- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- FAQ
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- FAQ
What is E-POS Data?
E-POS data refers to electronic point of sale data, which includes information collected from electronic devices used to process sales transactions. It encompasses details such as product names, prices, quantities, customer information, and payment methods. E-POS data is crucial for businesses as it provides insights into sales patterns, inventory management, and customer behavior, enabling informed decision-making and improved operational efficiency.
Examples of E-POS data include sales transactions, customer information, inventory levels, and product details collected from electronic point of sale systems. E-POS data is used for analyzing sales patterns, monitoring inventory, understanding customer behavior, and making informed business decisions. In this page, you’ll find the best data sources for E-POS dataset.
Best E-POS Databases & Datasets
Here is our curated selection of top E-POS Data sources. We focus on key factors such as data reliability, accuracy, and flexibility to meet diverse use-case requirements. These datasets are provided by trusted providers known for delivering high-quality, up-to-date information.
Basketview Signal CPG USA Data | SKU-Level Consumer Receipt Data & POS Data | CPG-Tagged Data on Leading Global Brands | 3-Day Lag, Daily Delivery
Ecommerce Merchant Data | Retail Executives Worldwide | Verified Emails & Phone Numbers for Global Leaders | Best Price Guaranteed
Fruit Juice Retail Mapping - Product availability, pricing, shelf visibility, and outlet attributes for packaged fruit juices across retail locations
Envestnet | Yodlee's De-Identified Company Data Research Panel | USA Employee Payroll Data covering 4800+ employers | Cohort Analysis
Retail Data | Retail Sector in Asia | Verified Business Profiles & Insights | Best Price Guaranteed
Envestnet | Yodlee's De-Identified Salary Research Panel | USA Employee Payroll Data covering 4800+ employers | Cohort Analysis
Envestnet | Yodlee's De-Identified Payroll Research Panel | USA Employee Payroll Data covering 4800+ employers | Cohort Analysis
Envestnet | Yodlee's De-Identified Bank Statement Research Panel | USA Employee Payroll Data covering 4800+ employers | Cohort Analysis
Envestnet | Yodlee's De-Identified Electronic Payment Research Panel | USA Employee Payroll Data covering 4800+ employers | Cohort Analysis
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Top E-POS Data Providers & Companies
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Chain of Demand
CoverageUSAUK+16Chain of Demand provides retail industry data spanning across the US, China and the European market. With three years of data available, we use our proprietary data mining techniques to collect insights, signals and revenue predictions that are powered by our artificial intelligence models.100M+data points168xfaster to analyze data1%variance in revenue prediction -
Edison Trends
Edison Trends data is owned directly by Edison. The data source are Edison’s consumer apps, Edison Mail and Edison Assistant. With users consent our apps access and process certain email messages to help Edison Trends aggregate anonymized purchase data. Our panel is longitudinal and representative.
Popular Use Cases for E-POS Data
E-POS Data is essential for a wide range of business applications, offering valuable insights and driving opportunities across industries. Below, we have highlighted the most significant use cases for E-POS Data.
Main Attributes of E-POS Data
Below, we outline the most popular attributes associated with this type of data—features that data buyers are actively seeking to meet their needs.
| Attribute | Type | Description | Action |
|---|---|---|---|
| String | The name of a company or business, might be the legal or brand name. | View 7 datasets | |
| String | The compensation of a job. | View 5 datasets | |
| Text | A description of a company. | View 3 datasets | |
| String | The approx. number of employees working for a company. | View 3 datasets | |
| String | The domain name of a company website. Potential ID to match company records. | View 2 datasets | |
| String | The industry classification of a company. | View 2 datasets |
Frequently Asked Questions
How is the Quality of E-POS Data Maintained?
The quality of E-POS Data is ensured through rigorous validation processes, such as cross-referencing with reliable sources, monitoring accuracy rates, and filtering out inconsistencies. High-quality datasets often report match rates, regular updates, and adherence to industry standards.
How Frequently is E-POS Data Updated?
The update frequency for E-POS Data varies by provider and dataset. Some datasets are refreshed daily or weekly, while others update less frequently. When evaluating options, ensure you select a dataset with a frequency that suits your specific use case.
Is E-POS Data Secure?
The security of E-POS Data is prioritized through compliance with industry standards, including encryption, anonymization, and secure delivery methods like SFTP and APIs. At Datarade, we enforce strict policies, requiring all our providers to adhere to regulations such as GDPR, CCPA, and other relevant data protection standards.
How is E-POS Data Delivered?
E-POS Data can be delivered in formats such as CSV, JSON, XML, or via APIs, enabling seamless integration into your systems. Delivery frequencies range from real-time updates to scheduled intervals (daily, weekly, monthly, or on-demand). Choose datasets that align with your preferred delivery method and system compatibility for E-POS Data.
How Much Does E-POS Data Cost?
The cost of E-POS Data depends on factors like the datasets size, scope, update frequency, and customization level. Pricing models may include one-off purchases, monthly or yearly subscriptions, or usage-based fees. Many providers offer free samples, allowing you to evaluate the suitability of E-POS Data for your needs.
What Are Similar Data Types to E-POS Data?
E-POS Data is similar to other data types, such as POS Transaction Data. These related categories are often used together for applications like Market Intelligence and Ecommerce Intelligence.
