
SnapBizz Database of FMCG/kirana Stores
# | Store id |
Store Name |
Installation Date |
Retailer Name |
POS Make |
No of POS |
Store Classification |
Cluster |
Zone |
Door No / Shop No / Floor No |
Building Name |
Landmark |
Sub-Locality |
Locality |
District |
Sub-District |
Pin Code |
Locality Profile |
How long your store has been opened |
Business hours |
Total Turnover Per Month (L-Lakh) |
No Of Employees attend to customers |
Self Service(Yes/No) |
Shopping Carts(Yes/No) |
Shopping Baskets(Yes/No) |
Air Conditioning / Air Cooling (Yes/No) |
Ethical Allopathic Drugs(Yes/No) |
Type Of The Store |
Store Area In sqft. |
Average Footfall per day |
Card Payment Acceptance(Yes/No) |
Electronic Weighing Scale(Yes/No) |
Visi Coolers (Yes/No) |
Other Coolers (Yes/No) |
Freezers(Yes/No) |
Refrigerators(Yes/No) |
|||||
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1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx |
2 | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx |
3 | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx |
4 | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx |
5 | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx |
6 | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx |
7 | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx |
8 | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx |
9 | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxx |
10 | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxx | xxxxx | xxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxxx |
... | xxxxxx | Xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
Store id
|
Integer | 10668 | |
Store Name
|
String | The Beauty Palace | |
Installation Date
|
String | 20-Jul-17 | |
Retailer Name
|
String | *********** | |
POS Make
|
String | Panache | |
No of POS
|
Integer | 1 | |
Store Classification
|
String | Android | |
String | Pune | City Name | |
Cluster
|
String | Pune | |
String | Maharashtra | State Name | |
Zone
|
String | West | |
Door No / Shop No / Floor No
|
String | *********** | |
Building Name
|
String | *********** | |
String | Address | ||
Landmark
|
String | Residential Building,Shopping Complex/ Commercial Buildin... | |
Sub-Locality
|
String | Pune | |
Locality
|
String | Pune | |
District
|
String | Pune | |
Sub-District
|
String | Pune | |
Pin Code
|
Integer | 411027 | |
String | Phone Number | ||
Locality Profile
|
String | Predominantly residential,Commercial with trading,Commerc... | |
How long your store has been opened
|
String | < 2 Years | |
Business hours
|
String | 10 Hours | |
Total Turnover Per Month (L-Lakh)
|
Integer | 5 | |
No Of Employees attend to customers
|
Integer | 2 | |
Self Service(Yes/No)
|
Boolean | f | |
Shopping Carts(Yes/No)
|
Boolean | f | |
Shopping Baskets(Yes/No)
|
Boolean | f | |
Air Conditioning / Air Cooling (Yes/No)
|
Boolean | t | |
Ethical Allopathic Drugs(Yes/No)
|
Boolean | f | |
Type Of The Store
|
String | Cosmetic (Main Selling products are beauty, personal care... | |
Store Area In sqft.
|
String | 300-500 | |
Average Footfall per day
|
String | <100 | |
Card Payment Acceptance(Yes/No)
|
Boolean | f | |
Electronic Weighing Scale(Yes/No)
|
Boolean | f | |
Visi Coolers (Yes/No)
|
Boolean | f | |
Other Coolers (Yes/No)
|
Boolean | f | |
Freezers(Yes/No)
|
Boolean | f | |
Refrigerators(Yes/No)
|
Boolean | f | |
String | *********** | Latitude-Longitude |
Attribute | Type | Example | Mapping |
---|---|---|---|
Type of Store
|
Grocery | ||
String | Karnataka | State Name | |
Pincode
|
625007 | ||
Average Footall per day
|
453 |
Description
Geography
History
Volume
100,000 | records |
Pricing
License | Starts at |
---|---|
One-off purchase |
$10,000 / purchase |
Monthly License | Not available |
Yearly License | Not available |
Usage-based | Not available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is SnapBizz Database of FMCG/kirana Stores?
Dataset contains all the kirana store details such as retailer name, contact details, location of the store etc,.
What is SnapBizz Database of FMCG/kirana Stores used for?
This product has 2 key use cases. Snapbizz recommends using the data for Retail Intelligence and Retail POS Data Analysis. Global businesses and organizations buy B2B Data from Snapbizz to fuel their analytics and enrichment.
Who can use SnapBizz Database of FMCG/kirana Stores?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for B2B Data. Get in touch with Snapbizz to see what their data can do for your business and find out which integrations they provide.
How far back does the data in SnapBizz Database of FMCG/kirana Stores go?
This Tabular Data has 5 years of historical coverage. It can be delivered on a weekly, monthly, quarterly, and on-demand basis.
Which countries does SnapBizz Database of FMCG/kirana Stores cover?
This product includes data covering 1 country like India. Snapbizz is headquartered in India.
How much does SnapBizz Database of FMCG/kirana Stores cost?
Pricing for SnapBizz Database of FMCG/kirana Stores starts at USD10,000 per purchase. Connect with Snapbizz to get a quote and arrange custom pricing models based on your data requirements.
How can I get SnapBizz Database of FMCG/kirana Stores?
Businesses can buy B2B Data from Snapbizz and get the data via Email. Depending on your data requirements and subscription budget, Snapbizz can deliver this product in .csv, .xls, and .txt format.
What is the data quality of SnapBizz Database of FMCG/kirana Stores?
Snapbizz has reported that this product has the following quality and accuracy assurances: 100% Retail Sore Profile. You can compare and assess the data quality of Snapbizz using Datarade’s data marketplace. Snapbizz appears on selected Datarade top lists ranking the best data providers, including Top 10 Point-of-Sale (POS) Data Providers.
What are similar products to SnapBizz Database of FMCG/kirana Stores?
This Tabular Data has 3 related products. These alternatives include 【Korea】 B2B Database: B2B Contacts and Company Data; 1,567,621 Companies and 5.5 Million Contacts, Business Data United States of America / Company B2B Data United States of America ( Full Coverage), and SIC Data Buy Business data worldwide by official SIC Codes. You can compare the best B2B Data providers and products via Datarade’s data marketplace and get the right data for your use case.