA Dallas TX Austin TX San Jose CA Fort Worth TX Jacksonville FL Columbus OH Charlotte NC Indianapolis IN San Francisco CA Seattle WA Denver CO Washington DC Boston MA El Paso TX Nashville TN Oklahoma City OK Las Vegas NV Detroit MI Portland OR Memphis TN Louisville KY Milwaukee WI Baltimore MD Albuquerque NM Tucson AZ Mesa AZ Fresno CA Sacramento CA Atlanta GA Kansas City MO Colorado Springs CO Raleigh NC Omaha NE Miami FL Long Beach CA Virginia Beach VA Oakland CA Minneapolis MN Tampa FL Tulsa OK Arlington TX Wichita KS Bakersfield CA Aurora CO New Orleans LA Cleveland OH Anaheim CA Henderson NV Honolulu HI Riverside CA Santa Ana CA Corpus Christi TX Lexington KY San Juan PR Stockton CA St. Paul MN Cincinnati OH Greensboro NC Pittsburgh PA Irvine CA St. Louis MO Lincoln NE Orlando FL Durham NC Plano TX Anchorage AK Newark NJ Chula Vista CA Fort Wayne IN Chandler AZ Toledo OH St. Petersburg FL Reno NV Laredo TX Scottsdale AZ North Las Vegas NV Lubbock TX Madison WI Gilbert AZ Jersey City NJ Glendale AZ Buffalo NY Winston-Salem NC Chesapeake VA Fremont CA Norfolk VA Irving TX Garland TX Paradise NV Arlington VA Richmond VA Hialeah FL Boise ID Spokane WA Frisco TX Moreno Valley CA Tacoma WA Fontana CA Modesto CA Baton Rouge LA Port St. Lucie FL San Bernardino CA McKinney TX Fayetteville NC Santa Clarita CA Des Moines IA Oxnard CA Birmingham AL Spring Valley NV Huntsville AL Rochester NY Cape Coral FL Tempe AZ Grand Rapids MI Yonkers NY Overland Park KS Salt Lake City UT Amarillo TX Augusta GA Columbus GA Tallahassee FL Montgomery AL Huntington Beach CA Akron OH Little Rock AR Glendale CA Grand Prairie TX Aurora IL Sunrise Manor NV Ontario CA Sioux Falls SD Knoxville TN Vancouver WA Mobile AL Worcester MA Chattanooga TN Brownsville TX Peoria AZ Fort Lauderdale FL Shreveport LA Newport News VA Providence RI Elk Grove CA Rancho Cucamonga CA Salem OR Pembroke Pines FL Santa Rosa CA Eugene OR Oceanside CA Cary NC Fort Collins CO Corona CA Enterprise NV Garden Grove CA Springfield MO Clarksville TN Bayamon PR Lakewood CO Alexandria VA Hayward CA Murfreesboro TN Killeen TX Hollywood FL Lancaster CA Salinas CA Jackson MS Midland TX Macon County GA Kansas City KS Palmdale CA Sunnyvale CA Springfield MA Escondido CA Pomona CA Bellevue WA Surprise AZ Naperville IL Pasadena TX Denton TX Roseville CA Joliet IL Thornton CO McAllen TX Paterson NJ Rockford IL Carrollton TX Bridgeport CT Miramar FL Round Rock TX Metairie LA Olathe KS Waco TX

With 172,000 Businesses in Saudi Arabia , Techsalerator has access to the highest B2B count of Data/Business Data in the country.

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all regions and cities in Saudi Arabia ( example) :

Hejaz Mecca Region Makkah Najd Riyadh Region Riyadh Eastern Arabia Eastern Region Dammam Southern Arabia 'Asir Region Abha Southern Arabia Jizan Region Jazan Hejaz Medina Region Madinah Najd Al-Qassim Region Buraidah Hejaz Tabuk Region Tabuk Najd Ha'il Region Ha'il Southern Arabia Najran Region Najran Badiah Al-Jawf Region Sakaka Hejaz Al-Bahah Region Al-Baha Badiah Northern Borders Region Arar

With 19.7 Million Businesses in India , Techsalerator has access to the highest B2B count of Data/Business Data in the country. .

