What is Retail Data? Examples, Datasets and Providers
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- Guide
- FAQ
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- Guide
- FAQ
What is Retail Data?
Retail data includes data related to sales, customer behavior, inventory, and pricing of retail goods. This data helps retailers gain insights into their business performance and enhance the overall retail customer’s experience. It’s used for analyzing consumer behavior, identifying trends, optimizing inventory management, and making informed business decisions e.g. retail site selection.
What Are Examples of Retail Data?
Key examples include:
- Sales Data: Revenue, transaction volumes, and average purchase value.
- Inventory Levels: Stock quantities, turnover rates, and restocking needs.
- Customer Demographics: Age, gender, location, and shopping preferences.
- Purchase Patterns: Frequency, timing, and seasonal buying trends.
- Product Performance: Best-sellers, slow-moving items, and returns.
- Foot Traffic: Customer visit counts and peak times.
- Competitor Comparison: Pricing, availability, and promotional strategies.
- And more.
Best Retail Databases & Datasets
Here is our curated selection of top Retail 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.
Retail Data | Retail Sector in Asia | Verified Business Profiles & Insights | Best Price Guaranteed
Retail Data | Malls & Shopping Centers in US | Tourist Attraction Data
Global Retail Data | Retail Store Data | In-Store Data | Retail POI and SKU Level Product Data from 1M+ Locations with Prices
Demographic Data | Segmentation Data | Retail Data | POI Data and Sentiment Data
Store Location Data | 230M+ Global POIs & Retail Locations | 5000+ Categories with Restaurant, Retail & Business Coverage
Global Independent & Multi-National Retail Location and Foot Traffic Data
Store location data | 164M Commercial Places - Stores | Global Retail Store Location Data
CPG Data | Retail Store Location Data | 52M+ POI | SafeGraph Places
Product Data | Google Shopping Data | Retail Data from Thousands of Retailers
Demographic Data | Saudi Arabia | Latest Estimates on Population, Consuming Class, Retail Spend | GIS Data | Map Data
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Top Retail Data Providers & Companies
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MealMe
CoverageUSAUK+248MealMe delivers real-time product availability data from restaurants, grocery stores, and retail stores. Our proprietary technology empowers businesses with actionable insights for competitive intelligence, pricing analysis, and market research, ensuring reliable, scalable data.GroceryTop 100 CoverageRestaurantTop 1000 CoverageRetailTop 100 Coverage -
Consumer Edge
CoverageUSAUK+248Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. Transaction data, scanner data, web data, and email receipt data spans dozens of industries, hundreds of companies, and global geographies to empower strategic decision-making.100M+Credit & Debit Cards30K+Public & Private Brands700+Parent Companies -
Xtract
CoverageUSAUK+248VIt has been a great experience working with Xtract.io. Their data quality was top-notch, helping us strategize new locations for business expansion. Thank you, team, for giving us the right datasets that we require to expand our store outlets.
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Silencio Network
CoverageUSAUK+236We empower users to share their smartphone-generated data ethically — and get rewarded for it. By combining privacy-first values, a user incentive system, and a unique profit-sharing model, we create a transparent data generation ecosystem where users benefit directly from value they help create.CCPA, GDPRCompliant100%Opted-In Users35 B +Data Points -
Huq Industries
CoverageUSAUK+247VHuq’s data is real-time and that makes the data in the TE dashboard even stronger and more valuable. Both local and central government bodies will be able to use it to get a clearer picture of the real economic impacts of Covid-19 across the country. Benjamin Lucas (PhD, FHEA), Managing Director at 3DI
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dataplor
CoverageUSAUK+247VMy firm decided to make the switch over to dataplor from a manual data collection effort once we saw measurable lift in accuracy, coverage, and cost-savings. Their resolution was far beyond what we could achieve on our own, and we no longer had to juggle the labor-intensive process of duplicates, new places, and pulling in updates. We now have a reliable stream of data in difficult markets that is high-fidelity and always current. And because they are global, adding new datasets is a breeze.
