Location Data | Global Store Location Data on 11M+ POI | SafeGraph Places
In-store density distribution heat maps dataset by PREDIK Data-Driven
Online Retail Database | eCommerce Stores List 2022 | 5 Million eCommerce Stores Worldwide | 15+ eCommerce Platforms | Real-Time Verified Data
SnapBizz Database of FMCG/kirana Stores
Shopper Data | Retail Transaction Data | In-Store Data on Spending Patterns
Lifesight App Data (Mobile App Usage DataMobile Audience DataApp Store Data)
Luz Store - Revenue Figures for Online Stores - Ecommerce Data (USA, Canada, EU-5)
Automaton AI Video Data - video of products in a retail store
Quadrant Point-of-Interest (POI) Data - Store Location Data - with custom, on-demand metadata and attributes
aKQired Ecommerce Database - Shopify Stores, Retail Database [Owners, Emails, Phone]
The Ultimate Guide to In-store Data 2022
What is In-Store Data?
In the simplest terms, in-store data tells you about what happens in-store. This can range from consumer footfall to optimizing inventory use, for example by highlighting where you have a surplus and could apply a promotion. These data-driven insights give business owners a clear look into their company and point out key areas that could be improved to generate greater profits.
Aside from administrative data, in-store data also tells you about the customers who visit your store. It can break down their behaviors and purchasing habits which allows you to better streamline your efforts to ensure they return results.
How is In-Store Data collected?
In-store data is collected byusing different types of data collection.
Location and footfall data is used to measure the number of customers who visit the store over a given period. This can highlight when peak and slack periods of businesses are and can be used to allocate staffing schedules accordingly.
Purchase data is collected whenever a customer makes a purchase. This records the cost and what was bought to highlight products which sell more or less well and how much revenue they generate.
Inventory data is compiled by recording what products are available at what times. Businesses use this information to pick and choose when to apply promotions or begin marketing campaigns to sell excess stock.
This is all analyzed and compiled into one big in-store dataset.
What are the attributes of In-Store Data?
Because there are so many sub-categories of data that is related to in-store data it is hard to define what information will typically make up an in-store dataset. It is therefore important to check that your chosen data provider’s data suits your personal needs.
An in-store dataset can provide foot traffic information, such as how many visitors came over a specific period and how long they stayed. This can be combined with purchase data to show how much revenue was generated from these visits and whether there is a discrepancy between visitors and number of sales mdade.
Inventory information will give you detailed insights into stock numbers which is used to highlight which products a promotion could be added to.
What is In-Store Data used for?
There are many different uses of in-store data, but essentially it all comes down to optimizing the efficiency of your store and driving up profit margins.
Businesses use in-store data to break down the running of their store. Foot traffic information can highlight when a store is at its most and least busy which means you can tailor staffing schedules around this.
Purchase data and analytics shows the volume and profits of sales that are being made. This can also give an insight in customer preferences and habits which can be used for running marketing campaigns. If you know one product is selling particularly well you can market this efficiently to encourage people to buy more.
Companies also use inventory details to highlight which products there is a surplus of so marketing campaigns can be run to sell these items and generate revenue at the same time.
How can a user assess the quality of In-Store Data?
As said above, because there is a wide range of details that makes up an in-store dataset it is important to check that your data provider’s information matches your businesses needs.
High quality in-store datasets will provide real-time intelligence and analytics about the running of your store and will cover the aspects you deem most important.
Always make sure to read the data provider’s reviews before buying to ensure that you are getting the best possible data for your personal needs.
Where can I buy In-store Data?
Data providers and vendors listed on Datarade sell In-store Data products and samples. Popular In-store Data products and datasets available on our platform are Location Data | Global Store Location Data on 11M+ POI | SafeGraph Places by SafeGraph, In-store density distribution heat maps dataset by PREDIK Data-Driven by Predik Data-driven, and Online Retail Database | eCommerce Stores List 2022 | 5 Million eCommerce Stores Worldwide | 15+ eCommerce Platforms | Real-Time Verified Data by Lead for Business.
How can I get In-store Data?
You can get In-store Data via a range of delivery methods - the right one for you depends on your use case. For example, historical In-store Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time In-store Data APIs, feeds and streams to download the most up-to-date intelligence.
What are similar data types to In-store Data?
In-store Data is similar to Consumer Review Data, Product Data, Ecommerce Data, Shopper Data, and Brand Data. These data categories are commonly used for Store Visit Attribution and Retail Intelligence.