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all cities and regions in India ( example ) :

Mumbai Maharashtra Delhi Delhi Bangalore Karnataka Hyderabad Telangana Ahmedabad Gujarat Chennai Tamil Nadu Kolkata West Bengal Surat Gujarat Pune Maharashtra Jaipur Rajasthan Lucknow Uttar Pradesh Kanpur Uttar Pradesh Nagpur Maharashtra Indore Madhya Pradesh Thane Maharashtra Bhopal Madhya Pradesh Visakhapatnam[4] Andhra Pradesh Pimpri-Chinchwad Maharashtra Patna Bihar Vadodara Gujarat Ghaziabad Uttar Pradesh Ludhiana Punjab Agra Uttar Pradesh Nashik Maharashtra Ranchi Jharkhand Faridabad Haryana Meerut Uttar Pradesh Rajkot Gujarat Kalyan-Dombivli Maharashtra Vasai-Virar Maharashtra Varanasi Uttar Pradesh Srinagar Jammu and Kashmir Aurangabad Maharashtra Dhanbad Jharkhand Gurgaon[5] Haryana Amritsar Punjab Navi Mumbai Maharashtra Allahabad Uttar Pradesh[6] Howrah West Bengal Gwalior Madhya Pradesh Jabalpur Madhya Pradesh Coimbatore Tamil Nadu Vijayawada Andhra Pradesh Jodhpur Rajasthan Madurai Tamil Nadu Raipur Chhattisgarh Kota[8] Rajasthan Chandigarh Chandigarh Guwahati Assam Solapur Maharashtra Hubli–Dharwad Karnataka Mysore[9][10][11] Karnataka Tiruchirappalli[12] Tamil Nadu Bareilly Uttar Pradesh Aligarh Uttar Pradesh Tiruppur Tamil Nadu Moradabad Uttar Pradesh Jalandhar Punjab Bhubaneswar Odisha Salem Tamil Nadu Warangal[13][14] Telangana Mira-Bhayandar Maharashtra Jalgaon Maharashtra Guntur[15] Andhra Pradesh Thiruvananthapuram Kerala Bhiwandi Maharashtra Tirupati Andhra Pradesh Saharanpur Uttar Pradesh Gorakhpur Uttar Pradesh Bikaner Rajasthan Amravati Maharashtra Noida Uttar Pradesh Jamshedpur Jharkhand Bhilai Chhattisgarh Cuttack Odisha Firozabad Uttar Pradesh Kochi Kerala Nellore[16][17] Andhra Pradesh Bhavnagar Gujarat Dehradun Uttarakhand Durgapur West Bengal Asansol West Bengal Rourkela Odisha Nanded Maharashtra Kolhapur Maharashtra Ajmer Rajasthan Akola Maharashtra Gulbarga Karnataka Jamnagar Gujarat Ujjain Madhya Pradesh Loni Uttar Pradesh Siliguri West Bengal Jhansi Uttar Pradesh Ulhasnagar Maharashtra Jammu[18] Jammu and Kashmir Sangli-Miraj & Kupwad Maharashtra Mangalore Karnataka Erode[19] Tamil Nadu Belgaum Karnataka Kurnool[20] Andhra Pradesh Ambattur Tamil Nadu Rajahmundry[21][22] Andhra Pradesh Tirunelveli Tamil Nadu Malegaon Maharashtra Gaya Bihar Udaipur Rajasthan Karur Tamilnadu Kakinada Andhra Pradesh Davanagere Karnataka Kozhikode Kerala Maheshtala West Bengal Rajpur Sonarpur West Bengal Bokaro Jharkhand South Dumdum West Bengal Bellary Karnataka Patiala Punjab Gopalpur West Bengal Agartala Tripura Bhagalpur Bihar Muzaffarnagar Uttar Pradesh Bhatpara West Bengal Panihati West Bengal Latur Maharashtra Dhule Maharashtra Rohtak Haryana Sagar Madhya Pradesh Korba Chhattisgarh Bhilwara Rajasthan Berhampur Odisha Muzaffarpur Bihar Ahmednagar Maharashtra Mathura Uttar Pradesh Kollam Kerala Avadi Tamil Nadu Kadapa[23] Andhra Pradesh Anantapuram[24] Andhra Pradesh Kamarhati West Bengal Bilaspur Odisha Sambalpur Odisha Shahjahanpur Uttar Pradesh Satara Maharashtra Bijapur Karnataka Rampur Uttar Pradesh Shimoga Karnataka Chandrapur Maharashtra Junagadh Gujarat Thrissur Kerala Alwar Rajasthan Bardhaman West Bengal Kulti West Bengal Nizamabad Telangana Parbhani Maharashtra Tumkur Karnataka Khammam Telangana Uzhavarkarai Puducherry Bihar Sharif Bihar Panipat Haryana Darbhanga Bihar Bally West Bengal Aizawl Mizoram Dewas Madhya Pradesh Ichalkaranji Maharashtra Karnal Haryana Bathinda Punjab Jalna Maharashtra Eluru[25] Andhra Pradesh Barasat West Bengal Kirari Suleman Nagar Delhi Purnia[26] Bihar Satna Madhya Pradesh Mau Uttar Pradesh Sonipat Haryana Farrukhabad Uttar Pradesh Durg Chhattisgarh Imphal Manipur Ratlam Madhya Pradesh Hapur Uttar Pradesh Arrah Bihar Anantapur Andhra Pradesh Karimnagar Telangana Etawah Uttar Pradesh Ambarnath Maharashtra North Dumdum West Bengal Bharatpur Rajasthan Begusarai Bihar New Delhi Delhi Gandhidham Gujarat Baranagar West Bengal Tiruvottiyur Tamil Nadu Pondicherry Puducherry Sikar Rajasthan Thoothukudi Tamil Nadu Rewa Madhya Pradesh Mirzapur Uttar Pradesh Raichur Karnataka Pali Rajasthan Ramagundam[27] Telangana Silchar Assam Haridwar Uttarakhand Vijayanagaram Andhra Pradesh Tenali Andhra Pradesh Nagercoil Tamil Nadu Sri Ganganagar Rajasthan ...

