What is In-store Data? Examples, Datasets and Providers
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- FAQ
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- FAQ
What is In-store Data?
In-store data is information collected and analyzed from various sources within a physical retail store. This data helps retailers gain insights into consumer preferences, optimize store operations, improve customer experience, and make data-driven decisions to enhance overall business performance.
What Are Examples of In-store Data?
Key examples include:
- Sales Transactions: Details of purchases, transaction amounts, and items sold.
- Customer Demographics: Information on age, gender, and shopping frequency of visitors.
- Inventory Levels: Real-time data on stock availability and restocking needs.
- Foot Traffic: Counts of visitors, peak shopping times, and popular areas within the store.
- Dwell Time: Time customers spend in specific sections, revealing product interest.
- Promotion Impact: Effectiveness of in-store promotions, such as discounts and special displays.
- And more.
Best In-store Databases & Datasets
Here is our curated selection of top In-store 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.
Point-of-Interest (POI) Data | Shopping & Retail Store Locations in US and Canada | Retail Store Data | Comprehensive Data Coverage
Retail Store Data | Retail & E-commerce Sector in Asia | Verified Business Profiles & eCommerce Professionals | Best Price Guaranteed
Global Retail Data | Retail Store Data | In-Store Data | Retail POI and SKU Level Product Data from 1M+ Locations with Prices
Global Convenience, Restaurant, Retail, & Grocery Store Data | 70M+ Points of Interest (POI)
eCommerce Company Data | 320K Stores | API & Bi-Weekly Updates | Technographic Insights
Retail Store Data: Accurate Places Data | Global | Location Data on 52M+ Places
Store location data | 164M Commercial Places - Stores | Global Retail Store Location Data
Google PlayStore & Apple App Store & Data: Price, App Category, App Description, Reviews, Ratings | Global Coverage
Shopify store lists - 1M+ stores worldwide | scrapelabs.io
Ecommerce Data | Store Location Data | Global Coverage | 61M+ Contacts | (Verified E-mail, Direct Dails)| Decision Makers Contacts| 20+ Attributes
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Top In-store Data Providers & Companies
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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 -
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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Grepsr
CoverageUSAUK+247VUtilizing Grepsr's data solutions, we employed their capabilities to efficiently retrieve a substantial volume of job posting information. The acquired dataset played a pivotal role in supporting our strategic marketing initiatives, demonstrating the versatility and effectiveness of Grepsr's data services in enhancing our business intelligence and outreach endeavors.
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GapMaps
CoverageUSACanada+153VWe use GapMaps’ data offering extensively to identify and approve locations for Anytime Fitness sites across Southeast Asia. This data helps Inspire Brands Asia make informed decisions, ensuring that each new gym location meets our strategic criteria and has the potential for success. The insights from GapMaps enable us to understand market demographics, competition, and accessibility, which are crucial for our expansion plans.
Popular Use Cases for In-store Data
In-store 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 In-store Data.
Main Attributes of In-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 |
|---|---|---|---|
| String | The address of a company or contact (street name, number, zip code, city, county, country). | View 283 datasets | |
| Float | The latitude of a point on earth's surface. Commonly abbreviated as "lat". | View 194 datasets | |
| Float | The longitude of a point on earth's surface. Commonly abbreviated as "long". | View 177 datasets | |
| String | The name of a neighborhood. | View 172 datasets | |
| String | The postal code of an address. | View 165 datasets | |
| String | The name of a city. | View 162 datasets |
Frequently Asked Questions
How is the Quality of In-store Data Maintained?
The quality of In-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 In-store Data Updated?
The update frequency for In-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 In-store Data Secure?
The security of In-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 In-store Data Delivered?
In-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 In-store Data.
How Much Does In-store Data Cost?
The cost of In-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 In-store Data for your needs.
What Are Similar Data Types to In-store Data?
In-store Data is similar to other data types, such as Retail Store Data, Retail Sales Data, and Kirana Data. These related categories are often used together for applications like Store Visit Attribution.