Popular Use Cases for Retail Data
Retail 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 Retail Data.
Main Attributes of Retail 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 |
|---|---|---|---|
| Float | The latitude of a point on earth's surface. Commonly abbreviated as "lat". | View 221 datasets | |
| Float | The longitude of a point on earth's surface. Commonly abbreviated as "long". | View 153 datasets | |
| String | The address of a company or contact (street name, number, zip code, city, county, country). | View 136 datasets | |
| String | The name of a city. | View 125 datasets | |
| String | The name of a country. | View 125 datasets | |
| String | The postal code of an address. | View 108 datasets |
Retail Data Explained
Retail Data Collection
Technological advancement has brought along a new wave of possibilities including a collection of retail information. Modern retail stores go the extra mile to develop customized apps for mobile phones and PCs. While these apps are important in enhancing customer experience, retailers also use them to monitor customer shopping habits and interests. This method of in-store customer data collection helps retailers to tailor services to each customer. Retailers can also collect retail data by the use of guest satisfaction surveys, as a result of helping the business point out the key trends in buying behavior and formulating initiatives to drive more sales. By tracking website activities, retailers can also gather retail data about the number of potential buyers that have shown interest in the business.
Key Attributes
The attributes of retail data are grounded on two factors: customer insights, and business insights. Retail data’s attribute of customer insight involves a information on customer behavior. Understanding customer behavior is crucial for retailers to improve customer satisfaction and as result bolster sales. On the other hand, business insight is data that pertains to the business’ supply chain and inventory tracking that can help retailers make procurement decisions to ensure a steady supply of goods. Taking command of the supply chain through retail analytics helps the business to be highly reliable because customers are sure to get what they need at the right time and place.
Use Cases
The benefits of retail data are twofold, helping retailers to optimize their supply chain and identify customer trends and preferences. As far as optimizing the supply chain is concerned, retailers can use retail data to revamp their inventory and procurement systems. This undertaking can be achieved by the use of predictive tools in which the business makes use of historical data and trend evaluation to define the order in which to bring in new products and the precise quantities of products needed. This inventory optimization is ensuring that customers get the products they need while reducing space usage in stores. Identifying customer trends and preferences is another important use of retail data analytics. Retailers can easily match up sales data to predict consumer behavior hence creating cross-functional marketing strategies that target just the right consumers.
How is Big Data changing retail marketing analytics?
Companies such as Amazon, Wal-Mart, eBay and Costco are prime examples of how modern retailers have successfully harnessed the real power of big data analytics across processes in the businesses. The companies, which are largely retail stores that are based online, have amplified the power of big data analytics by making retail data useable throughout company departments, hence leading to wide-spread optimization of core business goals and smaller routine daily tasks. Here’s some examples of how using big data in retail adds value to business operations:
• Pipeline development for store location
• Pricing optimization
• Personal data protection
• Customer service
• Inventory management
• Promotion intelligence
• Fraud protection and prevention.
How can a user assess the quality of Retail Data?
Users can assess the quality of retail data by its ability to accurately depict the current trends in market behavior both, for the customers and the business. Quality data is tailored to a specific business in terms of needs and trends. It is up-to-date and it accurately provides trends as far as the retailers’ line of business is concerned. Quality retail data also takes into account the aspect of consumer private data protection. When collecting retail data, the data provider must abide by data privacy regulations to ensure consumers’ PII isn’t compromised.
Frequently Asked Questions
How is the Quality of Retail Data Maintained?
The quality of Retail 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 Retail Data Updated?
The update frequency for Retail 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 Retail Data Secure?
The security of Retail 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 Retail Data Delivered?
Retail 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 Retail Data.
How Much Does Retail Data Cost?
The cost of Retail 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 Retail Data for your needs.
What Are Similar Data Types to Retail Data?
Retail Data is similar to other data types, such as Consumer Review Data, Product Data, Ecommerce Data, Online Purchase Data, and Online Shopping Data. These related categories are often used together for applications like Location Intelligence and Retail Site Selection.