Use our Owler companies dataset, a sales intelligence and business information research company, to map your ecosystem, and find market trends and investment opportunities. Access a database of competitors, revenue, employees, and funding for any company. Depending on your needs, you may purchase the entire dataset or a customized subset. The Owler companies information dataset offers public information on all companies listed in Owler. The dataset includes all major data points: Company size Revenue News Key executives Location Website and more. Freshness configuration: monthly refreshes refresh rate of up to 8 million records a month

Goals :

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description).

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

5. Sales Performance Dashboard:

To track the sales performance of the online retail company, a sales performance dashboard can be created. This dashboard can include key metrics such as total sales, sales by product category, sales by customer segment, and sales by geographical location. By visualizing the sales data in an interactive dashboard, it becomes easier to identify trends, patterns, and areas for improvement.

Research Ideas ****:

1. Inventory Management: By analyzing the quantity and frequency of product sales, retailers can effectively manage their stock and predict future demand. This would help ensure that popular items are always available while less popular items aren't overstocked.
2. Customer Segmentation: Data from different countries can be used to understand buying habits across different geographical locations. This will allow the retail company to tailor its marketing strategy for each specific region or country, leading to more effective advertising campaigns.
3. Sales Trend Analysis: With data spanning almost a year, temporal patterns in purchasing behavior can be identified, including seasonality and other trends (like an increase in sales during holidays). Techniques like time-series analysis could provide insights into peak shopping times or days of the week when sales are typically high.
4. Predictive Analysis for Cross-Selling & Upselling: Based on a customer's previous purchase history, predictive algorithms can be utilized to suggest related products that might interest the customer, enhancing upsell and cross-sell opportunities.
5. Detecting Fraud: Analysing sale returns (marked with 'c' in InvoiceNo) across customers or regions could help pinpoint fraudulent activities or operational issues leading to those returns
6. RFM Analysis: By using the RFM (Recency, Frequency, Monetary) segmentation technique, the online retail company can gain insights into customer behavior and tailor their marketing strategies accordingly.

**************Steps :**************

1. Data manipulation and cleaning from raw data using SQL language Google Big Query
2. Data filtering, grouping, and slicing
3. Data Visualization using Tableau
4. Data visualization analysis and result

Unlock the door to business expansion by investing in our real-time eCommerce leads list. Gain direct access to store owners and make informed decisions with data fields including Store Name, Website, Contact First Name, Contact Last Name, Email Address, Physical Address, City, State, Country, Zip Code, Phone Number, Revenue Size, Employee Size, and more on demand.

