Dataset

This dataset was created by ajay jha

Released under CC0: Public Domain

Contents

This statistic shows the share of customers in the U.S. and worldwide by if they have ever stopped doing business with a brand due to a poor customer service experience in 2018. During the survey, 62 percent of respondents from the United States stated that they have stopped doing business with a brand due to a poor customer service experience.

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

Dataset Description

This dataset contains 500 records of customer transactions across five distinct bakeries, providing a rich source of information for analyzing consumer behavior in the bakery industry. Each record is characterized by several key features:

• Bakery_ID: A unique identifier for each bakery, allowing for comparative analysis across different locations.
• Customer_ID: A unique identifier assigned to each customer, facilitating individual transaction tracking without personal identification.
• Items_Purchased: The quantity of items purchased in each transaction, which helps gauge customer buying habits and preferences.
• Amount_Spent: The total expenditure of each customer during their visit, serving as a primary metric for assessing customer spending behavior.
• Payment_Method: The method used by customers to complete their purchases, including options like Card, Cash, and Mobile Payment, which offers insights into payment trends and preferences.
• Loyalty Member: This column indicates whether the customer is a member of the bakery's loyalty program. Analyzing loyalty membership data can provide insights into customer retention and the effectiveness of loyalty initiatives in driving repeat business.
• Age: This column indicates the age of the customer at the time of purchase. It helps analyze spending patterns and preferences across different age groups.
• Gender: This column represents the gender of the customer, providing insights into purchasing behavior and preferences. Analyzing gender data can assist bakeries in tailoring marketing strategies and product offerings.
• Purchase_Date: This column records the date of each transaction, allowing for the identification of seasonal trends and peak shopping periods. It aids in understanding customer buying behavior over time.
• Time_of_Purchase: This column categorizes the time of day when the purchase was made. Analyzing this data helps identify peak hours for customer visits, enabling bakeries to optimize staffing and inventory.

This dataset is designed to facilitate various analyses, including spending patterns, payment method preferences, and overall consumer trends in the bakery sector. By utilizing this dataset, stakeholders can derive actionable insights to enhance customer engagement, optimize product offerings, and inform marketing strategies.

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

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. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

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

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

The size of the Business Data Visualization Tools market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

The statistic shows Beiersdorf AG's consumer business segment sales growth worldwide in 2024, by its different business units. Beiersdorf AG is a multinational corporation headquartered in Hamburg, Germany. The company's consumer segment focuses on the international skin and body care markets. In 2024, the NIVEA brand grew organically by 9 percent, with total sales amounting to 5.6 billion euros.

The US Cell Phone Database: Consumer & Business Contacts is AmeriList’s premier mobile-first dataset, built for marketers, agencies, and enterprises that demand accurate, compliant, and scalable U.S. cell phone data. Covering millions of verified consumer and business mobile numbers, and refreshed weekly for accuracy, this file is one of the most reliable and frequently updated cell phone databases available today.

Why Choose This Database? Today’s marketing success depends on reaching your audience where they are, and that’s on their mobile devices. With this dataset, you gain:

• Nationwide coverage of U.S. consumer and business cell phone contacts.
• Verified mobile numbers that can be DNC-scrubbed to support compliance and responsible outreach.
• Multi-channel readiness with delivery via CSV, API, SFTP, or cloud integrations (AWS, GCP, Azure).

Key Features: - Millions of verified consumer and business mobile phone numbers. - Weekly update cycle to maintain accuracy and compliance.

Schema Preview: First_Name, Last_Name, Phone_Number, DNC_Flag

Use Cases This dataset powers a wide range of mobile-first and cross-channel marketing strategies:

• SMS Campaigns: Deliver time-sensitive promotions and personalized offers.
• Outbound Calling: Connect directly with decision-makers and consumers.
• Mobile-First Advertising: Enhance digital campaigns with compliant mobile targeting.

Industries That Benefit - Retail & E-commerce: Deliver SMS promotions, loyalty program updates, and flash sale alerts. - Healthcare: Share wellness updates, insurance enrollment opportunities, and educational campaigns. - Financial Services & Insurance: Connect with prospects for loan offers, credit card promotions, or new insurance plans. - Real Estate & Home Services: Reach potential buyers, renters, and homeowners with property alerts and service offers.

Why AmeriList? For over 20 years, AmeriList has been a trusted leader in direct marketing data solutions. Our expertise in consumer and business contact databases ensures not only the accuracy of the phone numbers we provide, but also the compliance and strategic value they deliver. With a strong focus on TCPA and CAN-SPAM regulations, data quality, and ROI, AmeriList empowers brands and agencies to unlock the full potential of mobile-first marketing campaigns.

Scottish Annual Business Statistics includes detailed data on employees, turnover, gross value added, labour costs and other financial data. Data are shown by industry sector, local authority area and ownership. Source agency: Scottish Government Designation: National Statistics Language: English Alternative title: Scottish Annual Business Statistics

Comprehensive dataset containing 937 verified Appliances customer service businesses in United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 15 verified Appliances customer service businesses in Minnesota, United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 1 verified Appliances customer service businesses in Maine, United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 45 verified Appliances customer service businesses in Venezuela with complete contact information, ratings, reviews, and location data.

Graph and download economic data for Breakdown of Revenue by Type of Customer: Business Firms, Not-for-Profit Organizations, and Government (Federal, State, and Local) for Wired Telecommunications Carriers, All Establishments, Employer Firms (RPCBFNEF5171ALLEST) from 2013 to 2022 about telecom, wired, employer firms, nonprofit organizations, accounting, revenue, establishments, business, government, services, and USA.

