What is Retail Store Data? Examples, Providers & Datasets to Buy
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- Guide
- FAQ
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- Guide
- FAQ
What is Retail Store Data?
Retail store data is information collected and analyzed from various sources within a retail establishment. This data helps retailers make informed decisions regarding pricing, promotions, inventory management, and customer experience, ultimately improving their overall performance and profitability.
What Are Examples of Retail Store Data?
Key examples include:
- Foot Traffic: Counts of visitors entering the store, peak times, and customer flow within the store.
- Sales Data: Revenue per location, average transaction size, and units sold.
- Inventory Levels: Real-time stock availability, shelf restocking needs, and item turnover rates.
- Customer Demographics: Basic profile information like age, gender, and shopper frequency.
- Promotion Performance: Effectiveness of in-store promotions, discounts, and product displays.
- Point-of-Sale (POS) Data: Detailed transaction data, payment methods, and checkout times.
- And more.
Best Retail Store Databases, Datasets & API
The best retail store datasets provide actionable insights into store performance, inventory, and customer demographics. This curated list features the top datasets and APIs, selected for accuracy, quality, and trusted providers where you can buy retail store data.
Retail Store Data | Retail & E-commerce Sector in Asia | Verified Business Profiles & eCommerce Professionals | Best Price Guaranteed
Point-of-Interest (POI) Data | Shopping & Retail Store Locations in US and Canada | Retail Store Data | Comprehensive Data Coverage
Retail Store Data: Accurate Places Data | Global | Location Data on 52M+ Places
Global Retail Data | Retail Store Data | In-Store Data | Retail POI and SKU Level Product Data from 1M+ Locations with Prices
Store location data | 164M Commercial Places - Stores | Global Retail Store Location Data
Retail Transaction Data | Retail Store Data | Retail Sales Data | Global Coverage Local Precision | Trusted by 600 + Businesses
Store Location Data | 230M+ Global POIs & Retail Locations | 5000+ Categories with Restaurant, Retail & Business Coverage
Transact US Fashion & Apparel, Accessories, Footwear Transaction Data | Retail Store Data, Online Data | 100M Credit & Debit Cards, 600 Tickers
Global Convenience, Restaurant, Retail, & Grocery Store Data | 70M+ Points of Interest (POI) and Visitation Metrics
Store Location Data | Asia/ MENA | Latest Estimates on Population, Demographics, Consuming Class, Retail Spend | GIS Data | Demographic Data
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Top Retail Store Data Providers & Companies
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BestPlace
CoverageUSAUK+112BestPlace is a proven research and predictive geoanalytical tool that provides end-to-end visibility of local shopper patterns. We focus on quality of our products as the key value and do our best to maximise our client's benefits.108Countries>2000Data features for calculation+15%ML models accuracy growth -
SafeGraph
CoverageUSAUK+248VWe are using safegraph data to research on the foot traffic of airports in the U.S. We find it very helpful with lots of documentation. The customer support is awesome. You can post in the slack channel and got a response very quickly
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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.
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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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Sky Packets
CoverageUSAMexico+3Sky Packets provides high-quality datasets from WiFi and mobile networks in urban areas. We serve municipalities, business districts, and commercial properties with insights into foot traffic, network usage, and consumer behavior. Headquartered in the U.S., we offer flexible data access models.CCPACompliant100%WiFi users are opt in5 MillionUnique mobile users -
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
Popular Use Cases for Retail Store Data
Retail store data is vital for optimizing operations, understanding customers, and driving sales. Businesses use it for location analysis, identifying the best areas for new store openings or expansions. It supports inventory management, ensuring optimal stock levels and reducing waste. Additionally, sales analysis uses retail store data to evaluate performance and develop targeted marketing strategies, just to mention a few use cases.
Main Attributes of Retail Store 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 101 datasets | |
| Float | The longitude of a point on earth's surface. Commonly abbreviated as "long". | View 87 datasets | |
| String | The address of a company or contact (street name, number, zip code, city, county, country). | View 78 datasets | |
| String | The postal code of an address. | View 76 datasets | |
| String | The name of a brand. | View 57 datasets | |
| String | The name of a city. | View 57 datasets |
How is Retail Store Data Collected?
Retail Store Data is collected in various ways and sources. They are collected through online customer satisfaction surveys/forums, websites, manufacturer’s databases, the government, and the retail industry. Every retail store provides data that is based on their performance. The raw data is collected in the form of graphs, sales charts, customer satisfaction meter, and ROI models, These are then analyzed and cleaned to curate a more in-depth analysis of retail stores. Retail store data is useful in analyzing the 4ps of marketing: product, price, promotion, and place.
What Are the Typical Attributes of Retail Store Data?
Insights available from a retail store dataset are quite comprehensive, so here are the typical attributes you might encounter:
| Attribute | Description |
|---|---|
| Store Name | The official name of the retail store. |
| Location | Geographic coordinates, addresses, and regions of the stores. |
| Sales Metrics | Revenue, transaction counts, and average basket size. |
| Customer Demographics | Age, gender, and purchasing behavior of the store’s visitors. |
| Inventory Levels | Stock details, turnover rates, and replenishment needs. |
| Operating Hours | Store hours, including opening and closing times. |
| Competitor Data | Insights into competing stores nearby. |
| Employee Count | Number of staff members working in each retail location. |
How can I Assess the Quality of Retail Store Data?
Before buying Retail Store Data, users must secure that the data providers provide accurate, real-time, and clean data. See to it that data sources are included, and the process of extracting clean data is mentioned. Users can also read online reviews to filter bad reviews. Data providers should have 24/7 customer service in case users have questions and follow-ups. Users can also ask for a data sample to see if it fits their needs.
Frequently Asked Questions
How is the Quality of Retail Store Data Maintained?
The quality of Retail Store 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 Store Data Updated?
The update frequency for Retail Store 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 Store Data Secure?
The security of Retail Store 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 Store Data Delivered?
Retail Store 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 Store Data.
How Much Does Retail Store Data Cost?
The cost of Retail Store 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 Store Data for your needs.
What Are Similar Data Types to Retail Store Data?
Retail Store Data is similar to other data types, such as In-store Data, Retail Sales Data, and Kirana Data. These related categories are often used together for applications like Retail Site Selection.