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Dataset

This dataset was created by Satya Rohith

Contents

Data Broker Service Market Outlook

The global data broker service market size is projected to grow from USD 250 billion in 2023 to an estimated USD 450 billion by 2032, reflecting a compound annual growth rate (CAGR) of 6.7%. This substantial growth can be attributed to increasing digitalization, the exponential rise of data-driven decision-making across industries, and the growing realization of the value derived from data analytics. As businesses continue to recognize the potential of leveraging consumer, business, financial, and health data, the demand for data brokerage services is poised to expand significantly.

One of the primary growth factors for the data broker service market is the increasing importance of data in driving business strategies and operations. Companies are increasingly relying on consumer and market data to gain insights into market trends, consumer behavior, and competitive landscapes. This surge in data utilization across sectors such as retail, healthcare, and finance is propelling the demand for data brokerage services that can provide accurate and comprehensive data sets. The proliferation of digital platforms and the Internet of Things (IoT) has further amplified the volume of data generated, thus boosting the need for efficient data brokerage services.

Moreover, advancements in artificial intelligence (AI) and machine learning (ML) technologies are significantly contributing to the market's growth. These technologies enable enhanced data analysis, predictive analytics, and real-time decision-making, making data brokerage services more valuable. Businesses are increasingly investing in AI and ML to analyze large datasets more efficiently and extract actionable insights. Data brokers, in turn, are leveraging these technologies to offer more sophisticated and tailored data solutions, thus attracting a broader customer base.

Privacy regulations and data protection laws are also playing a crucial role in shaping the data broker service market. While these regulations pose challenges, they also create opportunities for compliant data brokers to differentiate themselves in the market. Companies are more inclined to partner with data brokers that demonstrate robust data governance practices and adhere to regulatory requirements. This trend is driving the market towards more ethical and transparent data brokerage practices, increasing the trust and credibility of data brokers among businesses and consumers alike.

The regional outlook for the data broker service market highlights North America as a dominant player, primarily due to the high adoption of data-driven strategies among businesses and the presence of major data brokerage firms. Europe follows closely, driven by stringent data protection regulations like GDPR, which necessitate secure and compliant data handling. The Asia Pacific region is expected to witness the fastest growth, fueled by the rapid digital transformation in countries like China and India and the increasing use of data analytics in various industries. Latin America and the Middle East & Africa regions are also showing promising growth, supported by the rising awareness of data's strategic value and increasing investments in data analytics infrastructure.

Data Type Analysis

The data broker service market by data type comprises consumer data, business data, financial data, health data, and other categories. Consumer data is one of the most significant segments within this market. This type of data includes information on consumer behavior, preferences, purchasing patterns, and demographics. Businesses leverage consumer data to tailor their marketing strategies, enhance customer experiences, and drive sales growth. The increasing use of digital platforms for shopping, social interaction, and information consumption is continually generating vast amounts of consumer data, thereby fueling the demand for consumer data brokerage services.

Business data, encompassing company profiles, industry trends, and competitive intelligence, is another vital segment. Organizations require business data to strategize market entry, expansion, and competitive positioning. Data brokers play a crucial role in aggregating and providing actionable business insights that help companies navigate complex market dynamics. The rise of global trade, the need for cross-border business intelligence, and the growing importance of data-driven decision-making in corporate strategies are driving the demand for business data brokerage services.

Financial data is crucial for sectors like banking, fina

CE Scanner US provides financial services investors with point-of-sale transaction data. Proprietary M&A attribution and volume equivalency offer rollup views to ticker and brand level with comparative detailed category/subcategory views into retail sales, volumes, distribution, and trends.

This data set contains actual sales data for a chain of Brazilian stores. I modified the names of products, customers, and employees to preserve their identity. I am making this data available so that they can help me get the most out of it, analysis such as:

• Sales forecast

• Customer segmentation

• Employee productivity

• Profitable products

• And everything else that can be extracted from it.

Columns description

Company Code - Affiliate code that sold Order Number - Unique code to identify the sale Employee - Employee who made the sale Product - Name of product sold Product Category - category the product belongs to Client - Name of the customer who made the purchase Client City - City name of the customer who made the purchase Sale Date Time - Date and time the sale was made Product Cost - Cost per unit sold Discount Amount - Total sale discount Amount - Item Quantity Total - Total item value Form of payment - Form of payment

The column values: - Client - Client City - Employee They were exchanged for fictitious names.