Online Retail Sales and Customer Data

Transactional Data with Product and Customer Details in Online Retail

By Marc Szafraniec [source]

About this dataset

The InvoiceNo column holds unique identifiers for each transaction conducted. This numerical code serves a twofold purpose: it facilitates effortless identification of individual sales or purchases while simultaneously enabling treasury management by offering a repository for record keeping.

In concordance with the invoice number is the InvoiceDate column. It provides a date-time stamp associated with every transaction, which can reveal patterns in purchasing behaviour over time and assists with record-keeping requirements.

The StockCode acts as an integral part of this dataset; it encompasses alphanumeric sequences allocated distinctively to every item in stock. Such a system aids unequivocally identifying individual products making inventory records seamless.

The Description field offers brief elucidations about each listed product, adding layers beyond just stock codes to aid potential customers' understanding of products better and make more informed choices.

Detailed logs concerning sold quantities come under the Quantity banner - it lists the units involved per transaction alongside aiding calculations regarding total costs incurred during each sale/purchase offering significant help tracking inventory levels based on products' outflow dynamics within given periods.

Retail isn't merely about what you sell but also at what price you sell- A point acknowledged via our inclusion of unit prices exerted on items sold within transactions inside our dataset's UnitPrice column which puts forth pertinent pricing details serving as pivotal factors driving metrics such as gross revenue calculation etc

Finally yet importantly is our dive into foreign waters - literally! With impressive international outreach we're looking into segmentation bases like geographical locations via documenting countries (under the name Country) where transactions are conducted & consumers reside extending opportunities for businesses to map their customer bases, track regional performance metrics, extend localization efforts and overall contributing to the formulation of efficient segmentation strategies.

All this invaluable information can be found in a sortable CSV file titled online_retail.csv. This dataset will prove incredibly advantageous for anyone interested in or researching online sales trends, developing customer profiles, or gaining insights into effective inventory management practices

How to use the dataset

Identifying Products: StockCode is the unique identifier for each product. You can use it to identify individual products, track their sales, or discover patterns related to specific items.

Assessing Sales Volume: Quantity column tells you about the number of units of a product involved in each transaction. Along with InvoiceNo, you can analyze overall sales volume or specific purchases throughout your selected period.

Observing Price Fluctuations: By using the UnitPrice, not only can the total cost per transaction be calculated (by multiplying with Quantity), but also insightful observations like price fluctuations over time or determining most profitable items could be derived.

Analyzing Description Patterns/Trends: The Description field sheds light upon what kind of products are being traded. This could provide some inspiration for text analysis like term frequency-inverse document frequency (TF-IDF), sentiment analysis on descriptions, etc., to figure out popular trends at given times.

Analysing Geographical Trends: With the help of Country column, geographical trends in sales volumes across different nations can easily be analyzed i.e., which location has more customers or which country orders more quantity or expensive units based on unit price and quantity columns respectively.

Keep in mind that proper extraction and transformation methodology should be applied while handling data from different columns as per their datatypes (textual/alphanumeric/numeric) requirements.

This dataset not only allows retailers to gain an immediate understanding into their operations but could also serve as a base dataset for those interested in machine learning regarding predicting future transactions

Research Ideas

• Inventory Management: By tracking the 'Quantity' and 'StockCode' over time, a business could use this data to notice if certain products are frequently purchased together or in specific seasons, allowing them to better stock their inventory.
• Pricing Strategy:...

This statistic shows how well brand marketers felt thier companies coverted trade show leads, contacts and conversation into customer business in the United States in the fourth quarter of 2012 and the first quarter of 2013. The statistic shows that though only *** percent of respondents reported that thier companies converted leads to business extremely well, ** percent of respondents said their companies did so moderately well.

Number of businesses in routes of 1 000 m x 1 000 m as of 01 January. The breakdown indicates the total number of businesses in the routes. Historical versions back to 2013.

The dataset "cpc_insts" has been discontinued since 05/02/2024.

In all three years examined (2022, 2023, and 2024), approximately a ***** of UK adults felt they could be influenced to trust businesses about their sustainability commitments if they had transparent, accountable, as well as socially and environmentally responsible supply chains. As in 2023, about a ***** of surveyed UK consumers said they would simply not know what information to trust in 2024, while nearly a ******* stated that nothing could convince them to trust brands more about their commitments to climate change.

With 1.8 Million Businesses in Sweden , 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.

Techsalerator covers all regions and cities in the country :

Blekinge Karlskrona Dalarna Borlänge Falun Gävleborg/Gavleborg Gävle/Gavle Gotland Visby Halland Halmstad Jämtland Östersund/Osterund Jönköping/Jonkoping Jönköping Kalmar Kalmar Kronoberg Växjö/Vaxjo Norrbotten Kiruna Luleå Örebro/Orebro Örebro Östergötland/Ostergotland Linköping/Linkoping Norrköping/Norrkoping Skåne/Skane Helsingborg Kristianstad Landskrona Lund Malmö/Malmo Trelleborg Södermanland/ Sodermanland Eskilstuna Nyköping/ Nykoping Stockholm Södertälje/ Sodertalje Solna Stockholm Uppsala Uppsala Värmland/ Varmland Karlstad Västerbotten/ Vasterbotten Umeå/ Umea Västernorrland/ Vasternorrland Sundsvall Västmanland/ Vastmanland Västerås/ Vasteras Västra Götaland/ Vastra Gotaland Borås/ Boras Gothenburg Lidköping/ Lidkoping Skara