The category of the products was maintained, but translated into English, the name of the product consists of the name of the category to which it belongs concatenated with a random number. The rule does not apply to products in the Fuel category, for these, fictitious names were invented.

12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by order Number and it's date, product type and it's quantity, cost and purchase address

Column descriptors : Each dataset has:

Order Id: The id of the order.

Product: The type of the product bought.

Quantity Ordered: How many of the product was ordered.

Price Each: The price of a single item.

Order Date: When the product was ordered including the year,month, day, hours and minutes

Purchase Adrress: Where to deliver the order.

Questions: Question 1: What was the best month for sales? How much was earned that month?

Question 2 : Which city had the highest number of sales?

Question 3: What time should we display advertisements to maximize the likelihood of purchasses and Sales?

Question 4: What products are most often sold together?

Question 5: Which product was sold the most?

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Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

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The annual Small Business Procurement Scorecard is an assessment tool to (1) measure how well federal agencies reach their small business and socio-economic prime contracting and subcontracting goals, (2) provide accurate and transparent contracting data and (3) report agency-specific progress. The prime and subcontracting component goals include goals for small businesses, small businesses owned by women (WOSB), small disadvantaged businesses (SDB), service-disabled veteran-owned small businesses (SDVOSB), and small businesses located in Historically Underutilized Business Zones (HUBZones). Each federal agency has a different small business contracting goal, negotiated annually in consultation with SBA. SBA ensures that the sum total of all of the goals meets the 23 percent target established by law.

for example

United States Report On Business: Purchasing Managers' Index data was reported at 48.700 NA in Apr 2025. This records a decrease from the previous number of 49.000 NA for Mar 2025. United States Report On Business: Purchasing Managers' Index data is updated monthly, averaging 53.400 NA from Jan 1948 (Median) to Apr 2025, with 928 observations. The data reached an all-time high of 77.500 NA in Jul 1950 and a record low of 29.400 NA in May 1980. United States Report On Business: Purchasing Managers' Index data remains active status in CEIC and is reported by Institute for Supply Management. The data is categorized under Global Database’s United States – Table US.S003: Institute for Supply Management: Purchasing Manager Index. [COVID-19-IMPACT]

Mr Purchase Manager Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Procurement Analytics Market size was valued at USD 4.85 Billion in 2024 and is projected to reach USD 25.18 Billion by 2031, growing at a CAGR of 25.21% during the forecast period 2024-2031.

Global Procurement Analytics Market Drivers

Data-Driven Decision Making: Businesses are realizing the value of data-driven decision-making procedures in a variety of departments, including purchasing. With the help of insights from data analysis, procurement analytics helps businesses reduce risks, find cost-saving possibilities, streamline their purchasing procedures, and enhance overall procurement performance.

Growing Complexity and Globalization of Supply Chains: As supply chains becoming more intricate and international, businesses must deal with issues including risk reduction, supplier management, and regulatory compliance. Advanced capabilities to track supply chain interruptions, analyze supplier performance, and optimize sourcing strategies across several geographies and suppliers are provided by procurement analytics solutions.

Demand for Cost Reduction and Efficiency Improvement: Companies are under pressure to lower costs and boost operational efficiency in the cutthroat business world of today. Through spend analysis, supplier consolidation, contract optimization, and demand forecasting, procurement analytics helps businesses find cost-saving options that eventually result in bottom-line benefits.

Russia LUKOIL Petroleum Products Purchase: Year to Date: Domestic Companies data was reported at 1,242.000 Ton th in Dec 2018. This records an increase from the previous number of 898.000 Ton th for Sep 2018. Russia LUKOIL Petroleum Products Purchase: Year to Date: Domestic Companies data is updated quarterly, averaging 893.500 Ton th from Mar 2004 (Median) to Dec 2018, with 60 observations. The data reached an all-time high of 2,298.000 Ton th in Dec 2013 and a record low of 86.000 Ton th in Mar 2009. Russia LUKOIL Petroleum Products Purchase: Year to Date: Domestic Companies data remains active status in CEIC and is reported by LUKOIL. The data is categorized under Russia Premium Database’s Energy Sector – Table RU.RBK003: LUKOIL Purchase.

Buy verified company data by official SIC codes. Access global business records from public trade registers—accurate, segmented, and up to date. Explore 380M+ companies worldwide via bulk files, API, or self-service tools with CompanyData.com (BoldData).